Podcasts about Toca

Google ha presentado los Pixel 11, el Watch 5 y el Pixel Tag. Toca subida de precios por la crisis de memoria, llega Gemini compitiendo contra la futura Siri, y vemos un claro un giro hacia fotos menos perfectas. Lo interesante, como siempre, está entre líneas. * * * Loop Infinito, podcast de Xataka, de lunes a viernes a las 7:00 (hora peninsular española). Presentado por Javier Lacort. Editado por Alberto de la Torre. * * * Contacto: lacort@xataka.com, @lacort en X.

Conviértete en un supporter de este podcast: https://www.spreaker.com/podcast/el-mananero-radio--3086101/support.

Conviértete en un supporter de este podcast: https://www.spreaker.com/podcast/el-mananero-radio--3086101/support.

"La Primera de El Economista" concentra las notas más relevantes de la portada del periódico, con el contexto y análisis que marcan la agenda económica del día.¡Síguenos en nuestras redes sociales para mantenerte informado!Twitter: https://twitter.com/eleconomista Facebook: https://www.facebook.com/ElEconomista.mxInstagram: https://www.instagram.com/eleconomistamxLinkedIn: https://www.linkedin.com/company/el-economista/#ElEconomista #EETV

Para democratizar o cinema, Sérgio Marques leva a todo o país cinema ao ar livre, e sobretudo filmes de qualidade às crianças com o seu cinema insuflável. “As crianças são exigentes com as histórias”See omnystudio.com/listener for privacy information.

Hay temporadas en las que lo que más nos agota no es lo que está pasando, sino todo lo que imaginamos que podría pasar. “Mi futuro está en tus manos.” Salmos 31:15 NTV David no dijo: “Mi futuro está claro”. Dijo: “Mi futuro está en tus manos”. Y quizá ahí está la paz que estabas buscando. No en conocer todos los detalles, sino en saber quién sostiene tus días. Haz lo que te corresponde hoy. Ora. Trabaja. Toca la puerta. Ten la conversación. Da el siguiente paso. Pero después suelta. Que algo haya salido de tus manos no significa que haya salido de las manos de Dios. No necesitas llegar antes de tiempo a un lugar donde Dios ya te está esperando. Y si hoy vuelves a preocuparte por eso que ayer ya habías entregado, vuelve a entregarlo. Cada vez que la preocupación regrese, conviértela en una señal para regresar a Dios. Tu futuro no está a la deriva. Tus tiempos no están abandonados. Tu historia no está fuera de control. Está en las manos de Dios. Las lecturas son: Esdras 10:1-44 1 Corintios 6:1-20 Salmo 31:9-18 Proverbios 21:3 www.wenddyneciosup.comSígueme en mis redes como @wenddyneciosup--Distribuido por: Genuina Media Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Carlos Iribarren | La música para piano nos ofrece un arsenal enorme de obras de todo tipo y en esta edición de Hoy Toca con sabor veraniego, queremos presentarte un disco y a su intérprete: la joven pianista murciana María Ángeles Ayala, quien ha seleccionado 5 piezas monumentales de la literatura pianística y las ha grabado en un álbum titulado “Miroirs de l´être” y que merece muchísimo la pena. Carlos ha seleccionado 3 de ellas y los apellidos de sus autores no dejan lugar a la duda, pues pertenecen al olimpo de la mejor música para un instrumento tan magnífico: Schubert, Scriabin y Chopin conforman un menú de 5 estrellas que te va a satisfacer. Carlos y Mario comentan junto a nuestra invitada un montón de aspectos sobre estas obras y sobre la trayectoria de María Ángeles, quien tiene su propio método de enseñanza del piano y que ya ha vivido numerosos momentos especiales en una carrera que no para de subir… Así de refrescante es la nueva entrega de Hoy Toca, el programa de Clásica FM que te quiere sorprender.

Durante siglos, la convivencia entre el lobo y el hombre en la Península Ibérica no se construyó sobre cuentos de hadas ni sobre romantizaciones infantiles, sino sobre un delicado equilibrio tallado a base de supervivencia, ganadería y respeto ancestral. Hoy, sin embargo, desde despachos climatizados a cientos de kilómetros del campo, se impone un dogma que ignora la realidad sobre el terreno y condena al abandono a quienes sostienen la España rural. El lobo ibérico ha pasado de ser un símbolo de la fauna salvaje a convertirse en la punta de lanza de un ecologismo de moqueta que prioriza la ideología sobre la gestión medioambiental real. Mientras los ataques al ganado se multiplican y la desesperación cunde en las aldeas, los relatos institucionales prefieren mantener un silencio cómplice para no fracturar la narrativa oficial. Toca hablar de biodiversidad; pero también de la soberanía del campo y su liquidación en favor de regulaciones desconectadas del suelo que pisan. ¿Es posible proteger nuestra riqueza natural sin destruir la vida de quienes alimentan al país? Para abordar la complejidad de esta guerra cultural y ambiental contamos con Laïkas, divulgador y creador de contenido al frente del canal @LaikasSombrasdeUdun . Gran conocedor de la naturaleza salvaje, la fauna ibérica y las dinámicas del mundo rural, Laïkas nos aporta una visión pragmática, directa y sin filtros sobre la verdadera situación de la especie y el impacto directo que sufren los ganaderos. Tertulia #334. Bienvenidos a la Terra Ignota. Emitido en YouTube el 9 de agosto de 2026: https://youtube.com/live/jYaE60Bquwk ________________________________________ Recuerda darle a suscribirse para no perderte futuros contenidos. Y si te gusta, te animamos a compartirlo con tus amigos y conocidos. Puedes acceder a todas las plataformas de Terra Ignota desde https://linktr.ee/TerraIgnota (Instagram, iVoox, Spotify y mucho más). ¡Échanos una mano convirtiéndote en Patrón! https://www.patreon.com/TerraIgnota Para adquirir productos del podcast: https://TerraIgnota.es/Tienda En https://www.arenashop.es tenéis descuentos usando el código IGNOTEROS

Hay cargas que no llegaron a tu vida con un nombre claro. No venían etiquetadas. Simplemente aparecieron. Y tú, en ese momento, hiciste lo único que sabías hacer: las tomaste. A veces por amor. A veces por miedo. A veces porque nadie más lo hizo. Con el tiempo, lo que tomaste se volvió costumbre. Y lo que se vuelve costumbre deja de cuestionarse. Así el corazón aprende a vivir cargando cosas que nunca le correspondieron. Y lo más delicado: puedes acostumbrarte tanto al peso que ya no sabes cómo vivir sin él. Pero hoy la Palabra revela otra lógica: “Confía tus cargas al Señor, y Él te sostendrá.” (Salmo 55,22) Dios no niega que llevas peso. Pero no dice “quédate con eso”. Dice “entrégalo”. El alma no fue creada para sostenerlo todo. Y soltar lo que no te corresponde no es abandonar. Es devolver. Devolver a Dios lo que siempre fue suyo. Devolverte a ti mismo tu lugar. María vivió llena de momentos que no podía controlar, y no se aferró. Su confianza no era pasiva. Era una entrega interior constante. Porque Dios no necesita que cargues todo. Necesita que confíes.

#tocadodragao #2026 #podcast #Spidermanbrandnewday #homemaranha #umnovodia #brandnewday #MARVEL #filmes #filmesmarvel #reviewEpisódio de hoje: Peter Parker fez o mundo se esquecer dele, mas ele não consegue se esquecer do Toca. AHA! Sente a pressão cabeça-de-teia!ENTRE NA COMUNIDADE DO TOCA! ⁠⁠https://cesber.wixsite.com/tocadodragao⁠⁠REDES SOCIAIS E MUITO MAIS!https://beacons.ai/tocadodragaoFAÇA SUA DOAÇÃO #APOIE a TOCA a partir de R$ 10,00/ mês - Estamos no Apoia.se!https://apoia.se/atocadodragaoDOADORES DE AGOSTO/2026 PAULO DEROS ELVE, BRUNO BRAZ, RODRIGO SILVA, MARCIA REGINA BERNARDES, MASON YEON, PAULA GESTAL, GABRIEL SCHADE, LELE DANTAS, JOÃO PANDA, CEZAR AUGUSTO, ANTHONY MARTINS, BRENDA NASCIMENTO, MISTER DOVAH, LENHORMAR, VICTOR FERNANDES, MATHEUS TRENTINI, ANDRIA SEDREZ.Agradecemos aos Inscritos do Podcast que fizeram suas doações pelo PICPAY nosso e-mail: tocadodragaopodcast@gmail.comGRUPO DO TELEGRAM https://t.me/+fn75BRye8sY2NDExGRUPO DO WHATSAPPhttps://chat.whatsapp.com/KUtDsVnnv7w6hcseloXqCQCASTERS NESSE EPISÓDIO: Richard (Ricky, O Bardo) e Rodrigo SilvaMÚSICAS ORIGINAIS DO TOCA #Compositor: Caio Varalta / Tema do Podcast: "Entrando na Toca" - Todos os Direitos Reservados

Más opiniones en: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.ultimahora.com

O Caixa de Música é exibido na TV Novo Tempo de segunda a quinta às 18h e, aos sábados, às 12h.Curta e siga o Caixa de Música nas redes sociais: Instagram: ⁠https://www.instagram.com/caixademusica/⁠Facebook:⁠ https://www.facebook.com/CaixadeMusica/⁠X: ⁠https://x.com/caixademusic

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¡Bienvenidos a una nueva emisión al rojo vivo del Daily Show de Fútbol de Primera Radio! Hoy analizamos todo lo que nos dejó la Jornada 3 de la Liga MX: las Águilas del América golean a Santos Laguna para subirse al liderato junto a Xolos, pero Guillermo Almada exige mucho más a su plantel. Además, la crisis en Tigres toca fondo mientras que los Rayados de Matías Almeyda logran un triunfo de oro para respirar tranquilos. En el plano internacional, Mauricio Pochettino renueva como DT de Estados Unidos hasta 2030 y nos comparte su proyecto a cuatro años. En Europa, el Real Madrid de José Mourinho no para: Vinícius Jr. se suma a la pretemporada y Bernardo Silva revela el peso decisivo de "The Special One" para su fichaje. Por último, desmenuzamos la reflexión táctica de Abel Ferreira y la bomba política del día: ¡la UEFA exige abiertamente la salida de Gianni Infantino de la FIFA! ¡Únete al directo! ¡Dale LIKE a la transmisión, SUSCRÍBETE al canal de Fútbol de Primera y dinos en el chat: ¿Te parece correcta la exigencia de la UEFA contra Infantino?

Alibaba presenta Qwen 3.8 Max con 2,4 billones de parámetros; GPT-5.6 acelera avances en criptografía cuántica; una prueba revela riesgos en los archivos digitales de análisis de ADN; varios estados de EE. UU. recortan ventajas fiscales a los centros de datos; y México se consolida como gran proveedor de servidores.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

Si los dos sabéis que esto no funciona — si los dos lo habéis pensado, lo habéis hablado, y aun así seguís ahí — este episodio es para vosotros. Os cuento la historia de Marta y David. Se conocieron hace tres años en una boda: ella admiró su calma, él admiró su seguridad. Cada uno vio en el otro justo lo que le faltaba a sí mismo. Y ese fue el principio de todo — también del final. Con el tiempo, la calma de él se convirtió en distancia. La entrega de ella se convirtió en agotamiento. Y llegó la noche en la que uno de los dos por fin dijo en voz alta lo que pensaba desde hacía meses: "creo que deberíamos dejarlo." La respuesta no fue un sí. Fue una pregunta: "¿y por qué no me dejas tú, entonces?" En este episodio te explico por qué esa pregunta no es real — es una forma de comprobar, sin arriesgarse del todo, si el otro tiene tanto miedo como uno mismo. Y por qué, cuando dos personas dependen la una de la otra y además lo saben, seguir ahí no es amor ni es indecisión: es parálisis con los ojos abiertos. Lo que diferencia este episodio: no hablo de la dependencia de quien no se da cuenta. Hablo de la que es más grave — la de dos personas que sí lo ven, lo han hablado entre ellas, y no se mueven igualmente. Y te enseño por qué, según el psicólogo Antoni Bolinches, en estas relaciones manda quien menos ama — no el que más grita, sino el que necesita menos, o el que mejor lo disimula. Si te has reconocido en la historia de Marta y David, o en la tuya propia, no hace falta que le sigas dando vueltas a solas. Puedes reservar tu consulta telefónica gratuita de 45 minutos en emocioteca.com/contacto y hablamos de vuestra relación en concreto — no de dependencia emocional en general. Encuéntrame también en YouTube: https://www.youtube.com/@emocioteca2114 y en Instagram: https://www.instagram.com/emocioteca/ Ninguno de los dos sabe estar solo consigo mismo. Y por eso, aunque cada vez se sientan más lejos, ninguno se va. #psicologia #psicologoonline #terapiaemocional #autoestima #crecimientoemocional #emocioteca #dependenciaemocional #parejatoxica #miedoalasoledad

Si los dos sabéis que esto no funciona — si los dos lo habéis pensado, lo habéis hablado, y aun así seguís ahí — este episodio es para vosotros. Os cuento la historia de Marta y David. Se conocieron hace tres años en una boda: ella admiró su calma, él admiró su seguridad. Cada uno vio en el otro justo lo que le faltaba a sí mismo. Y ese fue el principio de todo — también del final. Con el tiempo, la calma de él se convirtió en distancia. La entrega de ella se convirtió en agotamiento. Y llegó la noche en la que uno de los dos por fin dijo en voz alta lo que pensaba desde hacía meses: "creo que deberíamos dejarlo." La respuesta no fue un sí. Fue una pregunta: "¿y por qué no me dejas tú, entonces?" En este episodio te explico por qué esa pregunta no es real — es una forma de comprobar, sin arriesgarse del todo, si el otro tiene tanto miedo como uno mismo. Y por qué, cuando dos personas dependen la una de la otra y además lo saben, seguir ahí no es amor ni es indecisión: es parálisis con los ojos abiertos. Lo que diferencia este episodio: no hablo de la dependencia de quien no se da cuenta. Hablo de la que es más grave — la de dos personas que sí lo ven, lo han hablado entre ellas, y no se mueven igualmente. Y te enseño por qué, según el psicólogo Antoni Bolinches, en estas relaciones manda quien menos ama — no el que más grita, sino el que necesita menos, o el que mejor lo disimula. Si te has reconocido en la historia de Marta y David, o en la tuya propia, no hace falta que le sigas dando vueltas a solas. Puedes reservar tu consulta telefónica gratuita de 45 minutos en emocioteca.com/contacto y hablamos de vuestra relación en concreto — no de dependencia emocional en general. Encuéntrame también en YouTube: https://www.youtube.com/@emocioteca2114 y en Instagram: https://www.instagram.com/emocioteca/ Ninguno de los dos sabe estar solo consigo mismo. Y por eso, aunque cada vez se sientan más lejos, ninguno se va. #psicologia #psicologoonline #terapiaemocional #autoestima #crecimientoemocional #emocioteca #dependenciaemocional #parejatoxica #miedoalasoledad Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Si los dos sabéis que esto no funciona — si los dos lo habéis pensado, lo habéis hablado, y aun así seguís ahí — este episodio es para vosotros. Os cuento la historia de Marta y David. Se conocieron hace tres años en una boda: ella admiró su calma, él admiró su seguridad. Cada uno vio en el otro justo lo que le faltaba a sí mismo. Y ese fue el principio de todo — también del final. Con el tiempo, la calma de él se convirtió en distancia. La entrega de ella se convirtió en agotamiento. Y llegó la noche en la que uno de los dos por fin dijo en voz alta lo que pensaba desde hacía meses: "creo que deberíamos dejarlo." La respuesta no fue un sí. Fue una pregunta: "¿y por qué no me dejas tú, entonces?" En este episodio te explico por qué esa pregunta no es real — es una forma de comprobar, sin arriesgarse del todo, si el otro tiene tanto miedo como uno mismo. Y por qué, cuando dos personas dependen la una de la otra y además lo saben, seguir ahí no es amor ni es indecisión: es parálisis con los ojos abiertos. Lo que diferencia este episodio: no hablo de la dependencia de quien no se da cuenta. Hablo de la que es más grave — la de dos personas que sí lo ven, lo han hablado entre ellas, y no se mueven igualmente. Y te enseño por qué, según el psicólogo Antoni Bolinches, en estas relaciones manda quien menos ama — no el que más grita, sino el que necesita menos, o el que mejor lo disimula. Si te has reconocido en la historia de Marta y David, o en la tuya propia, no hace falta que le sigas dando vueltas a solas. Puedes reservar tu consulta telefónica gratuita de 45 minutos en emocioteca.com/contacto y hablamos de vuestra relación en concreto — no de dependencia emocional en general. Encuéntrame también en YouTube: https://www.youtube.com/@emocioteca2114 y en Instagram: https://www.instagram.com/emocioteca/ Ninguno de los dos sabe estar solo consigo mismo. Y por eso, aunque cada vez se sientan más lejos, ninguno se va. #psicologia #psicologoonline #terapiaemocional #autoestima #crecimientoemocional #emocioteca #dependenciaemocional #parejatoxica #miedoalasoledad

En 'Hoy toca cine', Áurea Ortiz nos habla sobre la Filmoteca d'Estiu.

Sergio Klatumm, asesor financiero, analiza la actualidad de los mercados en un contexto marcado por un ambiente político convulso con nuevos ataques entre Estados Unidos e Irán, que repercute directamente en los índices y mercados. En medio de una jornada marcada por el rebote de las bolsas europeas tras las caídas de víspera y en una jornada en la que el IBEX35 se mueve por encima de los 19.500 puntos. Sin embargo, el invitado apunta que “la verdad es que tenemos una resistencia bastante clara en la zona de los 19.800”. También señala que “a nivel técnico está formando lo que podría ser un doble techo y sobre todo lo que me parece más importante es que tenemos gaps”. Los cuatro magníficos de la tecnología Una vez más la tecnología se posiciona como un punto fundamental al hablar de los mercados, en un contexto en el que Meta ha sufrido una caída de alrededor del 8% y Microsoft presenta una tendencia bajista, el analista señala que “en este sector yo diría que hay que empezar a salir” además recuerda que un 3% de todos los hombres adultos han perdido su patrimonio en el sector tecnológico “las personas adultas en general han perdido todo su patrimonio justo por este sector e incluso bueno de apalancados y todo pero básicamente todo el sector de semiconductores y de la inteligencia artificial ya hay que cogerlo con pinzas”

Rosana Laviada entrevista a Andrés Samperio, alcalde de Navalagamella, con el fin de conocer la actualidad de la localidad respecto a los incendios.

Conviértete en un supporter de este podcast: https://www.spreaker.com/podcast/el-mananero-radio--3086101/support.

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

She didn't commit to soccer until the summer before her junior year — and still earned a Division I scholarship from a small club no college coach was watching.Former NCAA Division I player Jada Davis joins Chat By The Pitch to break down youth soccer player development from both sides of the touchline. A multi-sport kid from the DFW Metroplex who bounced from karate to dance to basketball before soccer ever stuck, Jada shares how a competitive streak, ID camps, and pure hustle carried her from a small private club to Prairie View A&M — where she collected an offer from every other school in the conference first, just to prove a point.Now a youth coach in Dallas with a background in personal training and sports nutrition, Jada opens up about the injuries that cut her college career short, the unexpected path into coaching, and how she builds a player-led culture where even her eight-year-olds run their own warmups and hold each other accountable. She also digs into the psychology of coaching girls through the Girls Academy Credential of Coaching Excellence — why words land differently than we intend, and why culture has to be player-driven.If you're a soccer parent heading into tryout season, a young player wondering if your club is “big enough” for college, or a coach trying to balance development over winning, this conversation is for you. Jada's advice is simple: find a coach who cares about the person first — and never let the size of your club decide the size of your dream.Key Talking PointsA multi-sport childhood — karate, dance, basketball, volleyball, track, and cross-country — and how cross-training made her stronger and always in shape when she committed to soccerFalling in love with competing before falling in love with the sport, and why she didn't choose soccer until the summer before her junior year of high schoolPlaying for a small private club instead of a big-name academy — and how ID camps, game film, and self-advocacy got her in front of college coaches anywayChoosing Prairie View A&M: putting an HBCU at the top of her list, having no plan B, and earning an offer from every other school in the conference firstTwo season-ending foot fractures, the decision to play through the pain, and what she'd tell players facing the same choice todayThe unexpected call from TOCA that turned a personal trainer into a youth soccer coach — and why she almost said noBuilding a player-led culture with eight-year-olds: player-run warmups, accountability between teammates, and zero tolerance for negative talkThe Girls Academy Credential of Coaching Excellence: the psychology of coaching girls and why it doesn't matter what a coach means — it matters how players interpret itParent buy-in, over-communication, and the ongoing conversation about development versus winning at the youth levelHer tryout-season advice for families: find a coach who values the person before the athlete, and a club with real standards, culture, and a pathwayQuotes from Jada Davis“I just like to compete. You could put Uno in front of me, and I'm probably gonna win nine out of 10 games.”“I don't think it matters what club you play on if you wanna play in college. You just have to make the effort yourself.”“She offered me the least, and I truly believed all I need is one season, and I was absolutely correct.”“I wanted to stick with something where I could just create my own path.”“I kinda steered away from coaching 'cause I was like, 'I don't wanna turn into the coaches that I hated.'”“It doesn't matter what we mean when we say it, it matters how they interpret it.”“It's their team. It's not my team. They're playing. I'm not playing.”“I don't care if you make mistakes as long as you fix them.”“You don't have respect for people when they don't care to get to know you.”“Sydney looked at me and she said, 'We got this.' It's something I live by. I think about it every day.”Connect with Jada Davis

✅ Toca aquí para Inscribirte al seminario "La iniciación del mago interior" https://edmundovelasco.me/magointerior Descubre cómo tu atención y tus creencias están moldeando la realidad que experimentas cada día. En este episodio, Edmundo Velasco comparte principios de conciencia, PNL y transformación interior para comprender el origen de tus resultados. Aprende por qué el cambio verdadero comienza dentro de ti y no en las circunstancias externas. Una conversación que te ayudará a observar tu vida desde una perspectiva completamente nueva. ️ Escúchalo completo y comienza a despertar tu poder creador.

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Quieras creer o no, las cartas te pueden mostrar el futuro. Escucha lo que viene esta semana para el horóscopo y qué debes hacer para perseguir la buena suerte. Mantente al día con los últimos de 'El Bueno, la Mala y el Feo'. ¡Suscríbete para no perderte ningún episodio!Ayúdanos a crecer dejándonos un review ¡Tu opinión es muy importante para nosotros!¿Conoces a alguien que amaría este episodio? ¡Compárteselo por WhatsApp, por texto, por Facebook, y ayúdanos a correr la voz!Escúchanos en Uforia App, Apple Podcasts, Spotify, y el canal de YouTube de Uforia Podcasts, o donde sea que escuchas tus podcasts.'El Bueno, la Mala y el Feo' es un podcast de Uforia Podcasts, la plataforma de audio de TelevisaUnivision.

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1.    España gana el Mundial 2026 con gol de Ferran Torres2.    Messi cierra su historia en los Mundiales con unlegado incomparable tras 20 años de trayectoria3.    Miles marcharon el sábado en el Viejo San Juan por laindependencia de Puerto Rico4.    Cuando regrese de su viaje, gobernadora se reunirá conFrancisco Domenech e Itza García, dijo el secretario de Asuntos Públicos5.   Junta Fiscal reclama control sobre asignaciónlegislativa de $20 millones para municipios.6.    Power Expectations pidecambios al contrato. Nuevos tropiezos para la generación de emergencia.7.    Perú: terremoto de magnitud5,7 deja al menos 6 muertos, 30 heridos y cientos de desplazados8.    Muere el tercer soldadoestadounidense en Irán Este es un programa independiente y sindicalizado. Esto significa que este programa se produce de manera independiente, pero se transmite de manera sindicalizada, o sea, por las emisoras y cadenas de radio que son más fuertes en sus respectivas regiones. También se transmite por sus plataformas digitales, aplicaciones para dispositivos móviles y redes sociales.  Estas emisoras de radio son:1.    Cadena WIAC - WYAC 930 AM Cabo Rojo- Mayagüez2.    Cadena WIAC – WISA 1390 AM Isabela3.    Cadena WIAC – WIAC 740 AM Área norte y zona metropolitana4.    X61 – 610 AM en Patillas5.    X61 – 94.3 FM Patillas y todo el sureste6.    WPAB 550 AM - Ponce7.    ECO 93.1 FM – En todo Puerto Rico8.    WLRP 1460 AM Radio Raíces La voz del Pepino en San Sebastián9.    WOQI 1020 AM – Radio Casa Pueblo desde Adjuntas 10. Mundo Latino PR.com, la emisora web de música tropical y comentario Una vez sale del aire, el programa queda grabado y está disponible en las plataformas de podcasts tales como Spotify, Soundcloud, Apple Podcasts, Google Podcasts y otras plataformas https://anchor.fm/sandrarodriguezcotto También nos pueden seguir en:REDES SOCIALES:  Facebook, X (Twitter), Instagram, Threads, LinkedIn, Tumblr, TikTok BLOG:  En Blanco y Negro con Sandra http://enblancoynegromedia.blogspot.com  SUSCRIPCIÓN: Substack, plataforma de suscripción de prensa independientehttps://substack.com/@sandrarodriguezcotto OTROS MEDIOS DIGITALES: ¡Ey! Boricua, Revista Seguros. Revista Crónicas y otrosEstas son algunas de las noticias que tenemos hoy En Blanco y Negro con Sandra. 

El agente de futbolistas Alejandro Camaño ha analizado la final del Mundial entre Argentina y España en el programa 'Especial Mundial' de Deportes COPE. Camaño, que representa a jugadores como Lautaro Martínez, Hakimi o Abdé, viajará a Nueva York para presenciar el partido, una cita que ha descrito como "tan soñada por todo el mundo que me parece que no se puede faltar". El representante ha confesado que tuvo que pedir disculpas a sus jugadores por no haber podido asistir antes al torneo, ya que estaba centrado en operaciones de futbolistas más jóvenes como Mario Gila o Álvaro Rodríguez.Camaño ha relatado la intensa emoción que sintió con el gol de su representado, Lautaro Martínez. Aunque lleva desde los 18 años en España, ha afirmado que "el fútbol te da un común sentido de pertenencia". La celebración fue explosiva, según sus palabras: "En mi casa yo creo que se movieron hasta las paredes".Tras el partido, el agente pudo intercambiar mensajes con el delantero ...

¿Sientes que haces todo lo posible, pero tu vida sigue repitiendo los mismos resultados? En este episodio Edmundo Velasco explica por qué muchas personas creen que su vida está bloqueada, cuando en realidad está funcionando según una programación mental que opera de forma inconsciente. ✅ Toca aquí para Inscribirte al seminario "La iniciación del mago interior" https://edmundovelasco.me/magointerior

Eddie Lewis - two-time World Cup player for the U.S. and founder of TOCA Soccer - on the two eureka moments that led to the creation of TOCA Social. TOCA supports training for high-level athletes but also a fun soccer experience for the casual fan. Listen to the full episode here: https://nvgt.com/podcast?ppplayer=1e977ebc536a4f7840f232ca6e253547&ppepisode=8e379d9055c0772740f5bb58db0f35c8 For more insights, visit our LinkedIn page or learn more about Navigate at https://nvgt.com/.

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José Luis Moro, Director Creativo y Cofundador de la agencia publicitaria Pingüino Torreblanca se ha propuesto este verano en Hoy por Hoy "vender motos imposibles", aquellos productos, personas y conceptos que, lejos de comprarlos, lo normal es que huyéramos de ellos sin mirar atrás. Los oyentes, sin embargo, le compraron hace una semana la campaña de que trabajar en verano es guay y, esta mañana, lo ha hecho con una supuesta orquesta que en las verbenas de verano única y exclusivamente toca canciones de Alex Ubago, la gran y ficticia orquesta "Desaliento". ¿Cómo lo ha hecho?

Toca comentar este especial aparecido el mes de Abri. Donde Marvel nos lleva a través de cinco números a lo que sería la continuación de la serie de animació de los años 90. Donde quedaba el tema bien abierto y que aquí han querido zanjar no pocos asuntos (como la desaparición de Mary Jane). Si bien un servidor (Néstor Gascón) y Carlos de Antonio no acabamos dando una valoración positiva de lo leído. Algo que explicamos en el Podcast. Casting: -Néstor Gascón -Carlos de Antonio

Quedan dos GG antes de marcharnos de vacaciones, así que no vamos a permitir que el desastre en el que se ha convertido la industria del videojuego nos arruine la fiesta de despedida. Habrá quejas, y habrá collejas, que cada vez se merecen más, pero también habrá una selección de juegardos que no la salta un caballo con herraduras nuevas. Toca repasar qué han roto esta vez desde PlayStation y Xbox en una situación cada vez más habitual, especialmente ahora que están rodando cabezas allí donde menos pensábamos que iban a hacerlo. De regalo, para endulzar un poco el asunto, Raza nos trae algunos jueguitos que ha estado probando. https://tiermaker.com/create/best-games-2026---gg-by-3djuegos-16162858

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** Ponte en presencia de Dios. Trata de hablar con Él. ** 10 minutos son 10 minutos aunque te puedas distraer. Llega hasta el final. ** Sé constante. El Espíritu Santo actúa “a fuego lento” y requiere constancia. Audios de 10 minutos que te ayudan a rezar. Un pasaje del Evangelio, una idea, una anécdota y un sacerdote que te habla y habla al Señor invitándote a compartir tu intimidad con Dios. Busca tu momento, piensa que estás con Él y dale al play. Toda la info en nuestra web: www.10minutosconjesus.org diezminutosconjesus@gmail.com Para recibir cada día tu meditación por Whatsapp pulsa aquí: http://dozz.es/nu36t

En la clavada telefónica de hoy llamamos a Luis para hacerle creer que el hotel donde se hospedó encontró una infestación de chinches en la habitación después de su salida y que tendría que hacerse responsable de un costoso cargo por los daños y la fumigación. Entre explicaciones, nervios y mucha confusión, la llamada tomó giros inesperados y provocó momentos llenos de risas que no te puedes perder.See omnystudio.com/listener for privacy information.

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O programa Meio-Dia em Brasília desta sexta-feira, 26, fala sobre o retorno emergencial de Valdemar Costa Neto ao Brasil para tentar conter a crise instalada no partido após Michelle Bolsonaro publicar um vídeo em que ela criticou publicamente o enteado e pré-candidato à Presidência da República, Flávio Bolsonaro.Além disso, o jornal também aborda a rejeição da proposta de delação premiada de Paulo Henrique Costa, ex-presidente do BRB, e sobre a primeira noite na papudinha do banqueiro Daniel Vorcaro. O programa elege também os melhores, os piores da semana e destaca das principais reportagens da Revista Crusoé.Meio-Dia em Brasília traz as principais notícias e análises da política nacional direto   de Brasília.     Com apresentação de José Inácio Pilar e Wilson Lima, o programa aborda os temas mais quentes do cenário político e econômico do Brasil.     Com um olhar atento sobre política, notícias e economia, mantém o público bem informado.    Transmissão ao vivo de segunda a sexta-feira às 12h no nosso canal do Youtube.  https://www.youtube.com/@OAntagonista  Apoie o jornalismo independente. Assine O Antagonista e Crusoé com 10% via Pix ou Google Pay:  https://assine.oantagonista.com.br/   Siga O Antagonista no X:  https://x.com/o_antagonista   Acompanhe O Antagonista no canal do WhatsApp. Boletins diários, conteúdos exclusivos em vídeo e muito mais.  https://whatsapp.com/channel/0029Va2SurQHLHQbI5yJN344  Inscreva-se no canal, ative as notificações e deixe sua opinião nos comentários!  Leia mais em www.oantagonista.com.br | www.crusoe.com.br #Michelle #PL #Política #Bastidores #Polêmica #Partido #Internet #Repercussão #Debate #Oposição #Notícia #Podcast #Áudio #YouTube #Engajamento #Tendência #Alta #Viral #Estratégia #Conjuntura

Eddie Lewis - Founder of TOCA Soccer - is changing the way that young soccer players train, while simultaneously building soccer's answer to Topgolf. He shares how he balances both objectives domestically and his plans for international expansion.   Timestamps: 1:50 - TOCA's Elevator Pitch 5:55 - TOCA Soccer vs. TOCA Social 8:15 - Youth sports 12:55 - The infrastructure of American soccer 16:50 - MLS Partnership 18:40 - International expansion 22:00 - Lessons from playing soccer 28:55 - Rapid Fire Questions     For more insights, visit our LinkedIn page or learn more about Navigate at https://nvgt.com/.

Esto es HistoCast. No es Esparta pero casi. Toca centrarse exclusivamente en la toma de Panzacola para hablar largo y tendido de ella. Y para ello tenemos a @cerveranavas junto a @danigalpe, @HugoACanete y @goyix_salduero.Secciones Historia: - Primeras consideraciones terminológicas y otros HistoCasts sobre el tema - 14:00 - Los antecedentes de la batalla de Pensacola: Gálvez en la Luisiana, la batalla de la Mobila, el fallido intento de octubre 1780 - 19:05 - El comienzo de la batalla, el desembarco en la isla de Santa Rosa, 9 de marzo de 1781 - 1:05:50 - Problemas con la barra de Pensacola - 1:28:50 - La historia del bergantín Galveztown - 1:38:17 - Entrevista sobre la fallida réplica del Galveztown con Manuel Olmedo Checa - 1:44:04 - Yo Solo, la entrada en solitario de Gálvez en la bahía de Pensacola - 2:01:11 - Debate sobre la ineficacia de la batería de las Barrancas Coloradas - 2:12:39 - La flota española entra en el Puerto de Pensacola y primeras operaciones en tierra firme - 2:25:00 - “El Teotoburgo” de Gálvez en Pensacola, 28 de marzo de 1781 - 2:35:30 - El motín, el fusilamiento en el campamento español y los primeros reconocimientos para iniciar el asedio - 2:43:51 - Gálvez es herido luchando en primera línea, 9 de abril de 181 - 2:52:40 - Entrevista a Elisabeth Wise, presidenta del capítulo español de las Hijas de la Revolución Americana - 2:57:13 - Un huracán azota el hospital donde está ingresado Gálvez - 3:20:28 - Llega Francisco Saavedra con la flota de Solano y del Caballero de Montreuil, 13 de abril de 1781 - 3:29:00 - Buques que participan en la batalla de Pensacola y en la batalla de Trafalgar - 3:40:10 - Descripción de los ejércitos español e inglés y de las operaciones de sitio por Daniel Galán - 3:45:28 - Se abre la trinchera la noche del 26 al 27 de abril de 1781 - 4:15:35 - El gran revés de la nueva paralela, 4 de mayo de 1781 - 4:21:59 - Análisis de la situación del sitio en el momento crítico por ambos generales - 4:28:30 - El asalto al fuerte de la media luna se cancela finalmente, 7 de mayo de 1781- 4:31:49 - El fuerte de la media luna vuela por los aires, 8 de mayo de 1781 - 4:36:07 - Campbell pide la apertura de negociaciones - 4:42:09 - La ceremonia de rendición y los informes de Gálvez a la corte en España - 4:46:12 - Bibliografía y cierre - 4:54:20

#gobierno #corrupción #pnp La escandalosa querella de Sebastian Negrón Reichard ante el FEI pone de manifiesto acusaciones de corrupción que comprometen la credibilidad de una Secretaria de Justicia al servicio de La Fortaleza. A 40 años de Nelson Martínez Acosta y la primera versión del caso de asesinato de Luis Vigoreaux. | Dios no quiso traer el agua esta madrugada, según la Gobernadora. ¡Conéctate, comenta y comparte! #periodismoindependiente #periodismodigital #periodismoinvestigativo Síguenos en nuestras redes sociales: tiktok.com: https://x.com/Bonita_Radio Facebook: / bonitaradio Instagram: / bonitaradio X: https://x.com/Bonita_Radio

Análisis, debate y opinión sobre el mercado rojiblanco

Los temas del "cafecito informativo" de este jueves 11 de junio de 2026: Toca fondo el transporte interprovincial en Cuba Brasil intercepta a 108 cubanos en la frontera "Agua y luz", el grito desesperado en las calles de Luyanó Exposición ‘Creatura' en La Habana

Esto es HistoCast. No es Esparta pero casi. Toca hablar de uno de esos desconocidos personajes de la Historia sin el cual esta hubiera sido muy distinta, Francisco de Saavedra y Sangronís. Para ello tenemos con nosotros a James Giesler Vila preguntado por @goyix_salduero.Secciones Historia: - Orígenes - 16:44 - La Habana - 39:56 - Etapa francesa - 1:16:19 - Plan De Grasse-Saavedra - 1:29:38 - Yorktown - 1:55:08 - Reconocimientos - 2:34:53 - Bibliografía - 3:12:30

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