Podcasts about Llama

Summertime Is Joke Time Laugh-o-Rama-Llama! 1: Lions, Shovels, and Holey SocksWe may be taking a mid-summer break… but that doesn't mean your ears have to! Join us for an extra special episode featuring your favorite jokes, a bucket of popsicles and spaghetti, and our new llama friend, Professor Pickles! Featuring jokes from: Kai (Age 7), Harper (Age 10), Jamie (Age 10), Jayna (Age 7), Layla (Age 7), Grace (Age 12), and Professor Pickles (Age 8 in llama years; 32 in human years)Links for Kids: Silly Summer Jokes for KidsWe'll be back with our regularly scheduled Sneak Attacks, Full Episodes, and Brain Breathers on Monday, 9/7/26. Until then, head to storypillar.com and catch up on your favorite episodes.Make a donation! Support Storypillar!https://ko-fi.com/storypillar Shop at: storypillarstore.threadless.comInfo/Get in Touch: Website: www.storypillar.com Instagram: @storypillar Join our mailing list. Created, Written, and Produced by: Meg Lewis Storypillar Theme Song: Lyrics by Meg Lewis Music by Meg Lewis, Andy Jobe, and Suzanna Bridges Produced by Andy Jobe Episode Cover Art: Mackenzie AllisonSound Effects and Additional Music: -https://freesound.org/ -Llama sounds: https://deadsounds.com/lama-sound#google_vignette, https://animalsounds.online/animals/llama -Joke Time Song: https://freesound.org/people/BlondPanda/sounds/659889/ Know a kid with great advice for Sticky Situations? Check out www.storypillar.com/unsticktricks.© 2026 PowerMouse Press, LLC

These days, AI is everywhere, and it's increasingly hard to separate the gains from the slop. With that in mind, we're re-releasing my conversation with Stanford Law professor Daniel Ho on the future of AI and the law. When we look for applications where AI can deliver measurable benefit, the legal profession stands out, both for its potential gains in efficiency and equity, and for how much is at stake if we get it wrong. Dan's research — from using AI to identify racist property covenants buried in county deed records, to mapping obsolete regulations that waste thousands of hours of government time — shows what's possible when the technology is applied with rigor and purpose. If you're curious about how AI can serve both justice and good governance, this one is well worth another listen. Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu. Episode Reference Links: Stanford Profile: Dan Ho Connect With Us: Episode Transcripts >>> The Future of Everything Website Connect with Russ >>> Threads / Bluesky / Mastodon Connect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / Facebook Chapters: (00:00:00) Introduction Russ Altman introduces guest Dan Ho, a professor of law, political science, and computer science at Stanford University. (00:02:19) Path into Legal AI How Ho's background shaped his interest in law, and technology. (00:03:35) What Lawyers Do What makes law a complex domain for AI. (00:05:28) Legal Hallucinations When AI  performs well and when it fails.  (00:07:52) Searching Legal Records in California How AI can help identify outdated, harmful, or legally important material. (00:10:28) Scaling Redaction How a model accelerated a process that overwhelmed county recorder offices. (00:13:04) Legal Reform at Scale How AI has supported legal reform by scanning massive bodies of law. (00:15:02) STARA & The City of San Francisco How AI was used to go through San Francisco's code and clean up reporting. (00:20:53) Outdated Obligations How “regulatory sludge” takes the time & resources of the public service (00:25:02) Open vs. Closed AI The differences and associated risks of the different AI systems. (00:30:58) Legal Chatbots Why legal chatbots are promising but risky. (00:33:42) Conclusion Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Daniel: el que busca y llama Las mejores oraciones casi siempre nacen de una Biblia abierta. En este episodio contemplamos cómo Daniel escuchó primero la Palabra de Dios y luego convirtió sus promesas en oración, descansando únicamente en la misericordia del Señor. Daniel 9:1–19 Escucha este episodio y recuerda que el Padre escucha a quienes se acercan a Él, no por sus méritos, sino por la gracia que ha dado en Jesucristo. __________________________________________________________________________________________________________ Cada domingo escuchamos la Palabra de Dios proclamada. Pero ¿cómo podemos seguir meditándola durante la semana? Te invitamos a escuchar Recordando el Sermón, el devocional diario de la Iglesia Cristiana Bíblica Ra'ah en Bogotá, Colombia. Breves reflexiones, aplicaciones prácticas y momentos de oración basados en la predicación del Día del Señor. Disponible de lunes a viernes.

Des pelleteuses SANS CHAUFFEUR construisent les data centers de l'IA

¿Alguna vez has sentido que una venta se te escapa por no saber cuándo dejar de hablar? En este episodio de Conversando de Ventas, abordamos uno de los errores más comunes y costosos en el mundo comercial: la sobreventa.Muchos vendedores creen que vender es hablar, pero hablar demasiado —especialmente por nervios o por querer explicarlo todo— puede aburrir, confundir y alejar al cliente. Aprende a usar el silencio estratégico y a comunicar exactamente lo que el prospecto necesita escuchar para tomar una decisión.

New names: Kimi K3, Llama, Nemotron, Mistral, Cohere, Deepseek, Phi-4 – these are just a few of the fast-growing open source models from major AI providers. These systems threaten the business models and financial plans of OpenAI, Anthropic, Google, and X.ai. They perform at levels close to Frontier models and the can run up to five-times cheaper on a variety of hardware platforms. What is the disruptive impact of these open source LLMs and how does this impact your AI investments? As you'll hear in the podcast, Open Source unleashes the opportunity for lower cost AI solutions and more vertical, specialized, application-focused solutions we need. And the business model for these systems moves away from the massive investments of the Frontier providers. The result is more complicated than “open means control.” Model tuning, performance, and optimization could be in your future – as AI moves from a platform to a true layered product set we can use as we need. Lots to learn about here, let us know if you have any questions. Additional Information What's the difference between closed, open source, and open-weight AI? A researcher explains What Is Open-Weights A.I.? Comparison of Open Source Models Chapters (00:00:00) - Open Source and the AI Industry(00:11:46) - The Future of AI Is Fully Integrated(00:15:35) - HR 2030

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Microsoft et Meta viennent de publier leurs résultats trimestriels… et le contraste est saisissant !

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Santiago González comenta el balance de Sánchez y la entrevista a Sarah Santaolalla en la que se llama idiota a sí misma.

What if living a longer, healthier life isn't about finding the next supplement or wellness trend, but about making better choices every day?Holistic health educator Emma Magnolia shares her approach to intentional living, exploring how strength training, hydration, sleep, simple daily routines and consistency can help support healthy aging.Rather than chasing quick fixes, this conversation focuses on practical habits that almost anyone can adopt to build a healthier future.Emma Magnolia is the founder of emmawellness.com ----DISCLOSURE: This podcast is supported by affiliate arrangements with a select number of companies. We have arranged discounts on certain products and receive a small commission on sales. The income helps to cover production costs and ensures that our interviews remain free for all to listen. Visit our LIVE LONG SHOP for more details: PartiQlar supplementsEnhance your wellness journey with PartiQlar supplements. No magic formulas, just pure single ingredients, like NMN, L-Glutathione, Spermidine, Resveratrol, TMG and Quercetin. Get a 15% discount with the code MASTERAGING15 at PartiQlarEnergyBits algae snacksA microscopic form of life that could help us age better. Use code LLAMA for a 20 percent discountPartiQlar supplementsEnhance your wellness journey with pure single ingredients. 15% DISCOUNT - use code: MASTERAGING15SiPhox Health home blood testingMeasure 17 critical blood biomarkers from home. Get a 20% discount with code LLAMA Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showThe Live Long podcast, a HealthSpan Media LLC production, shares ideas but does not offer medical advice.  If you have health concerns of any kind, or you are considering adopting a new diet or exercise regime, you should consult your doctor.

Joan García, reciente campeón del mundo con la selección, atiende a El Larguero desde su campus de porteros en Tordera (Barcelona). Mientras el conjunto blaugrana prepara la temporada 2026/27 en el centro de alto rendimiento de la Federación Inglesa, St. George's Park, el guardameta del equipo que ha conseguido la segunda estrella y pieza clave en el de Hansi Flick, valora en El Larguero el post Mundial y las expectativas el equipo culé... con la Champions muy presente. 

PODCAST LAS NOTICIAS CON CALLE DE 28 DE JULIO - 1200 millones menos entre fondos federales y presupuesto de PR para el próximo año fiscal - El Vocero Trump dice estar impresionado con Zelesnky y su capacidad militar - WSJHoy se reúne Trump con Netanyahu en la visita del primer ministro - NYTLa Fed decide mañana: ¿y si SUBE las tasas?Un momento para WindMar Home — la empresa con más de 20 años protegiendo los hogares puertorriqueños.Solar para bajar tu factura. Techo para proteger tu inversión. Agua para que nunca te quedes sin — especialmente con las sequías que se aproximan. Y batería para total independencia energética.Todo bajo una misma empresa. Un solo llamado. Llama al 787-489-1155 o visita windmarhome.comWindMar Home — los que se preparan hoy , duermen tranquilos mañana.#incluyeauspicio#windmarhome  Trump va a la Corte Suprema para impedir el voto por correo - CNNRacionamiento inminente en Carraízo y sus clientes - El Vocero Investigan casos de hospital por agua asquerosa - Primera Hora CRIM busca dueños de 55 mil propiedades que no aparecen - El Nuevo Dia Fonalledas apoyan a Jenniffer y dice que le dan la bienvenida a las primarias - El Vocero Alegan que Cosculluela llamó a joven para amenazarla por estar con otros tipos y la amenazó con matar a su familia - El Vocero Viva la ley de plásticos de un solo uso y todavía investigan si la van a implementar o no - El Vocero No sabemos qué hacer con el sargazo en PR - Primera Hora Alcaldes defienden cobro de impuestos a fondos federales - El Nuevo Día Menos protección para animales en peligro de extinción - El Nuevo Día No cuadran los números del fondo de desempleo, aparenta haber montones de fraudes - El Nuevo Día Entidades falsas creando estudiantes fatuos para cobrar becas Pell - El Nuevo Día Juramenta nueva presidenta hoy en Perú. Keiko Fujimori y la derecha conquista Latinoamérica - El Nuevo Día Trump quiere que MAHA le meta mano a eliminar las vacunas para niños - WSJEl SAVE Act no tiene los 60 votos, Trump exige aprobarlo sí o sí - Punchbowl News PR importó $3,254 millones en genéricos en 2025, por lo que los aranceles le darán oportunidad y tumbe a la vez -  LOS DATOS DEL DÍA (cierre lunes 27 jul) Brent≈ $83/barril · cae fuerte por pausa Irán-EEUU Diésel (retail EEUU)a la baja siguiendo al crudo (dato aprox.) S&P 5007,413.18 · +0.02% Dow Jones52,210.08 · +0.51% Nasdaq24,932.08 · -0.18% Bono 10 años≈ 4.65% Euro/USD1.1397 Gas natural$2.72/MMBtu · -1.75% Hipoteca 30 años6.58% (Freddie Mac) / ~6.75% (Bankrate)

Portsmouth is worse than Fanny could've imagined, and in these chapters, she tries to find a friend among her family. Mary sends a gossipy letter to Fanny, and Fanny kind of likes it. Fanny buys a knife for her little sister AND joins a library! Topics discussed include regency era dentistry, Fanny's llama mama, Mrs. Price's resemblance to her sisters, the social status of the Ward sisters, Spicy Susan, the Saturday half-holiday, the comparison of Portsmouth to celibacy, Mary poking the bear, Baron Wildenhaim's courtship of Julia, what qualities in a person are innate, and luxurious and daring wealth.Patron Study Questions come from Avi, Kate, Ghenet, and Linnea. Topics discussed include how Fanny's sisters could've benefitted if she'd come home earlier, Mrs. Price as a tragic figure, and Fanny's lack of a female friendship, where Fanny truly belongs, whether Portsmouth is an enticement or warning about marrying Henry, and Maria as a warning.Becca's Study Questions: Topics discussed include the picture Jane Austen is painting of the lower classes and how Susan complicates that picture, what role Susan plays in the narrative, whether Fanny really misses Mansfield, why Maria is unhappy, and predictions about our former main characters. Funniest Quote(s):“As to the little irritations, sometimes introduced by Aunt Norris, they were short, they were trifling, they were a drop of water to the ocean, compared with the ceaseless tumult of her present abode.”“I hope she will recollect it and be satisfied, as well she may, with moving the queen on a palace, though the king may appear best in the background.”Questions moving forward: How close will Fanny and Susan get? Will we go to the Rushworths' party? Will Edmund propose?Who wins the chapters? Mary Crawford!Glossary of People, Places, and Things: Arthur (Mr. Ratburn and the Special Someone), High School Musical, Is Your Mama a Llama?, Mean Girls, Rugrats, the White Lotus, Dr. Johnson, the Office, Money with MelNext Episode: Mansfield Park Volume III Chapters 10-11 or Chapters 41 and 42Our show art was created by Torrence Browne, and our audio is produced by Graham Cook. For bios and transcripts, check out our website at podandprejudice.com. Pod and Prejudice is transcribed by speechdocs.com. To support the show, check out our Patreon! Check out our merch at https://podandprejudice.dashery.com.Instagram: @podandprejudiceTwitter: @podandprejudiceFacebook: Pod and PrejudiceYoutube: Pod and PrejudiceMerch store: https://podandprejudice.dashery.com/

¿Error o burla? Trump llama a México "Me-hee-ho" durante un discursoEnlace para apoyar vía Patreon:https://www.patreon.com/julioastilleroEnlace para hacer donaciones vía PayPal:https://www.paypal.me/julioastilleroCuenta para hacer transferencias a cuenta BBVA a nombre de Julio Hernández López: 1539408017CLABE: 012 320 01539408017 2Tienda:https://julioastillerotienda.com/ Hosted on Acast. See acast.com/privacy for more information.

Sheinbaum destaca respaldo a limitar celulares en planteles educativos Protección Civil activa alerta por lluvias intensas en el paísIrán endurece postura frente a EE. UU.Más información en nuestro podcast#grc

Hallan restos humanos abandonados en Temixco, Morelos Renuncia ministro de Educación de India por filtración de exámenesDía Naranja llama a erradicar la violencia contra mujeresMás información en nuestro podcast#grc

This week Country life talks to an American poultry farmer about her bird flu experience, visits a llama farmer and meets a family who've been farming celery for more than a century.You can find photos and read more about the stories in this episode on our webpage, here.In this episode:0:59 - Rural news wrap5:45- What New Zealand can learn from a farmer who lived through H5N117:43 - Addicted to yarn: Llama herd follows love of fibre31:36 - Celery, butterflies and Travis the goatWith thanks to:Georgie Cartanza, University of DelawareJanette Buckingham, Thickthorne LlamasGraham Franklin, Lucy Franklin, Alan Franklin, Monique Franklin, Jasmine Franklin, Luke Franklin, Brian King, and Solola Manulele, Franklin FarmMake sure you're following us on your favourite podcast app, so you don't miss new episodes every Friday evening.Send us your feedback or get in touch at country@rnz.co.nzGo to this episode on rnz.co.nz for more details

It was the 1990s when many farms were looking to diversify. 'Why not llamas?', thought Southland spinner and farmer Janette Buckingham.You can find photos and read more about the stories in this episode on our webpage, here.With thanks to:Janette Buckingham, Thickthorne LlamasGo to this episode on rnz.co.nz for more details

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

Google, Apple e Meta: a guerra da inteligência artificial que vai decidir o futuro dos negócios e do mercado de trabalho. Neste episódio de Por Trás do Hype, Pedro Waengertner recebe André Lopes, repórter de tecnologia da Exame, para contar os bastidores da reportagem que mapeou toda a estratégia de IA do Google desde 2017. Você vai entender por que a Apple paga bilhões para usar o Gemini na Siri, por que a Meta não consegue destravar o Llama, o que o chip próprio da OpenAI significa na corrida contra a Nvidia, e por que só 17% do mundo realmente usa inteligência artificial no dia a dia. Um raio-x direto ao ponto sobre tecnologia, startups e o futuro do trabalho, pra quem empreende, lidera times ou quer sair na frente.Nos siga nas redes sociais: Pedro WaengertnerACE VenturesEXAME

¿Todavía usando awk para extraer información de ps aux o df? En 2026 hay herramientas mucho mejores. En este episodio te presento tres herramientas que forman un tridente imbatible para trabajar con información del sistema en formato JSON: jc, jq y gron.jc es un conversor de comandos Linux a JSON. Se instala con pip y un simple pipe convierte la salida de ps, df, free, ss, systemctl, lsblk y hasta 60 comandos más en JSON estructurado. Olvídate de awk y de los scripts frágiles que se rompen cuando cambia el orden de las columnas. Con jc, el JSON no depende del formato de salida. Tiene parsers específicos para cada comando, incluyendo crontab, last, lsof, pip list, lsmod, date y más. Si no encuentra el parser que necesitas, puedes crear el tuyo. Está escrito en Python y tiene licencia MIT.jq es la navaja suiza de los JSON. Te permite filtrar, ordenar, agrupar, seleccionar y transformar cualquier JSON con una sintaxis potente. Está escrito en Go y es maduro, estable y rapidísimo. Combinado con jc, puedes listar los procesos que más RAM consumen, los discos por encima del 80% de ocupación o los servicios que han fallado, todo en una sola línea. Si la sintaxis te parece liosa, puedes pedirle a cualquier modelo de lenguaje que te genere la expresión que necesitas.gron es el menos conocido pero igual de útil. Aplana un JSON convirtiendo cada valor en una línea independiente con su ruta completa. ¿Para qué sirve? Para poder usar grep directamente sobre un JSON. Si alguna vez has hecho un curl a una API y has intentado hacer grep sobre el resultado, sabes que no funciona porque todo está en una línea. Con gron, cada valor tiene su propia línea y puedes buscar con grep. Además permite la operación inversa con --ungron: modificas el JSON aplanado con sed y lo reconstruyes.En el episodio presento sysreport.py, un script en Python que junta toda la información del sistema en un solo JSON usando jc y luego te permite hacer preguntas en lenguaje natural usando Llama 3.2 con Ollama. Le preguntas qué procesos consumen más RAM, qué servicios están caídos o si hay algún disco lleno, y él te responde en lenguaje natural. Todo corriendo en local, sin gastar un euro en APIs.El script se puede usar como API local, se combina con watch para monitorización en tiempo real y con notify-send para notificaciones en el escritorio. Además se integra directamente con el nightly-runner del episodio 815 para incluir el estado del sistema en el resumen matutino.Capítulos:0:00 - Introducción: el problema de la salida en texto plano2:30 - jc: convierte comandos Linux a JSON5:00 - jq: la navaja suiza de los JSON8:00 - gron: haz greppable cualquier JSON10:30 - Combinando jc y jq para consultas del sistema13:00 - sysreport.py: el script que lo junta todo16:00 - Preguntando al sistema en lenguaje natural19:00 - Monitorización con watch y notificaciones21:00 - Ventajas: local, sin coste y sin dependencias23:00 - Cierre: el tridente JSONMás información y enlaces en las notas del episodio

Cuando una familia contacta por primera vez con el centro, muchas veces no llega únicamente buscando una cita. Llega con dudas, con preocupación, con prisas o sin saber muy bien cuál debe ser el siguiente paso.Antes de entrar en consulta hay una parte fundamental del proceso que no siempre se ve: escuchar, orientar, organizar agendas, coordinar gestiones y procurar que cada persona se sienta atendida desde el primer momento.Hoy me siento a charlar con May Jiménez, que forma parte del equipo de recepción y está en contacto directo con nuestros pacientes y sus familias. Queremos descubrir cómo es realmente ese trabajo, qué ocurre detrás de cada llamada y cuántas piezas hay que mover para que todo parezca sencillo cuando llegáis al centro.¡Dale al PLAY y nos vemos dentro!Si tienes un hijo con algún problema neurológico o sospechas que puede ser la causa de sus dificultades y quieres que te guiemos por el camino correcto, ve ahora mismo a descargar las guías gratuitas para padres que tengo en la web www.elneuropeditara.es. En menos de 15 minutos podrás tener una idea bastante clara de qué le pasa a tu hijo y los pasos a seguir para ayudarle.Si ya tienes claro que valore a tu hijo o quieres una segunda opinión, Ponte en contacto ahora mismo con nosotros para que analicemos tu caso y nos pongamos manos a la obra. Llama al 682 651 047 o escríbenos al mail recepcionista@elneuropediatra.es

En un mundo lleno de promesas rotas, donde las palabras cambian con el viento y las certezas se desvanecen, hay un ancla que permanece firme; se llama Jesús.

Fluent Fiction - Spanish: Guided by a Llama: Unspoken Lessons from Machu Picchu Find the full episode transcript, vocabulary words, and more:fluentfiction.com/es/episode/2026-07-22-22-34-01-es Story Transcript:Es: En medio de las nubes bajas de Machu Picchu y bajo un sol invernal, Esteban, Montserrat y Valentino llegaron al punto de encuentro para su tan esperada visita guiada.En: Amid the low clouds of Machu Picchu and under a winter sun, Esteban, Montserrat, and Valentino arrived at the meeting point for their long-awaited guided tour.Es: El aire estaba lleno de emoción y murmullos de otros turistas.En: The air was filled with excitement and murmurs from other tourists.Es: Esteban, aunque impresionado por la majestuosidad del lugar, estaba nervioso.En: Esteban, although impressed by the majestic place, was nervous.Es: Su miedo a las alturas le preocupaba, y quería que todo fuese perfecto y seguro.En: His fear of heights worried him, and he wanted everything to be perfect and safe.Es: Montserrat, por otro lado, sonreía emocionada, lista para descubrir los secretos de esta antigua ciudad inca.En: Montserrat, on the other hand, was smiling excitedly, ready to discover the secrets of this ancient Inca city.Es: Valentino ajustaba la lente de su cámara, buscando la mejor composición para su blog.En: Valentino was adjusting the lens of his camera, looking for the best composition for his blog.Es: De repente, el guía que todos esperaban no apareció.En: Suddenly, the guide everyone was expecting did not show up.Es: En su lugar, una llama con un poncho multicolor se acercó al grupo.En: Instead, a llama with a multicolored poncho approached the group.Es: La llama levantó su cabeza altivamente, como si supiera que era el protagonista del día.En: The llama raised its head proudly, as if it knew it was the protagonist of the day.Es: Montserrat rió con deleite y dijo: "¡Miren, nuestro guía es una llama!"En: Montserrat laughed with delight and said, "Look, our guide is a llama!"Es: Esteban frunció el ceño, no muy entusiasmado con la idea de seguir una llama.En: Esteban frowned, not very enthusiastic about the idea of following a llama.Es: "¿Cómo va a explicarnos la historia de Machu Picchu?"En: "How is it going to explain the history of Machu Picchu?"Es: murmuró preocupado.En: he murmured worriedly.Es: Montserrat, sin embargo, estaba encantada.En: Montserrat, however, was delighted.Es: "Esto es único, Esteban.En: "This is unique, Esteban.Es: ¡Podría ser parte de su mística!"En: It could be part of its mystique!"Es: Valentino vio el potencial para una historia increíble.En: Valentino saw the potential for an incredible story.Es: "Esto hará un gran artículo para mi blog.En: "This will make a great article for my blog.Es: ¿Quién más puede decir que fue guiado por una llama?"En: Who else can say they were guided by a llama?"Es: Pese a sus dudas, Esteban decidió seguir al grupo.En: Despite his doubts, Esteban decided to follow the group.Es: Caminaban por senderos bordeados de piedras antiguas y vegetación exuberante.En: They walked along paths bordered by ancient stones and lush vegetation.Es: La llama avanzaba con paso seguro, haciéndoles detener en puntos clave para disfrutar de las vistas.En: The llama advanced with a secure step, making them stop at key points to enjoy the views.Es: Había una pizca de magia en la manera en que guiaba sin palabras.En: There was a hint of magic in the way it guided without words.Es: Llegaron a un tramo más empinado y complicado.En: They reached a steeper and more challenging section.Es: Esteban, concentrado en mantener el equilibrio, tropezó cerca de un acantilado.En: Esteban, focused on maintaining his balance, stumbled near a cliff.Es: En ese instante de pánico, sintió un suave empujón que le devolvió la estabilidad.En: In that moment of panic, he felt a gentle nudge that restored his stability.Es: Era la llama, que usando suavemente su lado, ayudó a Esteban a recuperar el equilibrio.En: It was the llama, which, using its side gently, helped Esteban regain his balance.Es: El grupo observó en asombro mientras Esteban recobraba su postura.En: The group watched in amazement as Esteban regained his posture.Es: "Gracias," murmuró Esteban, casi sin creerlo.En: "Thank you," Esteban murmured, almost not believing it.Es: La llama parpadeó, como si comprendiese su gratitud.En: The llama blinked, as if it understood his gratitude.Es: Más relajado, Esteban comenzó a ver la aventura desde una nueva perspectiva.En: More relaxed, Esteban began to see the adventure from a new perspective.Es: Comenzó a reírse de la situación, sintiendo un alivio que nunca habría imaginado sentir en esa altura.En: He started to laugh at the situation, feeling a relief he never imagined he would feel at such a height.Es: Montserrat, Valentino y Esteban continuaron el recorrido, ahora disfrutando de cada momento bajo el liderazgo de su curioso guía de cuatro patas.En: Montserrat, Valentino, and Esteban continued the journey, now enjoying every moment under the leadership of their curious four-legged guide.Es: Al final del día, mientras contemplaban el atardecer sobre las ruinas majestuosas, Esteban finalmente comprendió.En: At the end of the day, as they watched the sunset over the majestic ruins, Esteban finally understood.Es: A veces, para aprender y crecer, uno necesita dejar de lado el control y confiar en el flujo de lo inesperado.En: Sometimes, to learn and grow, one needs to let go of control and trust in the flow of the unexpected.Es: Montserrat y Valentino capturaron cada momento con palabras y fotos, asegurando que nunca olvidaran aquel día donde una llama les mostró, no solo Machu Picchu, sino también una lección sobre la confianza y la aventura.En: Montserrat and Valentino captured each moment with words and photos, ensuring they would never forget that day when a llama showed them not only Machu Picchu, but also a lesson in trust and adventure. Vocabulary Words:the guide: el guíathe len: la lentethe mystique: la místicathe path: el senderothe cloud: la nubethe adventure: la aventuraancient: antiguothe secret: el secretothe composition: la composiciónthe stability: la estabilidadthe moment: el instantethe protagonist: el protagonistamajestic: majestuosochallenging: complicadothe perspective: la perspectivathe vegetation: la vegetaciónthe ruin: la ruinasuddenly: de repenteexcitedly: emocionadamenteto stumble: tropezarunexpected: inesperadothe cliff: el acantiladoto blink: parpadearto murmur: murmurarthe composition: la composiciónto approach: acercarsenervous: nerviosoto smile: sonreírthe sunset: el atardecerrelief: el alivio

PODCAST LAS NOTICIAS CON CALLE DE 21 DE JULIO -  Regresan las multas de AutoExpreso - El Vocero Hutíes plantean cerrar paso a barcos de Arabia Saudita disparando el precio del petróleo a 90 - BBC Miguel Romero pide sacar a Itza García y a Francisco Domenech - TeleOnce Nuevo operador para inspeccionnes de carros otra vez empieza el proceso - El Vocero Brasil le pasa a USA como exportador de alimentos mundial - Semafor  Quieren limitar la ciudadanía americana a nacidos en territorios de USA - El Nuevo Día Acuden al tribunal federal para ayudar a que PR no tenga que pagar la deuda y advierten que es falso que afectemos al mercado municipal de bonos - El Nuevo Día Un momento para WindMar Home — la empresa con más de 20 años protegiendo los hogares puertorriqueños.Solar para bajar tu factura. Techo para proteger tu inversión. Agua para que nunca te quedes sin — especialmente con las sequías que se aproximan. Y batería para total independencia energética.Todo bajo una misma empresa. Un solo llamado. Llama al 787-489-1155 o visita windmarhome.comWindMar Home — los que se preparan hoy , duermen tranquilos mañana.#windmarhome#windmar #incluyeauspicio Se disparan los impagos en préstamos estudiantiles, PR lidera todo USA  - El Nuevo Dia Frutos exóticos en San Sebastián, boricua siempre montones de frutas que se dan en PR que nadie imaginaría que se dan aquí - Primera Hora Carraízo, Cidra, Matrullas y Loco entran a embalses de observación por falta de lluvia - Primera Hora Hogar de envejecientes ceierra tras estar operando sin permiso - WAPA Federales arrestan sujeto por estar amenazando a pentecostales, judíos, soldados americanos y otros - Noticel Muere Granados Navedo, exvicepresidente de la Cámara bajo el PNP - Metro Trump vuelve a meterle aranceles a Canadá de 50%, papas y varios productos LOS DATOS DEL DÍABrent~$89/barril · tocó $90 WTI~$83/barril Gasolina EE.UU. (retail)>$4.00/galón (+13¢ semana) S&P 5007,443.28 (−0.19%) Dow Jones51,839.26 (−0.59%) Nasdaq25,508.07 (−0.05%) Bono 10 años4.59% Gas natural (Henry Hub)~$3.70/MMBtu (prom. 2026, EIA)Euro/USD e hipoteca 30 añossin confirmar hoyCierre del 20 de julio. En PR (DACO, mediados de julio): gasolina regular ~$1.05-$1.10/litro, diésel ~$1.23-$1.32/litro.

La Mesa Internacional - 21.07.2026 - EEUU llama a combatir el “terrorismo de extrema izquierda” by En Perspectiva

Does restricting frontier AI in the name of safety actually make us less secure? Joshua Saxe joins me to make the case that it does, and that AI cybersecurity will be won through defender adoption, not restriction.Josh has spent 15 years at the intersection of AI and security. He built and ran the machine learning program at Sophos, then led security for Llama at Meta, covering security post training, evals, agent guardrails, and prompt injection prevention. He recently left to co-found a startup reimagining vulnerability and exposure management agentically. He also writes one of the most cited blogs on AI and cyber policy.In this episode:- Why restricting frontier model access harms defenders more than attackers- How monitored closed models put threat actors at a structural disadvantage- The jagged frontier, and why attackers don't need frontier models for most of their tradecraft- The national security and supply chain risks of pushing the world onto Chinese open weights models- Why exploits don't cause cyberattacks, and which attacker constituencies AI actually unblocks- The dual use ceiling on guardrails and classifiers- Where defenders should be adopting AI right now, from access management to SOC automation- Using agents to burn down the mountain of security technical debtChapters:0:00 Intro0:42 Josh's background, from blackhat teen to Llama security lead3:07 The case for diffusion over restriction6:14 Why restriction hurts defenders more than attackers10:19 The jagged frontier and what attackers actually use models for12:49 National security and the supply chain risk of Chinese open weights16:08 Exploits don't cause cyberattacks20:20 Where defenders should adopt AI right now24:20 Guardrails, classifiers, and the dual use problem27:34 Reimagining vulnerability management with agents32:17 The structural advantage defenders hold35:15 Policy wishes and the attacker's Claude Code momentFollow Josh:LinkedIn: https://www.linkedin.com/in/joshua-saxe-01845a1Substack: https://joshuasaxe181906.substack.comFollow Resilient Cyber:Substack: https://www.resilientcyber.ioSubscribe for more conversations with security practitioners and leaders.#aisecurity #cybersecurity #vulnerabilitymanagement #aipolicy #opensourceai

¿Cansado de perder 15 minutos cada mañana revisando el tiempo, las noticias, las ofertas y el estado de tu servidor? En este episodio te muestro cómo automatizar todo ese proceso con un script en Python, un timer de systemd y un modelo de lenguaje local. Sin n8n, sin agentes, sin servicios externos de pago.Mucha gente piensa que para automatizar cualquier cosa necesitas un agente con montones de herramientas MCP, skills y configuración. Pero la realidad es que para muchas tareas cotidianas, un agente es como usar un lanzamisiles para matar una mosca. Consume demasiado contexto, demasiados recursos y al final no es la solución más eficiente.En este episodio te presento el patrón de las tres capas: un script que hace el trabajo, un timer que lo ejecuta a una hora determinada y un sistema de notificaciones que te envía el resultado. Con esto puedes automatizar cualquier cosa de forma sencilla, eficiente y completamente bajo tu control.Te explico cómo he creado el nightly-runner, un script en Python que cada madrugada recopila información de cuatro fuentes distintas. Primero consulta el tiempo en wttr.in, que te devuelve un JSON con la temperatura, el viento, la humedad y los rayos ultravioleta. Luego hace scraping con IA de tus fuentes de noticias favoritas, extrayendo titulares y valorando su relevancia. Después busca ofertas de zapatillas de running en varias tiendas, comparando los precios con los del día anterior. Y por último recoge información del sistema con df, free, uptime y ps aux para saber si tu disco se está llenando o te estás quedando sin RAM.Toda esa información se guarda en archivos JSON y luego se pasa por un modelo de lenguaje local, Llama 3.2 con Ollama, que genera un resumen en lenguaje natural. El resultado es un mensaje de Telegram con un tono cercano que te da los buenos días, te cuenta el tiempo que va a hacer, te destaca las noticias importantes, te avisa si hay una oferta que no puedes dejar pasar y te informa del estado de tu servidor. Todo en un solo mensaje.El timer de systemd con Persistent=true se asegura de que si tu equipo estaba apagado a las 4 de la mañana, el script se ejecute en cuanto se encienda. Y cada capa es tolerante a fallos: si wttr.in está caído, el script simplemente omite el tiempo y el resumen dice que no hay información meteorológica disponible. Si no hay ofertas nuevas, no las menciona. Si Ollama no responde, envía el resumen sin procesar.Lo mejor de todo es que no necesitas saber Python para montar esto. Puedes usar Open Code o Gemini para que te genere el script con solo explicarle lo que quieres. Y para ejecutarlo, Llama 3.2 en local es más que suficiente. Sin gastar un euro en APIs.Capítulos:0:00 - Crítica a los agentes como solución universal2:00 - El problema: 15 minutos perdidos cada mañana4:00 - La solución: tres capas (script, timer, notificación)5:30 - wttr.in: el tiempo en JSON con un curl7:30 - Noticias: scraping con IA para extraer titulares9:30 - Zapatillas: comparativa de precios contra caché11:00 - Sistema: df, free, uptime y ps aux13:00 - El resumen: todos los JSONs pasan por Llama 3.216:00 - Systemd timer con Persistent=true18:00 - Notificaciones a Telegram y notify-send20:00 - Tolerancia a fallos en cada capa21:30 - Genera el script con IA aunque no sepas Python23:00 - Comparación con Hermes: menos es másEste podcast pertenece a la red de Sospechosos Habituales. Más información en atareao.esMás información y enlaces en las notas del episodio

PODCAST LAS NOTICIAS CON CALLE DE 17 DE JULIO - Mega proyecto para arreglar en varias fases el super acueducto - El Nuevo Día500 mil de fianza a sujeto que iba a dejar morir o matar viejita en Las Piedras - El Vocero Se supone que al fin acabe el escaneo de furgones - El Nuevo Día Gas Natural de USA el gran ganador de guerra de Ormuz - Semáforo Trump jura que CHINA interviene en elecciones de USA y comete fraude - CNN Corrección podría mudarse por renta muy cara - El Nuevo Día Supremo no decide si va a resolver el caso de LUMA de arranque - El Nuevo Día Guerra de grandes ligas por pago de COBRA tras impuestos a la construcción de alcaldes - El Nuevo Día Siempre innovando y con los mejores beneficios, MCS Personal Directo te ofrece cubiertas accesibles para que cuides de tu salud y la de los tuyos.Con una amplia red de proveedores de más de 15,000 médicos de libre selección. Reembolso de hasta $40 mensuales por membresía a un gimnasio o por un entrenador personal debidamente certificado. Asistencia en el hogar para servicios de cerrajería, plomería y electricidad de hasta $350 por evento hasta 4 veces al año.¡Únete HOY a la gran familia de MCS!¡Salud que completa tu vida! Llama al 787.945.1259 y oriéntate.Endoso pagado#incluyeauspicio #MCSEmpezarán a cobrar el tren urbano otra vez - El. Vocero Educación dice que ha cumplido tanto con los padres de educación especial que merece que bajen multa de 11 mil diarios a mil - El Nuevo Día Demócratas inundados en dinero en comparación con republicanos para midterms - Punchbowl News Fin de semana sin IVU para escuelas arranca hoy - El Nuevo Día Buscan apoyo a proyecto de status - El Nuevo D´â Ahora Valerie Rodz dice que Domenech sí intervino para detener solicitud de propuestas - El Nuevo Día Le tendrán que dar a los fondos buitres de la deuda de la AEE expediente para ver si gastaron dinero que era para la deuda - El Nuevo DíaLOS DATOS DEL DÍA  Brent ~$84.93/barril · cerca de máximos de 1 mesDiésel/gasolina al alza siguiendo el crudo (dato PR sin confirmar)S&P 500 7,533.77 (−0.51%)Dow Jones 52,552.97 (−0.20%, −105.67 pts)Bono 10 años 4.59% (+0.04)Euro/USD 1.1433 (−0.27%)Gas natural Henry Hub sin confirmar; gas europeo €55/MWh (máx. desde marzo)Hipoteca 30 años 6.55%

El Padre Celestial nos ama personalmente, conoce toda nuestra historia y nunca nos ha abandonado. Cuando descubrimos quién es Él, la oración deja de ser una obligación y se convierte en el encuentro gozoso con nuestro Padre.

PODCAST LAS NOTICIAS CON CALLE DE 16 DE JULIO - Gobierno dice que LUMA tiene que irse en el Supremo de PR - El Vocero 200 millones para rehabilitar el tren urbano - El Vocero Nuevo arancel de 25% a Brasil entra el 22 de julio; escalada comercial en curso.Los demócratas se rompen por Israel, sobre 100 votan contra ayudar a Israel - Semafor Bonistas logran aliado en Rivera Schatz, alega que la Junta solo quiere ofrecer poco para seguir quedándose en PR y no negociar pa guisar en PR - Noticel AAA dice que dará créditos por falta de servicio - El Vocero Trump tiene mensaje especial esta noche a las 9PM Un momento para WindMar Home — la empresa con más de 20 años protegiendo los hogares puertorriqueños.Solar para bajar tu factura. Techo para proteger tu inversión. Agua para que nunca te quedes sin — especialmente con las sequías que se aproximan. Y batería para total independencia energética.Todo bajo una misma empresa. Un solo llamado. Llama al 787-489-1155 o visita windmarhome.comWindMar Home — los que se preparan hoy , duermen tranquilos mañana.#incluyeauspicio #windmarhomeAlcaldes se enteran por los medios de racionamiento de agua, Comerío, Cidra, Canóvanas y Río Grande, San Lorenzo no tendrá, no anunciaron en Carolina - El Vocero EEUU atacó un petrolero y objetivos en el norte de Irán; Teherán respondió contra bases estadounidenses en Baréin, Kuwait y Jordania, y advierte que resistirá "hasta el final".Líderes de inteligencia artificial piden regulación de la inteligencia artificial - Axios Al fin aprueban ley para prohibir el escaneo obligatorio de furgones - El Vocero LePen está al frente en las encuestas de Francia - Reuters Se harán 3 casinos más en PR - El Vocero No hay jurisdicción federal en caso de Domenech y Sebastián hasta ahora - El Nuevo Día Huevos bajan demasiado de precio tras dumpeo federal - El Nuevo Día Albergue se va a quedar con perro que atacó a secretario de Recursos Naturales - El Nuevo Día Corrección no paga la renta de su sede y quieren contrato de 4 millones por quedarse en oficinas - El Nuevo Día Tres testigos dicen que Elvia sacó algo punzante de cartera - El Nuevo Día Sigue la guerra de Paulson v. Ghaffar en el tribunal de PR - El Nuevo Día Este weekend es el back to school sin IVU   LOS DATOS DEL DÍA (cierre / referencia 15-16 jul) Brent$84.95 · +12% en 3 sesiones  Gasolina EEUU (AAA)$3.89/gal · +9¢ semana  S&P 500 (fut.)~7,610 · -0.1%  Dow (fut.)+145 pts · +0.3%  Bono 10 años4.57% Euro/USD1.1431 · +0.1% Gas natural (Europa)€53.1/MWh  Hipoteca 30 años~6.65%

Manuel, de Sevilla , vivió durante mucho tiempo atrapado en un bucle muy duro: ataques de ansiedad constantes (hasta 4 al día) que le dejaban completamente agotado, y un miedo muy frecuente pero poco comprendido: el miedo a atragantarse.La ansiedad lo tenía contra las cuerdas… hasta que un día su padre le regaló mi libro “Sin Miedo”, y ahí empezó su transformación. Poco a poco, entendiendo qué le pasaba y perdiendo el miedo al miedo, Manuel fue recuperando su calma, su energía y su alegría.Hoy está genial, más tranquilo, más fuerte y más feliz que nunca.Tú también puedes:

En este episodio te enseño a combinar ImageMagick, Tesseract y Llama 3.2 para crear un pipeline de OCR inteligente en Linux. Olvídate de reescribir capturas de pantalla a mano.¿Cuántas veces te ha pasado que alguien te envía una captura de pantalla con información que necesitas y tienes que copiarla a mano? O peor aún, tienes un PDF escaneado del que no puedes seleccionar texto. Hasta ahora la solución era pasar horas transcribiendo o conformarte con un OCR básico que devuelve texto lleno de errores. En este episodio te muestro cómo construir un pipeline completo que automatiza todo el proceso.Primero capturas la imagen o la recibes, luego la preprocesas con ImageMagick para mejorar el contraste y eliminar ruido, después aplicas Tesseract para el OCR y por último pasas el resultado por un modelo de lenguaje local, Llama 3.2, que corrige los errores y da formato al texto. Todo con herramientas de código abierto, sin enviar tus datos a servidores externos.El script ocr_ai.py que he creado es capaz de detectar automáticamente el tipo de contenido que estás procesando. Si es código fuente, aplica un pipeline de preprocesamiento más agresivo con threshold y sharpen intensivos, y un prompt de IA específico para restaurar la indentación y la sintaxis sin cambiar nombres de variables. Si es una tabla, preserva la estructura de filas y columnas, corrigiendo números mal reconocidos pero sin rellenar celdas vacías. Si es prosa, simplemente corrige acentos, puntuación y caracteres mal interpretados sin alterar el significado del texto.También te explico cómo monitorizar un directorio con inotifywait para que cualquier imagen que caiga en él se procese automáticamente, ideal para combinarlo con carpetas compartidas de Nextcloud o Syncthing y tener OCR automático desde el móvil. Y cómo combinar todo con wl-copy en Wayland o xclip en X11 para tener el texto directamente en el portapapeles con un solo atajo de teclado.Cubrimos Tesseract 5 y su motor LSTM, los modos de segmentación de página (PSM 3, 6, 7 y 11), los diccionarios personalizados con --user-words para dominios específicos, las operaciones de ImageMagick v7 como colorspace gray, normalize, threshold, deskew, sharpen, negate y morphology, y cómo ajustar cada parámetro según el tipo de imagen. Además hablamos de OCRmyPDF para añadir capa de texto a PDFs escaneados y convertir cualquier PDF en un documento seleccionable y buscable.El episodio incluye consejos prácticos para troubleshooting: cómo ajustar el threshold cuando el texto es borroso (valores entre 40% y 65%), cómo reducir el ruido con filtros de morfología, cómo redimensionar imágenes con texto muy pequeño usando -resize 200%, cómo manejar fotos de documentos con -median 3, y cómo crear diccionarios personalizados con --user-words para mejorar el reconocimiento en dominios específicos como facturas, logs o código fuente.Capítulos:0:00 - El problema de las capturas de pantalla2:00 - Motivación: running, Hermes y el pipeline completo4:00 - Captura y preprocesamiento con ImageMagick6:30 - Operaciones clave: normalize, threshold, deskew y sharpen8:30 - Pipelines personalizados según el tipo de imagen10:30 - Tesseract: instalación, modos y diccionarios14:00 - ocr_ai.py: detección automática del contenido18:00 - Corrección con Llama 3.2 y prompts específicos23:00 - Monitorización de directorios y automatización24:30 - Consejos prácticos y cierreMás información y enlaces en las notas del episodio

Señor, haz de mi corazón una tierra fértil para recibir tu Palabra y de mi vida un instrumento para sembrar esperanza, amor y fe. Que cada palabra, cada gesto y cada acción reflejen tu presencia, llevando frutos de paz a quienes más lo necesitan. Porque quien siembra contigo, cosecha vida eterna y participa de tu obra de amor. #EnLaHoraDelEncuentro

¿Qué ocurre cuando un niño tiene ganas y capacidad para practicar deporte, pero necesita ayuda para comprender una instrucción, anticipar un cambio, regularse o integrarse dentro del grupo?En muchos casos, su participación acaba dependiendo de que su padre o su madre puedan acompañarlo durante cada entrenamiento. Y cuando la familia no puede estar, el niño se queda fuera.Hoy me acompaña Manuel Moreno, impulsor de Proyecto Cohete, una iniciativa que nace de su propia experiencia junto a su hijo Manuel, un niño con autismo que encontró en el atletismo un espacio de bienestar, aprendizaje y pertenencia.De esa experiencia surgió una propuesta muy concreta: la creación de la figura del Auxiliar Técnico Deportivo, un profesional que ayude a que niños con autismo, TDAH u otras necesidades de apoyo puedan participar de verdad en el deporte base.Si tienes un hijo con algún problema neurológico o sospechas que puede ser la causa de sus dificultades y quieres que te guiemos por el camino correcto, ve ahora mismo a descargar las guías gratuitas para padres que tengo en la web www.elneuropeditara.es. En menos de 15 minutos podrás tener una idea bastante clara de qué le pasa a tu hijo y los pasos a seguir para ayudarle.Si ya tienes claro que valore a tu hijo o quieres una segunda opinión, Ponte en contacto ahora mismo con nosotros para que analicemos tu caso y nos pongamos manos a la obra. Llama al 682 651 047 o escríbenos al mail recepcionista@elneuropediatra.es

En esta Clavada Telefónica, alguien que asegura ser Lionel Messi llama a Rosa Melo para enfrentar una situación inesperada: ella nunca ha sido fan del astro argentino. Lo que sigue es una conversación llena de incredulidad, respuestas inesperadas y mucho humor, mientras el supuesto Messi intenta convencerla de que merece una oportunidad. ¿Caerá en la trampa o descubrirá la clavada antes de tiempo? Una llamada llena de risas que ningún amante del fútbol se puede perder.See omnystudio.com/listener for privacy information.

Modern life asks less and less of our bodies, while placing ever greater demands on our minds. We move less, sleep poorly and fill every spare moment with stimulation—often without realizing the long-term consequences.Oscar Trelles explores the connections between recovery, resilience and the way we age. His work—through the wellness and performance company Breathing Flame—focuses on helping people better understand the conditions that shape health and long-term wellbeing.In his forthcoming book, The Human OS Manual, he argues that a longer, healthier life depends less on isolated interventions and more on the rhythms and routines that shape our days.So have we lost touch with the conditions that help us thrive—and what would it take to restore them?----DISCLOSURE: This podcast is supported by affiliate arrangements with a select number of companies. We have arranged discounts on certain products and receive a small commission on sales. The income helps to cover production costs and ensures that our interviews remain free for all to listen. Visit our LIVE LONG SHOP for more details: PartiQlar supplementsEnhance your wellness journey with PartiQlar supplements. No magic formulas, just pure single ingredients, like NMN, L-Glutathione, Spermidine, Resveratrol, TMG and Quercetin. Get a 15% discount with the code MASTERAGING15 at PartiQlarEnergyBits algae snacksA microscopic form of life that could help us age better. Use code LLAMA for a 20 percent discountDisclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showThe Live Long podcast, a HealthSpan Media LLC production, shares ideas but does not offer medical advice.  If you have health concerns of any kind, or you are considering adopting a new diet or exercise regime, you should consult your doctor.

This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstBritain's most capable coding model can't be exported, and that ban is the whole reason Cosine set out to build one from scratch. Alistair Pullen, CEO and co-founder of Cosine, sits down with Tim Scarfe to explain how a frontier system he calls Fable, locked behind US export controls, became the founding case for a UK sovereign model trained on the Isambard supercomputer in Bristol.The bet underneath it is economic. Pullen argues that an inference company, rather than a training-first lab, doesn't need billions to compete: millions, a national compute allocation, and a consortium feedback loop can be enough. From there it gets into the machinery, why open-weight models still trail the frontier on size, active parameters and data, the mixture-of-experts versus dense trade-off and why active params dominate how a model actually feels, and the edge that real coding trajectories confer.The back half is about making agents trustworthy. Pullen makes the case for beating "slop" by rewarding the process instead of the final answer, reframes code review as runtime proof (spin the bug up in a VM and force the agent to actually exploit it), and walks through Swarm, Cosine's system running hundreds of sub-agents in one shot. It ends on why memory is still an unsolved hack, how synthetic graders let you run RL on tasks with no built-in test, and why Pullen reads US export controls as an accidental gift, with a supply-chain sting in the tail.---TIMESTAMPS:00:00:00 The sovereign mandate and the Fable ban00:04:02 Millions vs billions: the inference-company model00:07:19 The consortium feedback loop00:07:40 Why open models lag the frontier00:14:59 MoE vs dense, and why active params matter00:16:29 Trajectories: the process-data advantage00:19:48 Beating slop: reward the process, not the answer00:26:06 Reusable abstractions and the epistemic wall00:29:56 Code review becomes runtime proof00:37:32 Do agentic harnesses still matter?00:40:35 Swarm: orchestrating hundreds of sub-agents00:45:14 Why memory is still unsolved00:48:25 Synthetic data and graders for RL00:53:09 The US export gift and supply-chain risk---REFERENCES:organization:[00:01:15] Cosinehttps://cosine.sh[00:04:14] Mistral AIhttps://mistral.ai[00:05:50] Anthropichttps://www.anthropic.com[00:07:42] Coherehttps://cohere.com[00:08:36] DeepSeekhttps://www.deepseek.comtool:[00:02:52] Isambard-AIhttps://isambard.ac.uk[00:05:56] Colossus (xAI)https://en.wikipedia.org/wiki/Colossus_(supercomputer)[00:07:52] GLM (Z.ai)https://z.ai[00:11:52] NVIDIA B300https://www.nvidia.com/en-us/data-center/dgx-b300/[00:15:37] gpt-oss-120bhttps://huggingface.co/openai/gpt-oss-120b[00:15:52] Devstral 2https://mistral.ai/news/devstral[00:16:01] Llama 70bhttps://www.llama.com[00:17:05] Claude Codehttps://www.anthropic.com/claude-code[00:26:23] ARC-AGI (Francois Chollet)https://arcprize.org[00:40:38] Swarm (Cosine)https://cosine.sh[00:40:50] OpenAI Codexhttps://github.com/openai/codex[00:41:16] Lumen Outpost (Cosine)https://cosine.sh[00:41:18] Kimi K2 (Moonshot)https://huggingface.co/moonshotai/Kimi-K2-Instruct[00:49:55] SWE-benchhttps://www.swebench.com[00:52:40] SystemVeriloghttps://en.wikipedia.org/wiki/SystemVerilogperson:[00:23:40] Andrej Karpathyhttps://karpathy.aipaper:[00:27:10] GRPO (DeepSeekMath)https://arxiv.org/abs/2402.03300[00:27:13] GSPOhttps://arxiv.org/abs/2507.18071Incompressible Knowledge Probes, Bojie Lihttps://arxiv.org/pdf/2604.24827Estimating the Size of Claude Opus 4.5/4.6https://unexcitedneurons.substack.com/p/estimating-the-size-of-claude-opus---ReScript:https://app.rescript.info/session/5852d2b884c4ce4b?share=10b9799160845bb11779f8ac6cd3124f

Semillas que cayeron 1) Orilla: Si vos me traicionaste y yo te traiciono, mi traición no habla de tu actitud, sino de la mía. Trata de no perder nunca de vista eso, porque hasta justifican diciendo “Se lo merecía” y hasta usan el famoso “Quien roba a un ladrón tiene cien años de perdón” y no, quien roba a un ladrón es un ladrón. Capaz que vos te mereces que te traicione, pero ¿yo merezco ser un traidor? ¿Quiero vivir siendo un traidor? ¿Yo quiero ser eso solo porque vos lo fuiste? Cruzar a la otra orilla es aprender a ver más allá de mi vida y no hacer lo que otros me hagan.2) Multitud: Aunque mucho nos cueste entenderlo, no podemos sanar en el mismo ambiente donde nos enfermamos. Por eso hay que aprender de Jesús que sabe alejarse de esa multitud enfermiza, la multitud de cosas que haces o la multitud de gente con que te juntas. No sigas en la multitud que te enferma, porque esa multitud no te va a sanar, porque lo que te enferma no te sana. Llama a esa multitud como quieras: nombre de persona, gente, trabajo, tiempo, etc.3) Espinas: Las actitudes no deben estar a la altura del que las recibe, sino más bien de quien las da. Se cuenta una anécdota de Alejandro Magno, quien, yendo en uno de sus grandes viajes, se estaba muriendo de hambre. Fue recibido por una familia muy pobre, muy humilde, que lo alimentó y dio algo de tomar. Él les terminó agradeciendo regalándoles un imperio. El hombre de la casa le agradeció, pero le dijo “Es un regalo muy grande para nosotros” a lo cual Alejandro contestó “Pero yo no quiero agradecer de una manera más chica que esa, porque las actitudes hablan más de quien las da que de quien las recibe”. Así que los frutos son producto de nuestro corazón y no podemos conseguir grandes frutos de la vida y en la vida si no dejamos que la semilla de Dios entre en lo que le da sentido a nuestras vidas. Algo bueno está por venir.

PODCAST LAS NOTICIAS CON CALLE DE 10 DE JULIO - Israel dice que Irán iba a asesinar a Trump y por eso cambiaron de avión presidencial - CNN 22 personas y empresas se declaran en quiebra por día - El Nuevo Día Gobernadora entrega 200 títulos de propiedad - NEWSPR Acusan a Gian Carlo Piovanetti por vivir como rico cogiendo de tonto a clientes y fraude - El Nuevo Día Plantean darle alivios a ayunadores y cuidadores, costo de 300 millones - El Nuevo Día  PR es el líder en enfermedades raras en todo USA - El Vocero Ciencias Forenses busca encontrar personas desaparecidas para dar paz a familias que no encuentran a sus seres queridos - El Vocero Politank dice que se va a defender de todo esto sal pa fuera - El Vocero Agricultura y comida en aumento de precio por sequía y fertilizantes - El Nuevo Día Nombran fiscal investigadora en caso de Negrón Reichard - El Nuevo DíaUn momento para WindMar Home — la empresa con más de 20 años protegiendo los hogares puertorriqueños.Solar para bajar tu factura. Techo para proteger tu inversión. Agua para que nunca te quedes sin — especialmente con las sequías que se aproximan. Y batería para total independencia energética.Todo bajo una misma empresa. Un solo llamado. Llama al 787-489-1155 o visita windmarhome.comWindMar Home — los que se preparan hoy , duermen tranquilos mañana.#windmarhome #incluyeauspicioPetróleo/diésel: Brent ~$76; diésel de EE.UU. al alza más rápida en 4 años; Rusia prohíbe exportar diésel (≈30% de su refinación estuvo fuera el mes pasado)Segundo día de ataques Irán–EE.UU.OpenAI y Google vendieron modelos avanzados a subsidiarias en Singapur de Alibaba, Baidu y Tencent — empresas en la lista negra del Pentágono - FT  LOS DATOS DEL DÍA (snapshot Bloomberg, 10 jul) Brent$76.13 (-0.2%) S&P 500 (futuros)7,580.75 (-0.1%) Nasdaq 100 (futuros)29,817.25 (-0.4%) Bono 10 años4.53% (-0.02) Oro$4,104.95 (-0.5%) Diésel EE.UU.alza más rápida en 4 años (nivel s/c)

Andrew "Boz" Bosworth is the chief technology officer of Meta. Bosworth joins Big Technology to discuss why Meta fell behind in the frontier AI race and how it plans to turn its models, products, and distribution into an advantage. Tune in to hear his candid explanation of what went wrong with Llama, why the best AI products will use multiple models, and what it will take for consumer agents to break through. We also cover Meta's AI glasses, the future of augmented reality, employee tracking and training programs, AI companions, and the painful process of adapting a company to a technological revolution. Hit play for a revealing conversation about Meta's AI comeback and the products that could shape how we interact with computers. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Watch the full documentary here: https://www.gravitee.io/ai-agent-documentary Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

- 118 escuelas bilingües en PR Gobierno dice tiene superávit de 635 millones No hay break para sacar a la Junta dicen los expertos - El Nuevo Día Legislatura propone que CRIM tenga que publicar propiedades por embargar para que se puedan poner en el mercado de compra de vivienda - El Vocero Informe federal advierte que reconstrucción eléctrica de PR está demasiado segmentada - Noticel Vivienda contrata a jefe de OGPE bajo FEI - Jay Fonseca PR Siempre innovando y con los mejores beneficios, MCS Personal Directo te ofrece cubiertas accesibles para que cuides de tu salud y la de los tuyos.Con una amplia red de proveedores de más de 15,000 médicos de libre selección. Reembolso de hasta $40 mensuales por membresía a un gimnasio o por un entrenador personal debidamente certificado. Asistencia en el hogar para servicios de cerrajería, plomería y electricidad de hasta $350 por evento hasta 4 veces al año.¡Únete HOY a la gran familia de MCS!¡Salud que completa tu vida! Llama al 787.945.1259 y oriéntate.Endoso pagado#mcs#incluyeauspicio Abel Nazario niega gestiones con agricultor y cuadrarle reunión con gobernadora - Noticel Menos conserjes escolares, supuestamente 40% menos - El Vocero La semana que viene se va jefe de fiscalía federal - El Nuevo Día Sujeto trató de meter 356 pastillas de Suboxone a la cárcel federal - El Nuevo Día Más cargos contra sujeto que era asesor legislativo y a la vez empresario por los donativos legislativos a quebrada Margarita - El Nuevo Día Guerra por contratos de seguridad en el gobierno - El Nuevo Día Irán despide hoy al Ayatollah Ataques de Rusia contra Ucrania dejan 30 muertos - Reuters Gobernadora pide explicaciones la Junta de libertad bajo palabra - El Vocero Archivada querella contra Ferraiuoli - El Vocero Canadá tendrá sus NBA en juego que PR no tendrá a Alvarado - Metro  2,295 muertos por terremotos de Venezuela Se espera mega bajón de precios del petróleo, Citi dice que a 60 el barril - Bloomberg Peligroso químico en aeropuerto de Aguadilla Se casa Taylor Swift y Travis Kelce - Washington Post  • ⁃ Trump chotió plan de Israel para matar nuevo liderado de Irán