Podcasts about CV

Most dream jobs I've landed in the last seven years didn't come from applying online - they came from a completely different approach.In today's video, I break down the exact 7-step system I've used for 16+ years to attract opportunities, network intentionally, and position your LinkedIn profile as a value powerhouse, not a digital CV.You'll learn: How to identify your real dream role (and why it matters), how to build a high-impact LinkedIn profile that works for you, how to connect with the right people using smart filters, how to send effective, personalised connection requests, how to nail a virtual coffee chat, when and how to ask for referrals, the mindset shift that changes your entire job search.Looking to go from chaos and unpredictability to resilience in the world of AI? Start here with The Predictability Factor newsletter at The Monica Talks Cyber (https://www.monicatalkscyber.com).

Joe Woodward had what most people would call a dream job. Chief Marketing Officer for the Rajasthan Royals in Mumbai. Ten years building things in sport, music and entertainment. Then the pandemic hit, the rug came out from under him, and he moved home.He sat down to update his CV. He immediately got frustrated.That frustration became Vizzy — a platform built to replace the 500-year-old document Leonardo da Vinci invented, and give people a genuinely human way to show who they are. Not bullet points and PDFs. Not job titles and logos. Who they actually are.In this episode of Screw It Just DO It, Joe tells the full story. The flip chart moment with his sister Jess and her husband Chris. The investor conversation with Robert Dodds, Simon Fuller's business partner, that ended with four words: 'That's the idea. I'll back it tomorrow.' The cold start launch. The Instagram DM from a stranger who said Vizzy had just landed them their dream job. And the lesson from Burberry, Louis Vuitton, EY and Tiffany that the future of hiring is not about getting more applicants — it's about getting fewer, better ones.Key Takeaways- Why the flip chart moment changed everything — and what that looked like in practice- How Joe went from zero to Burberry and Louis Vuitton without a hiring background- Why the best founders are still personally obsessed with hiring at £2 billion- What Vizzy taught Joe about the difference between instinct and overthinking

¿Creéis que el nuevo y polémico Ferrari Luce eléctrico es el primero que consigue que los puristas de Maranello auguren el fin del mundo automovilístico? ¡Pues no! La historia de Ferrari está plagada de escándalos, herejías mecánicas y modelos que fueron repudiados cruelmente en su lanzamiento. Quizás la controversia siempre ha sido el motor secreto de la marca. Enzo Ferrari decía: “Yo no vendo coches, vendo motores”. Si “Il Commendatore” levantase la cabeza, muchos se preguntan qué diría del nuevo Luce, un eléctrico puro que ha incendiado las redes sociales. Pero antes de rasgarnos las vestiduras, repasemos las ocasiones en las que la marca ha roto sus propias reglas y ha enfurecido a sus seguidores. Los mayores "escándalos" de Maranello A lo largo de las décadas, Ferrari ha tomado decisiones que en su día parecieron auténticos sacrilegios para los más fanáticos de la marca: Dino 206 y 246 GT (1967): El Ferrari que nació huérfano. Enzo creía que un Ferrari siempre debía tener 12 cilindros, así que este V6 ni siquiera llevaba el escudo oficial. Hoy es uno de los deportivos más bellos, equilibrados y cotizados de su época. Ferrari 308 GT4 (1973): La traición geométrica de Bertone. Un diseño en forma de cuña, extremadamente anguloso, con motor central V8 y configuración 2+2. Lo acribillaron por sus proporciones, pero su chasis, afinado por Niki Lauda, ofrecía un tacto sublime. Ferrari Mondial 8 (1980): El "patito feo". Lastrado por las estrictas normativas de emisiones de EE. UU., su inyección redujo la potencia a unos raquíticos 214 CV. Las prestaciones iniciales fueron un desastre, aunque el modelo evolucionó hasta ser muy rentable. Ferrari 456 GT y GTA (1992): ¿El Cavallino domesticado? Su diseño burgués fue criticado, pero la verdadera herejía fue la versión GTA, que introdujo una arcaica caja de cambios automática. Ferrari F50 (1995): ¿Vivir a la sombra del mito? Suceder al todopoderoso F40 era una tarea imposible. Su motor V12 atornillado directamente al chasis transmitía cada vibración a la espalda del conductor. La prensa lo tachó de tosco, pero hoy es el Santo Grial analógico de los coleccionistas. Ferrari Enzo (2002): La nariz de la discordia. El diseño japonés de Ken Okuyama apostó por un aerodinamismo brutalista, inspirando su morro en la Fórmula 1. Al principio fue calificado de feo y exagerado, pero su velocidad acalló todas las críticas. Ferrari California (2008): El descapotable de bulevar. Primer V8 delantero, primera inyección directa, primer cambio de doble embrague y primer techo duro retráctil. Los puristas lo odiaron, pero atrajo a un 70% de clientes nuevos a la marca. Ferrari FF (2011): El Ferrari para ir a esquiar. Un formato "shooting brake" que muchos apodaron "el zapato de payaso", acompañado del primer y polémico sistema de tracción total de la marca. Ferrari Purosangue (2022): El “innombrable”. La herejía final: un vehículo de cuatro puertas y gran altura. A pesar de que Sergio Marchionne juró que jamás harían un SUV, su aplastante V12 atmosférico lo convirtió en un éxito tan violento que tuvieron que cerrar la lista de pedidos. Ferrari Luce (2026): El hereje silencioso Llegamos al presente con la mayor bomba de la historia del Cavallino: el Ferrari Luce. Se trata del primer vehículo 100% eléctrico de Maranello, y la polémica que ha desatado es incalculable. Sus revolucionarias cifras han provocado síncopes en los foros del motor: Crossover "liftback" de cinco puertas que supera los 2.260 kilos de peso. -Arquitectura de 800 voltios con una inmensa batería de 122 kWh. -Cuatro motores independientes que rinden más de 1.050 CV. -Aceleración de 0 a 100 km/h en 2,5 segundos. -Un precio estratosférico que supera los 550.000 euros. No hay rugido, no hay cilindros y no hay escapes. El habitáculo es una revolución digital sin relojes analógicos, diseñado en colaboración con los creadores de Apple. Los aficionados más radicales acusan a la marca de perder el alma y de crear un "electrodoméstico sobrepotenciado". ¿Y qué ha pasado en el mercado real? La producción está estrictamente agotada hasta finales de 2027. Conclusión La historia nos enseña que el inmovilismo es una muerte segura. Cada vez que Ferrari ha roto las reglas, los puristas han gritado y la prensa ha criticado, pero las ventas y el tiempo siempre han dado la razón a Maranello. El polémico Luce eléctrico no es el fin de la marca, es solo otro emocionante y tumultuoso principio.

Nick is joined on the eve of Royal Ascot by The Mirror's David Yates, and Nick at length speaks to the King and Queen's racing manager John Warren about the chances of a Royal winner this week. Michael Mulvany looks forward to his two 2-year-olds in the Coventry tomorrow before Adam Mills previews the St James's Palace, Prince of Wales's and Gold Cup at Ascot whilst Harry Eustace shares whether he believes Docklands can go back-to-back in the Queen Anne. Finally, Henri Bozo discusses Diamond Necklace's victory in the Prix de Diane yesterday, a race that was missing from Monceaux's CV.

Show Notes: Gaurav Bhosle talks about his coaching practice, which is split 50/50 between helping people get hired by consulting firms and coaching current consultants. He shares his background as an ex-McKinsey consultant and his MBA from HEC Paris, noting the lack of preparation structures for consulting firms in 2006-2007. Breaking into Consulting Gaurav recounts how a former HEC alum helped him prepare for consulting firms, leading to his success in joining McKinsey in Frankfurt. He explains his transition from McKinsey to coaching, driven by his passion for strategy and career development, and his decision to focus on career strategy for consultants. Gaurav discusses his realization that breaking into consulting is not the ultimate goal but thriving in it is. The Upskilling Journey He shares his journey of upskilling, including obtaining ICF certification, psychometric tools, NLP, and TA, to provide deeper career coaching. Gaurav explains his shift from helping people get into consulting to coaching current consultants on career strategies and performance improvement. He emphasizes the importance of career happiness and the need for consultants to thrive in their roles, not just get hired. The Three-step Process  Gaurav describes his three-step process: foundation, parallel tracks (networking and practice), and polishing. He emphasizes the importance of mindset, skill set, and tool set, particularly the mindset of preparing to be a good consultant rather than just cracking interviews. Gaurav details the foundation phase, which includes preparing for cases, understanding fit questions, and polishing the consulting CV. Gaurav outlines the practical steps for case interview preparation, including the importance of practicing with peers and AI. He explains the three-step process for data interpretation: data sanity check, extracting insights, and communicating findings. The Value of Top-down Communication Skills Gaurav emphasizes the importance of top-down communication and preparing fit answers with headlines first. He shares tips for practicing data analytics skills, including using charts as part of case interviews and focusing on the context and problem-solving. Gaurav discusses the challenges of developing top-down communication skills, especially for those from Eastern cultures or non-consulting backgrounds. He shares his personal journey of adapting to top-down communication in McKinsey and the importance of pushing oneself to communicate insights at a higher level. Gaurav explains the STAR format for storytelling in interviews and the importance of starting with headlines. He emphasizes the need for consultants to communicate crisply and lead the conversation, rather than providing lengthy explanations Coaching Practice and Processes  When asked the first step in his coaching process, Gaurav explains the importance of achieving orientation and having clear career goals beyond superficial reasons like travel or status. He shares his use of psychometric assessments and the "Why should we hire you?" question to gauge a candidate's value proposition. Gaurav highlights the need for candidates to have a clear understanding of their career motivations and the ability to articulate their unique value. Coaching Consultants on Performance Improvement The conversation turns to Gaurav's practice of coaching current consultants on performance improvement. He shares an example of a recent MBB consultant seeking promotion to engagement manager and feedback on case leadership. Gaurav explains the importance of understanding the root cause of feedback and implementing systems for continuous improvement. He emphasizes the need for consultants to seek frequent feedback, develop systems for transparency, and build checklists for effective project management.  "Fit-for-consulting" Assessment Gaurav discusses the use of psychometric assessments and other tools to understand candidates' personality and fit for consulting. He shares his experience coaching a diverse range of professionals, including US Marines, public servants, and athletes, to transition into consulting. Gaurav highlights the importance of having a clear value proposition and the ability to articulate it effectively, and he emphasizes the need for consultants to have a strong achievement orientation and the willingness to adapt to the demanding nature of the role. Timestamps: 03:53: Transitioning to Coaching Current Consultants  06:01: Gaurav's Coaching Approach for Aspiring Consultants 10:58: Practical Steps for Case Interview Preparation 22:06: Developing Top-Down Communication Skills  26:50: Clarifying Career Motivations and Goals  30:48: Coaching Current Consultants for Career Growth  34:46: Gaurav's Coaching Methodologies and Tools  35:11: Gaurav's Online Presence and Contact Information  Links:  Company website:  https://www.beingconsultant.com/ Linkedin:  https://www.linkedin.com/in/consultingcareercoach/ Email: gb@beingconsultant.com   This episode on Umbrex: https://umbrex.com/wp-admin/post.php?post=300254&action=edit#:~:text=https%3A//umbrex.com/unleashed/gaurav%2Dbhosle%2Dma%E2%80%A6%2Din%2Dbuild%2Dthrive/  Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com. *AI generated timestamps and show notes.  

Nick is joined on the eve of Royal Ascot by The Mirror's David Yates, and Nick at length speaks to the King and Queen's racing manager John Warren about the chances of a Royal winner this week. Michael Mulvany looks forward to his two 2-year-olds in the Coventry tomorrow before Adam Mills previews the St James's Palace, Prince of Wales's and Gold Cup at Ascot whilst Harry Eustace shares whether he believes Docklands can go back-to-back in the Queen Anne. Finally, Henri Bozo discusses Diamond Necklace's victory in the Prix de Diane yesterday, a race that was missing from Monceaux's CV.

Episode: 00323 Released on June 15, 2026 Description:  Chris Mason has spent more than two decades serving as a law enforcement analyst with the Riverside County Sheriff's Department. Along the way, he has worked assignments in criminal intelligence, homicide, station operations, and public health overdose surveillance. He also serves as a director for the Association of Law Enforcement Intelligence Units (LEIU). In this episode, Chris discusses his journey from aspiring police officer to analyst, the importance of networking, intelligence operations, interdisciplinary partnerships, leadership, adapting to change, and the role analysts play in supporting public safety beyond traditional crime analysis. He also shares lessons learned from career setbacks, the importance of marketing analytical value, and why understanding your "why" can help sustain a long and fulfilling career.

「「ウマ娘」新育成ウマ娘として★3［花宿しのクチュール］カレンブーケドール（CV：小田杏樹）が登場！」　Cygamesは、iOS/Android/PC向けに配信中のゲーム「ウマ娘 プリティーダービー」において、新育成ウマ娘として★3［花宿しのクチュール］カレンブーケドール（CV：小田杏樹）を追加した。

「「チェンクロ」エレミアのメイド修行を受けたミユキ（CV：立花理香）が“森永製菓コラボフェス”に登場！」　セガは、iOS/Android向けアプリ「チェインクロニクル第5部 ―未来への導標―」において、本日6月14日より「すこやかなる六花 ミユキ（CV：立花理香）」が登場する“森永製菓コラボフェス”を開始した。

On parle IA et recrutement cette semaine dans tech 45' avec AssessFirst.Fondée en 2002 par David Bernard sur une conviction : le CV ne suffit pas pour prédire la réussite d'un candidat. Soft skills, capacités cognitives, motivations mais aussi hard skills et langues. Aujourd'hui, AssessFirst réalise 18M€ d'ARR avec ses 100 collaborateurs en 100% remote.David, qui a tout construit en bootstrap avant de faire entrer CAPZA en 2021, est avec nous pendant 45 mn !Je suis Seb Couasnon, abonne-toi, mets des étoiles, laisse-moi un avis. Merci de ta fidélité, bon épisode !Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Barbie has quite the impressive CV. She has had over 150 careers including fashion editor, surgeon and astronaut - enough to inspire any young fan. But what she has career-wise, she lacks when it comes to inclusivity. Barbie is usually blond, perfect and impossibly proportioned - there is a reason Margo Robbie is playing her in the latest Barbie film. But this is finally going to change: Barbie is becoming more inclusive. Mattel is launching a new ‘Fashonista line' which will include barbies with various disabilities, making it the most inclusive Barbie line to date. What does an inclusive Barbie look like? Why has it taken so long to have an inclusive Barbie? In under 3 minutes, we answer your questions! To listen to the last episodes, you can click here: ⁠How can I reduce my belly fat?⁠ ⁠What are the health benefits of algae?⁠ ⁠Why am I getting bags and circles under my eyes?⁠ A Bababam Originals podcast, written and produced by Joseph Chance. First Broadcast: 18/10/2023 Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us Fan MailMitali Jalan was told she couldn't break into consulting as a chartered accountant. She was told it was unlikely she'd land Bain as an international MBA student. She got the offers anyway.She worked at Deloitte India and Bain London, then sat on the other side of the table as a Bain interviewer. She's coached over 350 candidates and knows where people lose the offer.The thing she kept seeing? Candidates who spent weeks on case prep, walked in technically ready, and still didn't get it. The gap wasn't knowledge.We cover:How to recruit for a firm in a country that isn't yoursWhy "nice" is not a soft factor at Bain – and how to train for itWhat made Mitali stop reading a CV in secondsHow to track your prep so you're fixing the right things, not just logging casesResources:Work with Mitali directlyReady to break into MBB? Black Belt is the case prep program for serious candidatesBook a free 15-minute call with Katie to map out your pathFree Consulting Prep Just Got a Whole Lot BetterCreate a free MC account for access to step-by-step learning pathways, a brand new case prep course, and more. Download the MC app to prep anywhere.Connect With Management ConsultedCreate a free MC account or download the MC app (Apple, Android) to start your prep todaySchedule a free 15min consultation with the MC TeamWatch the video version of the podcast on YouTubeFollow us on LinkedIn, Instagram, and TikTokJoin an upcoming live event – case interviews demos, expert panels, and more

"Maybe It's Just Me" The indie rock outfit Lost Leaders were formed in 2011 by pals Byron Isaacs and Peter Cole. Isaacs' resume was already pretty full at the time, thanks to his work as the bassist for the Lumineers and playing with the Levon Helm Band and Ollabelle. If that doesn't sound busy enough, Isaacs has also recorded and performed with Bruce Springsteen, Roseanne Cash, Jackson Browne, Amy Helm, and Joan Baez. As for singer/guitarist Cole, his CV was pretty full as well, thanks to his tenure in Lava Baby, long-serving the New York jazz scene and working as a professional audio engineer. Cole and Isaacs have been pals since the late '90s, playing in bands like Slink and Lowdowners in Stereom, but Lost Leaders is where the two musicians really hit their artistic stride. With a handful of winning albums under their belts like Hungry Ghosts, Promises Promises and their self-titled debut album, Lost Leaders' work is redolent with harmonic dexterity, melodic muscle and rootsy bliss. Their latest effort is producing the full soundtrack and composing the original score for the indie film RUN, which stars Sarah Levy, Adam Palley and Chris Redd. By the way, the soundtrack features Amy Helm and Samantha Fish and it's just wonderful Lost Leaders are about to hit the road opening for The Wallflowers, but before they do, they had a chat with us. And here it is... https://byronisaacs.com www.bombshellradio.com www.stereoembersmagazine.com www.alexgreenbooks.com (http://www.alexgreenbooks.com) Stereo Embers Threads + IG + BLUESKY: @emberspodcast Email: editor@stereoembersmagazine.com

I'M RICK JAMES, NICK! This week, we had the distinct pleasure of hosting New York's Own, Nick James, but not that New York–until recently, anyway. Nick has a “chaotic” CV, to hear him tell it, so we get into it: joining the Air Force when he was 17, Meteorology, Lab Science, Moving to The Big Apple, racial ambiguity, WESTSIDE GUNN, Buffalo, Birding, Military LARPers, lacrosse, thrifting, skate culture, his intro to the menswear community, and, as always, way more!

David est le fondateur et CEO d'AssessFirst.Sa conviction depuis le départ : le CV ne suffit pas pour prédire la réussite d'un candidat.Son logiciel aide les entreprises à évaluer le potentiel réel des personnes et cela marche = 18M€ d'ARR, 1 500 clients actifs, +20% de croissance, plus de 100 collaborateurs.Bon teaser ! Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Geoff Downes has quite the musical CV. Shooting to fame with The Buggles, then joining Yes alongside Trevor Horn for their 1980 “Drama” album, he then had massive success alongside Yes member Steve Howe with the supergroup Asia. He rejoined Yes in 2011 where he remains to this day. Geoff joined me from his home in Wales for a career-spanning conversation discussing songs by The Buggles, Asia, and the epic Countermovement from the new Yes album “Aurora”.   Please forgive the sound quality on this interview. It's perfectly listenable but not optimal.   Find me on Instagram @sendingsignalspodcast

Nigel's guest today is Matt Cain. Matt is a shining light in the LGBTQ+ world and has been for many years. His impressive CV has included being Channel 4 News' first ever Culture Editor and Editor-in-Chief of the UK's biggest selling magazine for gay men, Attitude. He is an ambassador for many queer charities and was awarded an MBE for services to LGBTQ+ culture in 2025. As an author he has penned many novels, his latest being The Castle Of Stories published through independent publishers, Pansy, a company Matt has set up with his husband specialising in queer books by queer authors for all readers.This series is a celebration of a beautiful queer community; people of all ages, people who have had to tread their own path to live their real truth, who have fought with their emotions and emerged victorious, who inspire, who aspire and always entertain. Hosted by Nigel May. Every episode Nigel speaks to a person from the LGBTQIA+ rainbow to hear their story; one person, one life, one conversation. And it always guarantees A Gay Old Time!Follow the podcast on TikTok @agayoldtime and on Instagram @agayoldtimepodcast Hosted on Acast. See acast.com/privacy for more information.

Welcome back to Truth, Lies & Work, the award-winning workplace podcast where behavioural science meets workplace culture, brought to you by the HubSpot Podcast Network. This week, hosts Al Elliott and Leanne Elliott unpack the sleep science taking over TikTok, a massive shift in how Google interviews tech talent, and the ethical dilemma of corporate accountability after a major cyberattack. Plus, we dive deep into the data to see if "years of experience" actually matters on a CV, and answer community questions from the experts at The Business Psycho.

En el mundo del motor actual, pocas preguntas generan debates tan encendidos en redes sociales, foros y barras de bares como la comparativa entre el coche eléctrico y el de combustión. Hoy dejamos de lado la ecología para centrarnos en la física pura y dura. Vamos a analizar por qué un coche acelera como lo hace y si el "territorio voltio" es realmente tan imbatible. La tiranía de la báscula frente al milagro del par motor Para entender la aceleración, primero debemos entender los dos factores que luchan entre sí: la fuerza que empuja el coche y la masa que se opone a ese movimiento. El gran talón de Aquiles del coche eléctrico, hoy por hoy, es su peso. Las baterías de iones de litio tienen una densidad energética muy inferior a la de la gasolina. Sin embargo, el motor eléctrico tiene un as bajo la manga: el par motor instantáneo. En un motor de combustión, la entrega de fuerza es progresiva; los gases tienen que mover una turbina, los pistones deben subir y bajar, y el motor tiene que alcanzar un rango de revoluciones óptimo. En un eléctrico, el par es como un interruptor de la luz: está ahí desde el primer milisegundo. El sprint corto: El dominio absoluto del motor eléctrico Si hablamos del 0 a 100 km/h, el peso importa, pero la capacidad de tracción y el par inicial mandan. En esta distancia corta, el coche eléctrico suele ser el rey absoluto por su facilidad para transmitir la potencia al suelo sin drama. Si comparamos cifras, vemos casos fascinantes. Un Caterham Seven 620R de “solo” 315 CV, que es la máxima expresión de la ligereza térmica con solo 520 kg, logra hacer el 0 a 100 en 2,8 segundos. Es una cifra impresionante, pero un Tesla Model S Plaid, que pesa 2.162 kg (cuatro veces más), detiene el crono en 2,1 segundos. La estirada larga: La gasolina recupera el terreno La situación cambia drásticamente cuando la meta se aleja y buscamos alcanzar los 200 km/h. A partir de los 120-130 km/h, entra en juego un factor determinante: la resistencia aerodinámica, que crece de forma exponencial con el cuadrado de la velocidad. Para vencer ese muro de aire invisible, ya no basta con tener mucho par inicial; necesitas potencia sostenida y una gestión eficiente de la energía a altas revoluciones. La mayoría de los coches eléctricos utilizan una sola marcha, lo que significa que, a velocidades muy altas, el motor eléctrico empieza a girar fuera de su zona de máxima eficiencia. Aquí es donde el motor térmico, apoyado en sus cajas de cambios de 7 u 8 velocidades, saca pecho. Un ejemplo claro es la comparativa entre dos hermanos de marca: el Porsche 911 Turbo S (térmico) y el Porsche Taycan Turbo S (eléctrico). Aunque el Taycan tiene más potencia (761 CV) y mucho más par, el 911 Turbo S le acaba ganando la partida antes de llegar a los 200 km/h por una sencilla razón: pesa 1.640 kg frente a los casi 2.300 kg del Taycan. La frenada: El factor que muchos olvidan Correr es relativamente fácil, pero detener una masa en movimiento es donde se separan los buenos diseños de los mediocres. La energía cinética que los frenos deben transformar en calor depende directamente de la masa. Aquí, Newton no perdona a nadie. En una frenada de emergencia de 100 a 0 km/h, la diferencia entre un deportivo térmico como un Corvette Z06 y un eléctrico potente como un BMW i4 M50 puede parecer pequeña (unos 4 metros), pero en el mundo real, esa distancia es la diferencia entre un susto y un accidente grave. El problema se agrava cuando subimos a los 200 km/h. Frenar desde esas velocidades requiere que los discos absorban una energía brutal. Los eléctricos confían mucho en la frenada regenerativa, pero en una frenada a fondo, el 90% del trabajo lo hacen los discos y las pastillas. Un coche pesado fatiga los frenos mucho antes, provocando el temido "fading" o pedal blando, especialmente en puertos de montaña o circuitos. El equilibrio perfecto: La hibridación moderna Llegados a este punto, cabe preguntarse si existe una solución ideal. El híbrido moderno, como el McLaren Artura o el Ferrari SF90, parece haber encontrado el camino. Utilizan pequeños motores eléctricos para hacer lo que se llama "torque filling" o relleno de par. El motor eléctrico se encarga de dar la patada inicial mientras los turbos del motor de gasolina cogen presión. Esto permite tener la respuesta instantánea de un eléctrico sin tener que cargar con 600 kg de baterías. Es, en términos de ingeniería actual, el equilibrio más razonable si lo que se busca es el máximo rendimiento en todas las circunstancias.

I want to tell you about a pair of hands. Hands that could lay a straight line of carpet across a living room floor, split a cord of firewood without breaking a sweat, or coax a stubborn CV joint back into place with nothing but basic tools and a whole lot of patience. Those were my dad's hands. The post SILY 701- Hands of a Father appeared first on Golden Spiral Media- Entertainment Podcasts, Technology Podcasts & More.

Episode: 00322 Released on June 8, 2026 Description:  What happens when a newspaper reporter becomes a crime and intelligence analyst? In this episode of Analyst Talk, Abigail shares her unique journey from journalism and marketing into law enforcement analysis. She discusses how storytelling became one of her greatest analytical strengths, why qualitative data can be just as powerful as statistics, and what it was like building an analysis program from the ground up in an agency that had never had an analyst before. Abigail also discusses her work supporting child exploitation and human trafficking investigations, the growing threat of online extremist groups targeting children, peer support for first responders, and the importance of networking across the profession. Along the way, she offers practical advice on writing, communication, training, and finding opportunities to demonstrate value within an agency. The episode also features another entertaining installment of "Shit You Hear in the Office," including stories involving ceiling crawl spaces, office tomato farms, and analysts watching movies on their second monitor. Whether you are a new analyst, an experienced practitioner, or someone interested in the future of public safety analysis, Abigail delivers valuable insights on leadership, service, and making an impact through analytical work.

If you are worried about China taking over due to having better robots than the yanks, I got mixed messages for ya here. This was created using DeepSeek v4 Pro. Remember when DeepSeek could do the same thing as chatGPT but on shitty processors and not much RAM? All those stocks shit themselves? Oh what memories. Would have been a great time to buy NVIDIA stocks. I didn't, if you're asking....It's pretty good but it really didn't follow the instruction in the prompt that Joel Hill is Jack the Insider on the transcript. So that's a minus point. But also, this took fucking ages to generate. It's better than lots of the yankee slop but damn son this took MINUTES. So they might take over if we are patient or whatever. Enjoy the episode. ----------------------------------------------Joel Hill (Jack the Insider) and Hong Kong Jack return for a sprawling episode that tackles two of the biggest stories shaping politics in 2026. The pair open with the jaw-dropping Redbridge poll putting One Nation at 31% of the primary vote — a number that would all but wipe the National Party off the federal map and potentially deliver Anthony Albanese a strengthened majority government by splintering the right. Joel and Jack clash over whether culture-war grievances or material concerns are driving the surge, while drawing historical parallels to Joh for Canberra and the DLP split of the 1950s.The conversation then crosses hemispheres for a tour through UK chaos: Peter Mandelson's leaked dossier exposing a rudderless No. 10 under Keir Starmer, Nicola Sturgeon's estranged husband pleading guilty to embezzling SNP donations on a surreal shopping spree of Lalique salt shakers, seven Dysons, and a motorhome with four miles on the clock, and a deeply troubling police body-cam incident that has reignited the two-tier policing debate ahead of three critical by-elections.The centrepiece of the episode is a sober, hour-long deep dive into the COVID-19 pandemic and what Australia has refused to learn. The Two Jacks lay out the true death toll (perhaps 22 to 69 million globally), the devastating scale of long COVID, the vaccine rollout failures, the absurdities of hotel quarantine with rubbish bags over heads, and why governments and public health officials are desperate to avoid a Royal Commission. They close by asking whether the next pandemic will meet a population that has permanently lost trust in its leaders — and whether we'll simply repeat the mistakes of both COVID and the Spanish flu.Sport provides a lighter coda: the Carlton revival under an interim coach, James Hird's awkward candidacy at Essendon, the expanded 48-team World Cup that nobody seems excited about, and a formidable New Zealand Test side taking on England at Lord's.00:00:25 — Introduction Joel welcomes listeners to Episode 159, recorded 4 June. Today: Australian political news, a check-in on the UK, and a deep dive into the COVID-19 pandemic.00:01:21 — The Redbridge Poll: One Nation at 31% The AFR's Redbridge poll: One Nation 31%, Labor 28%, LNP 20%, Greens 12%. The two-party preferred is now being calculated as One Nation versus Labor — a seismic shift in how Australian politics is measured.00:03:12 — Not Just a Protest Vote Jack argues this is real, not a re-run of Hanson's 1990s flash-in-the-pan. The South Australian state election and the Farrah by-election suggest One Nation support is durable. Joel counters that protest votes can be expressed at the ballot box and that Australians are tiring of pluralism.00:04:09 — If One Nation Succeeds, Labor Wins The cruel irony: One Nation's rise probably delivers Labor government. The National Party could simply disappear. The DLP kept the Coalition in power for decades as an anti-Labor party; One Nation may do the reverse.00:05:46 — Scrutiny and Splintering Joel notes One Nation's policies are "two-sentence fragments" and motherhood statements. When proper scrutiny arrives, the contradictions will surface. Hanson's parliamentary attendance is as poor as imaginable.00:08:22 — The Third Rail Jack argues populists succeed because they discuss what polite society won't: immigration, culture wars, welcome to country rituals. The major parties must engage these topics or cede the ground entirely.00:11:34 — Feeling Unheard The core driver, Jack contends: voters feel sneered at and silenced by mainstream politics. It's not about flag counts, it's about being listened to.00:13:50 — What Actually Drives Votes Joel pushes back: voting determinants are the household economy, migration, climate change — not culture war trivia. Culture wars "don't amount to a hill of beans" at the ballot box.00:14:51 — The DLP Parallel Both agree the One Nation phenomenon most closely resembles the DLP split of the 1950s and 60s — a right-wing fracture that delivered Labor government after Labor government.00:17:18 — The Republic Referendum Lesson Jack recalls the 1999 republic referendum: pro-republicans split between models rather than uniting, scuppering the whole project. Voters will vote their preference even knowing it helps their enemy.00:19:32 — UK Parallels: Accommodate or Fight? Significant figures in the UK Tory party are debating whether to fight Reform or reach an accommodation. Tony Abbott recently said the Liberal Party won't criticise Pauline Hanson.00:21:48 — Joh for Canberra Redux Imre Salusinszky's comparison: this is "Joh for Canberra" all over again. But Joel notes Joh's moment lasted months; One Nation's has already lasted years.00:24:08 — State Election Previews Joel predicts the Victorian state election will be chaotic and peculiar — a government that's been in power too long, an opposition that may not be up to the task, and One Nation peeling votes from safe Labor seats. NSW will give a clearer reading.00:25:44 — Hanson "Ready to Govern" — from the Senate? Pauline Hanson announced she's ready to govern. Joel asks: shouldn't she contest a lower-house seat first? Jack recalls the only precedent: John Gorton became PM while still a senator, but had to be eased into Kooyong.00:28:20 — The Mandelson Dossier: Starmer's Empty Suit Jack's read of the leaked Mandelson documents: ministers don't know what the PM wants, there's zero respect or fear of his authority. Starmer comes across as an empty chair. One minister's text: "Every meeting with Labour MPs — it's all about who can we tax to pay benefits to other people."00:30:50 — Mandelson's Legal Peril Mandelson is under police investigation for misconduct in public office. Could face charges — the seriousness depends on whether it's mere misconduct or genuine bribery for foreign interests.00:31:49 — The Nicola Sturgeon Saga Her estranged husband has pleaded guilty to embezzling roughly £400,000 in SNP donations. The shopping list: six high-end coffee machines, seven Dyson vacuums, Lalique salt and pepper shakers, Montblanc pens, Swiss watches, an iJag, part of a Volkswagen, and a motorhome with four miles on the clock parked at his 92-year-old mother's house. Nicola claims she "didn't go in the kitchen much."00:34:20 — The BBC Interview Laura Kuenssberg's forensic interview with Sturgeon — "not quite Prince Andrew, but not much better." Sturgeon has been cleared by Police Scotland, but her reputation, already damaged by the Alex Salmond trial, is now in tatters.00:35:05 — Will He Go to Prison? £400,000 is a substantial sum. With another £600,000 unaccounted for, a custodial sentence seems likely. The money was ring-fenced for a second independence referendum push.00:36:50 — Money Laundering or Conspicuous Consumption? Joel wonders if the bizarre purchases — multiple watches on the same day — were an amateur money-laundering attempt: buy goods with SNP funds, sell them quietly for cash.00:38:23 — UK By-elections: Makerfield Looms Three by-elections on 18 June, including the critical Makerfield contest. Andy Burnham, Greater Manchester's high-profile mayor, is the tepid favourite. Low turnout could help him return to Westminster.00:39:30 — The Body-Cam Incident A white teenager accused of racially vilifying a Sikh man was stabbed — and police arrested the bleeding victim, not the attacker. Body-cam footage shows the victim saying "I can't breathe, I've been stabbed" while officers dismiss him. Joel calls the footage "just awful."00:41:22 — Two-Tier Policing Jack traces UK policing's overcorrection: after the Macpherson/Lawrence report, guidelines were rewritten so aggressively that they've produced a pattern of questionable enforcement that devastates community trust — and plays directly into Tommy Robinson's hands.00:42:08 — NSW Police on Four Corners Joel recommends the harrowing Four Corners investigation: bashings in custody, false arrests, an officer who threw body-cam footage into Sydney Harbour, and two undercover officers jailed for a savage assault. The problem today is general duties policing, not the specialist squads of the 1980s. Some command areas are far worse than others — a leadership failure.00:44:55 — Victoria Police: Under-Resourced, Not Corrupt Joel shares an anecdote: two divisional vans for 80,000 people in outer-east Melbourne. Tough work being a police officer; even tougher being a good one.The COVID-19 Reckoning00:45:09 — Why This Matters Joel sets the frame: we parked COVID in 2023 with a hangover but never understood what we'd been through. Today's episode aims to crack that problem.00:45:51 — The True Death Toll Officially: 7 million dead. But most countries stopped testing and stopped reporting cause-of-death data to the WHO. Using excess mortality, the real toll is between 22 and 69 million — at the high end, exceeding the Spanish flu.00:47:02 — Long COVID's Shadow Roughly 400 million people globally (6% of the population) have experienced long COVID. In Australia alone, between 200,000 and 500,000 people are living with or have lived with the condition. Second infections can be worse. Emerging links to cardiovascular disease, type 2 diabetes, and accelerated dementia.00:49:43 — The Collective Amnesia Governments worldwide have "a collective embarrassment" about how they handled the pandemic, Jack says. They want it in the history books and forgotten. Joel says this is a grave mistake for public trust — and for public health, given COVID is now a permanent fixture alongside flu season.00:50:50 — Why Excess Deaths Are the Only Honest Metric All other figures are "kind of made up" because attribution methods vary wildly between countries. Excess deaths remain elevated in Australia and most nations.00:51:25 — Children and COVID Bobby Kennedy Jr. removed under-18s from government-supported vaccines in the US. Joel argues this is a disastrous move given mounting evidence that childhood COVID infection leads to higher rates of long-term chronic illness.00:52:47 — Why No Royal Commission? Not just politicians protecting themselves — public health officials and much of the media wanted to avoid scrutiny of their judgments and actions during the pandemic.00:53:32 — The Media's Abdication Jack watched "a lot" of Daniel Andrews's daily press conferences. Only two journalists ever asked pertinent questions: Rachel Baxendale and Leigh Sales. Nobody asked why curfews, why beach arrests, why the disparate impact on tradies and cafe owners while the "laptop class" actually made money working from home.00:56:14 — Andrews's Immense Popularity Joel adds context: Andrews was wildly popular at the time, which partly explains the media's deference — though Jack insists that shouldn't have mattered.00:57:34 — The Curfew Nonsense Curfews were about giving law enforcement the easiest possible environment, Joel says — and should have been acknowledged as such and wound back sooner. Meanwhile, Bondi's wealthy swam en masse while Western Sydney's working-class communities were treated harshly.00:57:59 — The Vaccine Rollout Failure The Morrison government bet everything on AstraZeneca — the non-mRNA, first-available vaccine. Then rare blood-clotting issues emerged (seven deaths, mainly men aged 40–49). Meanwhile, Australia was left waiting for Pfizer and other mRNA vaccines because no other supply deals had been secured.00:59:37 — Omicron Breaks the Pandemic's Back The Omicron variant emerged from South Africa: more infectious but far less lethal. Combined with 95%+ vaccination rates among Australians over 18, it effectively ended the acute phase — though at the cost of entrenched mistrust.01:00:38 — Government Overreach and Broken Trust Jack's core criticism: governments outsourced decision-making to public health officials rather than making political judgments that balanced competing interests. Joel counters that it would have been a "bold move" for politicians with no scientific background to contradict public health advice.01:02:19 — "Just Let It Rip" Was Never an Option The three countries with the highest COVID mortality — Brazil (highest), United States (second), India (third) — were all led by populist governments that largely refused mandates. Letting it rip was devastating.01:03:27 — The ADF Quarantine Scandal Scott Morrison refused to allow ADF quarantine facilities to be used for returning travellers. Instead, people were crammed into hotels with gaps under the doors. Joel recalls the "rubbish bags over heads" episode in Victoria — dark green plastic bags as infection control.01:05:00 — The Inquiry's Recommendations Create a proper Australian CDC. Release expert advice publicly. Better national planning with clear political accountability. And critically: politicians must own the big decisions on freedoms and spending instead of hiding behind experts.01:06:01 — The Next Pandemic There will be another one. If it's a respiratory, airborne pathogen like COVID, similar circumstances will return. Are we ready? Probably not. Will we close the country again? The economic damage — unemployment hitting 7.5% in 2020 — was enormous, even if it recovered to 3.5% by pandemic's end.01:08:06 — Who Was Left Behind? The arts community was inexplicably excluded from JobSeeker and JobKeeper. Meanwhile, the "laptop class" working from home effectively got a 15% pay rise by eliminating commuting costs. Bunnings did very well; so did companies that kept JobKeeper without passing it to employees.01:11:14 — The Human Cost of Lockdowns Public housing towers in Flemington were locked down. Joel recalls one family: an African-Australian single mother with nine children in a two-bedroom commission flat, trapped. Jack calls what happened with schools "disgraceful." But Joel notes the evidence now shows childhood COVID infection has serious long-term health consequences, complicating the retrospective judgment.01:13:59 — Will We Learn Anything? Jack's bleak prediction: the next pandemic is probably far enough away that we'll take no notice of COVID's lessons and make the same mistakes. Joel agrees — we didn't learn from the Spanish flu a century ago either.01:15:51 — Malcolm Roberts and Vaccine Misinformation The One Nation senator claims 70,000 Australians died from COVID vaccines — a figure with no evidentiary support, built by misattributing excess deaths. In reality, mRNA technology is now being deployed as a cancer treatment, showing promise against bowel and pancreatic cancers.01:17:36 — Trust Destroyed If the next pandemic arrives within this generation, governments will face a population that has lost faith. If it takes 50 years, the damage may have faded. Western Australia, meanwhile, locked itself down with negligible deaths and actually loved the isolation — provided the iron ore and LNG ships kept moving.01:20:37 — The Spanish Flu Echo Joel's closing historical note: Australia's response to the Spanish flu in 1919–1921 was nearly identical to COVID — lockdown disputes, police arresting people for not wearing masks, states fighting the newly created federal Department of Health. The whole thing collapsed into acrimony the moment state rivalries flared. A century later, nothing had changed.01:21:48 — Federation as Fatal Flaw Jack adds: the three high-mortality COVID countries (US, Brazil, India) share a feature beyond populist leaders — they're all federations where central government power is limited. When "the emperor is far away and the mountains are high," coordinated pandemic response is nearly impossible.01:23:40 — No Appetite for Truth Jack's final word: nobody wants a proper inquiry. Not politicians, not public health officials, not much of the media. Joel disagrees on the importance — the pandemic's legacy still shapes how Australians think, vote, and trust.Sport01:27:40 — AFL Coaching Carousel Essendon and Carlton both need permanent coaches. Joel asks: is James Hird the right man for Essendon? Jack: 17 other clubs wouldn't give him an interview, but the Bombers may have backed themselves into a corner where appointing him is the only way out.01:28:53 — Merit vs Member Sentiment Rowan Connolly's question: would you take James Hird or John Longmire (five grand finals, one premiership, 60%+ win rate)? The answer is obvious on merit — but members and fans want the fairy tale.01:29:47 — Carlton's Astonishing Revival Three straight wins. Ranked 16th in forward-50 entries a month ago; now second. The game style is unrecognisable — no more bombing the ball to non-existent power forwards. Mitch McGovern's low, flat kick to Patrick Cripps for the match-winner against Geelong was emblematic of the transformation. Seven players aged 21 or younger are now getting games and bringing energy.01:33:18 — FIFA World Cup 2026: Nobody's Excited Expanded to 48 teams, Scotland are going — and a Scot in his 30s told Jack that neither he nor any of his mates (all doing well financially, normally first on the plane) have any interest. Ticket prices are "extraordinary." The final is at MetLife Stadium in New Jersey — which Jack describes as "Waverley on steroids, but even more bleak."01:36:08 — Australia's Draw Socceroos face Turkey first up, then the United States. Jack suggests marketing it as "Gallipoli Round Two." Spain are favourites; England, Brazil, and Germany are in the chasing pack.01:37:06 — Cricket: England v New Zealand, First Test at Lord's Joel runs through New Zealand's likely top seven — Latham, Conway, Williamson, Ravindra, Mitchell, Blundell — noting the first four have all made Test double-centuries. "Just about the best first six in Test cricket." With O'Rourke's express pace and Henry's quality, this is a formidable Black Caps side.01:38:40 — Stump Speech & Next Week Listener mail (including an "exposé of who Jack is") held over for next episode. For the record: Hong Kong Jack's CV includes HSC at Assumption College Kilmore, a stint as a carpenter, a law degree from Melbourne University, stints at Holding Redlich and Slater & Gordon, work as a litigation and immigration lawyer, and an appointment to the Refugee Review Tribunal as a federal cabinet appointee.01:40:39 — Outro Joel thanks listeners for hanging in for an extra ten minutes. Back next week.The Two Jacks is recorded weekly. Send your questions and feedback to the show.

Most people arrive in Dubai thinking the opportunities will find them. Jason Grundy has spent 25 years watching that assumption play out badly.  As Managing Director for the Middle East and Africa at Robert Walters, Jason has seen every side of the hiring market. The candidates who oversell themselves. The companies that leave great people waiting a month for feedback and wonder why they lost them. The businesses generating AI written job descriptions that have nothing to do with the actual role. And the expats who land in Dubai assuming opportunities will fall into their lap, only to find one of the most competitive job markets on earth.  This episode covers what is really happening in the UAE job market right now, which industries are hiring, which have gone quiet, and when the bounce back comes. Plus the honest truth about Emiratisation, why culture retains talent more than salary, and why the best candidates are never on a job board.  If you are hiring, being hired, or just trying to understand where Dubai is heading, this one is worth your time.    Timestamps:  1:30 How Jason fell into recruitment and why one snap decision defined his entire career   6:40 How to choose the right recruiter, why trust matters, and what makes Robert Walters different   13:15 The Middle East versus Africa and the miracle of what this region has built in 50 years  17:30 Emiratisation: the honest answer, the real challenge, and the only playbook that works   23:50 What is hiring and what has gone quiet after the regional conflict   27:00 Jason's honest forecast: when Dubai will bounce back and what it will look like   31:54 Why badly trained interviewers are losing great candidates and how to fix it   36:32 AI in recruitment: what is actually happening, the quiet tap no bot can replace, and the one line that says it all   45:34 Why a third of candidates are hesitating and why two thirds are still saying yes to Dubai   53:40 Culture is the only real difference between companies that keep people and those that always hire   57:00 How to stand out as a candidate, what your LinkedIn is doing wrong, and why the CV is just the door   1:02:30 Degrees: do they still matter and what Jason told his own kids   1:07:00 Quickfire: red flags, overpaid professions, secretly dying careers, and Dubai versus Abu Dhabi    Follow Spencer Lodge on Social Media: https://www.instagram.com/madeindubaipodcast/?hl=en  https://www.facebook.com/profile.php?id=61586194260076  https://www.instagram.com/spencer.lodge/?hl=en  https://www.tiktok.com/@spencer.lodge  https://www.linkedin.com/in/spencerlodge/  https://www.youtube.com/c/SpencerLodgeTV  https://www.facebook.com/spencerlodgeofficial/    Follow Jason Grundy on Social Media:  https://www.linkedin.com/in/jasongrundy/  https://www.linkedin.com/company/robert-walters/posts/?feedView=all  https://www.instagram.com/robertwalterslife/?hl=en   

Ep 243 One World in a New World with Phillip HumphriesWhat happens when a retired CIA officer, police investigator, comedian, actor, and lifelong seeker sits down to discuss the biggest mysteries of existence?In this captivating episode of One World in a New World, Zen Benefiel reconnects with childhood friend Philip Humphries for a conversation spanning over five decades of life experience, curiosity, service, and exploration.Together they explore:

Passando a Limpo: No Passando a Limpo desta segunda-feira (8), Igor Maciel e a bancada do programa conversam com o ex-secretário nacional de Segurança Pública, Coronel José Vicente, sobre a classificação do PCC e CV como organizações terroristas pelos EUA. O programa também conta com Eliane Cantanhêde.

Trump declara PCC & CV grupos terroristas (Lula Reage) PSG campeão da Champions league Virginia é contratado pela globo e é vaiada no jogo do brasil Heroi tricolor barra bandeirao do Ifood por parecer do flamengo!⁠⁠- Brasil goleia Panamá (Copa do mundo chegando) ⁠- Empresas do Brasil se mudando para o Paraguai devido a flexibilidade que o Paraguai oferece.

Confira na edição do Jornal da Record desta sexta-feira (5): Já está valendo a lei que permite a renovação automática da CNH para motoristas sem infrações. Governo prorroga prazo de inscrição do Enem até o dia 12 de junho. A cada 12 minutos, uma criança ou um adolescente sofre abuso sexual no Brasil. Putin recusa encontro com Zelensky e afasta mais uma vez um acordo de cessar fogo. EUA oficializam a classificação do PCC e CV como terroristas. Paquetá e Igor Thiago, meio campo e ataque: as novidades de Ancelloti para o time que vai enfrentar o Egito no último amistoso antes da Copa.

Atıf Ünaldı ile Netizen'in bu bölümünde insan kaynakları alanında deneyimli isim Özge Korkmaz konuk oluyor. Yapay zekânın işe alım süreçlerine etkisi, CV hazırlamanın yeni kuralları, ATS sistemleri, LinkedIn'in profesyonel kariyerdeki rolü ve geleceğin çalışma hayatı detaylı şekilde ele alınıyor. Özge Korkmaz, "CV öldü" söylemlerinin gerçeği yansıtmadığını vurgulayarak özgeçmişlerin hâlâ işe alım süreçlerinin merkezinde olduğunu anlatıyor. Programda aday takip sistemleri (ATS), işe alımda yapay zekâ kullanımı, hibrit çalışma modelleri, portföy kariyer yaklaşımı ve geleceğin meslekleri üzerine önemli değerlendirmeler yer alıyor. Learn more about your ad choices. Visit megaphone.fm/adchoices

Coup double pour l'opération Pro Dream organisée cette année par PMU Play et qui a consacré deux vainqueurs qui nous font le plaisir d'une émission cette semaine :Yannis Lefur : breton et fan de sport, Yannis est ingénieur et a commencé sa carrière en Stratégie IT... Les années covid lui ont fait découvrir le poker et patiemment construire le projet de devenir pro après avoir sécurisé quelques années d'expérience sur le CV. L'aventure sera concrétisée dès janvier 2024.Florian Guimond : sudiste exilé à Rennes pour suivre des études d'ingénieur, il y découvre le poker, dépose 100€ et commence à gravir les échelons en MTTs. Il tente l'aventure pro après l'obtention de son diplôme et ça se passe plutôt bien depuis 7 ans.Présentation : Comanche et ShiShiStreaming : MarinRéalisation et montage : FannyClub Poker Radio vous est présentée par Winamax, le n°1 du poker en ligne. Perte d'argent, conflits familiaux, addiction… Les jeux d'argent sont interdits aux moins de 18 ans et peuvent être dangereux. En cas de besoin, contactez le 09 74 75 13 13.Ce podcast est hébergé par Podcastics, la plateforme pour créer et diffuser votre podcast facilement.

If you are focusing on improving your FatMax in 2026, you're likely wasting time and money. Not only is fat a more expensive substrate to burn than carbohydrate (you get less energy for the same amount of oxygen by oxidising fat), but the Fatmax number you see in your lab report is mostly noise and very little signal.  In today's episode of That Triathlon Show I'll explain exactly why that is, but I'll also give you a tool to evaluate any test or measure that you might (or might not) want to be tracking, from Time Trials to VO2max, HRV and various biomarkers like ferritin and testosterone.  HIGHLIGHTS AND KEY TOPICS:  What is Fatmax and Maximal Rate of Fat Oxidation?  How reliable is Fatmax testing?  How to measure the noise of a test using the Coefficient of Variation (CV) How to calculate the Smallest Detectable Change (SDC) of any test or measure Why the SDC of Fatmax is the equivalent of you having to go from 300 to 384W for your 20-minute power to be able to say that this was real improvement and not just noise (!!)  CVs and CV ranges for common tests and measures used in triathlon, Ironman and other endurance sports, including Time Trials, Time To Exhaustion, VO2max, lactate and ventilatory thresholds, economy and gross efficiency, lactate concentration, Critical Power and W', HRV, ferritin, testosterone, TSH and more Why carbohydrate is a 7% more efficient energy substrate than fat, and why you should be oxidising carbs in your next Ironman.  DETAILED EPISODE SHOWNOTES:  We have detailed shownotes for all of our episodes. The shownotes are basically the podcast episode in written form, that you can read in 5-10 minutes. They are not transcriptions, but they are also not just surface-level overviews. They provide detailed insights and timestamps for each episode, and are great especially for later review, after you've already listened to an episode.  The shownotes for today's episode can be found at https://scientifictriathlon.com/tts700/ LINKS AND RESOURCES:  Full bibliography in the shownotes: https://scientifictriathlon.com/tts700/ WHAT SHOULD I LISTEN TO NEXT? If you enjoyed this episode, I think you'll love the following episodes, related to sports science and (the third episode listed) fat adaptation and performance.  The replication crisis in sports science with Joe Warne, PhD | EP#468 The Skeptic's Guide To Sports Science with Nicholas Tiller, PhD | EP#239 High carbohydrate, low carbohydrate, or periodised carbohydrate intake with Louise Burke, PhD | EP#236 You can find our full episode archives here, where you can filter for categories such as Triathlon Training, Racing, Science & Physiology, Swimming, Cycling, Running etc. You can also find separate archives for specific series of episodes I've done, specifically Q&A episodes, TTS Thursday episodes, and Beginner Tips episodes.  LEARN MORE ABOUT SCIENTIFIC TRIATHLON:  The Scientific Triathlon website is the home of That Triathlon Show and everything else that we do Contact us through our contact form or email me directly (note - email/contact form messages get responded to much more quickly than Instagram DMs) Subscribe to our Newsletter Follow us on Instagram Learn more about our coaching, training plans, and training camps. We have something to offer for everybody from beginners to professionals.  HOW CAN I SUPPORT THAT TRIATHLON SHOW (FOR FREE)?  I really appreciate you reading this and considering helping the show! If you love the show and want to support it to help ensure it sticks around, there are a few very simple things you can do, at no cost other than a minute of your time.  Subscribe to the podcast in your podcast app to automatically get all new episodes as they are released. Tell your friends, internet and social media friends, acquaintances and triathlon frenemies about the podcast. Word of mouth is the best way to grow the podcast by far!  Rate and review the podcast (ideally five stars of course!) in your podcast app of choice (Spotify and Apple Podcasts are the biggest and most important ones). Share episodes online and on social media. Share your favourite episodes in your Instagram stories, start a discussion about interesting episodes on forums, reference them in your blog or Substack.  SPONSORS: Precision Fuel & Hydration produce our favourite gels, sports drinks, and electrolyte and carbohydrate products here at That Triathlon Show and Scientific Triathlon. Use the free Fuel & Hydration Planner to get a personalised plan for your carbohydrate, sodium and fluid intake in your next event, and get 15% off your first 2026 order by using the code TTS2026 at checkout. Rouvy is hands down the most complete indoor cycling platform for triathletes. Among their thousands of beautiful bike courses from all around the world, all filmed in stunning quality, they have over 75 IRONMAN and IRONMAN 70.3 race courses plus 20+ Challenge Family courses, so you can pre-ride your race from home. Real gradients, real visuals, and real feel! Head to rouvy.com and use the code TTS to get your first month free on top of a 7-day free trial. Effortless Swimming produce the best swim goggles for triathletes and open water swimmers. Their NanoClear anti-fog lenses give you clear, fog-free vision that lasts and doesn't wear off. Don't let foggy or leaky goggles ruin another swim. Go to shop.effortlessswimming.com and use the code TTS15 to get 15% off your goggles, and get a free two-month Effortless Swimming course membership.LEARN MORE ABOUT SCIENTIFIC TRIATHLON: The Scientific Triathlon website is the home of That Triathlon Show and everything else that we doContact us through our contact form or email me directly (note - email/contact form messages get responded to much more quickly than Instagram DMs)Subscribe to our NewsletterFollow us on InstagramLearn more about our coaching, training plans, and training camps. We have something to offer for everybody from beginners to professionals. HOW CAN I SUPPORT THAT TRIATHLON SHOW (FOR FREE)? I really appreciate you reading this and considering helping the show! If you love the show and want to support it to help ensure it sticks around, there are a few very simple things you can do, at no cost other than a minute of your time. Subscribe to the podcast in your podcast app to automatically get all new episodes as they are released.Tell your friends, internet and social media friends, acquaintances and triathlon frenemies about the podcast. Word of mouth is the best way to grow the podcast by far! Rate and review the podcast (ideally five stars of course!) in your podcast app of choice (Spotify and Apple Podcasts are the biggest and most important ones).Share episodes online and on social media. Share your favourite episodes in your Instagram stories, start a discussion about interesting episodes on forums, reference them in your blog or Substack. Hosted by Simplecast, an AdsWizz company. 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No Papo Antagonista desta quinta-feira, 04, falamos sobre o PCC e CV, como eles foram classificados como terroristas pelos EUA e também a Operação contra o filme de Bolsonaro.Papo Antagonista é o programa que explica e debate os principais acontecimentos do dia com análises críticas e aprofundadas sobre a política brasileira e seus bastidores.       O programa traz contexto e opinião sobre os temas mais quentes da atualidade.       Com foco em jornalismo, eleições e debate, é um espaço essencial para quem busca informação de qualidade.       Ao vivo de segunda a sexta-feira às 18h no nosso canal no Youtube.   https://www.youtube.com/@OAntagonista  Apoie o jornalismo independente. Assine O Antagonista e Crusoé com 10% via Pix ou Google Pay:  https://assine.oantagonista.com.br/  Siga O Antagonista no X:  https://x.com/o_antagonista   Acompanhe O Antagonista no canal do WhatsApp. Boletins diários, conteúdos exclusivos em vídeo e muito mais.  https://whatsapp.com/channel/0029Va2SurQHLHQbI5yJN344  Leia mais em www.oantagonista.com.br | www.crusoe.com.br #EUA #terrorismo #geopolítica #crime #segurança #facções #PCC #ComandoVermelho #narcotráfico #podcast #debate #atualidades #notícias #política #internacional #soberania #justiça #direito #fronteiras #intervenção

Tracey Cox joins In Her Shoes to explore the shift from chemistry to compatibility and what it reveals about the way we connect today.As decades of dating, relationships, and human behaviour collide with a world of apps, algorithms, and always-on communication, Tracey breaks down how “chemistry” has been over-romanticised and why true compatibility now sits at the centre of meaningful connection.But this isn't just about romantic relationships. We dive into the broader erosion of human connection how surface-level interactions, digital shortcuts, and performance-driven environments have reshaped the way we relate to each other, both personally and professionally.From dating to the workplace, what happens when connection becomes transactional? And what does it take to rebuild something deeper, more intentional, and actually sustainable?This episode challenges how we think about attraction, relationships, and the role human connection plays in every part of our lives including how we work, lead, and show up.This season of In Her Shoes is brought to you in partnership with ⁠⁠⁠RISER⁠⁠⁠. When we spoke to women about how they'd moved through the highs and lows of their careers, one thing came up again and again: network – visibility, connection and real relationships. RISER is the space built to help you do exactly that, at scale. Upload your 60-second video CV and let its responsible AI use context (not clichés) to understand who you are and connect you directly with hiring managers, so you can unlock the opportunities and financial gains you actually deserve.

⏱️ Capítulos do vídeo00:00 — A guerra silenciosa que o Brasil ainda não entendeu01:30 — A ameaça das tarifas de 25% e o prazo de 15 de julho02:30 — Como bancos e bandeiras ganham dinheiro — e por que o PIX acabou com isso06:55 — PCC, CV e a armadilha das sanções bancárias09:48 — O PIX vai acabar? A verdade que ninguém fala10:43 — Como proteger seu patrimônio agora

A century ago, when depositors lost confidence in a bank, they'd rush to withdraw their cash. In 1971, US president Richard Milhous Nixon faced a similar dilemma. But his problem wasn't ordinary citizens fearing for their savings. Instead, it was America's closest allies who were nervously eyeing the dwindling supply of gold in Fort Knox at a time when the dollar's value was tied to gold and allies' currencies were in turn tied to the dollar. And just like a beleaguered bank manager of yore, Nixon chose to shut America's doors to further withdrawals. His decision threatened to pull the plug on the entire international monetary system established at Bretton Woods in 1944. It was so unexpected and outrageous, it became known as the “Nixon Shock”. In the first of two episodes on the topic, hosts Gillian Tett and Robin Wigglesworth get the story from economist and ex-financier Jeffrey Garten – a man with a CV so long that he once even worked for the Nixon administration himself.Further reading:Three Days at Camp David: How a Secret Meeting in 1971 Transformed the Global Economy, by Jeffrey E Garten (2021)Gold and the dollar crisis, by Robert Triffin (1960)Credits: Getty Images, the Richard Nixon Presidential LibraryTo enjoy future episodes, be sure to subscribe to The Story of Money wherever you get your podcasts, also on the show's dedicated YouTube channel here: https://www.youtube.com/@FTTheStoryOfMoneyHosts: Gillian Tett and Robin WigglesworthProducer: Laurence KnightExecutive Producer: Manuela SaragosaOriginal music: Breen TurnerBroadcast engineers: Bianca Wakeman and Petros GioumpasisPodcast Development: Laura ClarkeVideo editor: Kristen Kenyon and Josh Divney at Podcast DiscoveryLearn more at www.ft.com/tsom or get in touch at thestoryofmoney@ft.com.Read a transcript of this episode on FT.com Hosted on Acast. See acast.com/privacy for more information.

US Secretary of State Marco Rubio designated the Comando Vermelho (Red Command, or CV) and the Primeiro Comando da Capital (First Command of the Capital, or PCC) as foreign terrorist organizations. The Brazilian Report's editor-in-chief, Gustavo Ribeiro, and the Brazil Office Alliance's president of the board, James Green, discuss the implications for Brazil.Send us your feedbackSupport the show

Boa terça, angulers! Um #335 recheado de assunto! Abrimos comentando a votação histórica na câmara que aprovou o fim da escala 6x1 e a redução da jornada de trabalho. O texto segue para votação no Senado, ainda sem previsão. No segundo bloco, a visita de Flávio Bolsonaro a Daniel Vorcaro, enquanto o ex-banqueiro ainda cumpria medidas cautelares após sua primeira prisão. Ainda no tema, Cláudio Castro foi alvo de operação que mira aportes em fundos do Banco Master. Além disso, o encontro de Flávio com Trump e a classificação do CV e do PC como organizações terroristas estrangeiras. Por fim, uma passada no primeiro turno das eleições presidenciais da Colômbia e as dicas culturais da semana que incluem a turnê do Martinho da Vila e Martnalia, entre outras. Sirva-se!Apoie o Angu de Grilo no apoia.se: apoia.se/angudegrilo ou na Orelo: orelo.cc/angudegriloCortes do episódio em vídeo no @angudegrilo no Instagram e Tiktok! Siga, curta e compartilhe! Edição e mixagem: Tico Pro @ticopro_Redes sociais: Claudio Thorne @claudiothorneCortes em vídeo: Nathália Dias Souza @natdiassouza

Send us Fan MailYou did the hard part last week. You sat with the feelings, let the thoughts tumble out, and didn't immediately sprint toward a job board. Good. Now we figure out what's actually next.Part 2 is where we get practical, but not in the way medicine trained us to be practical. We're not just dusting off the CV and hitting LinkedIn. We're doing something harder and more useful: getting clear on what we actually want before we start telling people we're available. Because the goal isn't just to land somewhere. It's to land somewhere better.We start by looking honestly at the finances, not from a scarcity mindset, but from real numbers. Because a lot of us feel more financial pressure than the math actually justifies, and that pressure is what sends us straight into the first job that looks familiar. We also ask the question that tends to stop people cold: what would you tell your friend to do if she were in your exact situation? And then, more importantly, why won't you allow that for yourself?From there, we work through a set of questions designed to help you use your lived experience as data. What did you tolerate that you won't anymore? What actually lit you up? What does your ideal Tuesday afternoon look like? And then we talk about how to get out there in a way that opens doors most people don't even know exist, because the best opportunities rarely show up on a job board.This one is worth listening to whether you've been laid off, are thinking about leaving, or are just quietly starting to wonder what else is possible.If you want to work through these questions with someone in your corner, I'd love to help. Book a free discovery call at https://calendly.com/healthierforgood/coaching-discovery-call and let's figure out your next move together.Connect with Megan:Instagram: @MeganMeloMDWebsite: healthierforgood.comEmail: megan@healthierforgood.com Support the showTo learn more about my coaching practice and group offerings, head over to www.healthierforgood.com. I help Physicians and Allied Health Professional women to let go of toxic perfectionist and people-pleasing habits that leave them frustrated and exhausted. If you are ready to learn skills that help you set boundaries and prioritize yourself, without becoming a cynical a-hole, come work with me.Want to contact me directly?Email: megan@healthierforgood.comFollow me on Instagram!@MeganMeloMD

ARTHUR DO VAL é político, DEL. HUGGO LEONARDO é delegado de polícia e CEL. BUSNELLO é coronel da PM. Eles vão bater um papo sobre a classificação do PCC e do CV como organizações terroristas. Já o Vilela ficou mais preocupado com a classificação do Corinthians na Libertadores.

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Episode: 00321 Released on June 1, 2026 Description:  This week on Analyst Talk with Jason Elder, we celebrate six years of LEAPodcasts and Analyst Talk. Jason is joined by co-founder Mindy Duong for a candid conversation reflecting on more than 300 episodes, the evolution of the law enforcement analysis profession, and the lessons learned from building a volunteer-driven podcast community. Together they discuss generational perspectives in the workplace, annual reviews, mentoring, information sharing, survey results, future podcast initiatives, and the importance of preserving analyst stories for future generations. The episode also provides a behind-the-scenes look at the podcast's growth since launching during the COVID era, including upcoming plans to explore professional associations beyond IACA and IALEIA, demystify vendor tools used by analysts, and expand educational content for the profession. As always, the show wraps up with a fan-favorite edition of "Don't Be That Analyst," featuring practical lessons and humorous reminders from listeners. Whether you've been listening since Episode 1 or recently discovered the show, this anniversary episode is a celebration of the analyst community and everyone who has helped define the profession one episode at a time.

No 3 em 1 desta segunda-feira (01), o destaque foi que a nova pesquisa Real Time/Big Data para as Eleições 2026 ligou o sinal de alerta no Palácio do Planalto. Embora o presidente Lula (PT) vença o senador Flávio Bolsonaro (PL-RJ) em um eventual segundo turno, o petista aparece em situação de empate técnico contra Romeu Zema (Novo-MG) e Ronaldo Caiado Partido Social Democrático (PSD-GO). A Polícia Civil de SP deflagrou a Operação Wi-Fi para investigar desvios em um contrato de R$ 108 milhões da prefeitura de São Paulo. A suspeita é de que verbas destinadas à internet gratuita foram usadas para financiar o filme Dark Horse, biografia do ex-presidente Jair Bolsonaro. O presidente do Senado, Davi Alcolumbre (União-AP), marcou para esta terça-feira (02) a reunião de líderes que definirá o rito de tramitação da PEC do fim da escala 6x1. A esperança de um acordo histórico ruiu após Irã e EUA suspenderem as negociações de paz nesta segunda-feira (01). O recuo de Teerã ocorreu imediatamente após uma nova onda de ataques de Israel contra o Líbano. A reportagem de João Vitor Revedilho traz os bastidores da sucessão no Rio de Janeiro, onde o deputado federal Carlos Jordy (PL-RJ) desponta como o favorito para herdar a vaga do ex-governador Cláudio Castro na disputa ao Senado. O ministro da Fazenda, Dario Durigan, viajará aos Estados Unidos com o objetivo de debater os impactos da classificação do PCC e do CV como grupos terroristas. A viagem tratará o suposto risco de sanções econômicas e possíveis interferências no sistema Pix. O presidente Lula (PT) convocou uma nova reunião ministerial para reorganizar o governo após a saída definitiva de 17 ministros, que deixaram suas pastas para disputar as Eleições 2026. O presidente Lula (PT) entrou em campo para selar a aliança em São Paulo e quer o ex-governador Márcio França (PSB) como vice na chapa de Fernando Haddad ao governo do estado. Em entrevista exclusiva ao 3 em 1, o líder do PL na Câmara, Sóstenes Cavalcante (PL-RJ), revelou que seu foco absoluto é a disputa pela presidência da Casa em 2027. No entanto, o deputado também não descartou a possibiliade de concorrer a uma vaga no Senado. Tudo isso e muito mais você acompanha no 3 em 1. Learn more about your ad choices. Visit megaphone.fm/adchoices

Alexandre Garcia comenta sobre Copa do Mundo, eleições, "Gilmarpalooza" e reação do governo brasileiro após Estados Unidos classificarem PCC e CV como organizações terroristas.

No episódio desta semana, falamos sobre a histórica aprovação da PEC sobre o fim da escala 6x1 na Câmara dos Deputados e todos os destaques dessa jornada, a decisão do STF de finalmente acabar com aposentadoria compulsória remunerada como maior punição a magistrados, novas pesquisas, a mansão de Bananinha, Flávio Bolsonaro mordomo e a decisão do governo Trump de classificar o CV e o PCC como organizações terroristas. FORMULÁRIO TODO MUNDO NO RIO!https://forms.gle/Q5JjXwHHNW89Hxsn8 APOIE financeiramente a continuidade do MIDCast: ------------------ - Apoia.se : https://apoia.se/midcast - Chave PIX : podcastmid@gmail.com ------------------ # COMPRE produtos na lojinha do MIDCast: colab55.com/@midcast # CANAL do MIDCast Política no WhatsApp: bit.ly/midcast-zap # GRUPO dos ouvintes no Telegram: bit.ly/midcastgrupo # LISTA de paródias do MIDCast: bit.ly/parodiasmidcast PARTICIPANTES: ------------------ Diego Squinello - https://bsky.app/profile/diegosquinello.bsky.social Rodrigo Hipólito - https://bsky.app/profile/rodrigohipolito.bsky.social Thais Kisuki - https://bsky.app/profile/thaiskisuki.bsky.social Victor Sousa - https://bsky.app/profile/vgsousa.bsky.social COMENTADO NO EPISÓDIO ------------------ Fim da escala 6x1: Câmara aprova PEC; como votaram deputados e partidos nos dois turnos 22 parlamentares votaram contra no 1º turno Câmara pauta PEC das Igrejas, que amplia a imunidade tributária para templos religiosos Câmara aprova projeto que permite a pais internarem adolescentes em comunidades terapêuticas Alcolumbre acelera PEC da oposição e impõe risco à tramitação do fim da 6×1 no Senado Primeira Turma do STF decide acabar com aposentadoria compulsória remunerada como maior punição a magistrados CNJ aprova e juízes terão contracheque único; objetivo é evitar salários acima do teto Datafolha: Lula tem 40%, e Flávio Bolsonaro, 31% das intenções de voto no 1º turno Meio/Ideia: Flávio perde jovens, alta renda e centro-direita Caiado diz que 'existe o sentimento' para uma aliança com Zema: 'Atestado de credibilidade para a população' Cláudio Castro é alvo de buscas da PF em operação contra aportes de R$ 3,7 bilhões pelo Rioprevidência Cláudio Castro avisa Valdemar que não será mais candidato ao Senado Vida de resort: veja fotos da mansão de Eduardo Bolsonaro nos EUA, avaliada em R$ 6 milhões EXCLUSIVO: Eduardo Bolsonaro mora em casa de luxo de R$ 6 milhões no Texas A contradição do Bananinha em vídeo Flávio Bolsonaro se encontra com Trump na Casa Branca Trump fez elogio a Lula em encontro com Flávio Bolsonaro na Casa Branca EUA anunciam que vão classificar PCC e Comando Vermelho como organizações terroristas

In Episode #543 of the Grit2Greatness Endurance Podcast, Coaches Rich Soares and April Spilde break down why Boulder is one of the most misunderstood and execution‑heavy races on the IRONMAN 70.3 calendar. This is a thinking athlete's race—where small mistakes stack up fast under altitude, heat, and patience‑testing terrain.We dive into the real reasons athletes DNF at Boulder, including: Overbiking relative to altitude Underfueling when effort feels “easy” Heat and hydration mismanagement Swim anxiety and disrupted breathing Aggressive early run pacing Missed bike lap cutoffs Ignoring early warning signsYou'll learn how to race Boulder with restraint, patience, and intention—so you're still strong when it matters most.We also debut a new fun segment: “Death, Taxes… or DNF?”—calling out the most predictable race‑day mistakes we see every year.If you're racing Boulder 70.3—or any altitude event—this episode is your race‑proofing checklist.This episode is brought to you by Vespa Power Endurance.Vespa Power Endurance helps you tap into steady, clean energy—so you stay strong, focused, and in the zone longer. Vespa is not fuel, but a metabolic catalyst that shifts your body to use more fat and less glycogen.✅ Less sugar✅ Higher performance✅ Faster recoveryVespa comes in CV‑25, Junior, and Concentrate.

There Are No Girls on the Internet is a weekly podcast hosted by Bridget Todd. Every week, we break down the tech and internet stories that deserve more attention — especially when they're about AI, power, gender, race, and who actually gets hurt when systems fail. This week: Elon Musk using a Hollywood casting decision to push white nationalist conspiracy theories. The government is surveilling people who oppose data centers as potential terrorists. The DOJ is going after a billionaire who helped fund E. Jean Carroll's lawsuit against Trump. And researchers who study online hate speech being threatened with deportation. If that sounds like your thing — Apple Podcasts | Spotify | and come back every week. HERE’S WHAT WE’RE WATCHING THIS WEEK:

O governo dos Estados Unidos oficializou a inclusão das facções brasileiras Primeiro Comando da Capital (PCC) e Comando Vermelho (CV) em sua lista de organizações terroristas estrangeiras. A medida altera o patamar de monitoramento, rastreamento de ativos e sanções aplicadas pelas agências americanas.Você já leu uma notícia hoje e sentiu que já viveu esse momento antes?   Essa sensação de déjà Vu não é coincidência. No Brasil, o que é manchete hoje costuma ser o eco de decisões e fatos que analisamos meses, ou até anos atrás.   Para celebrar os 8 anos da Crusoé, decidimos enfrentar esse ciclo. Pegamos o que nasceu no digital e, pela primeira vez, transformamos em um registro físico, tátil e permanente.   Chegou a edição especial Crusoé impressa.   É um item colecionável, atemporal e limitado. Uma revista feita para quem gosta de ler com calma, longe das notificações do celular. Um exemplar para guardar sobre o que realmente importa na história recente do brasil.   Esta edição é um presente exclusivo para novos assinantes do Combo de 2 anos O Antagonista e Crusoé.   Utilize o cupom 8ANOSCRUSOE e acesse o link:   https://bit.ly/crusoe-edicao-impressa  Papo Antagonista é o programa que explica e debate os principais acontecimentos do dia com análises críticas e aprofundadas sobre a política brasileira e seus bastidores.       O programa traz contexto e opinião sobre os temas mais quentes da atualidade.       Com foco em jornalismo, eleições e debate, é um espaço essencial para quem busca informação de qualidade.       Ao vivo de segunda a sexta-feira às 18h no nosso canal no Youtube.   https://www.youtube.com/@OAntagonista   Siga O Antagonista no X:  https://x.com/o_antagonista   Acompanhe O Antagonista no canal do WhatsApp. Boletins diários, conteúdos exclusivos em vídeo e muito mais.  https://whatsapp.com/channel/0029Va2SurQHLHQbI5yJN344  Leia mais em www.oantagonista.com.br | www.crusoe.com.br #PCC #CV #Terrorismo #EUA #Geopolítica #SegurançaPública #CrimeOrganizado #Narcotráfico #RelaçõesInternacionais #PolíticaExterna #BrasilEUA #Notícias #PodcastBr #AnálisePolítica #Debate #Atualidades #Facções #Sanções #EmAlta #Informação

No Papo Antagonista desta sexta-feira, 29, falamos sobre a decisão dos Estados Unidos de classificar o Comando Vermelho e o PCC como organizações terroristas.A decisão representa uma vitória política para Flávio Bolsonaro, que vinha defendendo junto ao governo americano o endurecimento contra os grupos criminosos brasileiros.Você já leu uma notícia hoje e sentiu que já viveu esse momento antes?   Essa sensação de déjà Vu não é coincidência. No Brasil, o que é manchete hoje costuma ser o eco de decisões e fatos que analisamos meses, ou até anos atrás.   Para celebrar os 8 anos da Crusoé, decidimos enfrentar esse ciclo. Pegamos o que nasceu no digital e, pela primeira vez, transformamos em um registro físico, tátil e permanente.   Chegou a edição especial Crusoé impressa.   É um item colecionável, atemporal e limitado. Uma revista feita para quem gosta de ler com calma, longe das notificações do celular. Um exemplar para guardar sobre o que realmente importa na história recente do brasil.   Esta edição é um presente exclusivo para novos assinantes do Combo de 2 anos O Antagonista e Crusoé.   Utilize o cupom 8ANOSCRUSOE e acesse o link:   https://bit.ly/crusoe-edicao-impressa  Papo Antagonista é o programa que explica e debate os principais acontecimentos do dia com análises críticas e aprofundadas sobre a política brasileira e seus bastidores.       O programa traz contexto e opinião sobre os temas mais quentes da atualidade.       Com foco em jornalismo, eleições e debate, é um espaço essencial para quem busca informação de qualidade.       Ao vivo de segunda a sexta-feira às 18h no nosso canal no Youtube.   https://www.youtube.com/@OAntagonista   Siga O Antagonista no X:  https://x.com/o_antagonista   Acompanhe O Antagonista no canal do WhatsApp. Boletins diários, conteúdos exclusivos em vídeo e muito mais.  https://whatsapp.com/channel/0029Va2SurQHLHQbI5yJN344  Leia mais em www.oantagonista.com.br | www.crusoe.com.br #PCC #CV #Terrorismo #Trump #Lula #EUA #Brasil #Geopolítica #SegurançaPública #CrimeOrganizado #Narcotráfico #Facções #PolíticaBrasileira #Notícias #PodcastBr #Atualidades #DebatePolítico #EmAlta #GiroDeNotícias #Informação

Você já leu uma notícia hoje e sentiu que já viveu esse momento antes?   Essa sensação de déjà Vu não é coincidência. No Brasil, o que é manchete hoje costuma ser o eco de decisões e fatos que analisamos meses, ou até anos atrás.   Para celebrar os 8 anos da Crusoé, decidimos enfrentar esse ciclo. Pegamos o que nasceu no digital e, pela primeira vez, transformamos em um registro físico, tátil e permanente.   Chegou a edição especial Crusoé impressa.   É um item colecionável, atemporal e limitado. Uma revista feita para quem gosta de ler com calma, longe das notificações do celular. Um exemplar para guardar sobre o que realmente importa na história recente do brasil.   Esta edição é um presente exclusivo para novos assinantes do Combo de 2 anos O Antagonista e Crusoé.   Utilize o cupom 8ANOSCRUSOE e acesse o link:   https://bit.ly/crusoe-edicao-impressa  Narrativas analisa os acontecimentos do Brasil e do mundo sob diferentes perspectivas.     Com apresentação de #MadeleineLacsko, o programa desmonta discursos, expõe fake news e discute os impactos das narrativas na sociedade.     Abordando temas como geopolítica, comunicação e mídia, traz uma visão aprofundada   e esclarecedora sobre o mundo atual.     Ao vivo de segunda a sexta-feira às 17h.   Siga O Antagonista no X:  https://x.com/o_antagonista   Acompanhe O Antagonista no canal do WhatsApp. Boletins diários, conteúdos exclusivos em vídeo e muito mais.  https://whatsapp.com/channel/0029Va2SurQHLHQbI5yJN344  Leia mais em www.oantagonista.com.br | www.crusoe.com.br #PCC #CV #Terrorismo #EUA #Geopolítica #SegurançaPública #Narcotráfico #Facções #CrimeOrganizado #PodcastNarrativas #MadeleineLacsko #Jornalismo #Notícias #Política #Debate #Atualidades #Brasil #PodcastBr #EmAlta #Segurança

Petistas criticam a medida e ação, nesse momento, é vista como uma vitória política de Flávio Bolsonaro.Você já leu uma notícia hoje e sentiu que já viveu esse momento antes?   Essa sensação de déjà Vu não é coincidência. No Brasil, o que é manchete hoje costuma ser o eco de decisões e fatos que analisamos meses, ou até anos atrás.   Para celebrar os 8 anos da Crusoé, decidimos enfrentar esse ciclo. Pegamos o que nasceu no digital e, pela primeira vez, transformamos em um registro físico, tátil e permanente.   Chegou a edição especial Crusoé impressa.   É um item colecionável, atemporal e limitado. Uma revista feita para quem gosta de ler com calma, longe das notificações do celular. Um exemplar para guardar sobre o que realmente importa na história recente do brasil.   Esta edição é um presente exclusivo para novos assinantes do Combo de 2 anos O Antagonista e Crusoé.   Utilize o cupom 8ANOSCRUSOE e acesse o link:   https://bit.ly/crusoe-edicao-impressa  Meio-Dia em Brasília traz as principais notícias e análises da política nacional direto   de Brasília.     Com apresentação de José Inácio Pilar e Wilson Lima, o programa aborda os temas mais quentes do cenário político e econômico do Brasil.     Com um olhar atento sobre política, notícias e economia, mantém o público bem informado.    Transmissão ao vivo de segunda a sexta-feira às 12h no nosso canal do Youtube.  https://www.youtube.com/@OAntagonista   Siga O Antagonista no X:  https://x.com/o_antagonista   Acompanhe O Antagonista no canal do WhatsApp. Boletins diários, conteúdos exclusivos em vídeo e muito mais.  https://whatsapp.com/channel/0029Va2SurQHLHQbI5yJN344  Leia mais em www.oantagonista.com.br | www.crusoe.com.br #Trump #PCC #CV #Terrorismo #SegurançaInternacional #Política #Geopolítica #Notícias #PodcastBr #Atualidades #CrimeOrganizado #Diplomacia #Debate #AnálisePolítica #Justiça

Você já leu uma notícia hoje e sentiu que já viveu esse momento antes?   Essa sensação de déjà Vu não é coincidência. No Brasil, o que é manchete hoje costuma ser o eco de decisões e fatos que analisamos meses, ou até anos atrás.   Para celebrar os 8 anos da Crusoé, decidimos enfrentar esse ciclo. Pegamos o que nasceu no digital e, pela primeira vez, transformamos em um registro físico, tátil e permanente.   Chegou a edição especial Crusoé impressa.   É um item colecionável, atemporal e limitado. Uma revista feita para quem gosta de ler com calma, longe das notificações do celular. Um exemplar para guardar sobre o que realmente importa na história recente do brasil.   Esta edição é um presente exclusivo para novos assinantes do Combo de 2 anos O Antagonista e Crusoé.   Utilize o cupom 8ANOSCRUSOE e acesse o link:   https://bit.ly/crusoe-edicao-impressa  Meio-Dia em Brasília traz as principais notícias e análises da política nacional direto   de Brasília.     Com apresentação de José Inácio Pilar e Wilson Lima, o programa aborda os temas mais quentes do cenário político e econômico do Brasil.     Com um olhar atento sobre política, notícias e economia, mantém o público bem informado.    Transmissão ao vivo de segunda a sexta-feira às 12h no nosso canal do Youtube.  https://www.youtube.com/@OAntagonista   Siga O Antagonista no X:  https://x.com/o_antagonista   Acompanhe O Antagonista no canal do WhatsApp. Boletins diários, conteúdos exclusivos em vídeo e muito mais.  https://whatsapp.com/channel/0029Va2SurQHLHQbI5yJN344  Leia mais em www.oantagonista.com.br | www.crusoe.com.br #Trump #PCC #CV #Terrorismo #Podcast #Notícias #Política #Segurança #Crime #Facções #Geopolítica #Atualidades #Justiça #AnálisePolítica #Mundo