Podcasts about churchill

La "Enciclopedia oculta de Guillermo Díaz" explora las "pifias bélicas", revelando errores y decisiones absurdas en la historia militar. En 1859, un cerdo casi desata una guerra entre Gran Bretaña y Estados Unidos en San Juan, evitada por un contralmirante británico tras un colono matar al animal. La campaña de Galípoli en la Primera Guerra Mundial, ideada por Churchill, es una catástrofe. El desembarco de 1915 causa más de 100.000 bajas aliadas por la fortificación otomana. El submarino alemán U-1206 se hunde en 1945 por un inodoro de alta presión. Un error en su uso genera cloro gaseoso, forzando al submarino a ascender y ser atacado por la RAF. En 1943, 34.000 aliados invaden Kiska tras bombardeos, descubriendo que los 5.000 japoneses ya han sido evacuados. La niebla y los nervios causan más de 300 bajas aliadas por fuego amigo. Estas historias demuestran cómo la "niebla de guerra", el miedo, la soberbia, la información incompleta y los errores humanos transforman buenas ideas en ...

Trump's approval with independents just hit 24%. Republicans are going home to their districts and getting an earful about an unauthorized, unconstitutional, and wildly unpopular war with Iran that is now pulling in the Saudis, the Egyptians, and Iranian proxies from Iraq to Yemen. Paul Rieckhoff calls it what it is — a sucking chest wound — and lays out why Senate Republicans are finally breaking, why Lindsey Graham comparing Trump and Netanyahu to Roosevelt and Churchill isn't landing, and why supporting the troops means demanding a coherent plan, not a blank check for one man's war. From there, the episode moves to John Cornyn refusing to be rolled on Trump's weaponization slush fund, Todd Blanche's Epstein problem and his condescending meeting with survivors, and the acting-AG limbo that could stretch into September. And it closes with a tribute to Coach Yeoman Wilder — the New York youth baseball legend who stood up to ICE agents at his practice, protected his players, and reminded a country full of capitulating law firms and universities what actual patriotism looks like. Righteous anger, patriotic hope, and a call to meet the moment. -WATCH full video of this episode here. -Millions of American veterans are being locked out of primary elections in the country they served. See what we're doing to change that. -Visit Kalshi and trade on anything. Use code [INDEPENDENT] to get ten dollars when you trade ten. -Join Noble Mobile today and get a $100 bonus when you use code PAUL and stay a member for 2 months! -Join IVA and help us get independent veterans elected to office. -Learn more about Paul's work to elect a new generation of independent leaders with Independent Veterans of America. -Learn more about American Veterans for Ukraine here. -Remember Independent is an Attitude. -Learn more about The Headstrong Project for Veterans, Tragedy Assistance Program for Survivors (TAPS), and Department of Veterans Affairs resources in your area. Seeking support is not a sign of weakness. It's a show of strength. If you or a loved one are in immediate crisis, dial 988 and press 1, or text 838255. Connect with Independent Americans: Subscribe on YouTube, Spotify, Apple Podcasts, and all podcast platforms Read more at Substack Support ad-free episodes at Patreon  Connect: Instagram  • X/Twitter • BlueSky • Facebook  Follow on social: @PaulRieckhoff on X, Instagram, Threads, and Bluesky -Join the movement. Hook into our exclusive Patreon community of Independent Americans. Get extra content, connect with guests, meet other Independent Americans, attend events, get merch discounts, and support this show that speaks truth to power.  -And get cool IA and Righteous hats, t-shirts and other merch now in time for the new year.  Independent Americans is powered by veteran-owned and led Righteous Media.  And now part of the BLEAV network!  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hello and welcome to conversations from the AandF podcast.  In this episode I to Niketa Sanderson Gillard, social worker, Churchill Fellow and Founder and CEO of Why Care. We chat about Niketa's Churchill fellowship, her work that led up to it and how her organisation Why Care has  influenced the DfE's draft standards that are currently being consulted on. Niketa and discuss the standards, the consultation and more. You can participate in the consultation and read more on the draft standards here.    As always if you've experience of adoption, fostering or special guardianship from any perspective personal or professional and would like share that on the podcast please get in touch through the Facebook page, BlueSky or email us at AandFpodcast@gmail.com

In this monthly episode of the Anglotopia Podcast, Jonathan Thomas returns with his August edition of What's On in London — and this one comes with a personal twist, because Jonathan will be in London himself later in the month. August is London's most contradictory month: the busiest tourist season of the year, a million-plus visitors, packed attractions and carnival streets — and yet simultaneously the month when Londoners themselves flee to Cornwall and France, leaving whole districts strangely quiet. Jonathan covers it all: the Notting Hill Carnival (August 30–31, its 60th anniversary this year, Europe's largest street festival, free entry); the major royal and historic building openings including Buckingham Palace State Rooms, Houses of Parliament summer tours, and the freshly restored Banqueting House reopening August 1st; London's brand new museum, Trent Park House of Secrets — just opened July 21st, telling the extraordinary wartime spy story that was kept under the Official Secrets Act for over 50 years; new West End openings including Jane Eyre the Musical and Kimberly Akimbo; the blockbuster exhibitions still running and closing this month; live music at Victoria Park and Wembley; family-friendly highlights; the VAT tourist discount reducing admission prices across major attractions this summer; and Jonathan's own personal list of what he's booked and planning to see — including the Ceremony of the Keys at the Tower of London, Buckingham Palace's East Wing tour, the Wallace Collection's Churchill as Painter exhibition, and Trent Park. Links The Big One — Notting Hill Carnival (August 29-31, 60th Anniversary) Notting Hill Carnival — Official Website Notting Hill Carnival 2026 Schedule & Info (Sat Aug 29 Steel Band Competition; Sun Aug 30 Children's Day; Mon Aug 31 Main Parade) Royal & Historic Building Openings Buckingham Palace State Rooms — Book Tickets (Open July 9 – September 27, 2026; daily 9:30–19:30 through August 31) Buckingham Palace East Wing Highlights Tour — Book Separately Houses of Parliament Summer Tours — Book Tickets (Reduced prices throughout summer recess; Westminster Hall, House of Commons, House of Lords) Banqueting House — Tickets & Info (Fully reopened August 1 after major restoration; open Thu–Mon 10:00–16:00; Adults £10, under 16s free) London's Newest Museum Trent Park House of Secrets — Book Tickets (Open since July 21; Tue–Sun 10:00–17:00; Adults £14/~$20; Oakwood tube station, Piccadilly Line) Ceremony of the Keys Ceremony of the Keys — Tower of London — Book Tickets (Next ticket release: August 3, 2026 at 13:00; suggested price £30, pay from £10; sells out immediately — set a reminder) Festivals & Outdoor Events Greenwich + Docklands International Festival — Free (August 21 – September 6, 2026; outdoor theatre, circus and dance across Greenwich, Woolwich and Docklands) Greenwich Fair — Free (August 22–23, Greenwich Park — no ticket required) 120 Years of the Piccadilly Line with Tim Dunn — London Transport Museum (August 22) All Points East — Victoria Park, East London (Two weekends; headliners include Lorde, Tyler the Creator, Twenty One Pilots) BBC Proms at the Royal Albert Hall (Running all month, ends September 12; day tickets available on the door) New West End Theater Openings Jane Eyre the Musical — UK Premiere (from August 28, Southwark Playhouse) Kimberly Akimbo (from August 28, Hampstead Theatre) The Mischievous Team — New spy farce (from August 1) West End Tickets via Londontopia TKTS Half-Price Tickets, Leicester Square Exhibitions — Running & Closing in August Frida Kahlo — Tate Modern (runs to 2027) (Plenty of time — but book ahead) Winston Churchill the Painter — Wallace Collection (until November 29) Richard Dadd — Royal Academy (running) Anish Kapoor — Hayward Gallery (until October) Schiaparelli: Fashion Becomes Art — V&A (until November) Marilyn Monroe — National Portrait Gallery (closing September) (Last chance — closing soon) Jurassic Oceans: Monsters of the Deep — Natural History Museum (Great for families) Star Trek at 60 Trail — Science Museum (closing September) (Free) Young Artist Summer Show — Royal Academy (until August 30, Free) Exhibitions Closing in August — See These Now Holy Pop at Somerset House — closes August 9 Patrick Caulfield — Royal Academy — closes August 16 Titanic Exhibition — Olympia — closes August 16 Fairy Tales — British Library — closes August 23 Zurbarán — National Gallery — closes August 23 Hervien Anderson — Tate Britain — closes August 23 David Hockney: A Year in Normandy — Royal Academy — closes August 23 Tracey Emin: A Second Life — Tate Modern — closes August 31 Family Activities Paddington Bear Summer Special — Immersive Experience The Big Dig: Archaeology Adventure (until August 21) Boleyn is Back — Tudor Living History, Tower of London (until August 14) Live Music All Points East — Victoria Park The Weeknd — Wembley Stadium (August 14–19) Practical Resources Met Office App — London Weather Forecast Londontopia London Events Calendar TfL Elizabeth Line — Air-Conditioned Travel Across London Anglotopia Wallace Collection Churchill Episode — Listen Now (Our earlier episode with Dr. Lucy Davis) Friends of Anglotopia Club Takeaways August 2026 is the 60th anniversary of the Notting Hill Carnival — Europe's largest street festival, free to attend, taking over Notting Hill and Ladbroke Grove on August 30–31. The Saturday Steel Band Competition (August 29) requires a ticket; Sunday Children's Day and Monday main parade are free. Arrive early, follow your nose to the food stalls, and keep your wits about you in the crowds. Trent Park House of Secrets — London's brand new museum, opened July 21st — tells the extraordinary story of the WWII intelligence operation where 59 captured German generals were housed in apparent luxury, never knowing the entire house was bugged. Often called "the other Bletchley Park." Tickets are £14 (~$20), open Tuesday–Sunday, and it's on the Piccadilly Line at Oakwood station. Jonathan is going. Buckingham Palace State Rooms are open daily through August 31, 9:30am to 7:30pm — the longest opening hours of the season. Jonathan has booked both the State Rooms tour and the newly opened East Wing tour. If you haven't booked yet, check for remaining availability immediately; the best slots are gone but cancellations do appear. Banqueting House on Whitehall — Inigo Jones's 1622 masterpiece with its Rubens ceiling, the building outside which Charles I was executed — reopens fully on August 1st after a major restoration. Adults £10, under 16s free, open Thursday–Monday. New lift for step-free access. One of London's most underrated experiences. The Ceremony of the Keys at the Tower of London — the world's oldest unbroken military ceremony, performed every night for at least 700 years — releases the next month's tickets on the first working day of each month. The next release is August 3rd at 1pm. Jonathan got a ticket; it requires an early wake-up and sells out within hours of release. The UK government has instituted a 15% VAT discount on tourist attraction admissions this summer — which means lower prices at major London attractions. If you booked in advance, you may be entitled to a partial refund for the difference; check with each venue. Lower prices also means slightly larger crowds. The Greenwich + Docklands International Festival runs August 21 to September 6 — 17 days of completely free outdoor theatre, circus and dance across Greenwich Park, Woolwich, and the Docklands. The two-day Greenwich Fair on August 22–23 in Greenwich Park is the unmissable centerpiece. No ticket required — just show up. Several major exhibitions close on or around August 23rd — a critical date. Zurbarán at the National Gallery, Fairy Tales at the British Library, David Hockney at the Royal Academy, Hervien Anderson at Tate Britain, and Patrick Caulfield at the Royal Academy all close that day. If you're arriving after August 23rd, these are gone. The BBC Proms at the Royal Albert Hall run all month and into September — the world's largest classical music festival, with day-of standing tickets potentially available on the door. Jonathan hasn't managed to go yet and still wants to. It's worth trying even if you can't book ahead. August in London is genuinely two cities at once: over a million tourists fill the attractions and the carnival streets, while large swaths of residential London go quiet as locals escape. The wise visitor uses this: the tourist attractions are busy, but the parks, the residential neighborhoods, and the early mornings are surprisingly peaceful. Soundbites "August is London's most contradictory month. Every guidebook tells you it's peak season — and every guidebook is right. And yet at exactly the same time, London kind of empties out. Locals flee to Cornwall, to France, to anywhere that isn't a stuffy office in a heat wave. Whole districts can go completely quiet." — Jonathan on August's strange duality. "The Notting Hill Carnival is that one weekend where London's multicultural and diverse character — which can feel oddly invisible at other times of the year — is front and center. It's loud. It's joyful. And it's entirely free." — Jonathan on why the carnival matters. "They captured high-level German officers and put them all in one place. What the prisoners didn't know was that the entire house was wired with microphones. And hidden in a concealed basement, around a hundred secret listeners — many of them German-speaking Jewish refugees who had fled the Nazis — were recording and transcribing every careless word." — Jonathan on Trent Park House of Secrets. "I have wanted to go inside Buckingham Palace for twenty years. I'm finally doing it this year. I've got my tickets booked. I'm going to do the State Room tour, I'm going to see the freshly rehung picture gallery, and I've booked the East Wing tour. That is going to take a whole Saturday. I cannot wait." — Jonathan on his long-awaited Buckingham Palace visit. "The Ceremony of the Keys is considered the world's longest military ceremony. They say it's been running for a thousand years — I'm skeptical, but at least as long as there's been a Tower of London. I woke up early and managed to snag a ticket before they sold out. Because they do sell out immediately." — Jonathan on booking the Ceremony of the Keys. "Banqueting House. Inigo Jones's masterpiece on Whitehall, with its Rubens ceiling. The one surviving fragment of the old Palace of Whitehall — the very spot where Charles I was executed in 1649. It reopens for the summer on August 1st. That is a lot of history in one place." — Jonathan on Banqueting House reopening. "The government has instituted a fifteen percent discount off the twenty percent VAT for tourists visiting tourist attractions this summer. So this month you will see lower prices at all of London's major tourist attractions. And if you booked in advance, you should be getting a refund." — Jonathan on the summer VAT tourist discount. "I love Star Trek. I have seen every episode of Star Trek dozens of times. You can't see over there, but I'm building the LEGO Enterprise D model right now. And I'm going to go see this exhibition because it's the 60th anniversary and the producers don't seem to want to celebrate it much — but everybody else is." — Jonathan on his plan to visit the Star Trek at 60 Trail at the Science Museum. "Several things close on August 23rd. I'm arriving after August 23rd. So I'm going to miss the David Hockney, the Zurbarán, the Tracey Emin. It's a bit annoying. But it is what it is." — Jonathan on the painful August 23rd exhibition closing cliff. "If you see a slightly sun-birthed Anglophile wandering the food stalls at one of the festivals going on later in August — do say hello. It might just be me." — Jonathan's closing invitation. Chapters 00:23 Introduction & Personal Note — Jonathan is heading to London this August 01:14 Weather Warning — England's driest summer in years, three heat waves, and what to expect 02:35 Episode Overview — Carnival, openings, exhibitions, practical tips 03:16 The Strange Duality of August — A million tourists arrive as Londoners flee 04:05 NOTTING HILL CARNIVAL — Europe's largest free street festival, 60th anniversary, August 29–31 05:28 Carnival Practical Tips — Go early, follow the food, wear comfortable shoes, stay safe 06:37 Festivals & Outdoor Highlights — Greenwich + Docklands International Festival (free, Aug 21–Sep 6) 07:07 Greenwich Fair — Free outdoor theatre in Greenwich Park, August 22–23 07:17 120 Years of the Piccadilly Line — Tim Dunn at the London Transport Museum, August 22 07:50 Edinburgh Fringe Previews & Al Fresco Dining — London venues hosting previews; Soho streets 08:22 ROYAL & HISTORIC BUILDING OPENINGS 08:25 Buckingham Palace State Rooms — Open daily; freshly rehung picture gallery; Jonathan's booked visit 09:31 Buckingham Palace East Wing — Newly opened tour; Jonathan's full Saturday plan 10:07 Houses of Parliament Summer Tours — Westminster Hall, Commons, Lords; reduced prices 10:36 Banqueting House — Fully reopens August 1st after major restoration; Rubens ceiling; £10 adults 11:03 TRENT PARK HOUSE OF SECRETS — London's brand new museum, opened July 21st 11:22 The Story of Trent Park — Philip Sassoon, 59 German generals, hidden microphones 13:03 The Secret Listeners — Jewish refugees transcribing Nazi generals in a concealed basement 14:16 Visiting Trent Park — Hours, tickets £14, Oakwood station, Piccadilly Line 15:43 NEW WEST END THEATER OPENINGS 15:53 The Mischievous Team — New spy farce from August 1st 16:04 Jane Eyre the Musical — UK premiere, Southwark Playhouse, August 28 16:19 Kimberly Akimbo — Tony-winning musical, Hampstead Theatre, August 28 16:32 BLOCKBUSTER EXHIBITIONS STILL RUNNING 16:47 Frida Kahlo at Tate Modern — Runs to 2027; Jonathan plans to go 17:03 Marilyn Monroe — National Portrait Gallery; closing September — last chance 17:13 Anish Kapoor — Hayward Gallery; Schiaparelli — V&A; Richard Dadd — Royal Academy 17:58 Winston Churchill the Painter — Wallace Collection; Jonathan booked since March 18:44 Royal Academy Summer Exhibition — Closes August 23rd; last chance 18:52 Jurassic Oceans — Natural History Museum; Jonathan has never been 19:03 Star Trek at 60 Trail — Science Museum; free; LEGO Enterprise D confession 19:30 EXHIBITIONS CLOSING IN AUGUST — CRITICAL DATES 19:37 August 9 & 16 — Holy Pop (Somerset House), Patrick Caulfield & Titanic (RA & Olympia) 19:54 August 23rd Mass Closings — Fairy Tales, Zurbarán, Hervien Anderson, David Hockney 20:16 August 31 — Tracey Emin: A Second Life (Tate Modern) 20:29 LIVE MUSIC 20:31 All Points East — Victoria Park; Lorde, Tyler the Creator, Twenty One Pilots 20:44 BBC Proms — All month to September 12; day standing tickets on the door 21:11 The Artist at Wembley — August 14–19; Luke Combs this weekend only 21:28 FAMILY ACTIVITIES — School holidays mean packed attractions 21:48 Paddington Bear Summer Special — Immersive experience for younger children 21:56 The Big Dig — Hands-on archaeology through August 21st 22:08 Boleyn is Back — Tudor living history at the Tower, through August 14th 22:16 Young Artist Summer Show — Royal Academy; free; closes August 30th 22:30 JONATHAN'S PERSONAL AUGUST ITINERARY 22:40 Ceremony of the Keys — Booked; how Jonathan got a ticket; arrive at 9:30pm 23:54 What Else Jonathan Is Doing — National Gallery, Wallace Collection, Royal Academy, Natural History Museum 24:49 Star Trek + Natural History Museum Double Bill Plan 25:05 Notting Hill Carnival — In London for it but sitting it out without his wife 25:32 PRACTICAL SECTION — CROWDS, WEATHER & SHOULD YOU GO? 25:37 August Weather Reality — Warm and dry; long days; twilight till after 9pm; pack layers 26:35 Peak Season & School Holidays — Why it's busy even when Londoners leave 26:39 Weather Detail — Pack layers; rain is rare but intense; humidity by the Thames 28:13 THE VAT TOURIST DISCOUNT — 15% off admissions; possible refunds for advance bookers 29:27 Should You Visit in August? — Jonathan's honest verdict 29:52 Top Tip — Don't travel over bank holiday weekend (Jonathan breaks his own rule) 30:51 Wrap-Up — Londontopia events calendar, Friends of Anglotopia, and see you in London Video Version

NonCensored is coming to an end. Our final show is at the London Podcast Festival on the 13th September 2026 at 4.30pm. Tickets are available here: https://www.kingsplace.co.uk/whats-on/podcast/noncensored/As we take a break over the summer, we're going to remind you of how fun our live shows are. This week, it's the first-ever live show, from the Edinburgh Festival Fringe in 2022.This week we bring you a very special LIVE episode of NonCensored, recorded as part of LATER at Paines Plough Roundabout. Harriet and Martin are joined by Diversity Correspondent Eshaan Akbar, who explains why Lawrence Fox is the most woke man alive, and by Culture Secretary For Culture And Digital Media Sport Nadine Dorries, who takes questions from the audience and reads an EXCLUSIVE extract from her forthcoming erotic political thriller His Front Bench Woman. There's also an extended interview with the man who brought down Theresa May, Simon Brodkin who talks pranks, arrests, and how being a character comedian means sometimes people get confused about whether you're real or not. With thanks to Rosie Holt, Brendan Murphy, Eshaan Akbar, Sooz Kempner, Simon Brodkin and Ed Morrish.Rosie's sitcom, Crossing The Floor, is available now on BBC Sounds. Her play, Churchill's Urinal, will be on at the Edinburgh Festival Fringe (tickets here), where she will also be doing a new character comedy/stand-up show, The Illegal Aliens Have Landed (tickets here).Brendan is taking a brand new show, Indy, to the Edinburgh Festival Fringe in August. It's a three-man retelling of Indiana Jones, and tickets are available here.Eshaan has started a new podcast called Adda With Eshaan which you can hear here, and contribute to here. His latest stand-up special, Fool Moon, is available on YouTube.Ed produces P.O.V., a scripted sketch show on BBC Sounds which has NonCensored regulars like Davina, Will and Sooz in it. He also produces Sound Heap With John-Luke Roberts, an award-winning improvised sketch show that features many NonCensored regulars like Rosie, Brendan, Will, Sooz and Joz.Show photography is by Karla Gowlett and design is by Chris Barker. Original music is by Paddy Gervers and Rob Sell at Torch and Compass.NonCensored is a Lead Mojo production Hosted on Acast. See acast.com/privacy for more information.

Dans le cadre de la course à la bombe entamée, à la fin de la guerre, entre les Nazis et les Alliés, ces derniers vont mener, à cinq reprises, des opérations militaires contre une usine productrice d'eau lourde en Norvège (l'eau lourde étant le surnom de l'oxyde de deutérium, essentiel pour la fabrication des réacteurs). L'émission est inspirée d'un chapitre du livre du regretté Bob Maloubier, Les coups tordus de Churchill, récemment réédité.Plongez au cœur des opérations secrètes menées par les Alliés pour empêcher l'Allemagne nazie de mettre la main sur l'eau lourde, un composant essentiel à la fabrication de la bombe atomique. Dans cet épisode captivant, Franck Ferrand nous entraîne dans les coulisses de la « Bataille de l'eau lourde », un affrontement dans l'ombre entre Churchill et Hitler pour le contrôle de cette arme de destruction massive. Découvrez comment les services secrets britanniques ont recruté des agents infiltrés, envoyé des commandos audacieux et orchestré des raids pour saper les efforts allemands. Suivez les péripéties de ces opérations à haut risque, menées dans des conditions extrêmes au cœur des montagnes enneigées de Norvège occupée. Avec le suspense d'un thriller d'espionnage, Franck Ferrand nous fait revivre cette course contre la montre qui a joué un rôle décisif dans l'issue de la Seconde Guerre mondiale. Une histoire méconnue de la conquête de la bombe atomique, à ne pas manquer pour tous les passionnés d'histoire

Michael is joined by Rear Admiral (Ret.) Mark Montgomery, Senior Director of the Center on Cyber and Technology Innovation at the Foundation for Defense of Democracies (FDD), who just concluded his seventh trip to Ukraine since the full-scale invasion. Mark delivers an operational update on his work training Ukrainian brigade commanders and assesses the strategic political reshuffle in Kyiv. He compares President Volodymyr Zelensky to a wartime Winston Churchill—brilliant yet fallible—and evaluates the new military leadership under Gen. Mykhailo Drapatyi. Finally, Mark draws on his naval expertise to argue why the U.S. Navy must commit to physical escort operations through the Strait of Hormuz to permanently break Iran's economic grip.

“The statesman is an educator who leads through language — who gives a people the language they need to think about themselves.” — Steven B. Smith on what makes a statesman Was Odysseus a statesman? The Yale political philosopher Steven B. Smith — who, by the way, can't beg, steal or borrow a ticket to Christopher Nolan's cinematic epic — offers a cautious yes. While Odysseus wasn't exactly a founder of states, he was, to borrow one of Homer's favorite words, “polytropos” — which translates as many-sided, a master of the political adaptability that Machiavelli demanded of his Prince. In his forthcoming On Statesmanship, Smith distinguishes the true statesman from the mere leader. All 195 states in the world have leaders, he says, almost none are blessed with statesmen. A genuine statesman, he explains, is an educator who leads his fellow citizens through language. So Lincoln's Gettysburg Address gave Americans new words to think about themselves. Just like de Gaulle's “certain idea of France” and Churchill's uncompromisingly defiant language in 1940. For statesmen, timing is everything. “Fortune is a woman,” Machiavelli advised his Prince, so she should be mastered adventurously. Many are called, few are chosen. But then they are unchosen. That same unbowed Churchill spent a decade in the political wilderness before 1940, then got booted out of office at the end of the war. Hegel called such figures world-historical individuals — used by history, then cruelly discarded. Ironically, it's “great” men of history like Odysseus and Churchill who are most vulnerable to its female fortune. On Statesmanship is equally clear about who doesn't qualify for this exclusive club. Smith argues that Lenin, Hitler, and Napoleon might have founded states, but weren't statesmen because they had no respect for the rule of law. Nor is the club purely Western, white, or male. Mandela, Havel, Gandhi, and MLK all qualify as “statesmen without states.” Singapore's Lee Kuan Yew makes the cut as a constitutionalist if not a democrat. Fortune might be a woman, but Margaret Thatcher and Indira Gandhi mastered fate with more adventure than their male colleagues. Political leadership these days, Smith says, is monopolized by either technocrats or demagogues. In fact, Smith can only name one true contemporary statesman — Volodymyr Zelensky who, he says, is “doing a stirring imitation of Winston Churchill.” Otherwise the world is drifting into Thucydides' trap where the strong do what they like and the weak suffer what they must. Thus our longing for many-sided leaders like Odysseus. Which is why Nolan's blockbuster is such a hit that Steven Smith has yet to see it. One small footnote to this conversation. Afterwards, Smith sent me a note expressing a correction of how he described Obama: “Reflecting back on the conversation, I was embarrassed about what I said about Obama lacking ‘civil courage.' That is not exactly what I meant. Probably a better formulation would be to say that he lacked what Plato called ‘thymos,' a kind of public-spirited anger. His rationality and ‘coolness' — in many ways admirable traits — gave the impression that he was too aloof from the rough-and-tumble of politics. Anger and indignation when judiciously employed, as my former student Amia Srinivasan has argued, remain formidable tools in the statesman's tool kit.” Five Takeaways •       A Leader Is Not a Statesman. There are 195 states in the world and every one has a leader; almost none has a statesman. The concept is an ideal of what leadership aspires to be, running from Plato's Politikos and the philosopher-king to the present. The statesman has the usual leadership virtues — foresight, courage, determination, a willingness to say unpopular things — but Smith's central claim is different: the statesman is an educator who leads through language, giving a people the words they need to think about themselves. Lincoln's Gettysburg Address shaped how Americans understand their republic; de Gaulle led with “a certain idea of France”; Churchill and Disraeli wrote the British self-image.•       Timing — and the Tragedy. Statesmanship is a matter of the ripe moment, and the moment is cruel. Churchill spent the 1930s in the political wilderness, changed parties, presided over Gallipoli — and yet when 1940 came, he was there, prepared; the instant peace was established, the voters discarded him. Lenin resolved the Marxists' endless debate about whether Russia was “ripe” by deciding that ripeness was a matter of willpower. Hegel called such figures world-historical individuals — Caesar, Napoleon, Alexander — whom history uses for its purposes and then throws away. Even without Hegel's grand narrative, Smith notes, statesmen are routinely and cruelly discarded by their own people. There is a tragedy built into the art.•       Who's Out — and Who's In. Lenin, Hitler, and Napoleon founded states, but founders of tyrannies are not statesmen: for Smith the modern statesman is inseparable from constitutionalism — rule of law, balance of powers, the capacity to listen and accept less than the whole loaf. Nor is the club white, Western, or male. Mandela, Havel, Gandhi, and Martin Luther King appear as “statesmen without states,” shaping nations before or without holding office; Lee Kuan Yew qualifies as a constitutionalist if not a democrat; Thatcher — who famously told a wobbling American president to stiffen — and Indira Gandhi practice statecraft at the highest level. As for the word itself: Smith keeps “statesman.” “Statesperson,” he rules, is ugly.•       The Anti-Statesmen: Technocrat and Demagogue. Smith's chapter on anti-statesmen identifies the two failed models of modern leadership: the technocrat, a problem-solver-in-chief without a narrative, and the demagogue — a Greek word, and a breed democracies reliably produce — who rules through fear, intimidation, and humiliation. Trump, mentioned barely twice in the book, is filed under the latter, classically. Obama remains Smith's enigma: civic seriousness and dazzling rhetorical gifts — yet lacking, Smith suggests on reflection, what Plato called thymos, a kind of public-spirited anger. Obama's rationality and coolness, admirable in themselves, gave the impression of a man too aloof from the rough-and-tumble of politics; anger and indignation, judiciously employed — as Smith's former student Amia Srinivasan has argued — remain formidable tools in the statesman's tool kit. The Democrats, Smith argues, are trapped between both failed models, and need what Lincoln, FDR, JFK, and Reagan had: an affirmative story of America to embed themselves in.•       One Statesman Standing. Asked to name a contemporary statesman, Smith offers exactly one — “and I say God bless him”: Volodymyr Zelensky, doing a stirring imitation of Winston Churchill, showing guts and magnanimity in a horrible moment for his people. Otherwise: seven British prime ministers in a decade, and a paucity everywhere else. Statesmen, Smith warns, cannot simply be willed into existence. And the alternative is already visible — the world of Thucydides, where the strong do what they like and the weak suffer what they must, carved into spheres of influence by Iran, Russia, and China. That is what a w...

SALUDOS amigos y amigas de FLASH BLUES, Creo que hoy hacemos el número 51 y hasta ahora no habíamos abandonado el blues pero hoy rescatamos del baúl de las joyas olvidadas un disco que es puro fuego: You & Me, de Chick Churchill.

College closures are becoming an increasingly common part of higher education, largely driven by declining enrollment, shifting federal funding, and rising operating costs. As more campuses face financial strain, what happens when a college can't survive on its own? Professor Mary Churchill shares the story of Wheelock College's merger with Boston University, widely viewed as one of the most successful mergers in higher education. She explains how leaders can protect a school's mission, support students and faculty through major change, and make difficult decisions before a crisis leaves them with no good options.  For more on this topic: Check out Churchill's book, When Colleges Close: Leading in a Time of Crisis, co-written with David Chard  

En mayo de 1940, Winston Churchill asumió como primer ministro de Gran Bretaña en el peor momento posible. Francia estaba a punto de caer, el ejército británico estaba atrapado y la mayoría del gabinete recomendaba abrir negociaciones con Hitler. Sin embargo, Churchill se negó en términos absolutos. No porque los números le favorecieran, sino porque su esperanza no se fundaba en los números. Años después afirmaría: “El éxito no es definitivo, el fracaso no es fatal; lo que cuenta es el valor de continuar”. La esperanza bíblica no es optimismo fundado en probabilidades favorables; es convicción arraigada en el carácter inquebrantable de Dios. El que prometió es fiel, y eso no depende del estado del mundo, ni del estado de nuestras circunstancias. La esperanza que viene de Dios no defrauda, no porque todo salga bien, sino porque Él está presente en todo. Por eso, renueva hoy tu esperanza. No en los resultados; en Él quien no cambia aunque todo alrededor se mueva. La Biblia dice en Romanos 5:5: "La esperanza no avergüenza; porque el amor de Dios ha sido derramado en nuestros corazones". (RV1960).

Nancy Churchill argues the 2020 election's vulnerabilities remain unaddressed, citing Chinese access to 220 million voter records and a February 2026 Harvard-Harris poll showing 71 percent of voters support the SAVE America Act. She calls on Congress to act before November 2026. https://www.clarkcountytoday.com/opinion/opinion-vindication-the-first-step-towards-secure-elections/ #ElectionSecurity #SAVEAmericaAct #MailInVoting #VoterID #ElectionIntegrity #NancyChurchill #WashingtonState #Congress #PaperBallots #Opinion ---

En mayo de 1940, Winston Churchill asumió como primer ministro de Gran Bretaña en el peor momento posible. Francia estaba a punto de caer, el ejército británico estaba atrapado y la mayoría del gabinete recomendaba abrir negociaciones con Hitler. Sin embargo, Churchill se negó en términos absolutos. No porque los números le favorecieran, sino porque su esperanza no se fundaba en los números. Años después afirmaría: “El éxito no es definitivo, el fracaso no es fatal; lo que cuenta es el valor de continuar”.La esperanza bíblica no es optimismo fundado en probabilidades favorables; es convicción arraigada en el carácter inquebrantable de Dios. El que prometió es fiel, y eso no depende del estado del mundo, ni del estado de nuestras circunstancias. La esperanza que viene de Dios no defrauda, no porque todo salga bien, sino porque Él está presente en todo.Por eso, renueva hoy tu esperanza. No en los resultados; en Él quien no cambia aunque todo alrededor se mueva.La Biblia dice en Romanos 5:5: "La esperanza no avergüenza; porque el amor de Dios ha sido derramado en nuestros corazones". (RV1960).

RUNDOWN   Mitch and Hotshot Scott open Episode 392 by marveling at the daredevils who parachuted off the Space Needle before celebrating the return of Seahawks training camp as a welcome distraction from another nerve-racking week of Mariners baseball. They react to Dom Canzone's season-saving heroics, Jerry Dipoto's puzzling coaching shakeup, and Seattle's looming trade deadline decisions before previewing the Seahawks' title defense and the inevitable Torrey Horton training camp hype. After Dominic Canzone's dramatic home run rescues the Mariners from a potentially devastating sweep, Mitch, Jason Churchill, and Brady Farkas debate whether the victory can truly change the trajectory of Seattle's season or merely delay the inevitable. Churchill also weighs Seattle's pitching surplus, discusses the futures of George Kirby and Logan Gilbert, evaluates top prospects Michael Arroyo and Lazaro Montes, and closes with an honest assessment of Cal Raleigh's prolonged slump. With Seahawks training camp underway, Mitch and Jacson Bevens examine how Seattle's Super Bowl-winning roster has changed and whether the team is equipped for another title run. They discuss the thinner roster, the uncertainty at running back, Sam Darnold's long-term future, and what Klint Kubiak's offense might look like with new weapons. Mitch samples three of the show's patron-exclusive regulars as Danny O'Neil weighs Tom Dundon's arena strategy and the Seahawks' upcoming Hard Knocks season. Jason Puckett breaks down the Mariners' trade deadline outlook, including Mason Miller, Logan Gilbert, George Kirby, and Seattle's payroll future, before Slickhawk closes with stories about Trader Joe's, a 26-pound brisket, Dino's Pub, and Ryan Grubb's coaching future.   GUESTS   Brady Farkas | Host, Refuse to Lose podcast Jason Churchill | Baseball Things Jacson Bevens | Cigar Thoughts Danny O'Neil | Host, The Dang Apostrophe Jason Puckett | KJ-Aren't / Puck Drop Slickhawk | Shooting the Shit with Slick   TABLE OF CONTENTS   0:00 | Mitch and Scott Welcome Football Back While the Mariners Hang On. 18:25 | Mariners No-Table: Jason and Brady Debate Canzone's Lifeline and Seattle's Deadline Dilemma. 49:55 | Seahawks No-Table: Jacson Bevens Sizes Up a Champion's Repeat Chances. 1:10:33 | Danny, Jason, and Slick: Patron Poo-Poo Platter Serves Up Blazers Drama, Hard Knocks, and Mariners Trade Talk. 1:32:33 | Other Stuff: Shaq names the only three players to dunk on him, Derrick Coleman's legendary defiance of Chuck Daly's dress code, Jackson Koivun's meteoric rise from college golf to PGA Tour winner, Phil Mickelson's marriage rumors, Kevin Calabro exits Portland after Tom Dundon's cost-cutting, ESPN layoffs, Pat McAfee's reported $60 million extension, LeBron James joins Philadelphia, Tony Romo DUI arrest, Everson Griffen arrested over interlock violation, Le'Veon Bell jailed over unpaid child support, Kenmore Air seaplane crash, Kyle Shanahan injured in car accident. HEADLINES: McDonald's sued over excessively hot french fries, Colorado woman's name too long for Medicare card causing claim denials, Christian men-only gym opens to avoid temptation, boat clocked at 157 mph in a 30 mph zone during New York charity event. RIPS: Jordan Devey (38), Peter Lassally (93), Dick Porn (87).

Dr. Larry P. Arnn, President of Hillsdale College, joins Hugh Hewitt on the Hillsdale Dialogues to continue a series on The Second World War, Churchill's sprawling memoir and history of World War II in six volumes.Release date: 24 July 2026See omnystudio.com/listener for privacy information.

Dr. Larry P. Arnn, President of Hillsdale College, joins Hugh Hewitt on the Hillsdale Dialogues to continue a series on The Second World War, Churchill's sprawling memoir and history of World War II in six volumes. Release date: 24 July 2026

Last time we spoke about the Japanese offensive at Changsha. In late September 1941, fighting in China had become a brutal struggle of endurance. Japanese forces kept pushing forward, but China—though battered by heavy losses—refused to yield. Help from abroad was slowly beginning to matter. American aid reached China through groups like the Red Cross, and volunteers later associated with the Flying Tigers offered crucial support to Chinese operations. Against this backdrop, Changsha in Hunan became the center of a dangerous autumn campaign. Chiang Kai-shek and the Nationalist leadership treated the city as strategically vital, knowing that its fate could influence the wider course of the war. In the end, the resistance at Changsha showed China's key advantage: resilience. The city's defense helped slow the Japanese advance, break up momentum, and create uncertainty as the campaign continued.   #212 The Autumn Battle of Changsha, 1941 Welcome to the Fall and Rise of China Podcast, I am your dutiful host Craig Watson. But, before we start I want to also remind you this podcast is only made possible through the efforts of Kings and Generals over at Youtube. Perhaps you want to learn more about the history of Asia? Kings and Generals have an assortment of episodes on history of asia and much more  so go give them a look over on Youtube. So please subscribe to Kings and Generals over at Youtube and to continue helping us produce this content please check out www.patreon.com/kingsandgenerals. If you are still hungry for some more history related content, over on my channel, the Pacific War Channel where I cover the history of China and Japan from the 19th century until the end of the Pacific War. In late September 1941, the war in China had already entered its fifth year. By then the conflict had lost whatever remained of novelty and had settled into something harsher and more exhausting: a long war of attrition fought across enormous distances, under conditions of chronic shortage, strategic uncertainty, and repeated local catastrophe. Japan still possessed formidable military strength. Its armies could strike hard, move fast, and exploit superior firepower, air support, and communications in individual campaigns. Yet the larger shape of the war had begun to change. China had not collapsed. The Nationalist government still stood. Chinese armies, however battered, still held the field. The line between survival and defeat remained dangerously thin, but it had not broken. This was the situation in which the autumn campaign in Hunan and Hubei unfolded.   The struggle around Changsha did not occur in isolation. It belonged to a wider military and political moment. In Europe, the war had already expanded into a continent-wide convulsion. Germany and the Soviet Union were locked in a massive struggle after the German invasion in June. Leningrad was under threat, Kiev had become the scene of disaster, and the approach of the battle for Moscow suggested that still greater bloodshed lay ahead. Stalin had pressed Churchill to open a second front in Europe and had been refused. The global balance of war was shifting, but not yet in any way that guaranteed relief to China.   In Asia, Japan continued to press forward. The pressure on China remained immense. Yet by mid-1941 China's position, though still precarious, no longer looked quite as desperate as it had in earlier years. International sympathy had grown. Material aid, however limited, was beginning to take more concrete form. In July, the American Red Cross sent China medical supplies worth millions of dollars. In August, more than a hundred retired American pilots and mechanics came together to aid China against Japan. The air unit they formed, later celebrated as the Flying Tigers, meant more than aircraft and personnel alone. It signaled that China's war was no longer quite so isolated. That change in atmosphere did not produce calm in Chongqing. It sharpened urgency.   For Chiang Kai-shek, the possibility that the international situation might slowly improve for China carried with it a hard implication: China had to endure long enough to benefit from it. That meant surviving not in abstract terms, but in particular places, against particular offensives, at moments when a local defeat might have consequences far beyond the immediate battlefield. In the autumn of 1941, one of those places was Changsha. Changsha had long occupied an outsized place in the military geography of the war. It stood as the capital of Hunan, a hinge between regions, and a vital position in the defense of central China. Its security affected the Xiang River basin, communications across the province, and the broader defensive posture shielding approaches toward the wartime capital. To lose it, even temporarily, would be more than a symbolic blow. It would threaten the larger balance of the front in south-central China and test whether Japanese offensives could still force dramatic strategic results.   At the same time, the battle for Changsha was not a simple contest between one Chinese army and one Japanese army facing each other across a fixed line. It was part of a larger operational struggle linking the Ninth War Zone with supporting actions from the Third, Fifth, and Sixth War Zones. It involved frontal resistance, hurried reinforcement, mobile defense, rear harassment, and the constant effort to turn Japanese momentum into Japanese overextension. Chinese commanders were trying not merely to survive a blow, but to absorb it, stretch it, and eventually strike back against an enemy whose speed could become a liability once supply lines lengthened and formations dispersed. This was the burden resting on the Military Commission in Chongqing.   There, while reports arrived in fragments and maps were revised hour by hour, Chiang Kai-shek spent long stretches in the operations room watching the situation in Hunan and Hubei develop. He had already ordered the various war zones to support the Ninth War Zone's operations, and now he followed the campaign with intense concentration. His attention moved repeatedly toward reports from the Sixth War Zone and Chen Cheng's counteroffensive toward Yichang, for Yichang was tied not only to local fighting in Hubei but also to the defense of the Yangtze approach and therefore to the security of Chongqing itself. In his mind, Hunan and Hubei had become the focal point of China's war effort in that season.   Meanwhile at the front, the situation was deteriorating fast. Japanese forces pressing into northern Hunan had already imposed severe strain on the Chinese defense. Blocking units had been battered back. Communications were uncertain. Some formations fought stubbornly but could not hold; others dissolved more quickly than hoped. The Japanese advance toward Changsha created exactly the kind of crisis Chinese commanders feared most: one in which the enemy moved with enough force and speed to threaten a major city before a coherent defensive response could be reassembled. Chinese units had to be thrown into action as they arrived, often after exhausting marches, often with incomplete knowledge of the situation, and often under immediate pressure from artillery, aircraft, and aggressive infantry assaults. Yet even at this dangerous stage, the campaign contained an opportunity as well as a threat.   The deeper Japanese units pushed into Hunan, the more exposed their rear became. Supply depots, transport lines, and communications stretched farther behind the spearhead. Chinese formations that had been driven aside were not always destroyed; some remained capable of reappearing to strike roads, depots, and isolated detachments. If Changsha represented the visible center of the struggle, the enemy rear represented its hidden vulnerability. The Chinese aim, therefore, was not simply to defend passively around the city. It was to combine stubborn resistance in front with harassment and disruption behind, so that Japanese advance would become increasingly difficult to sustain. The battle that followed unfolded under these pressures: a threatened city, a strained but unbroken defense, commanders forced to improvise under severe danger, and a wider international war that made every local decision feel heavier than the ground on which it was taken.   This subtle improvement in China's larger situation did not mean safety. China was still battered, under-resourced, and vulnerable. Japanese forces still possessed enormous advantages in mobility, firepower, and air power in many sectors. Large parts of the country remained occupied or threatened. Yet by mid-1941 something in the atmosphere had changed. In July, the American Red Cross sent China medical supplies worth millions of dollars. In August, more than a hundred retired American pilots and mechanics came together to aid China in its struggle against Japan. The air unit they formed would become famous under the name by which China knew them: the Flying Tigers. Their significance exceeded their immediate tactical value. They represented a visible sign that China was not entirely abandoned to isolation.   For Claire Chennault, romantic, aggressive, and optimistic by temperament, the new situation encouraged increasingly bold thoughts. For the first time he included in his planning the idea that Japanese home territory might one day be bombed from bases in China. Such schemes were ambitious and perhaps premature, but their very existence reveals an altered horizon of possibility. A country that imagines striking back is already psychologically in a different position from one concerned only with preventing collapse.     Yichang mattered directly to the fighting in Xiangbei, but it also mattered independently. It was tied to the security of the Yangtze approach and therefore to the defense of Chongqing. It belonged not only to the immediate problem of battlefield maneuver but to the larger question of whether China could do more than absorb Japanese offensives. Could Chinese forces exploit enemy overstretch? Could Japanese advances be turned into liabilities? Could local counterattacks and rear harassment generate effects greater than the raw size of the forces involved? By September 1941, these questions were no longer hypothetical. They were urgent. In Chiang's mind, Hunan and Hubei had become the focal point of China's war effort in that season.   The struggle around Changsha was central to that effort. From Chongqing, Chiang telegraphed Commander Xue Yue of the Ninth War Zone with instructions that made both the danger and the opportunity plain. The message emphasized that the enemy in northern Hunan, though still pressing hard, had already been bravely intercepted by Chinese units and was now exhausted, with lines of advance increasingly difficult to sustain. This, Chiang argued, created an excellent opportunity for Chinese forces to strike at the enemy's rear and seek annihilation rather than mere delay. He urged officers and soldiers alike to remain determined to destroy the enemy. Even if Japanese troops advanced to the vicinity of Changsha itself, Chinese forces were to renew their efforts, maintain their confidence in victory, and prevent the enemy from establishing a foothold.   The telegram also pointed to the broader operational picture. Japanese troops drawn from the sectors of the Fifth and Sixth War Zones had been committed to the invasion of northern Hunan, leaving their rear areas dangerously exposed. The Third, Fifth, and Sixth War Zones had launched supporting offensives on the twenty-third. Therefore, the Ninth War Zone had to strike resolutely and fiercely, preventing the Japanese from coordinating their forces and frustrating any attempt to occupy Changsha. The message was at once exhortation and operational logic. It reflected the core Chinese problem in the campaign: the Japanese thrust was dangerous and had to be blocked, but the very speed and depth of that thrust could also expose the attacker. Yet by the time Chiang sent his instructions, the danger around Changsha was already severe. After the 74th Army had been beaten back, there was no major Chinese force north of the city that could confidently block the Japanese advance in a straightforward, stable line. The Japanese 4th Division, together with the Hayabuchi Detachment, had defeated the 95th Division of the Chinese 37th Army and pushed toward Changsha. The prospect that the city might fall, at least temporarily, was no longer remote.   In response, Xia Chuzhong's 79th Army from the Sixth War Zone was ordered to move toward the city. Its vanguard was Wang Jiaben's 98th Division. That movement was a forced march in the literal sense. It was not a flourish of language but an admission of emergency. Troops were pushed beyond ordinary endurance because there was no time to gather strength under favorable conditions. Changsha could not wait for convenience, rest, or ideal concentration. Either reinforcements arrived in time to fight immediately, or they arrived too late to matter. Wang Jiaben was no stranger to battle. A veteran officer from the Yunnan Army, he had fought the Japanese repeatedly—more than twenty times since the outbreak of the War of Resistance. He belonged to that generation of commanders who had been shaped by the fragmented military politics of Republican China but whose careers were now being rewritten by the national emergency of the anti-Japanese war.   During the First Battle of Changsha, he had already distinguished himself. He led troops in a counterattack along the Tongcheng line in southern Hubei and intercepted retreating Japanese forces at Longmenchang and Changshoujie, killing more than a thousand enemy soldiers. He had experience, reputation, and a sense of what it meant to fight in the Changsha theater. This time he marched from Changde, driving his division forward with all possible speed. By noon on September 26 the 98th Division reached a position about two kilometers northeast of Changsha. There was scarcely time to settle the men. No careful preparation was possible. They had arrived because the city was in danger, and battle began almost at once. At dusk the division engaged the Hayabuchi Detachment.   The fighting continued until late in the night without decisive result. That in itself was costly enough. Troops who have just completed a punishing march are already close to the limits of their endurance. To be thrown immediately into combat magnifies every weakness: thirst, fatigue, confusion, and the difficulty of building fieldworks under pressure. What the soldiers of the 98th Division could do that night, they did with their hands and whatever tools they had. They scraped positions out of the earth. They assembled makeshift cover. Officers moved along the line trying to impose order on terrain that had become a battlefield almost as soon as it was occupied. At dawn on September 27 the Japanese intensified the pressure from the air.   More than twenty aircraft bombed the 98th Division's positions, tearing apart the earthworks that officers and soldiers had spent the night constructing. Bombing had a double effect. Physically, it damaged positions, wounded men, and disrupted preparations. Psychologically, it reminded defenders that they were being observed from above and struck by an enemy against whom they could often do little in the sky. Immediately after the bombing came a fierce infantry assault. Wang Jiaben and several other commanders went down to their respective regiments to supervise the fighting personally. This was dangerous, but under such circumstances commanders often believed that presence itself mattered. When fieldworks are collapsing, shell bursts are continuous, and communications are unreliable, authority must sometimes be carried by a body rather than a wire. The defenders held.   Again and again the Japanese attacked. Again and again they were repulsed only to reorganize and come on once more. Every minute the Chinese line remained in place was bought with blood. Fallen and wounded men were carried rearward in what seemed an endless stream. The cost of resistance accumulated visibly before the eyes of those still fighting. By noon even water could no longer be delivered to the front. That detail reveals the intensity of the battle more clearly than many grander statements could. A line that cannot be supplied even with water has reached a condition of severe strain. During brief pauses in the fighting, officers and soldiers pulled out dry rations and chewed them slowly, swallowing with difficulty while they waited for the next attack. Such moments are among the most characteristic in battle: not the long, smooth continuity imagined in peacetime rhetoric, but brief interludes of miserable necessity between explosions. At two in the afternoon the crisis deepened sharply.   A unit of the Japanese 4th Division worked its way behind the 98th Division's position and attacked from the rear. This was the kind of tactical development every defending commander dreads. A force can endure heavy frontal pressure if its line remains coherent and its rear reasonably secure. But when enemy troops appear behind a position, the entire structure of defense begins to dissolve. There, behind the line, were more than a hundred Chinese wounded awaiting evacuation. Japanese soldiers attacked them with bayonets. One of the wounded Chinese soldiers, already beyond ordinary capacity for resistance, rushed forward and detonated a grenade against a Japanese attacker. It was an act of desperation, but also of defiance. The episode captures something essential about the tone of the battle around Changsha. This was not a neat engagement between organized lines alone. It was a desperate collision in which even the badly wounded were drawn into last resistance. For Wang Jiaben, however, the tactical reality was impossible to deny. If he stayed where he was, the division might be annihilated. Personal courage could not alter encirclement. Resentfully, bitterly, and under the kind of pressure that leaves permanent scars on a commander's memory, he ordered retreat. When he led the survivors off the battlefield, fewer than half his men remained.   Reinforcements from another direction now entered the struggle. The Provisional 2nd Army of the Seventh War Zone dispatched the Provisional 8th Division under Zhang Junsong. At seven in the evening on September 27, Zhang's force arrived at Zuojiatang on the eastern outskirts of Changsha. Personnel of the Ninth War Zone waiting there reported grim news: a Japanese detachment had already entered the city that afternoon, and another detachment was only three kilometers away. The situation invited rashness. A commander arriving near a threatened city at dusk, with reports that the enemy is already inside, may feel compelled to hurl troops forward immediately in a gesture of resolution. Yet night fighting in an urban approach without adequate reconnaissance can turn courage into waste. Zhang Junsong considered the matter carefully. He concluded that to rush blindly into the city at night would likely produce confusion and heavy losses. Instead he first sought to strike the enemy outside the city. His troops deployed quietly and crept forward through the darkness, launching a probing attack. But they soon discovered that the Japanese were fully prepared and not vulnerable to easy surprise. The line could not be broken. With no prospect of success, the force withdrew.   The moment is revealing because it shows how narrow the margin had become. Chinese commanders were trying to recover initiative while still groping through uncertainty. Reports were incomplete, Japanese dispositions difficult to fix, and every hour dangerous. On the evening of September 28, the 79th Army's 6th Provisional Division occupied Yuelu Mountain west of Changsha. From that height officers and soldiers looked across the Xiang River toward the city. Changsha, for the first time, was in Japanese hands. The emotional force of that sight can only be imagined. Changsha had lived for years under threat. It had been defended, contested, prepared, and feared. The possibility of its loss had hovered over military planning and public consciousness alike. Now the danger had become fact. A city that had stood as an object of effort and anxiety was visibly occupied by the enemy. No single feeling could have governed the men who saw it. There must have been anger, shame, weariness, and dread—but also stubbornness. For in war a city seen in enemy hands is not always simply lost. Sometimes it is a summons.   Late that night Zhao Jiping, commander of the 6th Provisional Division, received a telegram from Xue Yue. He read it and then stood before the map for a long time. Days of anxiety began to lift. He was not alone in that reaction. Many Chinese generals involved in the campaign had felt that they were fighting a confused and chaotic battle. Japanese movements had been broad, rapid, and difficult to pin down. The Ninth War Zone had thrown numerous units into blocking actions, yet both well-regarded formations and weaker, improvised units had repeatedly failed to hold. After ten days of combat, Chinese losses were heavy. But Japanese forces too had been worn, stretched, and damaged. Only now, as the Japanese reached the apparent culmination of their advance, did the Ninth War Zone begin to recover rhythm. Chaos did not disappear, but it became more legible. The possibility of a more ordered counteroffensive emerged.   In the early hours of September 29, Zhao Jiping issued the order to force a crossing of the Xiang River and attack the enemy in Changsha. The preparations consumed the entire day. River crossings under combat conditions are among the most delicate operations in war. Boats must be assembled or found. Units must be organized into waves. Timing matters. Surprise matters. The location of landing points matters. The men themselves matter most of all, because they must sit exposed in fragile craft knowing that the real test will begin not in the middle of the river but at the far bank. Late that night the division crossed secretly by boat in six groups. The crossing itself went well enough. The river did not betray them. The groups made progress in darkness. Yet everyone involved understood that smooth movement on the water did not guarantee success. The crucial question was what waited at the landing points. It was expected that the troops going ashore at Daximen would be first to meet the Japanese. Once battle began there, the rest of the crossing accelerated.   Chinese soldiers landed, attacked fiercely, and drove the enemy back from the riverbank. The momentum of the assault mattered. In such moments hesitation on shore can doom an entire crossing, but aggression can seize a foothold before the defender fully stabilizes. By the time Chinese forces entered the city, dawn was breaking. Then came street fighting. Changsha had ceased to be an objective on a map. It became again what cities become in war: a place of corners, walls, alleys, debris, smoke, and sudden death. Fighting in a city strips away much of the abstraction through which campaigns are often remembered. Streets channel movement. Buildings become cover, obstacles, or traps. Distances shrink but danger multiplies. A few men behind masonry can delay many more. Noise magnifies confusion. The city itself seems to fragment into a hundred separate battlefields. For Changsha, the long season of anxious anticipation ended in direct flame.   Yet to understand the campaign only through the struggle at Changsha would be incomplete. While the city drew attention as the central prize, another set of operations was unfolding across Hunan and Hubei—less dramatic in appearance perhaps, but strategically significant. These were the actions of the 27th Army Group under Yang Sen. If the fighting around Changsha demonstrated the pressure of frontal battle, Yang Sen's operations illustrated another principle entirely: the stubborn, opportunistic use of weaker forces to harass, sting, and trouble a stronger enemy from behind. The 27th Army Group was composed largely of Sichuan troops. Their presence speaks to one of the larger political transformations of the war. Republican China had long been fractured by regional militarism. Armies had often owed stronger loyalty to commanders, provinces, or local power structures than to any unified national command. Yet the anti-Japanese war compelled a partial reordering of those loyalties. Many warlord forces, however imperfectly integrated, now turned their weapons outward under Nationalist command.   There were exceptions. Some men, like Han Fuqu, had preserved their own strength at the cost of military responsibility and lost both opportunity and honor in doing so. But in the main, especially among middle and lower-ranking officers and ordinary soldiers, the imperative of national survival overrode provincial identity. Men from distant regions marched and fought in terrains not their own because the war no longer permitted a narrow conception of interest. Yang Sen himself was an old warlord soldier, one of those figures formed in the fractured military world of earlier decades. By 1941 he was deputy commander of the Ninth War Zone and commander-in-chief of the 27th Army Group. Along the Hunan-Hubei-Jiangxi border he had long been cautious in his operations.   At the beginning of the northern Hunan battle, his troops proved unable to withstand the force of the Japanese offensive and retreated in disorder into mountains and forests. That fact should not be softened. They were pushed back. But retreat did not mean disappearance. This is where Yang Sen's role became important. Once the main Japanese force had advanced far ahead, the troops that had scattered began to reemerge. They knew the terrain. They knew the roads, villages, passes, and wooded approaches. If they could not defeat the Japanese in a grand collision, they could search for openings behind the advancing columns.   On September 20, as the Japanese main force drove into the heart of northern Hunan, Yang Sen led elements of the 4th, 58th, and 20th Armies in pursuit for an entire day. The main body of the 4th Army had already been badly beaten at the Xinqiang River line, and only the 60th Division, recently transferred from the 37th Army, remained relatively intact. That single fact sharply defined the weakness of his means. He was not pursuing with fresh and powerful forces prepared for a decisive encounter. He was pursuing with fragments, survivors, and such units as still retained some offensive potential. But the Japanese, intent on rapid advance southward, paid too little attention to what followed behind them. That was the opening Yang Sen needed.   On the morning of September 21, the 60th Division acted on information supplied by an old beggar. Such details often sound almost literary, but they remind us that war in China was never fought by armies alone. Villagers, refugees, peddlers, guides, boatmen, and the destitute all participated in the circulation of military knowledge. A beggar could see what a reconnaissance officer had missed. On the basis of that tip, the 60th Division launched a surprise attack on the supply depot of the Japanese 40th Division at Zhangjiayuan. Division Commander Dong Yu led the action.   The Chinese quickly overwhelmed the more than three hundred Japanese troops guarding the depot. Once they broke in, they discovered an astonishing quantity of matériel: shell boxes, ammunition, food, medicine, gasoline, explosives, protective clothing, telephone wire, body bags, stretchers, and more, all stacked beneath heavy canvas. For men accustomed to scarcity, it must have seemed almost unreal. Dong Yu, delighted, is remembered as exclaiming to his deputy commander, "It's practically a department store!" The line has a momentary comic brightness in the middle of war, and that is part of why it survives. But its deeper significance lies in what it reveals. A modern army advancing deep into hostile territory is sustained by accumulation—ammunition, fuel, wire, medicines, rations, transport materials. To seize such a depot is not merely to take objects. It is to grasp the hidden skeleton of movement. Dong Yu then issued a one-word order: "Move."   At once the whole division became a swarm of carriers. Officers and soldiers grabbed whatever they could and began hauling the captured supplies away. The scene must have been one of extraordinary improvised energy—riflemen transformed into laborers, war reduced for a moment to the brute physical act of lifting, dragging, and carrying. They moved like ants dismantling a storehouse. But there was too much to remove and too little time. Before even two regiments had completed the work, the Japanese returned in force. Enraged, they sent more than a dozen aircraft to bomb the troops carrying off the captured crates, while over a thousand infantrymen rushed in on the ground. Dong Yu immediately ordered the men to abandon what remained and destroy it. Amid deafening explosions, the 60th Division withdrew from Zhangjiayuan. The Japanese pursued hard, driving them back into the mountains before finally stopping. Measured purely in terms of territorial occupation, the action changed little. The Chinese did not hold the depot. They were forced back again. But judged in operational terms, the raid mattered. Supplies had been captured, destroyed, or disrupted. Japanese rear security had been embarrassed. Time and energy had been consumed in response. In a campaign where the Japanese were racing south and depending increasingly on stretched lines, such harassment was not trivial.   After September 23, Yang Sen expanded these efforts. He sent whole divisions to harass the Japanese rear while also dispatching battalions and companies to ambush supply columns and disrupt transport lines. This was not the kind of warfare that produces neat dramatic maps in conventional military histories. The actions were scattered, local, opportunistic, and often inconclusive. Yet precisely for that reason they can be misunderstood. Not every useful military action ends in the destruction of an enemy division. Sometimes its value lies in forcing a stronger opponent to spend attention where he had hoped to spend only momentum. These operations were costly and uncertain. Small Chinese units striking roads and depots risked encirclement or aerial punishment. Yet they possessed one advantage: they did not need to win a grand battle to matter. They needed only to make movement harder, supply more precarious, and the rear less secure.   On September 25, even after the 26th and 37th Armies had suffered defeat elsewhere, Yang Sen gathered the troops that still retained some offensive capacity and launched another attack. In these actions, the 90th Division captured Wukou; the 59th Division reached the north bank of the Miluo River; the 60th Division advanced to Donggang; and elements of the 58th Army controlled the Yuanba–Zheyangqiao highway line by the twenty-sixth. These gains were not the dramatic centerpiece of the campaign. But they formed part of the cumulative pressure on Japanese operations. Every road contested from the rear, every convoy threatened, every depot exposed, and every forced diversion of troops weakened the enemy's ability to convert tactical advance into stable success.   The battlefield was fluid enough that Yang Sen's headquarters itself had to keep moving—from the Miluo River northward to Pingjiang. Mobile headquarters in such conditions lived under constant uncertainty. Reports arrived late or contradictory. Rumors outran facts. The front could bend in unpredictable ways. A command post that seemed safely behind the line in the morning might by afternoon lie only a few kilometers from enemy forces. Years later Mao Jiuyin, who had then served as an operations staff officer under Yang Sen, recalled the old commander's outward behavior in those dangerous days. Yang Sen, Mao remembered, was always joking with the people around him and could still laugh and talk during battle. On one occasion he even joked with his bodyguard about putting chili oil in his tea. To younger staff officers, such behavior appeared almost absurd.   Yet they knew how dangerous their situation often was. Sometimes they were only a few kilometers from the main Japanese forces. There is more than anecdote in this memory. Old commanders often cultivated a style of ease under danger not because they felt no fear, but because they believed visible calm itself was part of command. Men take emotional bearings from those above them. A commander who panics spreads panic faster than any enemy breakthrough. Yang Sen was sixty years old during the Second Battle of Changsha, and he liked to remark that he was five years older than Chiang Kai-shek. Age had not removed him from the front. His function in the campaign was not to shatter the enemy in one decisive frontal blow. It was to persist, reappear, sting, and remain troublesome. The 27th Army Group followed the Japanese nearly all the way to the outskirts of Changsha.   They did not fight many large set-piece engagements. That was never their main value. But the Japanese could not ignore them. If Japanese units turned and tried to hit them directly, they tended to dissolve into the landscape. If the Japanese pushed on and ignored them, they reappeared to sabotage roads, hit supply lines, and create alarms in the rear. Even Japanese official histories later described them memorably. They called them "like a swarm of annoying flies." The phrase was meant as contempt. But as so often happens in war, an insult became an accurate description of effect. If these Chinese troops could not be tigers in formal battle, they could still become intolerable as flies. And under certain conditions—fatigue, heat, uncertainty, stretched supply, shaken morale—flies can become more oppressive than an enemy would like to admit.   When later generations look back on campaigns, they often search for a single explanatory image. But the fighting of September 1941 resists reduction to one symbol alone. What gives the campaign its force in memory is not one isolated episode, but the convergence of several pressures at once: a city under threat, a command trying to coordinate multiple fronts, exhausted reinforcements thrown directly into battle, Japanese forces advancing quickly but becoming increasingly vulnerable the farther they pushed, and Chinese troops in the rear refusing to disappear after defeat. Seen from Chongqing, the campaign was a matter of telegrams, maps, and the management of strategic risk. Seen from the front, it was a matter of thirst, mud, broken fieldworks, sudden bombing, exhausted marching, and the uncertainty of whether the next order meant reinforcement, retreat, or death. Seen from the Japanese side, it was initially an aggressive advance toward an important objective, but one increasingly troubled by stretched supply, difficult terrain, and the persistent reappearance of Chinese forces behind the line of movement.   At the same time, the campaign reveals a Chinese war effort that had become more resilient than in the earliest years of the conflict. The Japanese advance toward Changsha was dangerous and at moments appeared close to success. Chinese blocking forces suffered severe defeats. Units arrived exhausted and were hurled into action at once. Commanders lost men at devastating rates. Changsha itself briefly fell into enemy hands. And yet the city was not simply surrendered as an accomplished fact. Chinese forces regrouped. Orders were coordinated across war zones. Reinforcements marched at desperate speed. Counterattacks were mounted. Forces west of the Xiang prepared and crossed back into the city. Elsewhere, troops under Yang Sen gnawed at the Japanese rear and made advance more costly than a map of front lines alone might suggest. This combination of desperate frontal resistance and incessant rear harassment was one of the characteristic strengths of China's resistance in this stage of the war. China could not always match Japanese power in one concentrated blow. But it could absorb, delay, fragment, and trouble that power across space and time. That is why the struggle around Changsha in late September 1941 deserves to be remembered not merely as a sequence of engagements, but as a layered event in which tactical battle, morale, logistics, political endurance, and international context all converged.   I would like to take this time to remind you all that this podcast is only made possible through the efforts of Kings and Generals over at Youtube. Please go subscribe to Kings and Generals over at Youtube and to continue helping us produce this content please check out www.patreon.com/kingsandgenerals. If you are still hungry after that, give my personal channel a look over at The Pacific War Channel at Youtube, it would mean a lot to me. In late September 1941, Japan pushed deeper into China, but Chinese forces—supported gradually by international help—kept resisting. Changsha in Hunan became the central point of the conflict, seen as vital to strategic control in south-central China. Meanwhile, elsewhere in Hunan and Hubei, Yang Sen's 27th Army Group—often using smaller, opportunistic actions—reappeared behind Japanese lines to disrupt depots and convoys. Even when Chinese gains were temporary, the cumulative pressure delayed Japanese momentum.       

Dr. Larry P. Arnn, President of Hillsdale College, joins Hugh Hewitt on the Hillsdale Dialogues to continue a series on The Second World War, Churchill's sprawling memoir and history of World War II in six volumes.Release date: 24 July 2026See omnystudio.com/listener for privacy information.

When Ron Magill announced his retirement on February 15th, plenty of people assumed South Florida was losing its most recognizable voice in the animal world.   On this episode of Cut to the Chase: host Gregg Goldfarb sits down with Ron to set the record straight. He retired from the county, moved his office about 30 yards north, and took on a new role as Goodwill Ambassador and Conservation Liaison for the Zoo Miami Foundation. Ron walks through the work ahead of him, including building the Ron Magill Conservation Endowment, the largest conservation endowment at the zoo, where none of the money can be spent at the zoo itself and goes entirely toward protecting animals in the wild. He also shares the travel programs he's leading, from 11 national parks across five states to polar bears in Churchill and a return voyage to Antarctica.   The conversation turns personal when Ron shares the story of the animal that changed his life. It was a squirrel that came down from a tree during recess, at a time when he was a Spanish-speaking kid being bullied so badly he'd retreated into his shell. He also weighs in on why Miami has become what he calls the Ellis Island of exotic animals, what's realistically possible with pythons and iguanas at this point, and why he's profoundly concerned about the rollback of environmental protections at a national level.   Join Gregg and Ron Magill on Cut to the Chase: as they explore: Why "retirement" for Ron meant moving offices rather than stepping away, and what the Zoo Miami Foundation role actually involves How the Ron Magill Conservation Endowment works, and why none of that money can be spent at the zoo How listeners can join Ron's travel programs through the travel tab at zoomiami.org Why local conservation in your own backyard matters before global causes Whether you can actually make a living in wildlife and conservation work, and what it takes Why storytelling, not statistics or pie charts, is the skill that opens doors in this field What Miami is doing to build green corridors connecting parks for wildlife Why invasive species like pythons, iguanas, and lionfish are here to stay, and what management realistically looks like Why the python hunts matter for public education even when they don't dent the population   TIME STAMPS 0:00 – Intro: "The Ellis Island of Exotic Animals" 0:30 – Welcome to Cut to the Chase & Ron's 4-Decade Legacy 1:01 – Retiring from the County, Not From the Mission 1:38 – New Role: Zoo Miami Foundation & Conservation Endowment 2:05 – Upcoming Trips: Antarctica, Africa, National Parks & How to Join  3:36 – Conservation Work: Audubon, Wildlife Rescue & Protecting Our Backyard 4:22 – Giving Back: Scholarships for the Next Generation  5:06 – "Living a Scam": On Loving Your Job 5:37 – Can You Actually Make a Living Doing This? 6:53 – Why Storytelling Matters More Than Ever 7:11 – The Next Ron Magill? Rising Stars at the Zoo  7:59 – Conservation in Your Own Backyard (Inspired by Wild London) 9:17 – The Squirrel That Changed His Life 11:29 – Escaped Animals & Hurricane Andrew 12:08 – Miami's Invasive Species Crisis: Pythons, Iguanas & More 13:01 – Are We Making Progress? The State of Global Conservation 13:56 – Final Thoughts & Call to Action    Ron Magill has spent more than four decades educating millions about wildlife and inspiring generations to care about conservation. After 46 years working under Miami-Dade County, he now serves as Goodwill Ambassador and Conservation Liaison for the Zoo Miami Foundation, where he leads the foundation's travel program and continues building the Ron Magill Conservation Endowment. He is also a USA Nikon Ambassador and wildlife photographer who has set foot on every continent and visited Africa more than 50 times.   Contact / Follow Ron Magill: Website: https://zoomiami.org (travel programs under the Travel tab) LinkedIn: https://www.linkedin.com/in/ron-magill-0257bb8/   Want more conversations that cut through the noise on science, climate, and the issues shaping our future? Subscribe to Cut to the Chase: with Gregg Goldfarb for new episodes every week.

« Je suis le maître de mon destin, je suis le capitaine de mon âme. » 16 vers écrits dans une chambre d'hôpital, par un homme qu'on s'apprêtait à amputer... Récités dans une cellule par Nelson Mandela... Repris par Churchill, puis par Obama, et portés au cinéma par Clint Eastwood. Dans cet épisode, découvrez l'un des poèmes les plus puissants jamais écrits : Invictus. Bonne écoute !

The Story of Adel "156" Souto: Timescape Zero, Violent Deed, Feast of Hate and Fear, and MoreIIn this episode, we're joined by Adel Souto (Adel 156), vocalist for South Florida bands Violent Deed, Timescape Zero, Shroud, and Sound for Sound, and creator of the fanzine Feast of Hate and Fear.Adel reflects on growing up in Hialeah, discovering punk through mixtapes, college radio, and record stores, and becoming part of South Florida's underground music scene in the 1980s. He discusses early shows, the formation of Violent Deed, recording the band's demo, and opening for bands including Youth of Today, Dayglo Abortions, Death, and Death Angel. He also shares memories of the violence that often surrounded shows during that era and how the culture evolved throughout the late 1980s and 1990s.The conversation explores Adel's fanzine work, from Evolution to Feast of Hate and Fear, and his role in documenting Florida's punk and hardcore communities. We also discuss Timescape Zero, the band's recordings and releases, performances throughout Florida, and venues including Churchill's, Washington Square, Plus Five Lounge, and Blue Chair.Adel also shares stories about Shroud, None Dare Call It Treason, Sound for Sound, and the people and DIY community that helped shape South Florida's underground music scene, along with the lasting legacy of the bands, venues, record stores, fanzines, promoters, independent radio, and more.

He Wants All of It! - Bro. Churchill 07.23.26

NonCensored is coming to an end. Our final show is at the London Podcast Festival on the 13th September 2026 at 4.30pm. Tickets are available here: https://www.kingsplace.co.uk/whats-on/podcast/noncensored/As we take a break over the summer, we're going to remind you of how fun our live shows are, starting with this one - the London Podcast Festival in 2022, which we recorded two days after the Queen died.This week Harriet, Producer Martin and Diversity Correspondent Eshaan Akbar made an in-person appearance at the London Podcast Festival at King's Place, London, to really give the wokies in the basement of The Guardian what for. They pay their respects to the Queen and then reflect on whether paying respect is, in fact, "woke". They're also joined by Sir Edward Fry, CEO of energy company Petrocorp, and there's an extended interview with Tom Walker, the man most famous for his character "Jonathan Pie", who explains what could possibly be funny about pretending to be someone else just to get laughs.With thanks to Rosie Holt, Brendan Murphy, Eshaan Akbar, Matt Green, Tom Walker and Ed Morrish.Rosie's sitcom, Crossing The Floor, is available now on BBC Sounds. Her play, Churchill's Urinal, will be on at the Edinburgh Festival Fringe (tickets here), where she will also be doing a new character comedy/stand-up show, The Illegal Aliens Have Landed (tickets here).Brendan is taking a brand new show, Indy, to the Edinburgh Festival Fringe in August. It's a three-man retelling of Indiana Jones, and tickets are available here.Eshaan has started a new, live podcast called The Early Evening Show, every Sunday evening on YouTube, and his latest stand-up special, Fool Moon, is also available on YouTube.Ed produces P.O.V., a scripted sketch show on BBC Sounds which has NonCensored regulars like Davina, Will and Sooz in it. He also produces Sound Heap With John-Luke Roberts, an award-winning improvised sketch show that features many NonCensored regulars like Rosie, Brendan, Will, Sooz and Joz.Show photography is by Karla Gowlett and design is by Chris Barker. Original music is by Paddy Gervers and Rob Sell at Torch and Compass.NonCensored is a Lead Mojo production Hosted on Acast. See acast.com/privacy for more information.

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

HPI, TDAH, hypersensibilité : on en parle partout, mais est-ce que c'est vrai ?Dr Fanny Nusbaum, spécialiste du haut potentiel, démonte les idées reçues, sans langue de bois.Le test de QI mesure-t-il vraiment l'intelligence ? Sommes-nous tous devenus "neuro-atypiques" ? Le TDAH est-il parfois un totem pour se cacher derrière une étiquette ? Et surtout : faut-il être (beaucoup) plus exigeant avec ses enfants ?Un échange direct, croustillant, parfois à contre-courant de la bien-pensance. ———CHAPITRES00:00 Intro — "On va pas être méchants, mais on va pas être gentils"01:57 HPI et les 2,28 % : le mythe derrière le test de QI03:49 Le QI mesure-t-il vraiment l'intelligence ?07:35 Passer le test ou pas : se connaître sans s'enfermer11:25 Hommes / femmes : QI, testostérone et gestion de la pression16:18 Les vraies différences entre cerveau masculin et féminin45:00 TDAH : diagnostic réel ou "totem du super connard" ?53:23 Quand le TDAH est un vrai handicap au quotidien59:08 Hypersensibilité : trait de tempérament ou carte facile ?1:08:46 "Trop de confort nous tuera" : sommes-nous des enfants gâtés ?1:20:39 Faut-il être exigeant avec ses enfants ?1:24:27 "Faut-il maltraiter ses enfants ?" : l'antifragilité en éducation1:31:18 Mettre le système sous tension : l'exemple de sa fille1:39:59 Accepter d'être détesté par ses enfants1:47:19 L'exigence a-t-elle une date de fin ?1:56:51 La phrase de Churchill sur l'amour inconditionnel1:58:28 Le mot de la fin : "Je suis différent comme tout le monde"———

A growing chorus of psychologists, contemplatives, and systems thinkers argue that most of us, and most of our institutions, are running an outdated psychological and spiritual "operating system," one never built for the converging ecological, economic, and cultural crises we face. Technological and policy responses absorb nearly all of our attention and resources, yet the maturity of the humans making the decisions may be the most needed and least prioritized intervention of all.  Could ancient contemplative traditions, paired with modern psychology, offer a map for the kind of collective growing up this moment demands? In this episode, Nate welcomes Dr. John Churchill, a psychologist and former Buddhist monk, to explore a map of human development that runs from "first-person" perspective of the isolated self all the way to a felt, embodied sense of interdependence with the planet itself. Drawing on decades spent synthesizing Tibetan Buddhist contemplative science with Western developmental psychology, John walks Nate through why our centers of power remain frozen at the level of third-person individualism. John also explains why  "love wisdom" (the integration of heart and mind) may be the missing ingredient in how we respond to our more-than-human predicament. Along the way, Nate presses him on whether this maturation applies to individuals, cultures, or our entire species, and on how a 2,000-year detour through empire and the carbon pulse pushed these traditions to the margins. The conversation ranges from the lost sacred academies of Alexandria to a simple attention practice anyone can do, and it keeps circling back to a single throughline: that beauty, truth, and goodness are load-bearing structures for whatever comes next.  What do beauty, truth, and goodness have to do with finding alternative paths toward a more flourishing humanity? How would it feel to be truly at one with the planet, and how might it change the way we act? And what can an individual do, this month, with the time and resources they actually have, to begin living into that shift? (Conversation recorded on June 17th, 2026)   About John Churchill: John began his in-depth study of Buddhist Psychology while a Buddhist monk at Samye Ling Monastery in Scotland. He then spent 15 years training and teaching in "Great Seal" meditation in an Indo-Tibetan Mahayana lineage under the mentorship of the late senior Western teacher, translator, respected author, and clinical psychologist Dr. Daniel P. Brown. He is also a founding member of the Integral Institute led by esteemed Transpersonal/Integral philosopher, Ken Wilber.  John has received advanced training in: attachment therapy, hypnosis, positive psychology for peak performance, and the "Pointing Out" style of Mahamudra meditation. For the last 25 years, John has developed the Fourth Turning Planetary Dharma which includes: a redesigned Nine Stages of Calm-Staying practice path, and a somatically based contemplative practice path; Embodying the Open Ground, that integrates psychodynamic healing, adult development and meditation. John holds a Doctorate in clinical psychology from William James College, and is a practitioner of Traditional Chinese Medicine.   Show Notes and More   Watch this video episode on YouTube   Want to learn the broad overview of The Great Simplification in 30 minutes? Watch our Animated Movie.   ---   Support The Institute for the Study of Energy and Our Future   Join our Substack newsletter   Join our Hylo channel and connect with other listeners

On this episode of Animal Spirits: Talk Your Book, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Michael Batnick⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ben Carlson⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ are joined by ⁠⁠⁠Alona Gornick from Churchill from Nuveen to discuss: credit market cycles, an update on private credit, investing in the middle market and more. Find complete show notes on our blogs... Ben Carlson's ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠A Wealth of Common Sense⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Michael Batnick's ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Irrelevant Investor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Feel free to shoot us an email at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠animalspirits@thecompoundnews.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ with any feedback, questions, recommendations, or ideas for future topics of conversation. Check out the latest in financial blogger fashion at The Compound shop: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://idontshop.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Investing involves the risk of loss. This podcast is for informational purposes only and should not be or regarded as personalized investment advice or relied upon for investment decisions. Michael Batnick and Ben Carlson are employees of Ritholtz Wealth Management and may maintain positions in the securities discussed in this video. All opinions expressed by them are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. See our disclosures here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/podcast-youtube-disclosures/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ The Compound Media, Incorporated, an affiliate of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ritholtz Wealth Management⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, receives payment from various entities for advertisements in affiliated podcasts, blogs and emails. Inclusion of such advertisements does not constitute or imply endorsement, sponsorship or recommendation thereof, or any affiliation therewith, by the Content Creator or by Ritholtz Wealth Management or any of its employees. For additional advertisement disclaimers see here ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/advertising-disclaimers⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Nuveen Disclaimer: Nuveen/Churchill and Ritholtz are not affiliated. Views/opinions expressed by Michael Batnick and Ben Carlson do not necessarily represent views of Nuveen/Churchill, its affiliates, or its staff. This material, along with any views and opinions are for informational and educational purposes only as of its date and may change without notice/may not come to pass. There is no promise or warranty (express or implied) as to its accuracy or completeness and should not substitute for your own judgment. This is not a recommendation, investment advice, a solicitation, is not provided in a fiduciary capacity, and does not consider any investor's specific objectives. Consult your financial advisor before making decisions. Past performance does not guarantee future results. All investments carry risk, including possible loss of principal. Alternative investments are speculative and carry substantial risks, including limited liquidity, potential leverage, short sales, currency risk, concentrated holdings, complex tax structures, illiquid secondary markets, and high fees. Private credit/debt investments carry additional risks due to the typically lower credit quality of the underlying borrowers, including credit, interest rate, currency, prepayment/extension, inflation, and capital loss risks, concentrated investments, may involve complex tax structures and may not suit all investors. Learn more about your ad choices. Visit megaphone.fm/adchoices

Dr. Larry P. Arnn, President of Hillsdale College, joins Hugh Hewitt on the Hillsdale Dialogues to continue a series on The Second World War, Churchill's sprawling memoir and history of World War II in six volumes.Release date: 17 July 2026See omnystudio.com/listener for privacy information.

Dr. Larry P. Arnn, President of Hillsdale College, joins Hugh Hewitt on the Hillsdale Dialogues to continue a series on The Second World War, Churchill's sprawling memoir and history of World War II in six volumes. Release date: 17 July 2026

Dr. Larry P. Arnn, President of Hillsdale College, joins Hugh Hewitt on the Hillsdale Dialogues to continue a series on The Second World War, Churchill's sprawling memoir and history of World War II in six volumes.Release date: 17 July 2026See omnystudio.com/listener for privacy information.

Charles Spicer relates how, in the summer of 1939, Ernest Tennant undertook an incognito mission to meet Ribbentrop, hoping to find a path to peace. He returned with warnings that Hitler was focused on Poland and prepared for a ten-year war. While London believed the information, Chamberlain's antipathy toward Stalin led to a slow, failed attempt at an alliance with Moscow, allowing Ribbentrop to secure a pact with the Soviets first. Spicer echoes Churchill's sentiment that this was an "unnecessary war," highlighting multiple missed opportunities to support the German resistance and remove the dictator before the global catastrophe began. (12)1935

When the disciples cried out, “Lord, increase our faith,” Jesus pointed them to something small, stubborn, and invasive: the mustard seed. In the ancient world, mustard was not a polite herb, it was a weed that spread everywhere once it took root. That is what real faith looks like. It does not stay safe or ornamental. It disrupts, it grows, it takes over.In this message, Rev. Shawn Garan explores Luke 17:5–10, the surprising cultural background of the mustard seed, and what it means for us to nurture a living, persistent faith. Faith is not about quantity but about nature. Faith is Dory saying, “Just keep swimming.” Faith is William Wallace crying, “They may take our lives, but they cannot take our freedom.” Faith is Churchill declaring, “Never, never, never give up.”Join us as we discover that mustard-seed faith may be small, but it will not be stopped.

Durante a Segunda Guerra Mundial o Raj Britânico, atualmente correspondendo a basicamente à Índia, viveu uma das maiores catástrofes de sua história com milhões morrendo de fome. Pra muitos, a responsabilidade não só foi do Reino Unido, mas também pessoalmente de Winston Churchill. Separe trinta minutos do seu dia e aprenda com o professor Vítor Soares (@profvitorsoares) sobre o que foi a Fome de 1943 em Bengala-Se você quiser ter acesso a episódios exclusivos e quiser ajudar o História em Meia Hora a continuar de pé, clique no link: www.apoia.se/historiaemmeiahoraConheça o meu canal!https://www.youtube.com/@profvitorsoaresConheça meu outro canal: História e Cinema!https://www.youtube.com/@canalhistoriaecinemaViaje comigo, com o Vogalizando a História e com o Operação Barbarussa pra Grécia e Roma!https://partiu.vip/historiaecinema2026Ouça "Reinaldo Jaqueline", meu podcast de humor sobre cinema e TV:https://open.spotify.com/show/2MsTGRXkgN5k0gBBRDV4okAssista meu outro podcast, o História pros brother!https://open.spotify.com/show/04a8C8gXTLj68lmZiQD8vmCompre o livro "História em Meia Hora - Grandes Civilizações"!https://a.co/d/47ogz6QCompre meu primeiro livro-jogo de história do Brasil "O Porão":https://amzn.to/4a4HCO8Compre a camisa do História em Meia Hora: https://www.blablalogia.com/blablalojinha/akiralampiaoh30PIX e contato: historiaemmeiahora@gmail.comApresentação: Prof. Vítor Soares.Roteiro: Prof. Vítor Soares e Prof. Victor Alexandre (@profvictoralexandre)REFERÊNCIAS USADAS:- SEN, Amartya. Poverty and Famines: An Essay on Entitlement and Deprivation. Oxford: Oxford University Press, 1981.- MUKERJEE, Madhusree. Churchill's Secret War: The British Empire and the Ravaging of India during World War II. New York: Basic Books, 2010.- DAVIS, Mike. Holocaustos Coloniais: Clima, Fome e Imperialismo na Formação do Terceiro Mundo. Tradução de Alda Porto. Rio de Janeiro: Record, 2002.- TAUGER, Mark B. 'Entitlement, Shortage and the 1943 Bengal Famine: Another Look.' The Journal of Peasant Studies, v. 31, n. 1, 2003, p. 45-72. 

See omnystudio.com/listener for privacy information.

Welcome to this Friday edition of RealAg Radio with your host Shaun Haney! On today’s show, Haney is joined by Tyler McCann of CAPI, Lyndsey Smith and Kelvin Heppner of RealAgriculture for the RealAg Issues Panel. Also on today’s show, Curtis de Gooijer of Bourgault Ag joins Shaun for a product spotlight, and Anne Wasko... Read More

Welcome to this Friday edition of RealAg Radio with your host Shaun Haney! On today’s show, Haney is joined by Tyler McCann of CAPI, Lyndsey Smith and Kelvin Heppner of RealAgriculture for the RealAg Issues Panel. Also on today’s show, Curtis de Gooijer of Bourgault Ag joins Shaun for a product spotlight, and Anne Wasko... Read More

If you don't fill in this survey http://bit.ly/noncensored-survey, we will get off with your mum, AND your dad.This week Harriet Langley-Swindon is joined by Andy Burhnam, XXX; Chairman of South Central Thames Water Sir Lord Douglas Brown OBE CBE, who explains why his company going into profit is a good thing for the customers who've been charged more; and Eshaan Akbar has a White-Hot & Spicy Takeaway of the Week about Lord Of The Rings.If you're listening to the Patreon you'll also hear an interview with Jemma, a woman who's been banned from the Internet. If that sounds like something you'd like to hear or watch, then head on over to Patreon.com/NonCensored and sign up for just £4 or £8 per month; you'll also get every episode early and without adverts, exclusive bonus podcasts, and the warm fuzzy feeling you get from knowing you're supporting the people who make a thing you like.Please follow our social media accounts!Instagram: @noncensoredpodcastTikTok: @noncensoredpodWith thanks to Rosie Holt, Brendan Murphy, Eshaan Akbar, Will Sebag-Montefiore, Oliver Izod, Holly Burn and Ed Morrish.Rosie's sitcom, Crossing The Floor, is available now on BBC Sounds. Her play, Churchill's Urinal, will be on at the Edinburgh Festival Fringe (tickets here), where she will also be doing a new character comedy/stand-up show, The Illegal Aliens Have Landed (tickets here).Brendan is taking a brand new show, Indy, to the Edinburgh Festival Fringe in August. It's a three-man retelling of Indiana Jones, and tickets are available here.Eshaan has started a new, live podcast called The Early Evening Show, every Sunday evening on YouTube, and his latest stand-up special, Fool Moon, is also available on YouTube.Will will be at the Edinburgh Festival Fringe with his new show Game Of Phones. Tickets are available here.Oliver says he does't have anything to promote, so just go and follow him on Instagram.Holly also doesn't have anything to promote, so follow her Instagram account and also her podcast's Instagram account.Ed produces P.O.V., a scripted sketch show on BBC Sounds which has NonCensored regulars like Davina, Will and Sooz in it. He also produces Sound Heap With John-Luke Roberts, an award-winning improvised sketch show that features many NonCensored regulars like Rosie, Brendan, Will, Sooz and Joz.Show photography is by Karla Gowlett and design is by Chris Barker. Original music is by Paddy Gervers and Rob Sell at Torch and Compass.NonCensored is a Lead Mojo production Hosted on Acast. See acast.com/privacy for more information.

Former Senator Jim Talent joins Marc and Kim with 30 years of foreign policy wisdom, backing JD Vance's take that Israel is a valuable ally whose interests sometimes diverge from ours, just like any partnership — and dismissing the notion that Israel is losing ground anywhere except on the radical left. Talent also weighs in on Benjamin Netanyahu's political future, comparing him to Churchill and Thatcher, before turning to the local scene: Cori Bush's union and Democratic Socialists of America endorsements, and why a hard left lurch could actually help Republicans if primary voters reject it. Wisdom, experience, and America First values — that's the Marc Cox Morning Show. Hashtags: #MarcCoxMorningShow #JimTalent #Israel #JDVance #Netanyahu #CoriBush #DemocraticSocialists #StLouisRadio #ConservativeTalk #AmericaFirst

In this episode of Private Markets 360°, we welcome Alona Gornick, Managing Director, Senior Investment Strategist at Churchill Asset Management. Alona's journey from investment banking to deal origination at Churchill and now guiding wealth investors through a rapidly changing landscape highlights the importance of adaptability, discipline and transparency in today's market. She discusses Churchill's differentiated approach, the advantages of its integration with Nuveen's broader ecosystem, and its commitment to rigorous diligence and investor education. Alona also offers insights on the competitive dynamics of wealth management, the importance of consistency in deal structuring, and the critical questions high net worth investors should be asking in today's market.   More S&P Global Content:  Be the first to move on private markets value while it's still taking shape. Uncover Hidden Potential>   Credits:  Host/Author: Christina McNamara and Jocelyn Lewis Guests: Alona Gornick, Churchill Asset Management Producer: Georgina Lee Published With Assistance From: Sophie Carr, Kimberly Olvany   www.spglobal.com www.spglobal.com/market-intelligence

Londres, mai 1941. Pierre de Vomécourt, alias " Lucas ", est l'un des premiers agents " action " du SOE parachutés sur le sol français sur ordre de Churchill. Avec René Piercy et sa femme Thérèse, ils vont créer à Lyon le noyau dur du SOE dans le Rhône. Fondée sur des documents inédits, une incroyable histoire d'amour et de Résistance, par l'auteur du Nageur d'Auschwitz .Inspirée du destin de Thérèse Leblond, René Piercy et Pierre de Vomécourt, voici l'histoire authentique des espions français de ChurchillLondres, mai 1941. Bientôt un an depuis que Churchill a créé le SOE – Special Operations Executive – et lancé le mot d'ordre : " Et maintenant, mettez le feu à l'Europe ! "Sous le pseudonyme de Lucas, Pierre de Vomécourt est l'un des premiers Français parachutés. Il recrute René Piercy et Thérèse Leblond, un couple lyonnais décidé à tout risquer pour la liberté. Ensemble, ils bâtissent le noyau dur du réseau du SOE dans la région Rhône-Alpes, au cœur d'un combat souterrain où se mêlent héroïsme, espoir et délation.Car la trahison d'une agent double, Mathilde Carré, dite " la Chatte ", va briser leurs réseaux. Arrestations, torture, déportation – et au cœur du drame, la lutte de Thérèse, prête à tout pour retrouver son mari disparu.Une femme d'exception, témoin d'un amour plus fort que la guerre.L'auteur, Renaud Leblond, est notre invité en studio* Sélection du grand prix littéraire de l'armée de Terre – Erwan Bergot 2026Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Send us Fan MailTim O'Donnell is back and in this episode we all trades some stories and some crazier than others. We talk about the recent UFC fight at the White House. Kevin's shares a story on how to deal with no bathroom breaks while working a part of the stage crew and a few celebrity related stories that quite possibly no one has ever heard of.Also we review the song "Anymore" by Best Days Behind taped live at the famed  Churchill's Pub in Miami, FL.LISTEN to Tim's new music on Spotify: https://open.spotify.com/artist/6dtnEkPuGeNVIW2wa47rEA?si=QnKkTOlASPWEVenyt7Nk0wCheck out upcoming The Nouveaux Honkies and The Conchy Tonkers.

Steve Richards welcomes award-winning historian Richard Vinen to discuss The Last Titans, a compelling new look at Winston Churchill and Charles de Gaulle, two extraordinary leaders whose influence continues to shape the world.Watch and Listen now on Spotify.#RichardVinen #TheLastTitans #SpeakingOfWriters #HistoryPodcast #Churchill #CharlesDeGaulle #WorldHistory #Biography #MilitaryHistory #Leadership #Books #AuthorInterview

Nancy Churchill examines the four candidates vying for Washington Supreme Court Position 7 — a seat that will almost certainly decide the constitutional fate of the state's new income tax. Churchill weighs Chief Justice Debra Stephens' record against three challengers who each emphasize applying the Constitution as written. https://www.clarkcountytoday.com/opinion/opinion-supreme-court-position-7-will-they-guard-the-dam-or-let-the-tax-flood-through/ #WashingtonState #SupremeCourt #IncomeTax #JudicialElection #Opinion #Columns #Politics #ClarkCounty #DangerousRhetoric #InfluencingOlympia

In war and diplomacy, personal relationships between leaders can shape the course of events in unpredictable ways. Read by Leighton Pugh. Read the essay here: https://engelsbergideas.com/notebook/the-political-power-of-knowing-your-enemy/.Image: Cartoon from Punch showing from Roosevelt, Stalin and Churchill at the Yalta Conference in February 1945. Credit: Alamy

Mitch McConnell has been in hospital for three weeks. His office hasn't said why. This has happened before. Many times.From ancient Rome and Churchill's secret stroke in 1953, to Biden's very public deterioration on live television in 2024, this is the complete history of politicians who refused to leave, and the systems that helped them hide it. We also look at Robert Byrd, the West Virginia senator who went from a terrible person to civil rights advocate across a fifty-year career, and whether a very long political career can ever actually be justified. Plus: the case for younger leadership, from Zohran Mamdani freezing rents in New York at 34 to Jacinda Ardern passing gun reform in ten days at 37.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/HistorysGreatestIdiots⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/historysgreatestidiots⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://buymeacoffee.com/historysgreatestidiots⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Artist: Sarah Chey⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.fiverr.com/sarahchey⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Most speakers assume the flood of AI-generated content is bad news for them. In this solo episode, John Ball argues the opposite: as content gets cheaper and more abundant, a real human voice saying something only they could say becomes harder to ignore, not easier to overlook.John traces why speaking has held power for centuries, from Cicero to Churchill to Martin Luther King, and pulls in a callback to an early conversation with Stoic philosophy expert Donald Robertson on Marcus Aurelius, who treated rhetoric as a discipline rather than decoration. From there, John properly defines what a keynote actually is (and isn't), why the format exists, and why panels, workshops and webinars can't do the same job. He closes with a specific, evidence-backed prediction for where professional speaking is heading, drawing on a recent conversation with David Newman and a preview of an upcoming interview with Dominic Eldred-Earl of London Speaker Bureau.Get the email outreach templates to get you booked: https://present-influence.kit.com/96ec2d2b85In this episode:Why the post-Covid hunger for real human connection was the first sign of where this was headingWhat Marcus Aurelius and Stoic philosophy have to do with modern speakingA proper definition of a keynote, and why it varies in style but not in structural purposeWhy panels, workshops and webinars can't replicate what a keynote doesThe specific reason AI can't replace a speaker with a genuine point of viewAn early preview of what London Speaker Bureau is seeing in the market right nowChapters:0:00 Why speaking is becoming more valuable, not less1:00 The post-Covid hunger for real human connection3:00 Speaking as an ancient, powerful medium4:00 Historical speeches and why the medium still works5:00 Marcus Aurelius, Stoicism and rhetoric as discipline6:00 What a keynote actually is7:00 Why AI can't replace a real point of view8:00 What London Speaker Bureau is seeing in the market9:00 The prediction10:00 CTA and what's coming next4. FAQ Section (AI Retrieval Format)What does John Ball say about AI and the future of public speaking? John Ball argues that AI-generated content is making professional speaking more valuable, not less, because a real speaker's point of view is one of the few things AI cannot replicate.What is a keynote, according to John Ball? John Ball defines a keynote as a deliberate structural format built around one voice holding a sustained, undiluted block of audience attention, distinct from panels, workshops and webinars, though style and delivery can vary widely within that structure.Who is Donald Robertson and why does John Ball mention him? Donald Robertson is a Stoic philosophy expert and author who appeared on an early episode of Professional Speaking to discuss Marcus Aurelius' approach to rhetoric, which John Ball references as an example of speech treated as a serious discipline rather than performance.What did David Newman say about AI and content that John Ball references? David Newman argued on a previous episode of Professional Speaking that how-to content became commoditised once ChatGPT went public, leaving a speaker's way of thinking, beliefs and predictions as the remaining scarce value.Who is Dominic Eldred-Earl and what does he say about the speaking market? Dominic Eldred-Earl of London Speaker Bureau is an upcoming guest on Professional Speaking who reports that demand for professional speakers keeps increasing even as more speakers enter the market, with strong speakers continuing to get booked.Visit https://strategic-speaker.scoreapp.com to take the 2-minute Strategic Speaking Business Audit and find out what's blocking you from getting more bookings, re-bookings, referrals and bigger fees. There's a special surprise gift for everyone who completes the quiz.Want to get coached for free on the show? Fill in the form https://forms.gle/mo4xYkEiCjqtz9yP6, and if we think your challenge could help others, we'll invite you on.For speaking enquiries or to connect with me, you can email john@presentinfluence.com or find me on LinkedInYou can find all our clips, episodes and more on the Present Influence YouTube channel: https://www.youtube.com/@PresentInfluenceThanks for listening. Rating the show 5* on Spotify helps their algo recommend the show, so please take a moment to follow the show and leave a rating.

Have you ever transferred miles into a program and then realized you only ever use it to book the same handful of flights everyone else does?   Air Canada Aeroplan might be the most useful airline program that US travelers consistently overlook, and after this conversation I think you'll see why so many experienced points and miles enthusiasts treat it as a core part of their strategy. Today I'm talking with Anshul Singh of Points, Miles, and Bling, who has spent years inside the Aeroplan ecosystem and has used it to book everything from business class to Europe to trips most people never imagine points can unlock.   Anshul and I get into why Aeroplan hits a rare balance between being easy to use and deep enough to reward the people who dig in, and why its network of close to 50 airline partners is where the real opportunity lives. Listen as we talk about the overlooked partner bookings most travelers walk right past, like Brussels Airlines business class out of Washington Dulles and Emirates and Air Mauritius routes you would never expect through a Star Alliance program, how Aeroplan's stopover rules let you build a single ticket that visits more than one place, and the Hotel Savers chart that points airline miles at high-end hotels like Six Senses and Fairmont.    We also cover the smart way to think about buying Aeroplan points around a promotion, and my favorite part of the whole conversation, how the same program that flies you to Europe can take you to see polar bears in Churchill, the northern lights over the Canadian Arctic, and whale pods up and down both coasts. Whether you're brand new to Aeroplan or you've transferred miles into it before and never looked past the obvious flights, this episode will change how you see the whole program.   Get full show notes and transcript: https://pointmetofirstclass.com/air-canada-aeroplan-strategy    Eager to learn the secrets of award travel so that you can turn your expenses into unforgettable experiences? Join the Points Made Easy course waitlist here: https://pointmetofirstclass.com/pointsmadeeasy  

Winston Churchill was born in a palace and was given a funeral worthy of a king. His family had enjoyed an intimate association with the British monarchy stretching back centuries. As King Edward VIII said of him, 'I have never met anyone of royal blood who exemplified in such high degree the ideal of the 'good king.' Churchill and the Crown (Oxford University Press, 2026) tells the story of Churchill's relationship with the various kings and queens he served during his long political career, from young journalist under Edward VII, through his dramatic fall from grace in the First World War under George V, the frustrations of appeasement during the interwar period and his relationship with Edward VIII during the abdication crisis of 1936, culminating in his Finest Hour in the Second World War under George VI and the coda of Churchill's public service to his final monarch: Queen Elizabeth II. Ted Powell analyses Churchill's writings on monarchy and his role in preserving and establishing monarchies outside Britain. At the core of the book is a series of studies of Churchill's relationships with the monarchs he served. These studies offer a two-way perspective, examining both Churchill's view of individual monarchs and their attitudes towards him. They shed light not only on Churchill's career but also on the changing role of the monarchy in 20th century Britain. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Winston Churchill was born in a palace and was given a funeral worthy of a king. His family had enjoyed an intimate association with the British monarchy stretching back centuries. As King Edward VIII said of him, 'I have never met anyone of royal blood who exemplified in such high degree the ideal of the 'good king.' Churchill and the Crown (Oxford University Press, 2026) tells the story of Churchill's relationship with the various kings and queens he served during his long political career, from young journalist under Edward VII, through his dramatic fall from grace in the First World War under George V, the frustrations of appeasement during the interwar period and his relationship with Edward VIII during the abdication crisis of 1936, culminating in his Finest Hour in the Second World War under George VI and the coda of Churchill's public service to his final monarch: Queen Elizabeth II. Ted Powell analyses Churchill's writings on monarchy and his role in preserving and establishing monarchies outside Britain. At the core of the book is a series of studies of Churchill's relationships with the monarchs he served. These studies offer a two-way perspective, examining both Churchill's view of individual monarchs and their attitudes towards him. They shed light not only on Churchill's career but also on the changing role of the monarchy in 20th century Britain. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/history

A letter from Churchill demonstrates the value of courtesy, even—or perhaps especially—at moments of highest battle. Connect with Us: Email: podcast@gretchenrubin.com Website: gretchenrubin.com Instagram: @gretchenrubin | @lizcraft Learn more about Gretchen's Four Tendencies personality framework and take the free quiz. Enjoyed this episode? Leave us a review on Apple Podcasts or rate us on Spotify—it helps other listeners find the show! Find the transcript for this episode on the episode details page in the Apple Podcasts app.  Learn more about your ad choices. Visit megaphone.fm/adchoices