Podcasts about Halfway

It's Wednesday, and Prison Bound Week continues. We started Monday with the original 2018 call, and yesterday we heard from our caller nine months into her sentence. Today's follow-up comes from October 2019, about halfway through her time in federal prison. These follow-ups originally aired on Stitcher Premium, which means a lot of you have never heard them before. We'll keep following the story tomorrow, then on Friday we'll hear from the Prison Bound caller again in an all-new conversation.  And for our Touch Tone subscribers, you'll get exclusive access to her turn answering our 5 Random Questions.  For now, here's where things stood at the halfway mark. Sign up for Beautiful/Anonymous+ to get ad free episodes and access to exclusive audio including 5 Random Questions with this week's caller. Leave us a voicemail at (973) 306-4676 Head to chrisgeth.com for tickets to Gethard's upcoming shows, including This Helps Day in San Diego featuring Dinosaur Improv. Right now, Babbel is offering listeners up to 60% off. Go to Babbel.com/BEAUTIFUL. Ready to reach your goals? Visit forhers.com/beautiful to get personalized, affordable care that gets you. Visit upwork.com to connect with top talent ready to help your business grow. Go to zenni.com/podcast and use code PODCAST15 for fifteen percent off your first order, plus free shipping on all US orders over sixty-five dollars.

Episode 280- Listener Questions We open the mail bag to answer a bunch of questions from the Decision Space community!   Time Stamps 2:15- who would win? 4:50- what is a YOU game? 9:00- design choice you disagree with 18:00- design choice you love 23:20- games for roommates 27:30- games you wish existed 34:30- bad games we like 40:00- who you want to play a game with 44:30- spoiler strategies 50:00- emotional responses to games 53:00- how our tastes have changed 56:30- where do you see the pod in 5 years? 1:00:30- game night pick 1:02:45- deep dive redo 1:05:30- time for games 1:09:00- state of the hobby 1:13:15- superlatives 1:14:15- has the podcast changed you as a gamer? 1:16:45- host disagreements 1:18:00- dueling game advice   Preplanners We have a deep dive of Paolo Mori's Libertalia coming up!   Music and Sound Credits Thank you to Hembree for our intro and outro music from their song Reach Out. You can listen to the full song on YouTube here: https://www.youtube.com/watch?v=gQuuRPfOyMw&list=TLGGFNH7VEDPgwgyNTA4MjAyMQ&t=3s You can find more information about Hembree at https://www.hembreemusic.com/.  Thank you to Flash Floods for use of their song Palm of Your Hand as a sting from their album Halfway to Anywhere: https://open.spotify.com/album/2fE6LrqzNDKPYWyS5evh3K?si=CCjdAGmeSnOOEui6aV3_nA Intermission Music: music elevator ext part 1/3 by Jay_You -- https://freesound.org/s/467243/ -- License: Attribution 4.0 Bell with Crows by MKzing -- https://freesound.org/s/474266/ -- License: Creative Commons 0 hammer v2.wav by blukotek -- https://freesound.org/s/337815/ -- License: Creative Commons 0   Contact Follow and reach us on social media on Bluesky @decisionspace.bsky.social. If you prefer email, then hit us up at decisionspa@gmail.com. This information is all available along with episodes at our new website decisionspacepodcast.com. Byeee!

Ben Criddle talks BYU sports every weekday from 2 to 6 pm.Today's Host: Ronald Weaver III (@ronthe3manweav) and Co-Hosts: Subscribe to the Cougar Sports with Ben Criddle podcast: Apple Podcasts: https://itunes.apple.com/us/podcast/cougar-sports-with-ben-criddle/id99676

Nebraska Football is halfway through fall camp - what have we learned about the Huskers? Jay Foreman and Austin Oerman discuss their takeaways from the first two weeks of camp before discussing the impact Anthony Colandrea could have on the Nebraska Football rushing attack.Don't forget to like, follow, subscribe, and share the show!Follow The Ticket: @937theticket on social mediaFollow Jay on Twitter: @foreman5644Follow Austin on Twitter: @austin_oermanAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Mike coaches a struggling Pilates studio owner on phone sales live: avatar building, referral strategy, and a real walk-in prospect. This pilates studio sales coaching session picks up where Part 1 left off. In Part 1, Mike walked through Jess Landar's churn, lifetime value, and pricing. In Part 2, they build her customer avatar, run a full gym sales role play on her actual phone script, and design a gym referral program built around feeding children. Halfway through filming, a real prospect walked into Jess's studio while the cameras were rolling. Mike coached her through that conversation live, in real time. It has never happened on this podcast before. The gym membership sales tips in this pilates studio sales coaching session are not Pilates-specific. The same principles for how to sell gym memberships, plant a belief before price ever comes up, and control a sales call apply to any boutique gym. This is Part 2 of a continuing series. Jess has 30 days of ads running before she and Mike reconnect for Part 3. In this episode you'll learn: — The exact way to build a customer avatar most gym owners skip — A full gym sales role play using Jess's real phone script, line by line — What happened when a real prospect walked into the studio mid recording — How to plant a belief before a prospect ever asks about price — The moves that control a sales call instead of getting steered by it — A gym referral program built around feeding children instead of a discount — The welcome gift bag move that gets new members texting back after their first class — Why customer acquisition cost matters more than cost per lead — How to turn a low priced trial into a full membership without extra ad spend — The AI assistant setup Mike uses to manage ads, CRM, and follow up — The homework and 30 day timeline before Part 3 If you have ever fumbled a walk-in prospect or lost control of a sales call, this live session shows you exactly what to do differently. Timestamps: — 0:00 Welcome back for Part 2 — 4:54 Building Jess's avatar, meet Brenda — 10:05 Finding Brenda's biggest insecurity — 12:17 The fishing analogy for hooks, lines, and offers — 17:46 How many members her studio can actually hold — 24:31 Live roleplay: selling the $39 trial over the phone — 29:41 Planting a belief before the close — 32:04 Finding out how ready a lead really is — 41:12 The VIP referral pass strategy — 45:33 Amateurs add, professionals multiply — 49:39 The double call and video text combo — 1:00:26 Why customer acquisition cost matters more than cost per lead — 1:08:39 Designing a scarier, higher converting offer — 1:16:05 What value stacking actually means — 1:19:20 Setting up an AI assistant to run the business — 1:21:29 Automating follow up with Go High Level Connect with Jess Landar: Instagram: https://www.instagram.com/thereformerlibrary 100K Plan: https://www.youtube.com/watch?v=uMPx7b3_LOA Behind Gym Doors Podcast Playlist: https://www.youtube.com/playlist?list=PLnwaMl7Us7dAkfE6idQW56EYkEEQ2E5eE Go High Level (CRM Mike uses and recommends): https://www.gohighlevel.com/?fp_ref=gsd-company11 Viktor AI Assistant (referral credit link): https://ref.viktor.com/mike-arce Mike builds Jess's entire offer and sales script live, the same way GSD Gyms helps studios everywhere sell what they already have. The plan behind how we get gyms to $100K a month is right here. Turns out a $39 trial can do a lot of talking.

Episode 279- Sandbox Objectives and Frosted Blooms This week Brendan tries to convince his co-hosts that "sandbox objectives" and "point salad" are not not the same thing.  Jake isn't convinced.  Also the gang deep dives the new polyomino game Frosted Blooms, designed by Bruno Cathala and Ludovic Maublanc. Time Stamps 3:20- sandbox objective systems 30:00- Frosted Blooms deep dive   Preplanners Next week is listener questions!  Send them to us via email or join our Discord!   Music and Sound Credits Thank you to Hembree for our intro and outro music from their song Reach Out. You can listen to the full song on YouTube here: https://www.youtube.com/watch?v=gQuuRPfOyMw&list=TLGGFNH7VEDPgwgyNTA4MjAyMQ&t=3s You can find more information about Hembree at https://www.hembreemusic.com/.  Thank you to Flash Floods for use of their song Palm of Your Hand as a sting from their album Halfway to Anywhere: https://open.spotify.com/album/2fE6LrqzNDKPYWyS5evh3K?si=CCjdAGmeSnOOEui6aV3_nA Intermission Music: music elevator ext part 1/3 by Jay_You -- https://freesound.org/s/467243/ -- License: Attribution 4.0 Bell with Crows by MKzing -- https://freesound.org/s/474266/ -- License: Creative Commons 0 hammer v2.wav by blukotek -- https://freesound.org/s/337815/ -- License: Creative Commons 0   Contact Follow and reach us on social media on Bluesky @decisionspace.bsky.social. If you prefer email, then hit us up at decisionspa@gmail.com. This information is all available along with episodes at our new website decisionspacepodcast.com. Byeee!

So much better than we expected! Natalie Grace in Lee Cronin’s The Mummy (2026) ★★★★ The first half of 2026 certainly made a statement in the horror genre. Reverberations will be felt across Hollywood as studios reassess what constitutes a blockbuster. The recent critical success of Sinners, Weapons, and The Substance appears to be bearing fruit. The results may have led to a diverse slate of offerings for 2026, and it is reflected in the wide array of horror offerings so far this year. If variety is your preference, then you have been in luck, so far. The unimaginable financial and critical success of Obsession and Backrooms, particularly given their low cost, have re-written the horror producer’s playbook, and will have a lasting impact for years to come. The Scariest Things evaluated what we’ve seen so far, and have come back with a report card of our favorites. The Mid Major – Large Indie Productions: It will be interesting to do an autopsy of 2026’s horror fare when we do our year end assessment. This year’s crop has been notably light on mid-major productions so far. The roaring successes we’ve seen thus far have been from the deep indie filmmakers. Backrooms, Undertone, Hokum, and Obsession. All original titles, and produced for less than $5 million. These are not the type of films you would typically predict would be box office hits. But, they outperformed any reasonable expectation, particularly startling for Backrooms and Obsession, which will have cinema economists trying to figure out the alchemy. The Major Studios: There were only a few major studio franchise sequels this year. All of them capable, but none of them superseding their source material. Scream 7 did very well at the box office, but it failed to make a big impression. Evil Dead Burn was a very violent, very intense outing that didn’t connect in the way its predecessors have. Scary Movie also did well on its $30 million budget, earning $210 million, its long layoff didn’t hurt its popularity, despite the derivative humor. 28 Years Later: The Bone Temple was a hit with critics (including our own Liz Williams) but it’s turn from zombies may have hurt it in the box office, as it probably will not make its budget back. The notable big comeback was Lee Cronin’s The Mummy, which gives horror fans hope that Universal has gotten their monsterverse back on track, and remembered how to deliver a vicious movie with a good story. The Festival Circuit: Unfortunately, we haven’t been able to get out to as many film festivals this year as we would have liked, but we still managed to review a lot of great small indie horror films. Our traditional rotation of SXSW, Overlook, Portland Horror, and Popcorn Frights all provided very interesting films from emerging filmmakers. Even the bigger festival this year were heavy on small indie productions, and there were a lot of good offerings. We didn’t discuss all of them in our Podcast, but you can review a lot of the movies we saw with links on the images below. There is an emerging trend of release schedules for many of the best independent horror films, relative to the festival circuit. Open at SXSW (Or sometimes Sundance), get some buzz, go to Overlook to validate the buzz, and then release in the early summer. It can be a big momentum builder. Last year it was The Ugly Stepsister (Oscar Nom), and Good Boy that were the big beneficiaries of that combination. This year it was Obsession, Hokum, and Leviticus. Sometimes Panic Fest will factor into the equation, depending on when runs relative to Overlook. Popcorn Frights still brought a massive catalogue for viewing, but this year was more reliant on reperatory films. Some of my favorite films this year were horror adjacent, and all of their streaming content was small indie productions. Our favorite local festival, The Portland Horror Film Festival had an unusually large amount of feature films, and the best part was that almost all of the directors participated in the festival. We were fortunate to get interviews with all of them. Check out our Podcasts for the interviews. It was a diverse group of films, and not a dud among them. What is Still Coming in 2026? Probably the most intriguing film still due out is a new take on Resident Evil by Zach Cregger, the director of Barbarian and Weapons. This is a major tonal shift for Sony. No more Mia Jovovic or Paul W.S. Anderson. Video game translations are usually dubious, and the franchise squandered its potential. It became more superhero and less horror. However, Sony is gambling that with the right creator, it might revitalize the legacy, and Cregger may inject real scares into this franchise for the first time since the original. We shall see. The trailer looks great. Other Mommy, which arrives on October 9, has some incredibly disturbing visuals. Jessica Chastain stars, and Rob Savage (Host) directs. This uncanny-valley movie came late to my radar and is intriguing. Later this August, the Insidious franchise is issuing Insidious: Out of the Further. Lin Shaye returns, Leigh Whannell writes this, and it has a banger trailer. This franchise has maintained some of the scariest PG-13 fare, but the sequels have been a gradual decline in quality. We’ll see how this goes. Beware Boiúna might give us the Anaconda movie we wish we got earlier this year, and wipe the slate clean from the Jack Black/Paul Rudd disappointment. It’s R-rated, and features reliable scream queen Jessica Rothe, along with Logan Marshall-Green and Kiana Madeira in the Amazon. The trailer impressed me with the snake’s monstrousness. Another fascinating man-vs-nature survival tale is Whalefall, a terrifying concept in which a scuba diver is in jeopardy of being consumed by a sperm whale if the giant squid the whale has snagged doesn’t kill him first. Clayface will attempt to bridge the superhero and horror genres, which is no easy feat. Will it have enough horror for horror fans, and will it have enough fealty to the Batman universe to appeal to the comic book fans? Mike Flanagan wrote it, and it promises real R-rated body horror, so it could be very interesting. Lastly, Robert Eggers’ period piece Werwulf is getting the full Eggers treatment, in monochrome and as moody as you could possibly want. Hopefully it will get the stink of the Wolf Man out of our popcorn. Just give me a great transformation scene, please. Keep an eye out for It Ends, a festival darling from 2025 which is finally getting a limited release this year. It is a brilliant, if melancholy purgatory metaphor. If you enjoyed Backrooms, you should check this film out if you are lucky enough to have it showing in a theater near you. Of course, some smaller films will come out of the shadows and inevitably make our end-of-the-top-10 lists, but this is what we know so far. The Podcast: The Best Horror Movies at the 2026 Halfway Mark: Episode 214 Recommendations from the First Half of 2026: Here are all the movies that we have ranked ★★★ or better, so far in 2026. The End of Oak Street (2026) ★★★★★ 28 Years Later: The Bone Temple (2026) ★★★★ They Will Kill You (2026) ★★★★ Undertone (2026) ★★★★1/4 The Vampire Lestat (2026) Grind (2026) ★★★★ Ice Cream Man (2026) ★★★★ Send Help (2026) ★★★★1/2 Backrooms (2026) ★★★★ Obsession (2026) ★★★★ Evil Dead Burn (2026) ★★★ Big City Pizza (2026) ★★★★.5 Cat Cam (2026) ★★★1/2 Hokum (2026) ★★★★ Armageddon Road (2026) ★★★.5 Hungry (2026) ★★★ Break a Leg (2026) ★★★★1/2 Dracula: The Night Around Us (2026) ★★★★ Frogman Returns (2026) ★★★★ The Demonatrix (2026) ★★★ LandLord (2026) ★★★★ Leviticus (2026) ★★★★ Ready or Not 2: Here I Come (2026) ★★★.5 The Yeti (2026) ★★★★ Sitra Achra (2026) ★★★★ Parasomnia (2026) ★★★1/2 Thrash (2026) ★★★.5 Goody Goody (2026) ★★★★ Iron Lung (2026) ★★★★ American Dollhouse ★★★★ Buffet Infinity ★★★1/2 Faces of Death (2026) ★★★1/2 Pretty Lethal (2026) ★★★★ Friday the 69th (2026) ★★★ Ugly Cry (2026) ★★★★ Quince (2026) ★★★★ Cold Storage (2026) ★★★1/2 A Safe Distance (2026) ★★★1/2 Bagworm (2026) ★★★1/2 Bodycam (2026) ★★★ Marama (2026) ★★★★1/2 The Peril at Pincer Point (2026) ★★★ Dead Media (2026) ★★★.5 Affection (2026) ★★★1/2

Last time we spoke about the beginning of the Pacific War. In late 1941, the Second Battle of Changsha ended with Chinese forces claiming victory despite suffering nearly 100,000 casualties against roughly 1,670 Japanese losses. Chiang Kai-shek's government launched an aggressive propaganda campaign, flooding international press with fabricated triumph narratives to sustain national morale. Behind closed doors, Chiang was furious at his generals' tactical failures, executing commanders and dismantling units. Meanwhile, geopolitical forces threatened China's survival. Japan's 1939 defeat at Nomonhan crushed hopes for a Soviet alliance against Tokyo. Germany's pact with the Soviet Union left China isolated, while Western powers remained reluctant to commit meaningful support. Britain offered little; the US provided only modest loans. By 1941, facing potential collapse, Chiang desperately searched for allies while Wang Jingwei's Japanese-backed rival government beckoned as an alternative. China's exhausted people endured horror—including a 1941 bombing massacre in Chongqing that killed over 400 trapped civilians. Then Japan unleashed a surprise attack on Pearl Harbor.   #215 An Implosion in the East 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 the early morning of December 7, 1941, the US Pacific Fleet was anchored at Pearl Harbor in Hawaii. Two waves of Japanese bomber aircraft, launched from six aircraft carriers, attacked the vessels and their sleeping crews, destroying the battleship Arizona outright and damaging seventeen others, as well as most of the military aircraft parked nearby. Some 2,400 Americans died, and another 1,100 were wounded. Within a day Japanese invasion forces had attacked Siam, Malaya, and the Philippines. Chiang Kai-shek heard the news at one o'clock in the morning and immediately dictated a letter of support to President Roosevelt, pledging commitment to a new "common battle." The news reached Zhou Fohai in Shanghai as well. On December 8 across the international date line from Hawaii he had heard the sounds of firing as the Japanese took over the rest of the city, and then received reports that Japan had declared war on the Western powers. "I heard they'd bombed Honolulu, Manila, Singapore, and Hong Kong. From now on, the Pacific becomes a killing ground."   When Japanese carrier planes descended on Pearl Harbor on December 7, 1941, they were executing far more than a tactical strike. They were unleashing the culmination of a strategic vision that had shaped Japanese military planning since the 1920s: the Nanshin-ron, or "Southern Advance" doctrine.    The Nanshin-ron represented a critical strategic choice that emerged from internal Japanese debates during the 1920s and 1930s. Japan faced an inescapable vulnerability: it was an island nation with virtually no natural resources of its own. The nation's population had doubled since the Meiji Restoration, growing to over 70 million by 1941. An industrial economy of tremendous sophistication, capable of producing world-class warships, aircraft, and military equipment, depended entirely on imported raw materials. Oil, rubber, tin, iron ore, bauxite, copper, and manganese all had to be acquired through foreign trade or conquest. This resource dependency was not a luxury problem; it was an existential one. A nation that could not feed itself or fuel its economy was a nation vulnerable to strangulation by enemies who controlled the resources it needed. Japanese strategic thinkers divided into two camps on how to resolve this vulnerability. The Hokushin-ron advocates, primarily within the Army, pushed for expansion northward into Siberia and Mongolia, seeking to conquer Soviet territory and resources. The Soviet Union possessed vast tracts of land, mineral wealth, timber, and agricultural output. From a purely materialist perspective, the Northern Advance made geographic sense—it was contiguous with Manchuria, which Japan had already secured in 1931. Yet by the late 1930s, the Hokushin-ron strategy had suffered a catastrophic blow. In 1938 and 1939, Japanese and Soviet forces clashed at Khalkhin Gol in Mongolia in what the Japanese called the Nomonhan Incident. The battles revealed a terrifying truth: the Soviet Red Army, under General Georgy Zhukov, was far more formidable than Japanese military planners had anticipated. Soviet armor, air power, and coordinated tactics annihilated Japanese forces. Japan suffered over 60,000 casualties while inflicting perhaps 10,000 on the Soviets. The defeat was concealed from the Japanese public, but it was unmistakable to military planners. A direct clash with Soviet power could end catastrophically. The Northern Advance, it became clear, would almost certainly provoke war with a nation capable of destroying the Japanese Army. This strategic defeat tilted the balance decisively toward the Nanshin-ron. If Japan could not safely expand northward, it must expand southward, toward Southeast Asia, the Dutch East Indies, Indochina, and the western Pacific. The appeal of the Nanshin-ron was immediately apparent to the Navy, which had always been more oriented toward maritime commerce and expansion across the ocean. The territories to the south were not militarily formidable. The colonial powers, Britain, the Netherlands, France, were either already defeated in 1940 or distracted by their own desperate struggles leaving Britain fighting alone against Germany. The United States, though potentially a formidable adversary, was not yet at war, and there was a chance, however slender, that a decisive blow against American naval power might convince America to negotiate rather than fight. But beyond strategic opportunity, the Nanshin-ron offered something that the Hokushin-ron did not: it offered resources of staggering abundance. The Dutch East Indies possessed oil reserves that could dwarf Japan's consumption for years. Malaya produced the world's rubber. Thailand offered agricultural bounty and geographic position. Burma's timber and agricultural products could feed Japan's growing empire. The Philippines possessed harbors, airfields, and economic output. These were not marginal additions to Japan's economy—they were transformative. Together, they would convert Japan from a resource-hungry island nation into the master of an imperial domain encompassing hundreds of millions of people and containing most of the strategic materials necessary for sustained industrial warfare. In the last episode I explained the western sanctions and embargoes that drained Japan's military of the resources they required to continue the China War. By 1941, Japan faced a civilization crisis. Japan imported roughly 90 percent of its petroleum, and the bulk of that came from the Dutch East Indies and the United States. Suddenly, the American supply was cut off entirely. Reserves would last perhaps 18 months if Japan's military and industrial consumption continued at existing rates. After that, the military machine would sputter and die. The industrial economy would collapse. Even civilian life would become impossible to sustain. Japan was facing not merely economic inconvenience but literal strangulation by the Western powers.   Yet it would be a mistake to reduce Japanese motivation to just materialism. Japanese military leaders wrapped their conquests in an ideological framework, one that many of them genuinely believed, or wanted to believe, served a higher historical purpose. The concept of the Daitoa Kyoeiken, the "Greater East Asia Co-Prosperity Sphere," posited a new regional order in which Japan would play the leading role in liberating Asian peoples from Western colonial domination and creating an Asian-centered political and economic system. This ideology drew on genuine anti-colonial sentiment. For a century, Western powers had dominated Asia, treating Asian peoples and nations as subordinate, colonized territories existing to serve Western economic interests. Japan, which had modernized and Westernized enough to avoid colonization, saw itself as the natural leader of an Asian renaissance—a civilizing force that would restore Asian dignity and independence even as it established Japanese hegemony. Of course, this was propagandistic. Japanese conquest represented a substitution of one imperial master for another, and often a harsher and more brutal one at that. But for understanding Japanese decision-making, the ideology mattered profoundly. It provided Japanese leaders with a moral narrative for conquest. It allowed them to understand themselves as liberators rather than mere conquerors. It provided a rationale for extracting resources from occupied territories and for mobilizing subject populations to support the Japanese war effort. And it resonated with anti-Western sentiment throughout Asia, making Japanese occupation seem, in some quarters, preferable to continued Western colonial rule. The slogan Hakko ichiu—"eight corners under one roof", captured this ideological vision: a unified Asia and eventually world under Japanese guidance. Whether Japanese leaders truly believed this vision or cynically deployed it as propaganda, it shaped how they understood their actions and justified them to their subjects. Now I know this is a podcast dedicated to the century of humiliation, ie: China's experience. But I thought it was necessary just to have one episode explain what Dan Carlin titled "a supernova in the east" and I have titled an implosion in the east. I won't go into depth into the campaigns as some of them have a lot of Chinese involvement and we will be covering them in depth, but rather I will explain the rationale for Japan's numerous invasions following the surprise attack on Pearl Harbor.   Japan's assault on Malaya began within hours of the Pearl Harbor attack, indeed, Japanese troops were already landing on the Kota Bharu beachhead on the Malaysian peninsula before dawn on December 8, 1941 local time. Malaya was one of the world's largest rubber producers, and Singapore, at the tip of the peninsula, was the linchpin of British naval power in the Far East.The Japanese interest in Malaya was straightforward but crucial: rubber. The Japanese military consumed vast quantities of rubber for tires, gaskets, hoses, and countless other applications. American and Dutch rubber supplies were now cut off by embargo. Malaya's rubber plantations represented perhaps the world's richest reservoir of this strategic material. Beyond rubber, Malaya and the surrounding region were major tin producers—essential for alloys and manufacturing. For Japan to conduct prolonged warfare, Malaya was not a luxury target; it was a necessity. Singapore represented something different: a symbol of Western power in Asia and a strategic chokepoint. As the major British naval base in the region, Singapore served as the anchor of British imperial authority across the South China Sea. By seizing Singapore, Japan would not only capture crucial resources and port facilities but would also deliver a psychological blow to the entire edifice of Western dominance in Asia. The fortress city that the British considered nearly impregnable fell within seventy days, a stunning demonstration of Japanese military capability and the vulnerability of overextended Western empires. If Malaya was critical for rubber, the Dutch East Indies was essential for oil. The region, comprising present-day Indonesia, was the world's third-largest oil producer and had reserves that could sustain Japanese military operations for years. This was the jewel of the resource-conquest strategy. Japanese planners had long eyed the East Indies, but direct conquest had seemed risky while Britain and America remained neutral or opposed. With both now at war, the window of opportunity had opened. Japan needed the oil so desperately that when the Dutch East Indies Petroleum Company initially agreed to some Japanese oil purchases before the war, Tokyo was already planning its takeover. The conquest of the Dutch East Indies would provide not just oil but also tin, nickel, bauxite, and other materials essential to an industrial war machine. The islands would also provide air and naval bases from which Japan could defend its new empire and potentially threaten Australia. The Japanese invasion of the Dutch East Indies, which began in late December 1941 and accelerated through January and February 1942, was methodical and comprehensive. Various island groups were targeted systematically: Java, Borneo, Celebes, and the Moluccas. Dutch military forces, though brave, were overwhelmed. By March 1942, Japan had secured the entire archipelago,a stunning logistical and military achievement that delivered vast resources into Japanese hands. Thailand presented a fundamentally different case from Japan's other targets. Rather than conquest, Japan pursued alliance with military strongman Plaek Phibunsongkhram, who had seized power in a 1938 coup as a nationalist ideologue determined to reassert Thai sovereignty and recover territory lost to French colonialism in the nineteenth century. Thailand was the last independent state in mainland Southeast Asia, hemmed in by British-controlled Burma and Malaya to the west, and French Indochina to the east. For a century, Thailand had survived by playing European imperial powers against each other. But by the late 1930s, this strategy was becoming obsolete. Europe was distracted by Hitler's Germany. Colonial powers were weakening. Japanese power was visibly ascending. Phibunsongkhram recognized both danger and opportunity. He could resist Japan and invite invasion, or he could ally with Japan and profit from rising Asian power. Japanese diplomats approached Thailand with sophistication: they offered military alliance and support for Thai territorial expansion at French expense. This addressed Thailand's deepest national wound, the humiliation of nineteenth-century territorial losses to France. The Franco-Thai War of 1940-1941 became transformative. With Japan providing military support, advisors, tanks, aircraft, Thai forces defeated the French for the first time in history. By May 1941, Thailand had recovered 54,000 square kilometers of Cambodian territory and portions of Laos. For Phibunsongkhram, this was extraordinary: he had achieved what Thai leaders could not in a century, reclaiming territory from a Western imperial power. Domestically, his position was dramatically strengthened. The parallel to China's century of humiliation—the foundation of your podcast is striking: Thailand too was reversing colonial diminishment through association with rising Asian power. The Thai-Japanese relationship was unique. Unlike the Philippines, Malaya, or the Dutch East Indies, Thailand would retain nominal independence. Phibunsongkhram remained in power. Japanese occupation was light. Thai forces maintained their own command structure. Most importantly, Thailand's territorial gains from the Franco-Thai War were recognized and secured. Japan valued Thai cooperation more than conquest. Burma, controlled by the British, was the next target in Japan's southwestward advance. Japanese planners had multiple motivations for invading Burma. First, it was strategically positioned on India's eastern frontier, and Japan harbored ambitions of eventually striking toward India and linking up with Germany in the Middle East or at least destabilizing the British position in South Asia. Second, Burma was a significant producer of timber and agricultural products. Third, and perhaps most importantly, the invasion of Burma was meant to cut the Burma Road, the crucial supply line that allowed American aid to reach Nationalist China. The invasion of Burma began in late 1941 and accelerated through early 1942.  The invasion of the Philippines, which began on December 10, 1941, just days after Pearl Harbor, served multiple strategic purposes. Like the assault on Pearl Harbor itself, it was meant to neutralize American military power in the region, in this case, the American garrison under General Douglas MacArthur and the American colonial administration. But the Philippines were also valuable in their own right. The islands sat directly in the path of what Japan envisioned as its defensive perimeter. By controlling the Philippines, Japan would make it extraordinarily difficult for American forces to project power westward into Southeast Asia. The territory would provide naval and air bases critical to defending Japan's new empire. Additionally, the Philippines possessed some agricultural and industrial resources valuable to the war effort, and their conquest delivered a psychological victory, defeating American forces and ending American colonial rule in Asia, which appealed to anti-Western sentiment that Japan tried to cultivate. Beyond Southeast Asia and the Philippines, Japan rapidly invaded Guam and Wake island to extend Japan's defensive perimeter further into the Pacific and eliminate American air and naval bases that could threaten Japanese operations. Guam, seized on December 10, 1941, fell within two days, the American garrison was small and the island's strategic value lay primarily in denying the United States a forward staging ground for any counteroffensive. Wake Island, attacked the same day and finally captured on December 23 after a brief but heroic American defense, served similar purposes: cutting off American reconnaissance and interdicting potential supply lines to the Philippines.  Now I believe that's enough of the implosion in the east, let's get back to the situation in China. After Chiang Kai-shek sent his letter of support to President Franklin D. Roosevelt, pledging Chinese commitment to what he termed a new "common battle." he did something most revealing about his mood towards the global war. He played "Ave Maria" on his gramophone, celebrating with an almost spiritual gratitude the implications of what had just transpired in the American Pacific. This moment crystallized the profound difference Pearl Harbor would mean to different members of the emerging Allied coalition. For the United States, the attack was pure shock, a violation of the sanctity of American soil and the complacency of American isolationism. For Britain, it was like the pricking of a large, painful blister—painful but perhaps not entirely unexpected given Japan's regional ambitions. But for China, the relief that surged through the government and the consciousness of the Chinese people surpassed even Britain's response. For four long years, China had fought Japan almost alone, watching as Western nations remained neutral or limited their commitment to rhetorical support and modest material assistance. Now, suddenly, the trajectory of the entire conflict had shifted with breathtaking speed. Chiang's emotional state at this moment could scarcely be contained, and those close to him understood that beneath his carefully maintained Confucian composure burned an intoxicating sensation of vindication. These Western powers who had been so impatient with Chinese strategy, who had mocked the retreats of the Nationalist Army and spoken scornfully of Japanese capabilities, were now themselves in headlong retreat from those very islanders. In Hong Kong, Singapore, and Manila, British and American forces would be fighting desperately for their lives against the Japanese military that China had been contending with for years. Chiang could not have failed to perceive the irony: the Japanese had conquered vast stretches of China through relentless campaigns, yet Westerners had dismissed Chinese resistance as inadequate. Now the Japanese were proving that they posed a formidable challenge to Western military power as well. There was a bitter satisfaction in this vindication, even as Chiang understood that the strategic implications of American entry far outweighed any emotional satisfaction. What was pragmatic and strategically clear to Chiang, however, was far more important than any emotional satisfaction. The United States was now at war, and it was on China's side. This represented the transformation for which Chiang had labored for years, bringing the full weight of American industrial capacity and military might to bear against the Japanese enemy. The diplomatic foundations for this alliance had already been laid in the months preceding Pearl Harbor. Beginning in the spring of 1941, the United States had begun to commit significant resources to supporting China. In April 1941, following the conclusion of the Soviet-Japanese neutrality agreement, which had devastated Chinese morale by suggesting that Japan might attack the Soviet Union rather than move southward, the Roosevelt administration had determined to provide $45 million worth of militarily useful commodities to China as an immediate gesture of support. Then, on May 6, 1941, President Roosevelt had made a more formal commitment by announcing that the munitions provisions of the Lend-Lease Act applied to China, declaring a phrase that captured the strategic logic of American assistance: "defending China is the key to defending America." This declaration represented a significant shift in American policy, elevating China from a regional concern to a central element of American grand strategy in Asia. Subsequently, in July 1941, Roosevelt had sent Owen Lattimore to China to serve as political adviser to the Chinese government, a position that signaled deepening American engagement with Chinese affairs. In August, the administration appointed John Magruder as military attaché and head of the American Military Mission to China (AMMISCA), with the primary responsibility of overseeing the delivery of Lend-Lease material to the Chinese armed forces. On the diplomatic front, Chiang moved with remarkable alacrity. He had already severed relations with Germany on July 2, 1941, the very day after Hitler had formally recognized the Wang Jingwei puppet government in an attempt to appease Japan and encourage it to finally implement the Southern Advance. Now, on December 8, 1941, just one day after the Pearl Harbor attack, Chiang took the decisive step of formally declaring war against the Axis powers, Germany, Italy, and Japan. This declaration transformed China's position from a beleaguered nation fighting a war of survival into a formal member of the Allied coalition. Chiang had seized the opportunity that Pearl Harbor presented to ally China with a genuinely strong power against Japan, a gambit that fundamentally altered the diplomatic weight and international legitimacy of both the Chinese government and the Chinese cause. From a strategic perspective, the Japanese had committed what Chiang and others immediately recognized as a catastrophic blunder. On the operational level, Pearl Harbor was indeed a devastating tactical success, the American Pacific Fleet was crippled, and Japanese forces now enjoyed temporary naval superiority throughout much of the Pacific and Southeast Asia. Yet strategically, Pearl Harbor represented a failure of epic proportions, a miscalculation rooted in a fundamental misunderstanding of the intangibles of warfare: what motivated the Chinese people and what motivated the Americans. The Chinese had understood, through four years of brutal conflict, that warfare was about more than territorial conquest and resource acquisition. It was about will, about endurance, about the capacity to absorb punishment and continue fighting. The Japanese had learned this lesson imperfectly in China, where despite overwhelming military advantages they had been unable to force a decisive conclusion or compel Chinese surrender. The Americans would teach the Japanese this lesson even more emphatically. The attack on American sailors in Hawaii instantly galvanized the American population to prosecute an unlimited war, with Tokyo itself as the ultimate destination of American counterattack. While Americans would remember the "date that will live in infamy" primarily because of the assault on Pearl Harbor itself, the strategic consequence was that Japanese actions in Hawaii had introduced numerous new adversaries into the Pacific conflict: not only the United States, but also Britain, Australia, New Zealand, and the Netherlands. These additions transformed what had been, from the Japanese perspective, a regional conflict with manageable parameters into a global war that Japan could not possibly win through any conceivable means. What made this particularly disastrous for Japan was the context in which the new theater had been opened. Japan was already deeply mired in a quagmire in China—a "malignant quagmire," as one observer would later characterize it, that had consumed vast resources, inflicted enormous casualties, and failed to produce decisive victory. In military strategy, it is sometimes wise to open a new theater in order to attack an enemy from an additional and unexpected direction, thereby dispersing their forces and creating new challenges for them to manage. But opening a new theater that introduces an entirely new, powerful adversary, and doing so when one is already overextended in an existing theater is a prescription for strategic catastrophe. This was precisely what Japan had done at Pearl Harbor. Chiang Kai-shek understood the magnitude of Japan's miscalculation with crystalline clarity. He understood that whatever the military complexities that lay ahead, whatever the challenges of coordinating with Western allies, and whatever the limitations of Western military assistance to China might be, the fundamental equation had changed. The United States would ultimately defeat Japan. The Chinese did not need to defeat Japan by themselves; they merely needed to survive long enough for American industrial and military capacity to grind the Japanese military into defeat. In the immediate aftermath of Pearl Harbor, Chinese and American officials moved swiftly to formalize the alliance and establish the mechanisms of wartime cooperation. Roosevelt asked Chiang Kai-shek to serve as commander in chief of the Allied forces in the China theater, a designation that recognized China's central role in the Pacific War and elevated Chiang's international standing to something he had long sought: a position as an equal partner in the Allied coalition, a seat at the "top table of global decision-making" alongside Roosevelt, Churchill, and Stalin. To support this expanded role and to ensure effective coordination of the military effort, Roosevelt dispatched Lieutenant General Joseph W. Stilwell to China. Stilwell was not chosen randomly; he brought fifteen years of experience in China, a deep familiarity with the country, its military, and its complexities that few American officers possessed. Stilwell was appointed to serve simultaneously as the U.S. government's military representative in China, as chief of staff of the China Theater, and as commander in chief of the American forces in the China-Burma-India (CBI) theater. This appointment established the framework for joint Chinese-American military cooperation and represented an American commitment to the Asian theater that, while never receiving the resources devoted to the European theater, nonetheless signified recognition of China's strategic importance. Trust me, going forward we will be having a lot of time dedicated to the hilarious bromance / bro hatred of Vinegar Joe and Peanut.  Yet the formation of an alliance and the establishment of command structures was easier than the practical implementation of military cooperation. The fundamental challenge that would plague Chinese-American relations throughout the war emerged almost immediately: the problem of delivering military assistance to China. Before Pearl Harbor, the United States and China had reached agreement on three major points regarding military assistance. First, the United States would provide training and technical support to assist China in establishing a modern air force. Second, the United States would provide training and equipment for a ground army consisting of thirty divisions. Third, the United States would assist China in constructing railways and highways from Yunnan to Burma, along with the provision of rolling stock and vehicles to support these new transportation routes. These were ambitious plans, reflecting the extent of Chinese need and American willingness to support the Chinese war effort. However, the outbreak of the Pacific War threw American plans into disarray. The fundamental strategic doctrine guiding American policy was the "Europe first" strategy, which meant that the European theater and the defeat of Nazi Germany received priority over the Pacific theater. Additionally, China's geography made it extraordinarily difficult to supply. China could only be reached through supply lines of extraordinary length and complexity, either through the port of Rangoon in Burma and then overland via the treacherous Yunnan-Burma Road, or through the port of Hong Kong, which immediately fell into Japanese hands after Pearl Harbor. The first Sino-American loan agreement had specified that the United States would deliver commodities worth $45 million to China in 1941. However, shipping shortages meant that only $26 million worth of commodities actually left the United States. These commodities were shipped to the transit port of Rangoon, where a massive traffic jam developed as supplies accumulated faster than they could be distributed onward to China. Along the Yunnan-Burma Road, that "much-contested life line" as it came to be known, conditions were chaotic. The road had always been a source of trouble: it was long, extraordinarily difficult to keep in repair, fantastically dangerous to travel, and too narrow to permit much volume of traffic at any one time. Due to mismanagement and pilfering, the commodities that were shipped out of Burma suffered heavy losses in transit, and only one-third of them eventually arrived in Chongqing. Yet the United States had already established one valuable asset in China that could be deployed relatively quickly: an air force nucleus. Just before Pearl Harbor, a group of American aviators had been formally organized in Chongqing who worked on volunteer status for China under the command of Colonel Claire Chennault, an American aviation officer who served as instructor at the Chinese Air Force Cadet School. Some of these men had been flying in China since the Kuomintang first began purchasing American aircraft, having come to demonstrate the aircraft and remaining to pilot and teach. Others had hurried to China after being released from their volunteer service in Spain, where they had gained combat experience during the Spanish Civil War. It was a familiar joke that pilots from both sides of the Spanish conflict were likely to meet and work side by side in China, but political convictions proved immaterial to these professional aviators, they got on well enough with each other, mercenaries of the twentieth century united by their expertise and their willingness to fight. When American traffic on the Burma Road swelled in volume and became chaotic, with cargo lost to pilfering and trucks breaking down from mishandling, the air freight service into Chongqing became increasingly vital to maintaining Chinese military capabilities. The backbone of this organization was American, and it had been in operation for months before the Japanese attack. Halfway down the Burma Road, over the border in Burma, was a plane factory backed by American and Chinese capital, providing some degree of repair and maintenance capacity for the aircraft operating out of China. Recognizing the strategic value of these experienced aviators and the need to formalize and expand their operations, American and Chinese officials moved quickly to incorporate this volunteer group, formally known as the American Volunteer Group or "Flying Tigers," into the U.S. Army Air Force. This transformation of volunteer aviators into official American military personnel represented a commitment to providing air support to Chinese operations, recognizing that air power could compensate to some degree for Chinese numerical disadvantages in ground combat. In Chongqing, it was clear that among the United Nations fighting Japan, America was the only one taking China seriously, the only one prepared to invest significant resources in Chinese military capabilities and the Chinese war effort. Yet Pearl Harbor brought consequences that extended far beyond the strategic and diplomatic spheres. For Western civilians living in China,missionaries, teachers, doctors, businessmen, and journalists, the attack represented a catastrophic change in their status and their fate. Until December 8, 1941, Westerners residing in China had maintained a curious protected status as foreign neutrals, even in areas nominally controlled by the Japanese. This status had provided them with a measure of distance from, and protection against, the grinding reality of the war that had devastated Chinese populations. For a century, Americans had been part of an imperial presence in China, sometimes benevolent, sometimes violent, but always ultimately under Western control. That world came to an abrupt end on December 8, 1941. All across eastern China, Americans and Britons were rounded up and interned. The International Settlement of Shanghai—that remarkable oasis of neutrality that had persisted in the midst of a war-torn city—was now reunified under Japanese control. Thousands of foreigners with Allied nationality were sent to holding camps scattered throughout occupied China. Some, like the eleven-year-old British boy Jim Ballard, would remain imprisoned for the remainder of the war, enduring hunger, cold, and disease in camps like Longhua, which housed approximately 2,000 detainees. Forty years later, as the novelist J. G. Ballard, he would memorialized this experience in his semi-autobiographical novel Empire of the Sun. For missionaries like Velva Brown, the evacuation process was degrading and final. The missionary staff were permitted to pack no more than three suitcases with their belongings. As they prepared to leave, Japanese and Taiwanese "sightseeing" parties arrived to decide which houses they might want to occupy. The journey home aboard crowded Japanese ships offered no concessions to status or dignity; ambassadors, consuls, merchants, and missionaries alike were given "bed-sized strips of Chinese matting which we spread side by side on the iron floor of the hold," as Brown recorded. The humiliation was compounded by the finality of it all. For many, their decades-long presence in China was ending not in orderly retreat but in hasty evacuation, leaving behind "servants, many of whom had been with us for fifteen or twenty years." Despite these immediate human tragedies, the strategic transformation that Pearl Harbor wrought for China was undeniable. Chiang Kai-shek now embarked on an entirely new sort of life, heavily involved with the leaders and generals of the Western powers. He held diplomatic discussions with the leaders of the United Nations and military planning sessions with Western generals. In these unfamiliar circumstances, he did his best to live up to the Confucian ideal of the gentleman, seldom permitting the mask to slip—though the pressure of these new responsibilities and the intoxicating combination of vindication and anxiety would occasionally prove too much even for his considerable self-control. Within weeks of Pearl Harbor, Chiang demonstrated the value he could bring to the new alliance. He visited India and spoke with the leaders of the Indian independence struggle as an ally and friend, as a fellow non-European speaking to others seeking to escape from colonialism and imperial domination. He joined the Allies in their first joint campaign, not in China but in the jungles of neighboring Burma, committing Chinese forces to operations outside China's borders in support of the broader Allied military strategy. Yet the price that Chiang would pay for China's belated entry into the global alliance against the Axis powers would prove to be a heavy one. Chiang desperately needed his new American and British partners, but accepting this alliance would unleash forces within Chinese society that would threaten the very basis of his political control. The complexities of this bargain would become most visible in the four-year struggle between Chiang and General Joseph Warren Stilwell, the American officer who would become simultaneously his chief of staff and the administrator of American military assistance to China. This relationship, shaped by mutual suspicion, different strategic visions, and incompatible political agendas, would define Chinese-American military cooperation during the Pacific War and would have profound implications extending far beyond the battlefields of Asia. 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. Pearl Harbor's shock reverberated through Asia. Japan's assault on Hawaii, combined with invasions of Burma, the Philippines, Guam, and Wake Island, inadvertently transformed regional conflict into global war. Chiang Kai-shek, vindicated after four years of isolation fighting Japan, embraced American alliance with profound relief. The U.S. committed military aid, appointed General Stilwell as chief of staff, and recognized China as a co-belligerent. For Chiang, this partnership brought diplomatic status but also internal tensions that would define the Pacific War's final chapter.

We are just over the halfway point of the USC Trojans' fall camp. The season kicks off in two weeks, and the depth chart is beginning to come into focus. Here is my best guess at the defensive depth chart at this point. You can find my USC Trojans offensive depth chart on the Channel already. I will do another final depth chart installment at the end of camp, before the team releases the official one. Tune in and make sure to like and subscribe to the USC LAFB YouTube Channel! Become a member today and help support the USC LAFB Team! Become a member here: https://www.youtube.com/channel/UCZ3-rN0vKVT_XZVs-m6LXaw/join Join our USC LAFB Message Board for exclusive intel right here on YouTube: https://www.youtube.com/@USCLAFB/community Check out our USC Trojans LAFB Merch: https://lafbnetwork.myshopify.com/ Listen to our USC Football Trojans Podcast: https://podcasts.apple.com/us/podcast/usc-lafb-a-usc-trojans-show/id1602005638 Join our USC Trojans Message Board: https://www.lafbnetwork.com/forums/forum/usc-trojans/ Go to www.LAFBNetwork.com for FREE full access to all of our podcasts and join the community! Twitter: @RyanDyrudLAFB | @LAFBNetwork | @Tim_Prangley Lincoln Riley is the USC Trojans Football Head Coach for the 2026 College Football Season. The Trojans look to capitalize on an offseason full of momentum and improve their Big Ten play for 2026. Tune in for up-to-date USC Trojans news, opinion, and recruiting intel. Plus, film review, game previews and breakdowns, and our weekly LIVE LAFB Conquest Call-In Show every Thursday evening! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Travis Currah and Sheldon Jones preview Week 11 of the 2026 CFL season!   00:00 - Open 04:25 - Mailbag 16:07 - Sheldon's CFL Power Rankings 18:53 - Halfway point of the season 23:28 - BC Lions @ Calgary Stampeders  35:38 - Ottawa RedBlacks @ Winnipeg Blue Bombers   49:18 - Toronto Argonauts @ Edmonton Elks  01:01:05 - Saskatchewan Roughriders @ Hamilton Tiger-Cats   Become a YouTube member: https://www.youtube.com/channel/UCp1-WTbs82THRNHc-RQbCVA/join    2 and Out Merch: https://2-and-out-cfl.myspreadshop.ca/    Patreon: https://www.patreon.com/2andOutCFLPodcast 

Episode 278- Who, What, When, Where, Why, How? That types of questions do games ask us as players?  When should I score?  Who can I trust? Why did they do that?  How should I sequence this?  This week we dig into what the central questions are in different games and think about how this can help us as players and designers. Time Stamps4:30- Who? 18:00- What? 26:00- When? 34:00- Where? 42:00- Why? 54:00- How?   Games Mentioned Magic the Gathering, The Resistance,  Root, Castles of Burgundy, A Feast for Odin, Tag Team, Bus, Dominion, Medina, Ra, El Grande, Star Wars Rebellion, The Crew, Root, Zoo Vadis, Tikal, Bus Preplanners Email us or join our discord to submit questions for a future episode! Also we'll be doing a deep dive on Frosted Blooms soon!   Music and Sound Credits Thank you to Hembree for our intro and outro music from their song Reach Out. You can listen to the full song on YouTube here: https://www.youtube.com/watch?v=gQuuRPfOyMw&list=TLGGFNH7VEDPgwgyNTA4MjAyMQ&t=3s You can find more information about Hembree at https://www.hembreemusic.com/.  Thank you to Flash Floods for use of their song Palm of Your Hand as a sting from their album Halfway to Anywhere: https://open.spotify.com/album/2fE6LrqzNDKPYWyS5evh3K?si=CCjdAGmeSnOOEui6aV3_nA Intermission Music: music elevator ext part 1/3 by Jay_You -- https://freesound.org/s/467243/ -- License: Attribution 4.0 Bell with Crows by MKzing -- https://freesound.org/s/474266/ -- License: Creative Commons 0 hammer v2.wav by blukotek -- https://freesound.org/s/337815/ -- License: Creative Commons 0   Contact Follow and reach us on social media on Bluesky @decisionspace.bsky.social. If you prefer email, then hit us up at decisionspa@gmail.com. This information is all available along with episodes at our new website decisionspacepodcast.com. Byeee!

Halfway through 2026, it's time to look back at all the C-dramas I've watched so far and, more importantly, the ones that have stolen my heart!

Mac, the creator of Halfway Anywhere and the annual PCT Thru-Hiker Survey, joins the show to talk about hiking Corsica's GR20, how the Pacific Crest Trail has changed, and whether modern thru-hiking has lost some of its mystery.We compare our experiences on the GR20, including huts, snow, intense heat, crowded trails, French hiking culture and the unexpected luxury of buying sandwiches in the mountains. Then we dig into the evolution of the PCT, from paper maps and hitchhiking to FarOut comments, scheduled rides, heavier gear and thru-hikes that now average around $10,000 to $11,000.Mac also shares how Halfway Anywhere became his full-time work, what more than a decade of PCT survey data reveals about backpacking gear, why experienced adventurers have to work harder to find the unknown, and what he is planning next in Japan.Before the interview, I recap running an 18:52 5K in Crocs and explain why the weird goals nobody else understands can sometimes be the most meaningful.Learn more from Mac at Halfway Anywhere: https://www.halfwayanywhere.com/Support our Sponsors: Sawyer: https://sawyerdirect.net/Janji (code: Freeoutside): https://snp.link/a0bfb726CS Coffee: CSinstant.coffeeGarage Grown Gear: https://snp.link/db1ba8abSubscribe to Substack: http://freeoutside.substack.comSupport this content on patreon: HTTP://patreon.com/freeoutsideBuy my book "Free Outside" on Amazon: https://amzn.to/39LpoSFEmail me to buy a signed copy of my book, "Free Outside" at jeff@freeoutside.comWatch the movie about setting the record on the Colorado Trail: https://tubitv.com/movies/100019916/free-outsideWebsite: www.Freeoutside.comInstagram: thefreeoutsidefacebook: www.facebook.com/freeoutside#Trailrunning #Runningnews #Outdoors #Outdooradventure

0:00 - We're a little over halfway through Broncos training camp. What have we seen so far? THANKFULLY there's only been one season-ending injury **knock on wood** How are we feeling about the team? Also, welcome back Kaner.14:59 - Sean Payton and several other Broncos coaches and players attended Drew Brees' HOF induction ceremony this weekend in Canton. In his induction speech, Brees praised Peyton and his insane football knowledge and overly meticulous preparation. The relationship between your head coach and QB makes all the difference.32:55 - The Buffalo Bills unveiled their new stadium this weekend, and the crowd went...mild. It's a very average/underwhelming stadium for how much money they poured into it. 

A sermon by the Rev. Canon George Maxwell on the Eleventh Sunday after Pentecost (August 9, 2026) at the Episcopal Cathedral of St. Philip, Atlanta

Which players have adapted to Roberto De Zerbi's system and which areas still need work? Professional coach and A licence holder JJ breaks down the Getafe friendly and what it tells us about Spurs' pre-season. The vertical play is back, Kinsky is clipping balls over the press, and Archie Gray got a live coaching lesson from De Zerbi on the touchline. But the creativity in the final third? Still a work in progress. Chapters: 00:00 What's working in De Zerbi's pre-season? 00:30 Vertical play returns — Spurs go down the middle 02:38 Kinsky playing over the press 04:03 The goal, broken down: Tonali to Gallagher 07:45 Tonali isn't Pirlo — so what is he? 10:16 Van Hecke's aerial surprise 11:40 Halfway verdict: three big ticks 12:27 Archie Gray and the De Zerbi touchline moment 15:02 Defensive distances: no more open motorways 16:49 The big area of improvement: final-third creativity 19:36 What to watch in the next friendly Follow JJ (The Fullco) for more coaching content — channel linked above. What did you spot in the Getafe game that we missed? Tell us in the comments. #KeepItLilyWhite #OhSo33 Learn more about your ad choices. Visit podcastchoices.com/adchoices

Show #496 “La Sigua Cigars And Overlooked American Whiskey" (Halfway to 500!) Special Guests: Mark And Brian from La Sigua Cigars Beverage Of Mystery ?????? Go Brewing Salty Lime AF Chilada Smoke Anything Interesting? La Sigua Cigars Beer Tasting: Charro Brewing Company Mexican Pilsner (Mexico City, Mexico) Beer Tasting: Saloon Door Brewing "Crank Dat AC" Texas Coast IPA (Webster, TX) Beer Tasting: Evil Twin Brewing "Great Northern Series 58" Barrel-Aged Barleywine (New York, NY) Spirit Tasting: Four Roses (Kentucky) DRINKING NEWS: “Do NOT Invite Great-Grandpa To Thanksgiving Dinner..." DRUNKEN FACT: “I'm Sorry...You Want Me to Cut Off WHAT?..." ---Our Smokin' & Toastin' OH Ingram Barrel Pick Is Almost Here!!---

Join insiders Joel A. Erickson and Nathan Brown as they recap the first of Indianapolis Colts training camp.

We build durable cyclists. New performance videos every week on YouTube:

Episode 277- Deckbuilding 301 and Slay the Spire Trick-taking wiz Taylor Reiner joins Pete, Jake, and Brendan to take the next step on our deckbuilding exploration.  This time we're going digital!  How do games like Balatro and Slay the Spire build on physical games and where do they diverge?  Also how are we seeing the reverse influence on tabletop games? Check out Taylor's amazing Youtube Channel: https://www.youtube.com/@ClaudeAndTaylor Time Stamps 4:00- introduce Taylor Reiner 9:30- digital card games 21:30- deckbuilding roguelikes 46:45- Slay the Spire  1:26:15- Balatro 1:32:00- digital games inspiring tabletop design   Preplanners I actually don't know what the next episode will be about!   Music and Sound Credits Thank you to Hembree for our intro and outro music from their song Reach Out. You can listen to the full song on YouTube here: https://www.youtube.com/watch?v=gQuuRPfOyMw&list=TLGGFNH7VEDPgwgyNTA4MjAyMQ&t=3s You can find more information about Hembree at https://www.hembreemusic.com/.  Thank you to Flash Floods for use of their song Palm of Your Hand as a sting from their album Halfway to Anywhere: https://open.spotify.com/album/2fE6LrqzNDKPYWyS5evh3K?si=CCjdAGmeSnOOEui6aV3_nA Intermission Music: music elevator ext part 1/3 by Jay_You -- https://freesound.org/s/467243/ -- License: Attribution 4.0 Bell with Crows by MKzing -- https://freesound.org/s/474266/ -- License: Creative Commons 0 hammer v2.wav by blukotek -- https://freesound.org/s/337815/ -- License: Creative Commons 0   Contact Follow and reach us on social media on Bluesky @decisionspace.bsky.social. If you prefer email, then hit us up at decisionspa@gmail.com. This information is all available along with episodes at our new website decisionspacepodcast.com. Byeee!

Your best people rarely quit in a dramatic moment. They leave a little at a time, and by the time they give notice the decision was made weeks ago. Gallup ties about 75% of voluntary turnover to the manager, and our own national research found that 35% of workers open to manufacturing have never had a single one-on-one development conversation with their manager. You can't keep people you never really talk to. Darcy Eikenberg, executive coach and author of Red Cape Rescue: Save Your Career Without Leaving Your Job, joins Trevor to turn the retention problem around: instead of replacing the people who leave, how do you help a good employee rescue the job they already have? A practical look at employee retention and manufacturing team leadership for plant managers, operations leaders, and frontline supervisors tired of watching good people walk out the door.

Halfway through 2026, what's actually moving the needle in marketing and what's just noise? David Kryszczak, Founder & President of Spartan Digital, joins the show for a mid-year reality check on AI, search behavior, and where trades businesses should be spending (and not spending) their marketing dollars. From AEO and "robots tricking robots" to the return of direct mail and the truth about AI voice agents, David breaks down what's working, what's hype, and how to keep your business visible no matter how fast the algorithms change.

Send us Fan MailYou know that feeling when an older anime comes on and your shoulders drop? We start with that exact vibe and pull the thread hard, asking why classic anime from the 80s and 90s can feel more relaxed, more rewatchable, and somehow easier to live inside, even when modern anime is visually stunning. From You're Under Arrest to Yu Yu Hakusho and Cowboy Bebop, we talk about pacing, character time, and the kind of storytelling that lets you chill without worrying you missed a crucial plot beat.We also get honest about the modern side: Demon Slayer and Attack on Titan look incredible, but the speed and intensity can make them harder to treat like comfort anime. We dig into attention spans, how anime got more mainstream, and what it means to write for a younger, wider audience. Then we ask the long-game question that always starts arguments: decades from now, which current hits will actually get rewatched as nostalgia classics, and which ones will fade when the hype passes?Halfway through, the nostalgia jumps mediums and lands in gaming. We vent about Bethesda priorities, react to an Xbox “Project Helix” leak around Xbox 360 backward compatibility and a possible disc-to-digital feature, and talk about why going all-digital still feels risky for players who care about ownership. And because we cannot resist a good memory, we close with Halo first impressions and a launch-night story that captures what gaming used to feel like when releases were events.If you've been stuck rewatching older shows, or you're trying to figure out why new anime doesn't hit the same, this one's for you. Subscribe, share the episode with a friend who loves classics, and leave a review. What's your comfort anime that never gets old? Support the showhttps://www.carolinaotakus.com/

Send us Fan MailWe've officially passed the halfway point of the 2026 WNBA season and it's since we've missed a week, we have A LOT to discuss! We go through each team discussing news before checking the standings for risers and fallers. 0:10 - Agenda1:17 - Raegan Pebley fired24:10 - Jovana Nogic suspended for the season27:00 - Napheesa Collier is BACK32:00 - Aces sign Mai Yamamoto33:10 - Sandy Brondello gets suspended47:50 - Sabally sister's injuries 53:55 - Betnijah Laney-Hamilton gets her 5th DNP56:24 - Dallas' 6-game win streak & Paige Bueckers' injury58:05 - Angel Reese's injury + Bri Jones BACK + Jaylyn Sherrod signed1:02:49 - A Fever rundown: Caitlin, Kelsey, Sophie1:07:05 - Caitlin (& Angel) vs the officiating1:16:00 - Aneesah Morrow might get traded &  Leila Lacan1:18:15 - Meg (Gustafson) DiLeo is married!1:19:05 - Olivia Miles is a mean girl1:21:40 - Risers: Valkyries, Dream, Mystics, Lynx, Fever1:32:30 - Fallers: Liberty, Toronto1:35:39 - Chicago Sky needs to close out games1:42:43 - It's over for the Phoenix Mercury1:46:00 - Who's blowing it up before the trade deadline?1:59:20 - who's winning the three point contest?2:06:50 - Who are the shooting stars??https://linktr.ee/pullup3 | Distributed via SteadyHype Studios

Halfway through the year feels like the perfect moment to pause, reflect, and ask: What have we learned? Before taking their summer break, Karen and Cecilia look back at the conversations that have stayed with them most and the ideas that challenged their thinking throughout the first half of the year. Together, they discuss: Conversations that surprised them Topics that guests kept bringing up Learnings that stayed with them They also give you a sneak peek at what's coming in September, including conversations on reconnecting with yourself at work and redefining career ambition. Tune in for a quick catch-up and hear Cecilia's and Karen's take on the first half of the year on the podcast

DJ Joe Mfalme Presents #TheDoubleTrouble 116 – MID YEAR BANGERS 2026 Halfway through the year, and we're running back the biggest bangers of 2026 so far! From the tracks taking over the clubs to the anthems running the streets and playlists, Mid Year Bangers brings nonstop energy from start to finish. Turn it up. No skips. Just BANGERS. Shot on location at XADO East Africa Ltd by Actionpac Media. Like | Comment | Subscribe | Share Catch my other mixes here Audio Links :- - https://www.mixcloud.com/DjJoeMfalme/ - https://soundcloud.com/dj-joe-mfalme-mixxtapes - https://hearthis.at/dj-joe-mfalme/ Video Links :- - https://www.youtube.com/c/DJJoeMfalme/videos Download Links :- - https://deejayjoemfalme.com/index.php/mixes #DJJoeMfalme #TheDoubleTrouble116 #MidYearBangers #MrSherehe #DJMix

DJ Joe Mfalme Presents #TheDoubleTrouble 116 – MID YEAR BANGERS 2026 Halfway through the year, and we're running back the biggest bangers of 2026 so far! From the tracks taking over the clubs to the anthems running the streets and playlists, Mid Year Bangers brings nonstop energy from start to finish. Turn it up. No skips. Just BANGERS. Shot on location at XADO East Africa Ltd by Actionpac Media. Like | Comment | Subscribe | Share Catch my other mixes here Audio Links :- - https://www.mixcloud.com/DjJoeMfalme/ - https://soundcloud.com/dj-joe-mfalme-mixxtapes - https://hearthis.at/dj-joe-mfalme/ Video Links :- - https://www.youtube.com/c/DJJoeMfalme/videos Download Links :- - https://deejayjoemfalme.com/index.php/mixes #DJJoeMfalme #TheDoubleTrouble116 #MidYearBangers #MrSherehe #DJMix

2026 is officially halfway over — and if you're a dentist or dental practice owner, that means it's time for an honest mid-year check-in. In this episode of The Lifestyle Practice Podcast, Matt Vogt breaks down the real difference between dentists who are winning in 2026 and dentists who are losing, based on patterns he sees across practice ownership, dental practice management, and everyday life. Dr. Matt shares three signs you're losing this year: being reactive to every up and down in your dental practice, blaming your team or "bad luck" instead of taking ownership, and coasting through your career without intention. Then he flips it — the three signs you're actually winning: staying the course through the ups and downs of practice ownership, taking extreme ownership of your results (including how you handle a tough Google review or patient complaint), and leading your day, your team, and your practice with real intention instead of drifting. Whether you're a general dentist, a specialist, or a practice owner working on treatment acceptance, staffing, or burnout, this episode is a reset for the second half of 2026. If you want to build a practice and a life that actually feel like a win — not just look like one — this one's for you. Connect with us: Take our FREE lifestyle and practice assessment: https://thelifestylepractice.com/practice-assesment/ Learn more about 1-on-1 coaching: https://thelifestylepractice.com/coaching-services/ Get access to TLP Academy: https://thelifestylepractice.com/coaching-services/ Get the TLP Student Academy for $20 (lifetime access): https://the-lifestyle-practice.teachable.com/p/studentacademy Subscribe to The Lifestyle Practice Podcast: https://podcasts.apple.com/us/podcast/tlp-podcast-for-dentists/id1476544801 Email Derek at derek@thelifestylepractice.com Email Matt at matt@thelifestylepractice.com Email Steve at steve@thelifestylepractice.com 

Watch the full episode on YouTube:We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3:And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF:Three years ago, inference engineering barely existed as a category.Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem.In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out.Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles.In this episode, Baseten's Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API.We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model.The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them.We discuss:* What happens when a 200,000-token request enters an inference system* Cache-aware routing and reusing previously computed KV cache* Why prefill and decode are increasingly handled by different GPUs* When dedicated deployments become cheaper and more reliable than shared APIs* How speculative decoding uses a smaller model to accelerate a larger one* Tool calling, structured outputs, and what LLMs actually do* What it takes to support a new open model on day zero* Grafting Kimi's vision encoder onto GLM-5.2* Retrofitting inefficient model layers with components from other architectures* Why models sometimes collapse into repeating the same token* How hardware, kernels, and race conditions create nondeterministic failures* Preserving model fidelity while making inference faster* How quantization errors can cancel each other out* Why inference optimizations still deliver gains of 20%, 100%, and 200%* How optimized serving can make a model up to 10× faster* NVIDIA Dynamo, KV-aware routing, and distributed model serving* Speculative decoding the speculative decoder* Why local AI is about making models less dumb while data-center AI is about making them less slow* Tensor, expert, and pipeline parallelism across GPUs* Hardware-aware model design, auto-tuning, and the case against mega kernels* Rubin and why inference is becoming a systems problem* Whether modern GPUs are evolving into programmable AI ASICs* Why enormous models like Kimi K3 require GB300-class hardware* Why open-source video generation still trails Veo, Kling, and other closed models* The quadratic attention bottleneck behind long-form AI video* Autoregressive video, real-time generation, and compounding quality drift* Why future video systems may combine autoregressive and diffusion architectures* Training for inference and inference for training* Continuous post-training, deployment, evaluation, and improvement loops* How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself* Why faster networking could unlock dramatically faster decoding* Continual learning, KV-cache compaction, and persistent model memoryShow Notes* How to build a day-0 API for Kimi K3* 22580: From GPT2 to Kimi3, ExplainedPhilip Kiely* LinkedIn: https://www.linkedin.com/in/philipkiely* X: https://x.com/philipkiely* Inference Engineering: https://www.baseten.co/inference-engineering/Ali Taha* LinkedIn: https://www.linkedin.com/in/aliestaha/* X: https://x.com/waterloointernTimestamps00:00:00 Introduction and the 200K-Token Prompt00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling00:11:26 Launching Production-Ready Open Models00:19:06 Model Retrofits, Failure Modes, and Nondeterminism00:28:22 Quantization and Canceling Errors00:32:15 The Race to 10× Faster Inference00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips01:10:03 Giant Models and the Limits of GPU Memory01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation01:21:47 Audio, Images, and Diffusion Models01:27:32 Training, Self-Optimizing Models, and Continual Learning01:40:06 Closing ThoughtsTranscriptIntroduction: Baseten, Waterloo Intern, and Inference EngineeringSwyx [00:00:00]: Okay, we're here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you've done, you and I have done before, as well as Ali. Welcome.Ali [00:00:15]: Pleasure to meet you.Swyx [00:00:15]: Waterloo intern.Ali [00:00:16]: Waterloo intern, always.Swyx [00:00:17]: When did you get “Waterloo intern” as a handle?Ali [00:00:19]: As a handle? Oh.Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.”Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer.Philip [00:00:30]: So we have to figure out who's gonna get the handle.Ali [00:00:33]: Well, I'll pass the torch over to the next intern.Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad.Ali [00:00:37]: To another Waterloo intern. No, bruh.Philip [00:00:39]: Yeah.Ali [00:00:39]: Intern.Swyx [00:00:40]: Intern, yeah.Ali [00:00:40]: And no.Philip [00:00:41]: You gotta get an intern from Waterloo.Ali [00:00:42]: Yeah, I've gotta get an intern from Waterloo.Swyx [00:00:44]: Right.Ali [00:00:44]: But they have to follow the path.Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it's like whoever Baseten gets from Waterloo.Ali [00:00:48]: Right.Swyx [00:00:49]: Has the title of Waterloo.Ali [00:00:50]: It stays in the ecosystem.Philip [00:00:51]: Exactly.Ali [00:00:52]: Halfway through the internship, you either get it or you're out.Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle.Ali [00:00:59]: Just say it.Philip [00:00:59]: For everybody.Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you're an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten's inference? What's the process of query through GPU model routing, balancing, all that? What is all the stuff that we don't think about?Long Context Requests, KV Cache, and Cache-Aware RoutingPhilip [00:01:26]: With a long query specifically, the first thing that I'm gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it's gonna be a lot easier for me and a lot cheaper for you. So the first thing that we're gonna look at is some cache-aware routing, where we're going to see, we probably have a number of instances, a number of replicas up serving whatever model you're hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you're doing two hundred thousand tokens, it's probably coding or a multi-turn agent or something where you would expect to have that cached. If you don't, we're gonna have to send it to a prefill worker. We've at least on certain models disaggregated prefill and decode, so you're going to have one set of GPUs that's solely going to process the input, create the KV cache, and get you your first token, and then that's going to be passed over to a separate set of GPUs, which is going to run decode. We're going to iteratively make those tokens. We're probably going to have some speculator model in front of that. I'm going to assume that you're doing coding, and because of that, our speculator model, which assumes you're doing coding, is gonna have a high draft token acceptance rate. If I'm wrong and you're asking me to summarize every Harry Potter book, it's gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?”Swyx [00:03:04]: Except Baseten doesn't charge by pennies.Philip [00:03:07]: Well, yeah, we charge. I'm assuming that we're talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it's not pennies.Public APIs vs. Dedicated DeploymentsSwyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it's up to you to figure out how to saturate the box.Ali [00:03:31]: And more often than not, it's, like, way cheaper if you're pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token.Philip [00:03:37]: Yeah, they do. I think that we've increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that's really sticky, then they move over to dedicated.Swyx [00:03:51]: Is there a best practice on when it's time to swap over?Philip [00:03:54]: Couple reasons. Yeah, reliability, that's a big one, right?Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic.Swyx [00:04:04]: Spec dec is speculative decoding.Speculative Decoding and Custom SpeculatorsAli [00:04:05]: Speculative decoding, yeah.Swyx [00:04:07]: You have to explain.Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you're summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I'm gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn't be able to provide this to you if you're a shared endpointSwyx [00:04:53]: YeahAli [00:04:53]: ‘cause I have no idea if you're doing Harry Potter, if you're doing coding, if you're doing English. We don't know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that?Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you're trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn't pass your benchmarks and you wanna run a model at higher precision, you could do that. There's just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don't have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users.Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it's people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you're generating JSON or is there more complication beyond that?Tool Calling, JSON, and Structured OutputsAli [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that's not just, like parse a file or go find the weather. It's something that's very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn't require its own like sandbox. It's not like it's going to use that tool calling to like escape a sandbox or like it doesn't have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you're dealing with all of the JSON outputs, if it doesn't like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn't see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model.Philip [00:06:56]: Yeah, that's a challenge on the training side and then on the inference side, there's work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember backSwyx [00:07:27]: Yeah, the specific grammar is,Philip [00:07:29]: Yeah, exactlySwyx [00:07:30]: GML had this thing.Philip [00:07:31]: Yeah. So it's like the old-school “make sure this is only JSON”, return only JSON orSwyx [00:07:38]: YeahPhilip [00:07:38]: Grandma's gonna die type of prompts.Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR.Philip [00:07:47]: In our inference system, it's just a specified output format. And you get the guarantee that your output's gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn't solve the certainty problem but it at least solves the output structuring problemSwyx [00:08:10]: YeahPhilip [00:08:10]: Within tool calls.Swyx [00:08:12]: And MCP is just another form of tool, right.Philip [00:08:14]: Yeah, exactly.Swyx [00:08:15]: As far as there's no special thing there.Philip [00:08:16]: The thing I'm always like explaining to people is the LLM is not capable of doing anything. It's only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs.Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you're right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don't know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don't have the same exact quality outputAli [00:08:56]: Right.Swyx [00:08:57]: When you just swap from a big model, right?Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper.Ali [00:09:04]: But, I had expected that something would replace JSON because it's hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it's hard to parse something or validate something while it's being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it's something like TOML, something like YAML. But JSON seems to be dominant still.Philip [00:09:30]: The JSON outputs aren't that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it's a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn't be as valuable, but maybe I'm wrong about that.Ali [00:10:02]: I think you're also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you'- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn't be that much of a difference. Also more profitable if it outputs more tokens probably.Swyx [00:10:25]: Depends on your business model.Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there's paragraphs in every field because I'm trying to structure it, right?Philip [00:10:44]: Right.Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let's, let's recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there's a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let's call it GLM-5.2, Kimi K3. I had previously assumed, especially if it's like, well, GLM 5 to 5.1 to GLM-5.2, like that you've supported them before. Is it that much work?What It Takes to Support a New Open ModelAli [00:11:26]: It's a lot of work.Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I'm like, “Yeah, of course we support it.” But what goes into that? What goes intoPhilip [00:11:40]: I think it's more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we're at 150. The nextSwyx [00:11:55]: I kinda kicked that off with the GLM-5.2.Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views,Ali [00:12:02]: Based on being numberSwyx [00:12:03]: YeahAli [00:12:04]: Or it's for something else.Swyx [00:12:05]: Yeah. Which,Ali [00:12:06]: Oh my GodSwyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and,Philip [00:12:14]: There's a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model.Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there's going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar.Quantization, Speculators, and Production ReadinessAli [00:13:16]: Yeah. It was pure continued post-trainingPhilip [00:13:18]: YeahAli [00:13:18]: If I remember correctly.Philip [00:13:19]: Even in those cases, there's still stuff you have to do. You have to redo the quantization work. You're taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we're not causing any regression in the model's intelligence. And then we also have to train the speculator, as we've talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don't know exactly the traffic that people are sending us, but we know what's popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you're getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there's that process which you need the real model weights for. And then there's of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there's a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 hadAli [00:14:53]: Sparse attention.Philip [00:14:54]: Yeah,Ali [00:14:54]: YeahPhilip [00:14:54]: the DSA.Ali [00:14:55]: Right. Which is brought from DeepSeek.Philip [00:14:57]: Yeah. AndAli [00:14:59]: So you can copy-paste then?Philip [00:15:01]: It kindAli [00:15:01]: I don't know how this works.Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you're right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn't have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2.Retrofitting Vision into GLM-5.2Ali [00:15:27]: We'll be training the projector.Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there's the encoder, which is the part that looks at the image and turns it into latent information, and then there's the projector which likeAli [00:15:38]: You can say latent space. It's okay.Philip [00:15:41]: And then there's the projector that maps it onto, the model itself, and then there's the model weights. You don't wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters.Ali [00:16:02]: That would be, yeah.Philip [00:16:02]: Yeah.Ali [00:16:03]: Can you show the training one?Ali [00:16:04]: Like the way it groksPhilip [00:16:05]: YeahAli [00:16:06]: Very interesting.Philip [00:16:06]: And maybeAli [00:16:07]: That right therePhilip [00:16:07]: Maybe Ali, you should take it from here. You've got a betterAli [00:16:10]: Ooh, double the sandPhilip [00:16:11]: Understanding of this than I do.Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here's a picture of a mountain. Can you describe what's in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we're trying to teach it is to translate the encoded. Like it's already taken the encoder from Kimi K. It's taken the image. It'Philip [00:16:31]: Yeah. FrozenAli [00:16:31]: FrozenPhilip [00:16:32]: With adapter.Ali [00:16:32]: Exactly.Philip [00:16:33]: Yeah.Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It's just we're tryingPhilip [00:16:37]: AlignAli [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he's like, “Oh, can you describe what's in this image?” And he's like, “Oh, it's a mountain,” or it's a person or it's a human, whatever the case is. But that didn't cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn't perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn't get it, but it will say something like, “This is Albert Einstein.” Like it still understandsPhilip [00:17:25]: Close enoughAli [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that's like really cool.Philip [00:17:32]: Yeah. So, we've covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that's very foundational work for anyone who hasn't done vision work before.Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questionsPhilip [00:17:47]: RightAli [00:17:47]: Off the image and how much better you can get performance.Philip [00:17:50]: Right. Right. Right. Yeah. But what's, what's so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It's not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you're running this model, you haven't suffered any loss on your GLM-5.2 quality. If you don't have an image, it'll just behave exactly the way it used to. And ultimatelyAli [00:18:14]: Which in the inference code you literally do not include the other part, right?Philip [00:18:18]: Yeah. You would just skip the encoder if you don't have an image input.Ali [00:18:22]: Okay.Philip [00:18:22]: Just confirming.Philip [00:18:23]: YeahAli [00:18:23]: Does it affect a lot on the overall inference side? Like you're not adding much, you're adding a very small vision encoder. These are typically likePhilip [00:18:30]: They're super fineAli [00:18:31]: Less than a billion parameters, right?Philip [00:18:32]: Yeah. It's, - There's a little bit less standardization among vision encodersSwyx [00:18:37]: YeahPhilip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it's a pretty, it's a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model.Open Source Model Grafting and Franken-MergesPhilip [00:18:56]: And that's, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that's better than anyoneSwyx [00:19:05]: YeahPhilip [00:19:05]: Can be individually.Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take likePhilip [00:19:10]: YeahSwyx [00:19:10]: Layers from each model.Swyx [00:19:11]: Does anyone do that anymore?Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you're doing auto-regressive token generation for three tokens, and you're doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it's not sparse, it's not top K. So we find it better to like, okay, we're gonna replace this, we're gonna replace this layer with a layer from another model that's using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That's like, I feel like more and more becoming true.Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready?Loop Detection, Race Conditions, and Non-DeterminismPhilip [00:20:26]: Yeah. I think that there's also a question of just, we can test a model to a pretty extensive degree, but we're trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there's going to be, so many more varieties of things given to it that you're able to, discover and patch things. So it's not just a, day zero process, it's then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance?Ali [00:21:21]: What do you mean you don't want your model outputting S?Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising.Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it's four times the same token, it's probably collapsed.Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that?Ali [00:21:48]: You want that?Ali [00:21:50]: I think there's a way that we have to handle it. I'm not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there's a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters.Swyx [00:22:07]: Yeah.Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2Swyx [00:22:11]: OhAli [00:22:11]: And I think it was DSV 4 as well. Like you'd just have like looping issues where like you literallySwyx [00:22:17]: ItAli [00:22:17]: Just have like S.Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomlyAli [00:22:21]: It just seems to be the one token involved.Swyx [00:22:23]: Yeah. And it'Philip [00:22:24]: Is thereSwyx [00:22:24]: And it's only temperature 0Ali [00:22:27]: NoSwyx [00:22:27]: Even at other temperaturesAli [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse.Swyx [00:22:30]: That's weird, right?Ali [00:22:30]: It's, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we'll find that it fixes it. Or oftentimes this will only happen in an inference engine that you're using like SGLang. But if you were to switch to vLLM, that isn't the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It's not like a weights problem. Like I'- we'll say like, “Oh, it's a problem with the quant. We did PTQ wrong,” right? But that isn't, that doesn't make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it's, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem.Swyx [00:23:19]: Oh my God.Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn't. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We're gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware?Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right?Ali [00:23:46]: Right.Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won't always get the same output.Swyx [00:23:52]: Even-- But I'm surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order.Ali [00:24:02]: Well, yeah, true. Like I'm not, I'm not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that's like ‘cause you want to do that because there'sSwyx [00:24:12]: It's like pipeliningAli [00:24:12]: Expense. Exactly.Swyx [00:24:13]: Yeah.Ali [00:24:13]: But it'- But you don't do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you're designing a kernel and you want it to make it to be very fast, if you don't test it extensively, you'll, you'll have certain threads access data points from registers before they've been written to by other threadsSwyx [00:24:36]: YeahAli [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, andSwyx [00:24:42]: And there's no like borrow checkerAli [00:24:45]: What does that mean?Swyx [00:24:46]: Like Rust. Like the. If you're trying to have like memory safety It sounds like a comparable problem.Ali [00:24:52]: Well, yes, but you're working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that's what modular is supposed to do. I don't know.Quantization Quality and Vendor FidelityVibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoderAli [00:25:07]: RightVibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarksAli [00:25:22]: YeahVibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes intoPhilip [00:25:27]: There's a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you're preserving all the outliers. There's other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what's gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn't need the full million token context, for example, you can get them better performance. I don't know if that's exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model.Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it's getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking hereAli [00:27:41]: YesPhilip [00:27:41]: Where they haveAli [00:27:42]: They released an actual vendor benchmark.Philip [00:27:43]: Exactly, yeah.Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi's benchmark.Philip [00:27:50]: Yeah.Philip [00:27:51]: So, with Reflect we probablyVibhu [00:27:52]: This was a long time ago, right?Philip [00:27:54]: No.Ali [00:27:54]: Yeah, like threeVibhu [00:27:55]: They alsoAli [00:27:55]: Four, five months agoVibhu [00:27:57]: This also happened with, I don't remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have beenPhilip [00:28:03]: Kimi Vendor Verifier.Ali [00:28:04]: Yeah.Philip [00:28:05]: Yeah.Ali [00:28:05]: Yeah, ‘cause you, ‘cause you'd be pissed, right? Like if you'Philip [00:28:07]: Yeah.Ali [00:28:07]: If like if I'm a consumer and I'm using like Amazon's endpoint for instance, and I've used Kimi and I'm like, “Oh my God, like this is bad,” I'm not gonna say, “Oh, Amazon quantized the model in a bad way.” I'm gonna say, “Oh, Kimi sucks.” Right?Philip [00:28:17]: Yeah.Ali [00:28:17]: So it seems like that makes sense.Philip [00:28:19]: Yeah, they care. They care.Vibhu [00:28:21]: Justifiably.Ali [00:28:21]: Yeah, justifiably.Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization?Philip [00:28:28]: Yeah.Vibhu [00:28:28]: Like, is quantization always strictly worse?Ali [00:28:30]: Well technicallyVibhu [00:28:32]: NoAli [00:28:32]: It's a lossy. QuantizationPhilip [00:28:33]: YeahAli [00:28:33]: Is a lossy, it's a lossy implementation.Philip [00:28:36]: Speed improvesVibhu [00:28:36]: Speed improves.Ali [00:28:37]: It the number, likeVibhu [00:28:38]: No, I' always look for inverse scaling laws.Philip [00:28:40]: Yeah.Ali [00:28:40]: Yeah.Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do.Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your,Ali [00:28:52]: YeahPhilip [00:28:52]: NVFP4 quant is like, two basis points higher than yourAli [00:28:56]: No, it's noise. It's noise.Philip [00:28:57]: Yeah, exactly. I'm like, yeah, it's, it's within. That's why I always say within margin of error.Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we're barely inside of that to the worst, so we're saying. But yeah, sometimes it's just like, gives you a higher output score. But like Ali said, that's noise. To my knowledge, you're not necessarily making the results better. You're just trying to, again, like keep your fidelity as close to 100% to the original model.Layer Selection, KL Divergence, and Better QuantizationAli [00:29:27]: There is, to your point, research that we did on MP. I don't know if you are able to pullPhilip [00:29:31]: YeahAli [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it's a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It's. You're compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you're losing some information, and you're trying to minimize that. And so when I say that I'm gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don't quantize modulation layers, and I don't quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn't have the. Yeah. It's a long paper. I don't know if I can findVibhu [00:30:25]: If there's a part to search or it's probably in the thread.Ali [00:30:28]: It's probably in the thread.Vibhu [00:30:29]: Yeah.Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that's 20% more quantized than another provider, so you get 20% more throughput of it because there's more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you're probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it's gonna be, ‘cause the more loss you introduce. That's not exactly, not necessarily true. So yeah, doesn't improve it, but can cancel out.Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers.Philip [00:32:03]: But very interesting. Didn't know this was a whole paper you guys put out.Ali [00:32:06]: It's. Fun fact, it was originally 72 pages, this paper, and then we decidedPhilip [00:32:11]: WowAli [00:32:11]: We can't tell. We couldn't release it. So it's now 45.Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what's possible in terms of speedup? Like it's like probably like the numberInference Speedups and BenchmarkingSwyx [00:32:25]: Thing that people do wanna care about, and it's something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing?Philip [00:32:36]: So what's cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you're in finance, you measure how much better you got in basis points. It's like, “Oh, I got five basis points better, like twentieth of 1% better,” that's huge news because everything is so optimized. When we publish optimizations, it's 20%, it's 100% it's 200%. So there's still probably like a lot further to go, honestly. Like you'll, you'll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something.Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would findAli [00:33:27]: And like 20%, tens of percent.Swyx [00:33:29]: That's. Yes.Philip [00:33:29]: Yeah.Swyx [00:33:30]: And now it'Philip [00:33:31]: Tiny fractionsSwyx [00:33:32]: For those people interested, look up Andrew Lo's paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool.Philip [00:33:48]: Exactly, and we're at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there's so many variables that go into it. What hardware are you using? How much load do you have on the system? What's the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you're looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there's two tokens per second. There's tokens per second, the throughput number, and the latency number.Ali [00:34:31]: TTMT, yeah.Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don't.Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that's an 8X gain. That's the order of magnitude that we're working with in this space. We're trying to make things substantially faster, not just go from like 70 to 90.Swyx [00:35:38]: Are you saying you've. You have done that?Philip [00:35:40]: So let's say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you're just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you're, you're probably, yeah, looking at that like 30 to 40. You think that's like a reasonable baseline?Swyx [00:36:12]: Right. Right.Philip [00:36:12]: To get to something like 10X, there's a lot of trade-offs that you're making. If we're running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It's oftentimes maybe more of a four to six times improvement. But that's the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens.Stacking Optimizations: NVFP4, Speculation, and DisaggregationAli [00:37:19]: It's also, like, hardware dependent. Like, ifPhilip [00:37:20]: YeahAli [00:37:20]: If you have a thing where you're serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs.Philip [00:37:35]: Yeah. Then you're looking at, like, a two to 4X improvementAli [00:37:38]: Right. RightPhilip [00:37:38]: Depending on the inference optimizations. So yeah, it's. Some of it's, what's the call, and some of it's who's the driver.Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2Ali [00:37:51]: YeahVibhu [00:37:51]: On B200sAli [00:37:53]: YeahVibhu [00:37:53]: Single node, right? What's, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right?Ali [00:38:01]: Spectre quantization. Yeah.Vibhu [00:38:03]: Spectre quantization.Ali [00:38:04]: That's, that's, that's like 95%. LikeVibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it?Philip [00:38:23]: If you're doing it up front, it's quite a lot of work. If you're doing it today, there's going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we're thinking about, like, what are the 2Xs we're stacking, going from, BF16 to NVFP4 is, it's not quite a 2X, right? It's like. I think it's about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn't quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you're able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that's how it stacks up.Ali [00:39:21]: YeahPhilip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they're doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you haveAli [00:39:39]: Once set up. Once set up. YeahPhilip [00:39:40]: Yeah, getting disagg working for the first time, I'm saying, of course, is very difficult.Philip [00:39:44]: The marginal implementationAli [00:39:48]: Like, if you're just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you're wondering, “How can I just host it myself?” You don't need to quantize the model yourself. There's always gonna be, like, an open source quantized checkpoint. NVIDIA's gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they've trained as well. You don't need to train your own spec dec. You can just use that as well.Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP.Ali [00:40:13]: Right. Right.Vibhu [00:40:14]: What's multi token prediction?Philip [00:40:15]: Yes.Ali [00:40:16]: I'm justVibhu [00:40:16]: Can you explain that?Ali [00:40:16]: I'm just an expert.Ali [00:40:18]: I can do it for you in case I get it wrong?Vibhu [00:40:20]: No.Vibhu [00:40:21]: Yeah, you should correct if we're wrong, but their multi-token prediction can be used for self-speculative decoding.Ali [00:40:27]: I'm not sure. I'm not gonna correct that.Vibhu [00:40:28]: Okay. I'm semi-confident in thatAli [00:40:30]: Okay. YeahVibhu [00:40:30]: But someone can check. But it's useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM.Ali [00:40:48]: Right.Vibhu [00:40:49]: I was waiting for a mention of Dynamo.Vibhu [00:40:51]: I feel like, that's supposed to be the baseline that you measure against.Dynamo, KV Routing, and Disaggregation ToolkitsPhilip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA.Ali [00:41:17]: We've done a pod with KylePhilip [00:41:18]: OkayAli [00:41:19]: Kyle Cranin.Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting.Ali [00:41:28]: But it's just a router, it's not like an optimizer layer.Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around.Philip [00:41:49]: That doesn't mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It's more of a developer toolkit.Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out.Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we've got to, we've got to benchmark against, like, what we're seeing in the wild.Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-SpecVibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book.Philip [00:42:31]: Yeah.Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLEPhilip [00:42:35]: YeahVibhu [00:42:36]: 524 on gram.Philip [00:42:37]: It's 55, would be disaggregationAli [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bitPhilip [00:42:44]: YeahAli [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques.Philip [00:42:51]: Yeah.Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe.Vibhu [00:42:55]: Medusa is quite old.Philip [00:42:56]: Yeah, Medusa's old.Ali [00:42:58]: It was old.Vibhu [00:42:58]: But is it in the book as a good, here'sPhilip [00:43:01]: BaselineVibhu [00:43:01]: Baseline vanilla understand it?Philip [00:43:02]: Like you should know this.Vibhu [00:43:03]: Like I read the paper, I'm like, “ it makes so much sense.”Philip [00:43:05]: Yeah.Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there's DFlash, dSpark. There's, there's newer techniques even than EAGLE, although EAGLE is still very commonly used.Ali [00:43:51]: SpecSpecta.Philip [00:43:52]: Yes. Speculative decoding.Vibhu [00:43:54]: What canAli [00:43:56]: Oh, it's a paper by Tri Dao and it's like, it's doing speculative decodingVibhu [00:44:00]: HuhAli [00:44:01]: For the speculative decoder.Philip [00:44:02]: Oh, in spec- oh my God.Ali [00:44:02]: It's literally just an another. It's like, yeah, that's the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it's almost like in our mind at least, it's almost as complex as training GANs. Like it's like a very delicate balance and oftentimes you, it's just but yeah, it's literally speculative decoding on speculative decoding.Vibhu [00:44:21]: Speculative.Ali [00:44:22]: Yeah. We saw this paper.Vibhu [00:44:24]: It's interesting, right?Ali [00:44:24]: Yeah.Vibhu [00:44:24]: I wouldn't even expect it to be very particular to train, I wouldAli [00:44:29]: Right.Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder.Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It's like, it's like almost like the iPhone auto predict version but for a normal model, right? Like you're just, you're just, generating three tokens and you're like, okay, I'll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model?Ali [00:44:53]: The other question there is what are the size of speculators? So say forPhilip [00:44:58]: Right. It's like a billion parameters.Ali [00:45:01]: Like for MiniMax, it's. Yeah. It's like one layer. It's like one 60th of the original model usually.Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office.Philip [00:45:10]: SpeculativeAli [00:45:11]: SpeculativePhilip [00:45:11]: Decoding.Ali [00:45:13]: No, it's, it does seem like how, when do you stop? But then it also seems like if you're able to train spec-spec decode for instance, right? Like if you're able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model's gonna predict, then why not just use that smallest model directly, right?Vibhu [00:45:34]: Yeah. This isAli [00:45:35]: Like it seems likeVibhu [00:45:35]: Adjacent to the routing problem.Ali [00:45:36]: Right.Vibhu [00:45:36]: Yeah.Ali [00:45:36]: Right.Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you're running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process.Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it's the same thing, it's just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same?Local AI vs. Data Center InferencePhilip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it's how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it's how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don't touch, in the pruning, in the distillation, in the, layer removal. There'Ali [00:47:42]: Layer removal matters less.Philip [00:47:43]: Yeah. There'Ali [00:47:44]: No one loves pruning really.Philip [00:47:45]: Yeah. Well, but the, but they doVibhu [00:47:46]: Which is surprising, right? But that's, that's a whole different thingPhilip [00:47:48]: Just to fit something on the laptop.Ali [00:47:50]: Right.Philip [00:47:50]: So yeah, it's a, it's an interesting, it's an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire.Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we've seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don't need decrease the storage that much. You don't need to do, FP4 KV cache. You don't need to use a requant. There's, there's, there's better optimizations to be made. But on Edge devices, it's extremely important, it's extremely useful. So, seems to be, like, different optimizations there, but then they're all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with bothPhilip [00:49:18]: Principles.Ali [00:49:19]: Yeah, exactly. Exactly. Exactly.Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks.Ali [00:49:35]: Yeah, this is the Exo Labs guys.Philip [00:49:36]: Yeah. You have, a nu

TURF NERDS SOCIAL MEDIA LINKS: https://linktr.ee/turfnerdspod Evan's Walker's: https://amzn.to/4wTxZ0O Use code TURFNERDS for 5% off orders $600 and up at Magna-Matic! Use code NERDS to save 10% on Spencer Products! Evan and Greg hit the halfway mark of the season with a midseason charge straight from Scripture. Genesis 2:15, Colossians 3:23-24, and Luke 16:10 on working hard, working with your hands, and being a good steward even when clients don't appreciate it. Plus: a non-paying client who tore up his own check, defending against a "you broke my window" accusation without filing an insurance claim, torn-up vinyl siding,  burned bushes from hedge trimming, a Billy Goat leaf vac price comparison ahead of fall cleanup season, and the real hourly rate lawn care pros need to charge to actually turn a profit.   Tap Here for Turf Nerds Merch!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Look! We Have A Website!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Don't forget to check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Green Frog Web Design⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and tell them the Turf Nerds sent you. Or Greg will scalp your lawn! Use promo code TURFNERDS for 50% off Equip Expo 2026 registration! Shoot us an email! Evan@TurfNerdsPod.com ⁠⁠Instagram⁠⁠ ⁠⁠Facebook⁠⁠ ⁠⁠TikTok⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe on YouTube: ⁠⁠⁠https://www.youtube.com/@TurfNerdsPodcast?sub_confirmation=1⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#LawnCare #LawnMaintenance #Mowing #MowingGrass #LawnCareBusiness #Toro #ToroMultiforce #CubCadet #BibleStudy #Bible #Christian #Business #Entrepreneurship #Comedy #2024 #Marketing #Advertising #TipsAndTricks #Tips #Success #Yakta #YaktaMowers #YaktaOutdoor #Spring #SpringRush #FYP #Mower #NewMower #UsedMower #RouteDensity #EquipExpo #EquipExpo2024 #Echo #Stihl #RedMax #Shindaiwa #StringTrimmer #WeedWhip #GreenFrogWebDesign #WebDesign #EzraMcCarthy #Aerator #Aeration #ZAerate #Bobcat #BobcatMowers #Husqvarna #HusqvarnaGroup #HYGREENTOOL #GOMOW #ThunderLightingSupply #ChristmasLights #Christmas #Trump #DonaldTrump #PresidentTrump #ElectionDay #EZDumper #DumpInsert #StempkyNursery #Mulch #MulchInstallation #TurfNerds #Newsmax #NewsmaxTV #CarlHigbie #CharlieKirk

Show #495 “Is A 25-Dollar Bottle America's Best Straight Bourbon?" (Halfway to 500!) Beverage Of Mystery: Jose Cuervo Cocktails Sparkeling Classic Margarita (spoiler alert... it's good!) Smoke Anything Interesting? Cruze: Romeo & Julieta Reserva Real Nicaragua Midnight Twist Toro Ian: My Father Blue Beer Tasting: Karbach Brewing Company "Love Street Summer Peach" Fruited Blonde Ale (Houston, TX) Beer Tasting: Belching Beaver Brewery "Death By Blueberry" Wheat Ale (Oceanside, CA) Beer Tasting: Burlington Beer Company "North Of Kathmandu" Bourbon Barrel Aged Coconut Stout (Kansas City, MO) Spirit Tasting: Campo Azul Tequila Gran Clasico Anejo (Jalisco, Mexico) DRINKING NEWS: “This Is Worse Than 'Dumpster Diving.' MUCH Worse..." DRUNKEN FACT: “Today's DRUNKEN FACT could SAVE THE WORLD!!!..." ---Our Smokin' & Toastin' OH Ingram Barrel Pick Is Almost Here!!---

Send us Fan MailPumped Halfway Recap , what's changed , what's hard , What's next .Instagram: https://www.instagram.com/p/C5536OnOTy_/?utm_source=ig_web_copy_link

The summer before the boat...

Episode 276- SD HistCon North 2026 Pete is joined by Cardner Babakitis of Odd Candy Games and Buried Giant Studios.  They discuss the historical games they played at the inaugural SD HistCon North.   Check out Cardner's games here: https://www.oddcandygames.com/ Time Stamps 15:00- Hellraisers in Kanawha County 19:30- An Infamous Traffic 2nd Edition 27:30- Baltic Empires 33:00- Penitent 42:00- Nicaea 49:00- John Company megagame 1:01:50- Oath with New Foundations 1:08:05- Muddy Buddies 1:09:15- Virgin Queen 1:23:00- Big Shot   Preplanners The next episode is Deckbuilding 301 which focuses on the relationship of Slay the Spire to tabletop games!   Music and Sound Credits Thank you to Hembree for our intro and outro music from their song Reach Out. You can listen to the full song on YouTube here: https://www.youtube.com/watch?v=gQuuRPfOyMw&list=TLGGFNH7VEDPgwgyNTA4MjAyMQ&t=3s You can find more information about Hembree at https://www.hembreemusic.com/.  Thank you to Flash Floods for use of their song Palm of Your Hand as a sting from their album Halfway to Anywhere: https://open.spotify.com/album/2fE6LrqzNDKPYWyS5evh3K?si=CCjdAGmeSnOOEui6aV3_nA Intermission Music: music elevator ext part 1/3 by Jay_You -- https://freesound.org/s/467243/ -- License: Attribution 4.0 Bell with Crows by MKzing -- https://freesound.org/s/474266/ -- License: Creative Commons 0 hammer v2.wav by blukotek -- https://freesound.org/s/337815/ -- License: Creative Commons 0   Contact Follow and reach us on social media on Bluesky @decisionspace.bsky.social. If you prefer email, then hit us up at decisionspa@gmail.com. This information is all available along with episodes at our new website decisionspacepodcast.com. Byeee!

Halfway through a heated argument, have you ever wondered what you're even fighting about? In this episode of DarrenDaily On-Demand, Darren Hardy reveals why almost no conflict is actually about the thing being argued, and shares the emotional intelligence move that lets you defuse tension instead of escalating it. Using the story of a CEO he mentors whose offhand dinner comment triggered a full-blown blowup, and a senior executive whose reaction in a meeting seemed wildly out of proportion, Darren shows what was really driving each one. This episode dives into the two questions that reveal what the other person actually needs, a leadership and relationship skill that works just as well at home as it does at work. Get more personal mentoring from Darren each day. Go to DarrenDaily at http://darrendaily.com/join to learn more.

If you've ever struggled with feeling like a control freak, fighting perfectionism, or wondering if you would lose your mind because you were consumed by fear, this is an episode made especially for you.Jill is a fellow podcast host turned real-life friend, and I'm so pleased to introduce her to you this week!Host of the Halfway to Sunday podcast, Jill has a knack for conversations. She was honest and forthright in this episode talking about becoming a teen mom, fighting to find truth in faith, and struggling to find her place in a community she unexpectedly found herself in.Connect with Jill:Jill's Podcast: https://podcasts.apple.com/us/podcast/the-grief-of-unanswered-prayers-w-val-kleppen-ep-116/id1583685375?i=1000770949082Follow Jill on Instagram: https://www.instagram.com/halfwaytosunday/____________________Want to be a guest on The Motherhood Experience? Send Val Kleppen a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/1758742098661627c9cc46f40

I made you a ⁠⁠⁠⁠⁠⁠⁠⁠FREE companion guide⁠⁠⁠⁠⁠⁠⁠⁠ for this episode! This FREE instant access PDF will give you 11 easy and enjoyable ways to celebrate Lammas (the halfway point between Summer Solstice & Autumn Equinox) + 8 journal prompts to help you reflect and process the inspiration of this season. ⁠Download it now!⁠⁠Lammas, also known as Lughnasadh, is a festival celebrated on August 1st, marking the halfway point between Summer Solstice and Fall Equinox, and the first harvest of the year in the Wheel of the Year. It's a time where Nature is modeling for us the themes of gratitude and abundance, harvest and reaping, transformation and sacrifice, community and sharing, and reflection and planning.Did you know?The word Lammas actually comes from the Old English "loaf mass." People baked a loaf of bread from their first harvest and brought it to the church to be blessed. This bread was believed to have healing and protective properties, so they placed it in the corners of their homes and barns for protection and blessing.Even though the exact date is August 1, we can celebrate this whole season. In this episode, you'll learn...the origins, history, and symbolism of Lammas what Nature is modeling for us physically, spiritually, and energetically, and how we can align 11 easy and enjoyable ways to celebrate Lammas 8 journal prompt themes to help you reflect and process the inspiration of this seasonRelated episodes:-229. How to Rewrite and Rewild Your Relationship with Your Cycle - with Em Dewey -228. The 4 Feminine Archetypes - what's yours? -227. Using the superpowers of your menstrual & moon cycle to enjoy your life moreWays we can walk together this Summer:Learn about one-on-one guidance ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Let's connect on⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!Grab any of my Free Resources ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here.⁠⁠⁠⁠⁠⁠⁠⁠Sign up for a free curiosity call ⁠⁠⁠⁠⁠⁠⁠here.⁠

The Learning Leader Show with Ryan Hawk www.LearningLeader.com New Book - The Price of Becoming - www.LearningLeader.com/Becoming This is brought to you by Insight Global. If you need to hire one person, hire a team of people, or transform your business through Talent or Technical Services, Insight Global's team of 30,000 people around the world has the hustle and grit to deliver. My Guest: Joanna Stern is an Emmy Award-winning technology journalist, chief technology analyst for NBC News, and founder of the independent media company, "New Things." She is best known for her 12-year tenure at The Wall Street Journal, and for authoring the New York Times bestseller I Am Not a Robot. Key Learnings  Joanna dedicated her book to her parents "who taught me to think for myself, and the AIs, robots, and machines that made me wonder if I really was." In an age when AI can do the thinking for us, the most valuable thing we can teach our kids is to think for themselves. Walt Mossberg invented tech journalism for humans. His first column: "Computers are too hard to use, and it's not your fault." Without Walt, we don't have a category where technology built for humans is reviewed by humans. AI won't replace the radiologist. It'll make them better. Joanna sat with her doctor while AI scanned her mammogram. The AI flagged three suspicious spots. Two the doctor had already dismissed as benign. But one was something the doctor hadn't caught. She marked it for follow-up. Then the doctor pointed to other cases where SHE had caught things the AI missed. It's not a replacement. It's a partnership. The same technology that finds cancer is the same technology that can autonomously send missiles. That's the great tension of our time. Every powerful technology has good and bad uses. AI colleagues are coming. Joanna has two employees and one AI agent. She predicts that within a year, she'll have full AI employees. Great management books haven't been written yet about how to lead a mixed team of humans and agents. Someone is going to write them. Concert ticket prices tell you everything about human connection right now. Every artist is selling out football stadiums. People are craving in-person interaction more than ever. Bot Girl Summer. Joanna tried to have an emotional relationship with a chatbot boyfriend named Evan for 48 hours. She didn't fall in love. But she noticed how easy it was to talk to something that only wanted to hear about her problems and told her she was great at everything. That's the danger. The AI therapist shut itself down when it realized there was a real therapist in the room. Joanna brought her AI therapist "Ash" into her actual therapy session. Halfway through, Ash said, "I'm sorry, I can't continue this conversation. It sounds like there are multiple people in the room." AI won't replace therapists. It'll help with the shortage and the stigma. Some people don't want to walk into a therapist's office. But they'll open their phones. That's a bridge to real help for people who wouldn't otherwise get it. Kara Swisher's career advice to Joanna: "Leave your fucking job." That was the shortest, cleanest advice Joanna got when deciding whether to leave the Wall Street Journal after 12 years. Joanna used AI as a co-founder for the biggest decision of her career. She uploaded 12 years of notes into ChatGPT and asked it to help her decide whether to leave the Journal. It didn't decide for her. But it structured the risk analysis, laid out the escape hatches, and helped her see the shape of the decision. The muscles atrophy if you don't use them. Writing is meant to be hard. Thinking is meant to be hard. If you outsource the hard work, you stop getting stronger at the hard work. Research is where you learn the most. If you have AI pull the memo for you and you get up and read it, you don't know what you're really talking about. You have to do the reps to know the material. AEI: Already Enough Intelligence. Sam Altman is obsessed with building superintelligence. Joanna proposes we already have enough intelligence to work with. Maybe the goal isn't to build smarter models. Maybe it's to figure out what to do with what we already have. The AI on-ramp for leaders: Take one document you make all the time (a memo, a PowerPoint, an email template). Upload it. Ask AI to make a template out of it. Next time you need it, you're twice as fast.  Push yourself to do the harder work. Joanna's analogy: marathon runners are insane. They just want to run. Nobody wants to do the harder work. But that's where the growth is. Joanna's champagne moment a year from now: milestones for her new company. She's already hit 100,000 YouTube subscribers three months after her first video. But she also just wants a nap. Reflection Questions Where in your work are you outsourcing the thinking to AI? Are the muscles you actually need atrophying because you're skipping the reps? If you accepted that you already have enough intelligence to work with, what would you actually build with what's in front of you right now? More Learning #679: Kat Cole - The Four Mindsets Every Leader Needs #605: Seth Godin - The Power of Remarkable Ideas #697: Dan Smith & Ryan Hawk - The Price of Becoming Podcast Chapters 00:00 The Price of Becoming - Pre-Order Now!  01:44 Meet Joanna Stern  02:31 Teach Your Kids to Think in the Age of AI  03:25 The Parents and Mentors Who Shaped Her Career  04:16 Lessons From Walt Mossberg  05:55 Why Joanna Spent a Year Living With AI  14:47 How AI Can Make You a Better Leader  17:30 Why People Crave Human Connection More Than Ever  21:00 Bot Girl Summer: Dating a Chatbot Named Evan  25:13 The AI Therapist Trial 29:51 Using ChatGPT for a Career Change 33:29 Don't Let Your Thinking Muscles Atrophy  40:20 Sam Altman on Superintelligence, and Joanna's Case for "AEI"  42:51 Joanna's Advice for College Students  45:29 How Joanna Actually Uses AI to Write  47:17 Practical AI for the Fortune 500 VP  50:17 The Champagne Question 52:43 EOPC

Website

How do you keep giving to a relationship that isn't giving much back? How do you know when you've finally done enough? In this Q&A episode, Whitney answers two questions that arrived from very different places but land in the same spot: radical acceptance. First, a woman exhausted by in-laws who never make the drive, never offer help, and never seem to notice the effort going one direction. Second, a woman whose gone no contact with parents who keep leaving her voicemails claiming they have no idea why.Whitney Goodman is a Licensed Marriage and Family Therapist (LMFT) and the founder of Calling Home, a membership community that helps people navigate complex family dynamics and break harmful cycles.Have a question for Whitney? Send a voice memo or email to whitney@callinghome.coJoin the Family Cyclebreakers Club: https://callinghome.coFollow Whitney on Instagram | sitwithwhitFollow Whitney on YouTube | @whitneygoodmanlmftOrder Whitney's book, Toxic Positivity: https://sitwithwhit.com/toxic-positivitySign up for updates on Whitney's new book: https://cmnyyv4kpyt.typeform.com/to/PHMzjy0oThis podcast is for informational purposes only and is not a substitute for professional mental health advice. Hosted on Acast. See acast.com/privacy for more information.

Screaming tribesmen  / move a little closer / 12 inch ep   x / Half Way around the world / tales from austrailian undergroundTrash Kickers / What a Waste / way out somewhere  Brimestone Howl / Like a Dog / Bang! Bang!Bang! Bang!Brash Habits / Vampire / feeling the Light Digger and the Pussycats / sergi / watch yr back Chunks / Nicotine / vol 1 ep Fewww / Track 7 / babyalone ..Fuck Yeah Dinosaurs / Tar Pit / jurassic Drunks Fun Things / (i aint got) time enough for love / Murder Punk 2 Died Pretty / Winterland / Lost..man Twisted Teens / Florida Water Blues / florida water bluesYuasa-Exide/Waylon Thornton / Tristan Tzara / U.S. Hypothetical The Saints / Im Stranded / i'm stranded ...Sex Mex / I Like it / 21:12 Jacket Burner / Highschool Weirdo / pig vomit sessionsThe Drones / Baby 2 / Wait Long By The River And The Bodies Of Your Enemies Will Float By God's Hand / Police Lorry / a side single punk band from iowa city Lime spiders / Slave Girl / slave girl comp / 84 single.Sorcerer / Spectators / Spectators The Scientist / We Had Love 84 mottey^3@gmail.com

Episode 275- Inside the Deck This week Jake, Paul, and Brendan explore the different deck structures of card games.  Things get mathy in this design focused discussion about how combinations of suits and numbers create different decision spaces in games.  Time Stamps 2:00- Air Land and Sea 13:00- The Fox in the Forest 21:45- Jekyll vs Hyde 27:45- Dracula vs Van Helsing 34:30- Coloretto 42:00- Pet Quartet 45:00- Flip 7 50:00- The Mind 55:00- No Thanks 1:01:30- Arboretum 1:09:30- Lost Cities 1:14:00- Schotten Totten 1:16:15- Scout and dnup   Preplanners Up next is a recap from a historical game convention.   Music and Sound Credits Thank you to Hembree for our intro and outro music from their song Reach Out. You can listen to the full song on YouTube here: https://www.youtube.com/watch?v=gQuuRPfOyMw&list=TLGGFNH7VEDPgwgyNTA4MjAyMQ&t=3s You can find more information about Hembree at https://www.hembreemusic.com/.  Thank you to Flash Floods for use of their song Palm of Your Hand as a sting from their album Halfway to Anywhere: https://open.spotify.com/album/2fE6LrqzNDKPYWyS5evh3K?si=CCjdAGmeSnOOEui6aV3_nA Intermission Music: music elevator ext part 1/3 by Jay_You -- https://freesound.org/s/467243/ -- License: Attribution 4.0 Bell with Crows by MKzing -- https://freesound.org/s/474266/ -- License: Creative Commons 0 hammer v2.wav by blukotek -- https://freesound.org/s/337815/ -- License: Creative Commons 0   Contact Follow and reach us on social media on Bluesky @decisionspace.bsky.social. If you prefer email, then hit us up at decisionspa@gmail.com. This information is all available along with episodes at our new website decisionspacepodcast.com. Byeee!

We're halfway through the year, halfway through summer, and somehow already halfway through July! In this episode, we're checking in on life, talking about what's been happening lately, reflecting on the first half of 2026, and sharing what we're looking forward to in the months ahead.New episodes are uploaded weekly on Saturday mornings at 7am Pacific / 10am EasternSupport us and the LeggLife Podcast by becoming a patron at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  / legglife  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Learn more about LeggLife by following us on:YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.youtube.com/legglifeak/?su...⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  / legglifeak  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  / legglife  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You can reach us via email at legglife@gmail.com

GAMBLING PROBLEM? CALL 1-800-GAMBLER or 1-800-MY-RESET, 800-327-5050/visit gamblinghelplinema.org (MA). Call 877-8-HOPENY/text HOPENY (467369) (NY). Call 888-789-7777/visit ccpg.org (CT), mdgamblinghelp.org (MD), 800-981-0023 (PR). Wagering offered by DK Sportsbook: 21+. Present in most states. (18+ DC/KY/NH/PR/WY). Void in ONT. On behalf of Boothill Casino (KS). Pass-thru of per wager tax may apply in IL.Event Trading offered by DraftKings Predictions, CFTC-registered: 18+. Trading involves risk of loss. Market availability varies. Predictions offer void in NY. General: 1 per new DraftKings customer. $5+ deposit req. Trade $5, get $200 Prediction Dollars (1-year expiry) issued as $50 increments every 7 days via click-to-claim for 21 days; or bet $5, get $200 Bonus Bets instantly (7-day expiry and stake removed from payout). 7 days = 168hrs. Rewards are non-withdrawable. Terms: dkng.co/offer. Ends 7/19/26 at 11:59 PM ET. Sponsored by DK.You can find every episode of this show on Apple Podcasts, Spotify or YouTube. Prime Members can listen ad-free on Amazon Music. For more, visit barstool.link/wegottabelieve

What happens when The Rizzuto Show tries to remember everything that's happened so far this year? Absolute chaos.The crew attempts a halfway-through-the-year quiz, and let's just say... history, sports, movies, award shows, and basically every major headline take a beating. Somehow everyone becomes confidently wrong while still arguing like they're experts, which is pretty much the perfect recipe for a daily comedy episode.Then it's time for Crap on Celebrities, where Jay-Z shatters Yankee Stadium records with an unbelievable three-night concert event featuring an all-star lineup that somehow keeps getting bigger. Billy Corgan pitches a dream Smashing Pumpkins Sphere residency, The Rock refuses to give up on his Broadway dreams with Kevin Hart, and Hollywood keeps rebooting everything whether we asked for it or not.The gang also dives into blockbuster movie rumors, Batman casting speculation, Hunger Games returning to theaters, Michael Jackson's record-breaking biopic, American Horror Story returning to its witchy roots, and why Love Island somehow managed to hijack everyone's television. There are passionate debates about depressing movies, surprisingly heartfelt tributes to entertainment legends, and several moments where everyone completely loses the plot.You'll also hear recommendations for an incredible revenge movie you probably haven't seen yet, reactions to new music releases, celebrity birthdays, pop culture nonsense, sports memories that may or may not be accurate, and enough sarcastic commentary to make your commute significantly more entertaining.As always, The Rizzuto Show manages to turn everyday headlines into complete insanity with quick jokes, ridiculous tangents, questionable facts, and the kind of conversations that only happen when friends who've worked together forever are allowed near microphones.Whether you're here for celebrity gossip, weird news, movie talk, sports disasters, or just want to laugh at grown adults confidently misremembering recent history, this daily comedy episode delivers exactly the kind of unpredictable fun you've come to expect.If you love hilarious conversations, pop culture commentary, entertainment gossip, celebrity news, and a funny morning show that never takes itself too seriously, you've found your people.Thanks for making The Rizzuto Show part of your day. Share the episode with someone who also can't remember who won anything this year.This daily comedy podcast is proudly brought to you by the crew at 105.7 The Point in St. Louis, where sarcasm, laughs, and questionable trivia are always on the schedule.Follow The Rizzuto Show → https://linktr.ee/rizzshow for more from your favorite daily comedy show.Connect with The Rizzuto Show Comedy Podcast online → https://1057thepoint.com/RizzShow.Hear The Rizz Show daily on the radio at 105.7 The Point | Hubbard Radio in St. Louis, MO.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Jerry & John Franco breaking down where both teams are as the All-Star break is here.

"Would I have allowed fear to steal my destiny?" Ryan Clark  A Friday episode of The Pivot Podcast with the fellas. Ryan Clark, Fred Taylor, and Channing Crowder kick back for a classic Friday conversation, catching up on life, sports, and everything in between. The trio opens with heartfelt thoughts on Chris Johnson's ALS diagnosis, reflecting on the football brotherhood and the importance of supporting those who have given so much to the game. They also have an honest conversation about the fear of CTE—the reality that comes with playing football at the highest level. While they acknowledge the risks every player accepts, all three agree that, knowing everything they know today, they still wouldn't change a thing about the game that shaped their lives. The conversation shifts to the NBA as they debate the buzz surrounding a potential Jaylen Brown trade and what it says about today's business of sports. From there, the guys dive into a bigger question: What truly defines a great leader? Whether it's in the locker room, on the field, or in everyday life, they share the qualities they believe separate good leaders from great ones. With July marking the halfway point of the year, Ryan, Fred, and Channing each open up about the personal goals, habits, and areas they're focused on improving during the second half of 2026. Ryan also shares stories from attending Travis Kelce and Taylor Swift's wedding, giving the guys plenty to laugh about as they react in true Pivot fashion. It's thoughtful, hilarious, honest, and everything people enjoy about the locker room feel of the guys simply talking about life. No guests—just The Pivot doing what it does best. Learn more about your ad choices. Visit megaphone.fm/adchoices

Halfway through the year, it's your Stock Market Scoreboard… Nasdaq's high, Korea's higher, & memory's highest (and Peloton's back?)Starbucks' new moonshot bet… is to turn its baristas into paid influencers.The fastest-growing buyer of vintage trucks is women under 40… It's Beyonce's Single Ladies Economy.Plus, how did Taylor Swift pull off a wedding at MSG?... She used Navy Seal military tactics. $F $SBUX $SPYGrab your Tickets to the IPO Tour: Our In-Person OfferingSan Francisco 9/23: https://www.ticketmaster.com/event/1C0064AFB5F688BDBoston 10/14: https://tickets.citywinery.com/event/tboy-the-ipo-tour-in-person-offering-8cdhupSeattle 11/4 (21+): https://www.axs.com/events/1446394/the-best-one-yet-ticketsNEWSLETTER:https://tboypod.com/newsletter OUR 2ND SHOW:Want more business storytelling from us? Check our weekly deepdive show, The Best Idea Yet: The untold origin story of the products you're obsessed with. Listen for free to The Best Idea Yet: https://wondery.com/links/the-best-idea-yet/NEW LISTENERSFill out our 2 minute survey: https://qualtricsxm88y5r986q.qualtrics.com/jfe/form/SV_dp1FDYiJgt6lHy6GET ON THE POD: Submit a shoutout or fact: https://tboypod.com/shoutouts SOCIALS:Instagram: https://www.instagram.com/tboypod TikTok: https://www.tiktok.com/@tboypodYouTube: https://www.youtube.com/@tboypod Linkedin (Nick): https://www.linkedin.com/in/nicolas-martell/Linkedin (Jack): https://www.linkedin.com/in/jack-crivici-kramer/Anything else: https://tboypod.com/ About Us: The daily pop-biz news show making today's top stories your business. Formerly known as Robinhood Snacks, The Best One Yet is hosted by Jack Crivici-Kramer & Nick Martell. Hosted on Acast. See acast.com/privacy for more information.

Why summer is the perfect time to revisit your goals for the year. Gretchen and Elizabeth share a hack for making socializing easier. Build Your Social Muscles: The more you socialize, the less mental energy it costs  [00:39] Spotlight on a Tool: July 2nd is the halfway point of the year, so it's a great time to revisit the goals and lists you made in January  [18:24] I've Been Meaning to Ask You: We talk about happiness all the time. Does we ever get tired of thinking about how to be happier?   [10:56] I've Been Meaning to Tell You: Gretchen finds reassurance in Elizabeth's recent take on the challenge of watching TV [19:14] Mentioned in this Episode: Design Your Year / Halfway Day resources When Zero Isn't Nothing: The Fresh Eyes of a Basketball Beginner 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