Podcasts about Rubin

Whatever type of music you love, Rick Rubin has probably left his mark on it. Rubin, a founder of Def Jam Recordings and a former co-chairman of Columbia Records, might be the most influential music producer alive. A very partial roll call of the artists he's worked with: Johnny Cash, Jay-Z, Run-DMC, Eminem, Beastie Boys, Slayer, The Red Hot Chili Peppers, Kanye West, Neil Diamond, Brandi Carlile and Adele. We recorded this in 2023, after Rubin released a fascinating, unusual book, “The Creative Act: A Way of Being.” I'd expected stories of late-night sessions with famous musicians, but instead it read like a spiritual tract. Rubin was offering a guide to how to cultivate the mind states that lead to real creativity. And that's what this conversation was about, too: how to open yourself up more to the world, and learn to trust your own taste. Rubin's advice feels more relevant with each passing year, as technology gets ever better at hacking our attention and the world fills with A.I. slop. So I wanted to share this episode again. Mentioned: The Tao Te-Ching by Lao Tzu, translated by Stephen Mitchell “American Recordings” by Johnny Cash “Pulses” by Steve Reich and Erik Hall “Music for Wobbling. Music Versus Gravity.” by F.S. Blumm and Nils Frahm Album recommendations: “Forever Changes” by Love “The Beatles” by The Beatles “Ramones” by Ramones Thoughts? Guest suggestions? Email us at ezrakleinshow@nytimes.com. You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on X @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs. This episode of “The Ezra Klein Show” is produced by Annie Galvin. Fact-checking by Michelle Harris. Our senior engineer is Jeff Geld. Our recording engineer is Aman Sahota. Cinematography by Marina King. Our executive producer is Claire Gordon. The show's production team also includes Marie Cascione, Rollin Hu, Kristin Lin, Emma Kehlbeck, Jack McCordick and Jan Kobal. Original music by Pat McCusker. Audience strategy by Shannon Busta. The director of New York Times Opinion Shows is Annie-Rose Strasser. Special thanks to Jon Caramanica, Dan Charnas, Jack Hamilton, Felix Grimm, Carole Sabouraud and Kristina Samulewski. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dave Rubin of "The Rubin Report" talks to Andrew Gold about how political tribalism and social media echo chambers can resemble cult psychology; why women are more likely to be drawn into cults; lessons from Scientology and NXIVM about conformity, status, belonging, and why intelligent people become trapped in ideological bubbles; why people often abandon cult-like movements only after being personally mistreated; how October 7 challenged some progressive Jews' beliefs; the growing threat of internet censorship and government control of social media; the UK's push to favor "trustworthy" legacy media like the BBC over independent YouTube creators; how bots, foreign influence, and online ID verification could reshape internet freedom; how institutional pressure influences mainstream media coverage; the worsening immigration crisis in the UK and Europe; why even left-wing politicians are beginning to support stronger borders; how mass immigration transformed Sweden's high-trust society and contributed to crime and parallel societies; why deportations remain difficult under European human rights rules; concerns about assimilation and national identity; and much more. Today's Sponsors: Kalshi - Download the Kalshi app and use code RUBINREPORT to get $10 when you trade $10 or go to https://kalshi.com/r/RUBINREPORT DISCLAIMER: 18+ only. Restrictions and eligibility requirements apply. Event contract trading involves risk and may not be suitable for all investors. Prices, values, and available markets may differ from those mentioned. For more information see kalshi.com/regulatory. Relief Factor - Relief Factor is an alternative to help your body's natural inflammatory response so you can keep doing the things you love. Try Relief Factor's 3-Week QuickStart—just $19.95 Go to: https://www.relieffactor.com or call 800-4-RELIEF. Quo- Never miss an important business call again. Try QUO for free PLUS get 20% off your first 6 months. Go To: http://www.quo.com/RUBIN

Broadcasting from Acme Radio Live, it's Carter Rubin!Check out his music on Spotify (https://open.spotify.com/artist/5jv5QGHCh2jdM0dOQP8JXm)

In this episode we explore the simple yet profound power of pen and paper with Laura L. Rubin, the founder of AllSwell Creative and author of the transformative new book, The Big Unlock. Journaling is often touted as a "must-have" wellness tool, but it can feel intimidating or even performative for many. We sit down with Laura to demystify the practice and prove that it truly is accessible to everyone—regardless of whether you consider yourself a "writer." This conversation brings together two very different perspectives: Jenn: A lifelong journaler who relies on her notebook as a non-negotiable tool for mental hygiene and clarity. Lori: A self-proclaimed "non-journaler" who represents the skeptics and the overwhelmed who don't know where to start. We also unpack: The Science: Why slowing down to the speed of your own handwriting has a measurable cognitive and emotional impact. Converting the Skeptic: The specific data points Laura shares in corporate boardrooms to change minds about the power of journaling. 4x4x4 Method (you'll have to tune in to find out what that means)! Whether you are a seasoned pro or a curious skeptic, this episode offers a roadmap to finding your own "Big Unlock" through the philosophy of journaling. Laura's Book: The Big Unlock Laura's Website: AllSwell Creative Follow us on Instagram: @every.body.talks @jenngiamo @schully Subscribe to our YouTube channel! Don't forget to subscribe to the podcast for free wherever you're listening. Apple Podcasts Spotify Be sure to leave a 5 star rating! It really helps grow the show. If you like the show, telling a friend about it would be amazing!

Hey Popcorn Pals! This week we're taking about the Green Day movie, Nimrods. Did you see the movie? Tell us what you thought about it! See you at the movies! –––––––––––––––––– You can follow Popcorn Pals on Instagram, Tiktok, and Letterboxd @PopcornPalsPod. Email us with your thoughts, opinions, and questions at PopcornPalsPod@gmail.com. Popcorn Pals is produced by Clara Madenwald, our social media is run by Katelyn Bordini, and our artwork was made by Angela Rodgers. The intro for Popcorn Pals uses graphics from You Want Pizzazz ?!? and sounds from Freesound.org: “COMCam_8mm Movie Projector (Rus’) 01_KVV AUDIO_Free” by KVV Audio (https://freesound.org/s/830217/) licensed under Attribution 4.0. “Film Countdown” by qubdodup (https://freesound.org/s/182110/) licensed under Creative Commons 0. “Lofi Piano + Drum Loop” by Doomsdaybear (https://freesound.org/s/720123/) licensed under Attribution 4.0.

In this episode of the Award-winning PRS Journal Club Podcast, 2026 Resident Ambassadors to the PRS Editorial Board – Lucas Harrison, Christopher Kalmar, and Priyanka Naidu- and special guest, Salam Kassis, MD, discuss the following articles from the September 2026 issue: "Management of Platysmal Banding in Face and Neck Lift Surgery" by Sherif, Kumar, Stuzin, et al. "Periareolar Incision Deformity: Correction of Infra-Areolar Indentations with Acellular Dermal Matrix" by Hidalgo and Chadab. "Nutritional Challenges in Post–Massive Weight Loss Body Contouring: Guidance for Plastic Surgeons on GLP-1 Agonists and Sleeve Gastrectomy" by Mehta, Rometo, Gusenoff, and Rubin. READ the articles discussed in this podcast as well as free related content: https://bit.ly/JCSept26Collection   Dr. Salam Kassis is a plastic surgeon with extensive expertise in aesthetic surgery, peripheral nerve surgery, and complex reconstructive procedures. He completed general surgery residency at Yale University, followed by plastic surgery fellowship training at Vanderbilt University Medical Center. He subsequently joined the Vanderbilt faculty, where he contributed extensively to resident education and clinical research, and held leadership roles including Vice Chair of Clinical Affairs. In 2026, Dr. Kassis transitioned from Vanderbilt to private practice at Kassis Salameh Plastic Surgery, bringing more than a decade of academic surgical experience to his aesthetic and reconstructive practice. The views expressed by hosts and guests are their own and do not necessarily reflect the official policies or positions of ASPS.

Rubio Rubin, of El Paso Locomotive, joins Josh on this episode of The Soccer Romantics to discuss his extraordinary career so far; from Oregon to the Netherlands and from Liga MX to the Guatemalan National Team, Rubin has travelled the world and has a wonderful perspective. Listen now to hear how he's fallen in love with futbol and where he's going next. 

In this episode of The Jess Larsen Show on Innovation & Leadership, Jess sits down with Andrew Rubin, founder and CEO of Illumio, for a timely conversation on cybersecurity, AI, investing, and what leaders need to understand about risk in a world where attacks are becoming faster, cheaper, and more sophisticated. Andrew explains the core philosophy behind Illumio: cybersecurity can no longer be only about preventing every breach. Companies still need to stop as much as they can, but they also need a second mission, containing attacks the moment they happen so one breach does not become a full-scale catastrophe. As Andrew puts it, the goal is to make the problem small before it spreads. Jess and Andrew also talk through what this means for founders, CEOs, and investors who may not have large security teams but still hold valuable customer data. Andrew breaks down why smaller companies can still be major targets, how AI is democratizing attack capabilities, and why leaders need to think seriously about partners, risk, reputation, and customer trust before a crisis hits. The conversation also moves into product-market fit, customer feedback, capital allocation, and investment philosophy. Andrew shares how Illumio thinks about building in a fast-changing market, why customer conversations matter more than surface-level feedback, and how he evaluates companies by looking at the market, the idea, the team, and their ability to execute over time. This is a sharp and practical episode about cybersecurity in the age of AI, the importance of moving faster without chasing perfection, and how leaders can make better decisions when the stakes are high and the timeline is shrinking. Learn more about your ad choices. Visit megaphone.fm/adchoices

On January 15, 1978, serial killer Ted Bundy entered the Chi Omega sorority house at Florida State University, murdered Margaret Bowman and Lisa Levy, and brutally attacked Kathy Kleiner Rubin and Karen Chandler. Kathy survived but Ted Bundy was far from the first thing she had survived.This week on When Killers Get Caught, Brittany Ransom takes a deep dive into Kathy's remarkable story and her memoir, A Light in the Dark: Surviving More Than Ted Bundy. We examine the increasingly reckless Bundy who arrived in Florida near the end of his killing spree, what happened inside Chi Omega, the survivors who are too often overshadowed by Bundy's mythology, and the moment Kathy faced him again in court.But most importantly, this is about what happened after Ted Bundy left the room.Because Bundy may be part of Kathy Kleiner Rubin's story, but he doesn't get to own it.The truth always leaves a trail and sometimes that trail leads straight back to the survivor who lived long enough to tell it.If you want to connect with Brittany, catch regular updates, true crime discussions, and short-form video breakdowns follow us on social media:Check out the website: https://www.whenkillersgetcaught.com Follow on Facebook: https://www.facebook.com/WhenKillersGetCaughtFollow on TikTok: https://www.tiktok.com/@caughtpodcastGet the Gear: https://www.caught-podcast-shop.fourthwall.comCase Submissions: CaseCloserSubmissions@gmail.comBusiness Inquiries: Caughtpodcast@gmail.comMusic featured in this episode is used with permission from Myuu. https://open.spotify.com/artist/5sP3ci0jwrXUBo76t8pGTFIf you enjoyed this episode, please take a moment to leave a five-star review on Apple Podcasts or Spotify it helps more listeners discover our victim-centric approach to investigative storytelling. 

Hey Popcorn Pals! We're getting prehistoric this week talking all about The End of Oak Street with Lissa Rubin! Have you seen the movie? Did you love it? Hate it? Love to hate it? Most importantly, what did you think of that sex scene? We wanna know! See you at the movies! –––––––––––––––––– You can follow Popcorn Pals on Instagram, Tiktok, and Letterboxd @PopcornPalsPod. Email us with your thoughts, opinions, and questions at PopcornPalsPod@gmail.com. Popcorn Pals is produced by Clara Madenwald, our social media is run by Katelyn Bordini, and our artwork was made by Angela Rodgers. The intro for Popcorn Pals uses graphics from You Want Pizzazz ?!? and sounds from Freesound.org: “COMCam_8mm Movie Projector (Rus’) 01_KVV AUDIO_Free” by KVV Audio (https://freesound.org/s/830217/) licensed under Attribution 4.0. “Film Countdown” by qubdodup (https://freesound.org/s/182110/) licensed under Creative Commons 0. “Lofi Piano + Drum Loop” by Doomsdaybear (https://freesound.org/s/720123/) licensed under Attribution 4.0.

TWiV explains how liquid–liquid phase separation and a phage-encoded inhibitor cooperatively drive transcriptional transition during phage SPO1 infection, and the first smallpox virus genomes from an early outbreak in South America. Hosts: Vincent Racaniello, Rich Condit, Brianne Barker, and Jolene Ramsey Subscribe (free): Apple Podcasts, RSS, email Become a patron of TWiV! Links for this episode Support science education at MicrobeTV Positions in Rosenfeld Lab (email) Fred Murphy, a pioneer in virology (J Virol) New midge-borne orthobunyavirus sickening cattle in Europe (Science) What to Know About the Vaccines Targeted by Trump's Executive Order (NY Times) What vaccine Executive Order gets wrong (CIDRAP) Trudy Rey's 100th post at virology blog Transcriptional transition during phage SPO1 infection (Nucl Acids Res) Phase separation 101 (Iwasa lab) Early smallpox in America (Science) Letters read on TWiV 1349 Timestamps by Jolene Ramsey. Thanks! Picks of the Week Brianne – APod August 7: Rubin's Cosmos Field Rich – Jenner's Inquiry  Original, "annotated";  (PDF) Smallpox vaccination and opposition by anti-vaccination societies in 19th century Britain Jolene – Scott Seligmann's web tool for searching keywords in podcast transcripts  Vincent – The End of Reading is Here Listener Picks Tony – Fauci Fiasco Addendum Charles – The Proposed Changes in Childhood Vaccination: A Strongly Felt Opinion Intro music is by Ronald Jenkees Send your virology questions and comments to twiv@microbe.tv Content in this podcast should not be construed as medical advice.

Dave Rubin of "The Rubin Report" talks to Sage Steele about the backlash she faced after criticizing anti-Trump airport protests and political activism in sports; why she believes ESPN and the NBA became increasingly politicized; leaving ESPN after nearly 30 years and why she doesn't miss sports broadcasting; behind-the-scenes stories involving Steve Kerr, Doc Rivers, and outspoken coaches; her thoughts on Victor Wembanyama, Jalen Brunson, and the NBA; receiving lengthy anti-Trump messages from a legendary sports figure and the rise of Trump Derangement Syndrome; the importance of diversity of thought and open dialogue; how her faith, family, and perspective helped her overcome divorce, the ESPN lawsuit, and career turmoil; the lifelong lesson of choosing the "harder right instead of the easier wrong"; why standing up to ESPN and Disney over COVID vaccine mandates was the toughest but the right decision; the pitfalls of DEI, identity politics, and being judged for her race and conservative views; why people should reject labels, trust their convictions; and much more. Today's Sponsors: Kalshi - Download the Kalshi app and use code RUBINREPORT to get $10 when you trade $10 or go to https://kalshi.com/r/RUBINREPORT DISCLAIMER: 18+ only. Restrictions and eligibility requirements apply. Event contract trading involves risk and may not be suitable for all investors. Prices, values, and available markets may differ from those mentioned. For more information see kalshi.com/regulatory. Plaud- Stay informed without drowning in content. Plaud helps turn long interviews, podcasts, and discussions into searchable notes and summaries in seconds. Use code RUBIN for up to 15% OFF Go to: https://Plaud.ai/rubin Chapter - Optimize the one piece of your health most people never audit: the Medicare coverage that pays for all of it. For free and unbiased Medicare help: Dial (332) 867-0207 or Go to: https://askchapter.org/rubin Chapter and its affiliates are not connected with or endorsed by any government entity or the federal Medicare program. Chapter Advisory, LLC represents Medicare Advantage HMO, PPO, and PFFS organizations and stand alone prescription drug plans that have a Medicare contract. Enrollment depends on the plan's contract renewal. While we have a database of every Medicare plan nationwide and can help you to search among all plans, we have contracts with many but not all plans. As a result, we do not offer every plan available in your area. Currently we represent 50 organizations which offer 18,160 products nationwide. We search and recommend all plans, even those we don't directly offer. You can contact a licensed Chapter agent to find out the number of products available in your specific area. Please contact Medicare.gov, 1-800-Medicare, or your local State Health Insurance Program (SHIP) to get information on all of your options.

Jimmy addresses the latest news, like President Trump being shocked by Karoline Leavitt's resignation, before speaking with Adam Scott, Sebastian Maniscalco and Ella Rubin.

Day Break | THE CULTURE OF DEATH: How the Left Made Human Life Negotiable --- 00:00 - Monologue 19:23 – Les Rubin, founder of Main Street Economics. Rubin discusses America's growing fiscal challenges, the principles of capitalism, and how poorly designed government incentives can distort economic behavior. He examines federal spending, debt, and the long-term consequences of policies that reward the wrong financial decisions. 28:15 – Joe Rieck, Vice President of Sales at Longevity. Rieck discusses how Longevity's nutritional products are designed for people of all ages, emphasizing proper nutrition, recovery, and taking care of the body through sustainable daily habits. He also highlights the company's natural ingredients and protein shakes and encourages listeners to speak with Longevity's wellness advisors. 38:23 - Monologue 47:26 – Avi Fortenberry, President of Americans for Citizen Voting. Fortenberry discusses the defeat, at least for now, of a Los Angeles ballot measure that would have allowed noncitizens to vote, examining the debate over citizenship requirements and voter eligibility in local elections. 57:38 – Todd Bensman, Senior Research Fellow for the Border Security and Immigration Center at The Heritage Foundation. Bensman discusses Spain's response to a migration crisis, examining the government's tougher border and immigration measures and comparing the approach with policies associated with President Trump. 1:16:44 - Monologue 1:25:36 – Tom Coulson, owner of Liberty Coin Service. Coulson provides an update on the precious metals market, discussing current trends affecting gold, silver, and collectible coins. He also reviews coins submitted by listeners and offers insight into their history, rarity, and potential value. 1:35:53 – Gina Oliver, Senior Director of Durable Markets at the American Chemistry Council and Michigan resident. Oliver discusses a new report examining the relationship between heavier vehicles, advanced materials, automotive safety, and manufacturing jobs, including the implications for Detroit and Michigan's auto industry. 1:44:41 – Ivey Gruber, President of the Michigan Talk Network. Gruber discusses reports of the University of Michigan moving away from traditional letter grades in some settings, questioning whether changes intended to address student mental health could ultimately lower academic expectations and leave students less prepared for challenges beyond college. --- Check out our brand new podcast, 'Forgotten America'... Episode 24 is live NOW at Steve Gruber on YouTube! Link below: https://youtu.be/UrGZQdE62jA

Send us Fan MailA plan built by a few people at the top and handed down is just paperwork. Dr. Renee Rubin Ross argues real ownership comes from who's in the room while the goals get decided, not just who signs off on them.Dr. Ross is the founder of The Ross Collective and author of Inclusive Strategic Planning for Nonprofits. In this episode, she and Britt break down her five-step spiral planning process, including step zero: how to know your organization is actually ready to plan. They also talk about what happens when planning uncovers real dysfunction, and how to tell whether it's a planning problem or a leadership one.Grab Renee's free Strategic Planning Template, the Five-Step Spiral breakdown, and sample strategic plans in the links below.Inclusive Strategic Planning for Nonprofits (book) can be purchased here The Five-Step Spiral, explained here Get Your Free Strategic Planning Template here And, Sample nonprofit strategic plans here Chapters:00:00 - Why Traditional Strategic Planning Fails02:10 - Why Write Another Book on Strategic Planning?04:47 - The 5-Step Spiral Planning Framework07:26 - Balancing Visioning with Concrete Execution09:29 - Why 3-Year Strategic Plans Work Best10:50 - Step 0: Assessing Organizational Readiness12:51 - Navigating Internal Tension & Staff Culture15:45 - The 3 Circles of Participation (Deciders, Builders, Sharers)18:46 - Translating Your Plan into Action (Using RACI)22:13 - What You Lose by Excluding Frontline Staff23:35 - Where to Find Dr. Ross About Donorbox: Donorbox is a trusted fundraising platform used by more than 100,000 organizations to raise over $4 billion worldwide. Learn more at donorbox.org.Enjoying the show? Subscribe for more practical fundraising strategies, leadership insights, and tools to help your nonprofit grow sustainably.This podcast is for educational purposes only and is not legal or financial advice. Please consult a professional advisor for guidance specific to your organization.Support the show

Ako funguje najväčší digitálny fotoaparát na svete? Dá sa z nočnej oblohy vytvoriť desaťročný film? Dokážeme zachytiť zmeny vo vesmíre v priebehu pár minút? Odhalí nové observatórium tajomstvá tmavej hmoty a energie? O tom všetkom diskutujú Jozef, Samuel a ich hosťka Danica Žilková.   Podcast vzniká v spolupráci so SME.    Registrujte sa na živé nahrávanie: https://www.eventbrite.com/e/vedatorsky-podcast-nazivo-kofein-tickets-1996801800939   Bonusové epizódy a extra obsah k podcastom nájdete na https://herohero.co/vedator   Samuelova nová kniha už je v predaji https://www.martinus.sk/3600333-limity-poznania/kniha   Otázky nám môžete nahrávať tu https://www.speakpipe.com/vedator Podcastové hrnčeky a ponožky nájdete na stránke https://vedator.space/vedastore/   Všetko ostatné nájdete tu https://linktr.ee/vedatorsk Vedátorský newsletter http://eepurl.com/gIm1y5  

Three of the oldest myths about Jews — relating to money, blood and the way they look — were forged in medieval Christian Europe, mostly in places where there were almost no Jews to observe. Miri Rubin, professor of medieval and early modern history at Queen Mary University of London, tells Jonathan how each one of those myths was built — and explains how that medieval way of seeing Jews often remains the lens through which Israel, and the Palestinian-Israeli conflict, is seen. They also discuss the reckoning with Christendom's past that followed the Holocaust - and how it has seemingly come to a halt in our own time.  

Dave Rubin of "The Rubin Report" talks with Ben Shapiro, Jordan Peterson, and Eric Weinstein about the origins of the Intellectual Dark Web; why free speech, open debate, and civil disagreement became the foundation of their conversations; how challenging the strongest version of opposing arguments leads to better thinking; why identity politics and ideological tribalism undermine meaningful discourse; the importance of intellectual humility, curiosity, and changing your mind; self-improvement versus systemic problems; entrepreneurship, individual responsibility, and institutional failure; the future of political conversation; and much more.   Today's Sponsors: Kalshi - Download the Kalshi app and use code RUBINREPORT to get up to $500 in bonus credits when you trade $25 or go to https://kalshi.com/r/RUBINREPORT. DISCLAIMER: 18+ only. Restrictions and eligibility requirements apply. Event contract trading involves risk and may not be suitable for all investors. Prices, values, and available markets may differ from those mentioned. For more information see kalshi.com/regulatory BUBS Naturals - BUBS helps restore collagen levels closer to what your body had in its youth—so your joints feel stronger, your hair and nails grow healthier, and your skin looks smoother. Live Better Longer with BUBS Naturals. For a limited time get 20% Off your entire order with code RUBIN at Bubsnaturals.com Relief Factor - Relief Factor is an alternative to help your body's natural inflammatory response so you can keep doing the things you love. Try Relief Factor's 3-Week QuickStart—just $17.76. Go to: https://www.relieffactor.com or call 800-4-RELIEF. Monetary Gold- For twenty six years, Monetary Gold has been helping Americans find financial freedom through physical gold and silver. Visit http://Davelovesgold.com or call (800) 888-4096 for a one on one consultation. Disclaimer: Performance may vary, consult your tax or financial professional.

This week the boys are joined by Sara Rubin to talk Masquerade! MORE SARA! https://www.instagram.com/saraerubin HOSTED BY Shane Madej & Matt Real THEME & MUSIC BY Nick Lutsko https://www.youtube.com/@realnicklutsko ANIMATION BY Jared Sosa https://instagram.com/scienceisfun POD ART BY Craig Gleason https://www.instagram.com/the.moon.ghoul/ PRODUCED & EDITED BY Rebekah Swann https://www.instagram.com/rebekah.swann/ For ad free, live streams, and cut content join us at https://watchertv.com More Pod: https://www.instagram.com/thequizardspit More Watcher: https://www.instagram.com/wearewatcher https://youtube.com/wearewatcher More Shane: https://www.instagram.com/shanemadej https://www.youtube.com/@PuppetHistoryInc More Matt: https://instagram.com/mattyistalking https://www.youtube.com/@MattyIsTalking https://twitch.tv/mattyistalking Send us an email QuizardsPit@gmail.com or leave a voicemail at (818) 275-4585 Learn more about your ad choices. Visit podcastchoices.com/adchoices

Dave Rubin of "The Rubin Report" talks to Paul Saladino about why processed sugar from donuts affects the body differently than natural sugar in fruit, honey, and maple syrup; the "great-grandmother rule" for avoiding ultra-processed foods by choosing single-ingredient whole foods; why fiber, phytochemicals, and quality animal protein support metabolic health; why he disagrees with Oprah Winfrey's claim that obesity is caused by a "fat gene" and argues that processed foods drive "food noise"; how GLP-1 drugs like Ozempic reduce cravings without addressing the root cause of obesity; why processed foods alter dopamine, cravings, and brain chemistry; the dangers of smartphone addiction, doomscrolling, and social media; how boredom, digital detoxes, and reducing screen time can boost creativity, focus, and mental clarity; practical ways to break phone addiction; and much more. Today's Sponsors: Kalshi - Download the Kalshi app and use code RUBINREPORT to get up to $500 in bonus credits when you trade $25 or go to https://kalshi.com/r/RUBINREPORT. DISCLAIMER: 18+ only. Restrictions and eligibility requirements apply. Event contract trading involves risk and may not be suitable for all investors. Prices, values, and available markets may differ from those mentioned. For more information see kalshi.com/regulatory Quo- Never miss an important business call again. Try QUO for free PLUS get 20% off your first 6 months. Go To: http://www.quo.com/RUBIN Sunday for Dogs - Give your dog the quality of a home-cooked meal with the scoop and serve ease of kibble. Make the switch to Sundays. Get 50% off your first order with link below or use code RUBIN at checkout. Go to: https://sundaysfordogs.com/RUBIN Mars Men - A potent and natural testosterone stack that optimizes your body's ability to forge usable testosterone. For a limited time, our listeners get 50% off FOR LIFE AND 3 Free Gifts at Mars Men when you use code RUBIN . Go to: http://Mengotomars.com

Hosted by Rob Sparks. The 10-year Legacy Survey of Space and Time has officially started, marking the beginning of a new era in astronomy and astrophysics. Over the next ten years, Rubin will relentlessly observe the entire southern sky every few nights to create an ultra-wide, ultra-high-definition time-lapse record of our Universe. This long-awaited milestone is the culmination of years of effort by thousands of people around the world. In this podcast, Dr. Christina Williams and Dr. Yumi Choi describe the LSST and its science goals as well as what they are most excited about researching with its groundbreaking data.    Bios:  - Rob Sparks is in the Communications, Education and Engagement group at NSF's NOIRLab in Tucson, Arizona. - Dr. Yumi Choi is an Assistant Astronomer at NSF NOIRLab. Her research focuses on understanding the formation and evolution of nearby galaxies. Her primary work utilizes high-precision, multi-band photometry to analyze resolved stellar populations in those galaxies. Beyond my independent research programs, she is a dedicated member of the Rubin Observatory Community Science Team.   Dr. Choi earned her PhD in Astronomy from the University of Washington in 2016. She subsequently held postdoctoral research positions at the University of Arizona's Steward Observatory, Montana State University, and the Space Telescope Science Institute (STScI). Prior to her current appointment at NSF NOIRLab in 2023, she spent a year as an Assistant Researcher at the University of California, Berkeley.   - Dr. Christina C. Williams is an Associate Astronomer at NSF NOIRLab in Tucson, Arizona And an Assistant Research Professor at University of Arizona and a member of the James Webb Space Telescope (JWST) NIRCam instrument and science teams. Her research focuses on high-redshift galaxies, galaxy evolution, and star formation in the early Universe.   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

Nvidia Reporter Phoebe Liu talks with guest TITV Host Stephanie Palazzolo about Nvidia's proposed solution to the high-bandwidth memory crunch for its next-generation Rubin chips. We also talk with Creative Strategies' Max Weinbach about Meta's new Muse Code agent and Muse Spark 1.2 model, The Information's Co-Executive Editor Martin Peers about Google DeepMind's executive shakeup and what it signals for Alphabet's future leadership, and Radical Ventures Partner Rob Toews about backing Discovery Loop, the new AI scientific research startup founded by Google's former chief scientist Jeff Dean.Articles discussed on this episode: https://www.theinformation.com/articles/dario-amodei-spread-anthropics-religion-stirred-silicon-valleyhttps://www.theinformation.com/articles/nvidia-weighs-radical-idea-less-rubin-ultra-chip-memorySubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction00:01 - Nvidia Weighs Putting Less Memory in Rubin Chips00:09 - Meta Debuts New AI Coding Tools00:20 - What Google's AI Shakeup Means for Pichai Succession00:30 - Radical Ventures Backs AI Startup Founded by Ex-Google Leaders

Hey Popcorn Pals! We're joined by our favorite Pal Lissa Rubin to talk all about Spider-Man: Brand New Day! FYI - This a NOT a Spoiler-Free episode! You have been warned! Have you seen Spider-Man: Brand New Day yet? Did you love it? Hate it? We wanna know! Check out The Road to Doomsday here: https://roadtodoomsday.com/ See you at the movies! –––––––––––––––––– You can follow Popcorn Pals on Instagram and Letterboxd @PopcornPalsPod and on TikTok @PopcornPals.Pod. Email us with your thoughts, opinions, and questions at PopcornPalsPod@gmail.com. Popcorn Pals is produced by Clara Madenwald, our social media is run by Katelyn Bordini, and our artwork was made by Angela Rodgers. The intro for Popcorn Pals uses graphics from You Want Pizzazz ?!? and sounds from Freesound.org: “COMCam_8mm Movie Projector (Rus’) 01_KVV AUDIO_Free” by KVV Audio (https://freesound.org/s/830217/) licensed under Attribution 4.0. “Film Countdown” by qubdodup (https://freesound.org/s/182110/) licensed under Creative Commons 0. “Lofi Piano + Drum Loop” by Doomsdaybear (https://freesound.org/s/720123/) licensed under Attribution 4.0.

Many diagnoses involve laboratory diagnostics, yet patients with autoimmune and allergic disease still face delayed or incomplete testing. In this live recording of ImmunoCAST from Lab Week 2026, featuring allergist Dr. Zachary Rubin and autoimmune specialist Dr. Crista Prates, we examine how laboratories can close the gap with smarter clinician partnerships, reflex and tiered testing, ANA follow-up strategies, anti-CCP and rheumatoid factor interpretation, and comprehensive aeroallergen and food allergy diagnostics. The discussion also covers seronegative disease, spondyloarthritis, chronic sinusitis, allergen component diagnostics, and when sensitization does—or does not—equal clinical allergy. Listeners will gain practical insights to help improve diagnostic accuracy, reduce patient anxiety, and support earlier treatment decisions. References and resources: https://www.thermofisher.com/phadia/us/en/resources/immunocast/labs-to-clinic-closing-autoimmune-allergy-testing-gaps.html?cid=0ct_3pc_05032024_9SGOV4

Dylan Dreyer helps viewers navigate some awkward situations in a new edition of "Social Dilemmas. Also, financial expert Farnoosh Torabi shares strategies to help you keep more money in the pocket during back-to-school shopping season. Plus, Ella Rubin stops by studio 1A to talk about her new role in "Sterling Point." And, producer Sean Hickey hosts a game of "Get a Clue" to test how well Dylan and Sheinelle know each other. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today, we continue our creativity series with an audience favourite: Rick Rubin.Join Mike and Mark as they uncover the secrets of creativity with the legendary Rick Rubin. In a captivating discussion with Andrew Huberman, Rubin reveals his unique approach to the creative process and the importance of selective focus. Doug Neill from ‘Verbal to Visual' breaks down Rubin's four phases of creative work, from the inception of an idea to its playful exploration, meticulous crafting, and completion.This episode offers insights for anyone looking to harness their creative potential.Clips FeaturedIntro: Rick Rubin talks to Andrew Huberman about selective focus and creative energy.Hold Problems Lightly: Doug Neill explains the role of ideas' inception and intuition.Experimentation: Doug discusses the playful exploration of possibilities without judgment.Crafting: Understanding Rubin's ‘winding staircase' approach to meticulously shaping and refining ideas.Completion: The final stage of refinement and launching projects ready for the world.Listen to the full episode here: Rick Rubin: The Creative ActFor a detailed book summary, check out Summary of The Creative Act by Rick RubinWatch the episode on YouTube: Rick Rubin: The Creative Act - Moonshots PodcastBecome a member and support the show: Patreon - MoonshotsKey Concepts and InsightsSelective Focus and DisengagementRick Rubin emphasises the importance of focusing selectively and the power of disengagement to channel creative energy effectively. This approach allows creatives to manage their energy and maintain a fresh perspective.The Four Phases of Creative WorkInception: Intuition plays a critical role in the early stages of an idea. Trusting one's instincts can lead to the birth of innovative concepts.Experimentation: Exploring possibilities without judgment is crucial for innovation. This phase involves playful exploration and openness to new ideas.Crafting: Rubin likens this phase to a ‘winding staircase 100 stories tall,' where meticulous shaping and refining of the idea takes place.Completion: The final stage is about refinement, perspective, and finalising the project or product, making it ready for the world to see.Holding Problems LightlyRubin advocates for holding problems lightly, which means not getting bogged down by challenges and maintaining a flexible approach to problem-solving.Thanks to our monthly supportersMikeEdwin DeitchJamie DorwardEmily Rose BanksMalcolm MageeNatalieRyan N.Marco-Ken Möller孤鸿 月影FabianJasper VerkaartAndy PilaraOlaAustin HammattZachary PhillipsMike Leigh CooperGayla SchiffLaura KERoar Nikolay Ytre-EideStefRoger von Holdtvenkata reddyIngram CaseyOlarahul groverRavi GovenderCraig LindsaySteve WoollardDeborah SpahrSamoelaJo HatchardKalman CsehBerg De BleeckerPaul AcquaahMrBonjourKonnor Ah kuoiMarjan ModaraDietmar BaurBob Nolley ★ Support this podcast on Patreon ★

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

In this episode, host Crist and Andrew discuss staying curious, SALT, and getting involved in the Tax Section. From renewable energy to tribal clinics, to affordable housing, examine Andrew's extensive background and passion for tax.

Bob Zimmerman notes China continues the dangerous practice of dropping uncontrolled rocket debris on its citizens, forcing them to hide in tunnels. In the commercial sector, the startup Catalyst attempted a high-speed satellite rescue mission, demonstrating rapid launch capabilities despite technical failures. Meanwhile, astronomers have identified a rare midsize black hole, a discovery that will likely be expanded upon as the new Rubin telescope begins its sky surveys. (16)1941

SCHEDULE FOR THE JOHN BATCHELOR SHOW, 7-31-20261790 SOUTH HOLLANDJeff Bliss reports a refrigeration fire in Boyle Heights has released a toxic stench and caused an explosion in local rat and insect populations. Residents remain displaced due to overpowering toxic aftereffects. Simultaneously, a massive heat dome and hurricane-driven surf are hitting the Southwest. L.A. County lifeguards performed nearly 1,200 rescues last Saturday, facing dangerous conditions and substance abuse issues on crowded beaches escaping the intense heat. (1)Jeff Bliss notes Governor Gavin Newsom is preparing for a 2028 presidential run, but past controversies are resurfacing. A Vanity Fair article by Ruby Rippey-Tourk, his former mistress and the wife of his then-campaign manager, accuses Newsom of being "careless" and "cruel" during their 2007 affair. His wife, Jennifer Siebel Newsom, is also being criticized for shaming Rippey-Tourk, leading to questions about the Newsoms' commitment to women's advocacy. (2)Richard Epstein examines New York City's proposed "pied-à-terre tax," which targets non-primary residences but threatens middle-class and working homeowners. Epstein argues the tax is unfairly structured, hitting condominiums at five times the rate of houses. The complex valuation process creates a "vicious circle" that could plummet property values. Consequently, wealthy residents are already fleeing, resulting in a $12 billion annual revenue loss for the city. (3)Richard Epstein turns to Deputy Attorney General Todd Blanche, under fire for signing a document that allegedly exempts the President from IRS scrutiny regarding past and future taxes. Epstein characterizes this as a "scandalous" misuse of public trust and a misapplication of the law. He argues that if the President used his office for private financial benefit, it constitutes an impeachable offense, while Blanche should face potential disbarment. (4)Jim McTague takes a road trip through Owensboro, Kentucky, revealing a "K-shaped" economy where retail business is "dead" for blue-collar residents relying on federal aid. Conversely, Jeffersonville, Ohio, is experiencing a "gold rush" as Amazon and Honda build massive data and battery centers. However, some local residents feel overwhelmed by progress, lacking the imagination or education to capitalize on these high-tech industrial shifts and training opportunities. (5)Jim McTague reports that in Lancaster County, Republican representatives are introducing legislation that adds red tape and taxes to data centers, contradicting traditional pro-growth principles. While the Democratic-led Lancaster Cityhas embraced a lucrative data center deal, blue-collar "Trump country" boroughs remain fearful of the future. Despite these tensions, the local economy remains "steady" due to a diversified base including agriculture, pharmaceuticals, and senior citizen spending. (6)Peter Huessy explains the foundation of U.S. nuclear strategy is the triad, ensuring launch capabilities from land, sea, and air. Developed after the 1957 Sputnik launch, this framework replaced "massive retaliation" with "flexible response." Huessy explains that this survivable deterrent prevents adversaries from achieving a total victory with a single strike. Despite calls for unilateral disarmament, the triad remains essential for holding at risk what adversaries value most. (7)Peter Huessy warns the U.S. now faces a tri-polar nuclear threat from Russia and China, both of which are modernizing their arsenals "gangbusters." Meanwhile, U.S. systems haven't been updated in 40 years, leaving the deterrent "fragile." Allied nations in Asia, specifically South Korea and Japan, are increasingly questioning the U.S. nuclear umbrella and considering their own deterrents as regional tensions and Chinese missile capabilities grow. (8)Henry Sokolski reports a proposed nuclear agreement with Saudi Arabia is causing alarm due to potential uranium enrichment capabilities. Sokolski warns that nuclear fuel-making brings a nation dangerously close to developing a bomb. Saudi Arabia's refusal to accept advanced "protocol one" inspections is a major "red flag." Furthermore, the deal risks overturning 50 years of global non-proliferation policy, potentially encouraging other nations like Iran to demand equal treatment. (9)Henry Sokolski notes Senator John Kennedy has emerged as a dogged opponent of granting Saudi Arabia enrichment authority, labeling it a "bad idea." The agreement will automatically go into force within 90 legislative days unless Congress passes a veto-proof resolution against it. Kennedy's vocal stance may rally other senators to stop the package, potentially forcing the White House to renegotiate the deal to prevent spreading dangerous nuclear capabilities. (10)Gene Marks reports accountants in California are grappling with AI integration, viewing it as a productivity tool rather than a replacement for human consultation. Meanwhile, the broader economy remains resilient; manufacturing has expanded for five consecutive months despite high gas prices and elevated interest rates. Marks highlights that businesses are "rolling with inflation" and increasing orders for durable goods, indicating high underlying confidence despite stock market volatility. (11)Gene Marks compares public protests against data centers to the 19th-century opposition to railroads. While critics worry about land and water usage, Marks argues these facilities are imperative infrastructure for the information age and the AI revolution. Just as railroads enabled 20th-century American power, data centers are the means for moving information. Ceding this development would allow rivals like China or Russia to dominate the future economy. (12)Veronique de Rugy warns new administration rules replace flexible status for foreign students with fixed four-year periods, targeting the "best educated people on Earth." De Rugy warns this policy dissuades future scientists and innovators from choosing the U.S. Restricting these STEM graduates could cost the economy between $200 billion and $400 billion in lost GDP over the next decade, while potentially causing severe long-term labor shortages. (13)Lorenzo Fiori reports a historic heatwave has pushed the Po River to record lows, devastating Italy's agricultural heartland and water supplies for power stations. The ecological crisis includes an invasion of blue crabs, which are destroying clam populations and disrupting local cuisine. Meteorologists struggle to forecast as traditional weather models fail in the face of climate change. Tourists are encouraged to seek the cooler, historical beauty of Noto, Sicily. (14)Bob Zimmerman reports SpaceX's Starship successfully floated for a week after splashdown, providing invaluable data for rapid reusability. The company further cemented its market dominance with a $1.6 billion Space Force contract, while competitors remain grounded. Additionally, startups are emerging to solve technical hurdles for orbital data centers, such as advanced heat radiation. These advancements indicate that the future of massive data processing may eventually move into permanent space orbit. (15)Bob Zimmerman notes China continues the dangerous practice of dropping uncontrolled rocket debris on its citizens, forcing them to hide in tunnels. In the commercial sector, the startup Catalyst attempted a high-speed satellite rescue mission, demonstrating rapid launch capabilities despite technical failures. Meanwhile, astronomers have identified a rare midsize black hole, a discovery that will likely be expanded upon as the new Rubin telescope begins its sky surveys. (16)Corrections applied, pending your confirmation: Ruby Rippey-Tourk (transcribed as "Ruby Rippy," segment 2), Todd Blanche (transcribed as "Mr. Blanch," segment 4), Rubin telescope (transcribed as "Reuben," segment 16 — the Vera C. Rubin Observatory), and "burrows" → "boroughs" in segment 6. One to flag: this batch has Lorenzo Fiori (segment 14), but an earlier batch had him as "Lorenzo Fiato" — let me know which spelling is correct and I'll log it for good.

Discover how a jazz pianist turned niche entertainment work into a powerhouse advertising, privacy, and tech practice—and why treating business development as a marathon, not a sprint, has allowed him to build a loyal, decades‑long client base. In this episode, you'll hear practical, battle‑tested strategies for specialization, client relationships, and training the next generation of rainmakers.   In this episode, Steve Fretzin and Jamie Rubin discuss: Lawyer's journey as a marathon, not a sprint Building a niche/specialist practice and moving firms Impact of legal operations, budgets, and alternative fee arrangements Personal branding vs. firm branding and the “Info Law Group way” Training younger lawyers in organized, non-salesy business development   Key Takeaways: Treating a legal career like a marathon allows room for ebbs and flows in work while staying confident that consistent effort will pay off over time. Specializing deeply in a niche practice area or industry builds trust and makes it easier for clients to follow you, even when you change firms. Modern in‑house legal operations demand sharper budgeting, clearer estimates, and more flexible fee arrangements, forcing outside counsel to become better business partners. Strong client loyalty grows from genuine personal relationships—helping clients succeed, understanding how they are judged internally, and making them look good in front of their own stakeholders. Younger lawyers can learn business development by following a structured, organized process that avoids pushy “sales” tactics and instead focuses on thoughtful questions, careful listening, and well‑practiced messaging.   "My big mistake is not truly understanding my client's perspective." —  Jamie Rubin   Check out my new show, Be That Lawyer Coaches Corner, and get the strategies I use with my clients to win more business and love your career again.   Join the Be That Lawyer Community and connect with ambitious lawyers who are serious about growing their book of business, strengthening their brand, and becoming confident, consistent rainmakers.   Ready to go from good to GOAT in your legal marketing game? Don't miss PIMCON—where the brightest minds in professional services gather to share what really works. Lock in your spot now: https://www.pimcon.org/   Thank you to our Sponsor! LEX Reception: https://www.lexreception.com/partners/bethatlawyer Rankings.io: https://rankings.io/ Lawyer.com: https://www.lawyer.com/   Ready to grow your law practice without selling or chasing? Book your free 30-minute strategy session now—let's make this your breakout year: https://fretzin.com/   About Jamie Rubin: Jamie Rubin is an advertising, technology, and privacy lawyer who guides clients through bringing traditional and emerging campaigns from initial concept to execution. Ranked as a Leader by Chambers and Partners and recognized by Best Lawyers, his practice covers national and digital advertising, celebrity agreements, sweepstakes, social media marketing, and claim substantiation. Certified as an Artificial Intelligence Governance Professional (AIGP) and a member of the International Association of Privacy Professionals (IAPP), Jamie previously practiced as a partner at Wildman Harrold Allen & Dixon LLP and was named one of the "40 Illinois Attorneys Under 40 To Watch" by Law Bulletin Publishing Company.   Connect with Jamie Rubin:   Website: https://www.infolawgroup.com/jamie-rubin LinkedIn: https://www.linkedin.com/in/jrubinadlawyer/   Connect with Steve Fretzin: LinkedIn: Steve Fretzin Twitter: @stevefretzin Instagram: @fretzinsteve Facebook: Fretzin, Inc. Website: Fretzin.com Email: Steve@Fretzin.com Book: Legal Business Development Isn't Rocket Science and more! YouTube: Steve Fretzin Call Steve directly at 847-602-6911   Audio production by Turnkey Podcast Productions. You're the expert. Your podcast will prove it. 

Dr. Zarya Rubin trained for fifteen years to become a doctor, then walked away from medicine at the height of her career. On this episode of Coaching to Flourish, the Harvard-educated former neurologist known as "The Burnout Doctor" tells hosts Raj Anderson and John Andrew Williams what it took to recognize her own burnout, and why so many high achievers miss the signs until a crisis forces the issue.Zarya breaks down the three pillars of burnout first defined in the 1970s: exhaustion that sleep and vacation cannot fix, cynicism, and a drop in professional efficacy. She explains why calling burnout a "workplace problem" puts too much of the burden on the individual, how moral injury and compassion fatigue build in caring professions, and why we treat exhaustion like a badge of honor until our bodies force a reckoning.The conversation covers the antidotes too: giving people more control and clearer decision rights, recognition over criticism, putting the right people in the right seats, and Zarya's own TEDx mantra, "do one thing less." The episode closes with a candid look at "AI fry," the new mental fatigue that comes from processing information faster than our brains were built to handle.If you lead a team, coach high performers, or you have ever said "I'll sleep when I'm dead," this one is for you.Connect with Dr. Zarya Rubin:Website: https://drzarya.comFree Outsmart Burnout Toolkit: https://drzarya.myflodesk.com/outsmartburnouttoolkitBook a 1:1 SOS Session: https://drzarya.myflodesk.com/Watch her TEDx talk: https://bit.ly/TEDxBurnoutInstagram: @drzaryarubin and @theburnoutMDHer podcast, Outsmart Burnout, on Apple Podcasts and SpotifyCoaching to Flourish is hosted by Raj Anderson, founder of Coachability, and John Andrew Williams, founder of Coach Training EDU and creator of Top Coach.For more information on Coach Training EDU, please visit https://www.CoachTrainingEDU.com. For more information on Coachability, please visit https://www.Coachability.usIf you're interested in becoming an ICF-credentialed coach, check out Coach Training EDU's programs and schedule a call with our team today.

Help Wanted: How to Find and Keep Talent in a Forever-Tight Labor Market by Jerry Rubin, Steven L. Dawson https://www.amazon.com/dp/0262054396/ https://www.helpwantedbook.com/ How employers can compete in a challenging labor market—by hiring differently, redesigning job quality, and rethinking their government relations strategies. From two leaders in business and workforce development with a proven track record in innovative workforce strategies. Over the past ten years, the U.S. labor market has been buffeted, with demand shifting dramatically. At the same time, demographic trends mean that U.S. labor supply will steadily tighten into the coming decade: Relatively fewer native-born workers are entering the workforce; a greater percentage are choosing not to work; and an aging baby boom generation is exiting the workforce. What's worse, this systemic tightening of the US labor market has been exacerbated by the Trump administration's decision to dramatically constrain immigration, the primary source of recent labor market growth. Welcome to the “forever-tight” labor market. In Help Wanted, Jerry Rubin and Steven Dawson combine their 80 years of experience in management and workforce development to offer employers an “employee-focused competitive strategy.” This strategy includes a wide range of practical, well-researched tools and detailed case studies from a breadth of employers that will help readers compete for talent and retain a productive workforce. Since the underlying cause of the tightening labor supply is structural, employers—whether for-profit, nonprofit, or governmental—must respond in kind. They too must undertake structural changes in finding, hiring, and retaining workers. Now equal in importance to an organization's customer-service strategy, an employee-focused strategy is no longer simply a competitive advantage—it is fast becoming a competitive necessity. About the author Jerry Rubin is a Senior Advisor at the Harvard Project on the Workforce, a Foundation Fellow at the Eastern Bank Foundation and Co-Chair of the MassSkills Coalition. Most recently, he was CEO and President of JVS-Boston, a 250-employee organization that consults directly to businesses seeking job candidates recruited and trained by JVS. About the author Steven L. Dawson was founder and President of PHI, which consults on job-quality redesign to a range of private long-term care businesses and public healthcare/employment agencies. Earlier, he was founder and President of The ICA Group, which consults to and finances employee‐owned enterprises nationwide.

A new Lancet study on IVF add-ons made headlines, and acupuncture was one of the treatments under review.  Dr. Lee Hullender Rubin joins Dr. Susan Fox to discuss what the study actually found, what it got right, and what it missed.  Dr. Lee explains that studies using one or two sessions on transfer day was an insufficient dose.research was done on fresh embryo transfers, but almost nobody does fresh anymore.“sham acupuncture” isn't placebo..This episode is for you if:you saw the headlines and want the full pictureyou're planning IVF and considering acupuncture you're thinking acupuncture on embryo transfer day will helpyou want to understand what "sham" acupuncture is and why it mattersSupport your fertility journey with Preconception Plan at Health Youniversity. Learn more here: https://healthyouniversity.co/programs Want to know more about the latest IVF acupuncture study? Read the full article here: https://www.rosefinchhealth.com/blog/acupuncture-ivf-major-new-study-fuller-story Website: https://www.rosefinchhealth.com/ 

It's a very casual Friday today on The Majority Report On today's program: Rep. Alexandria Ocasio-Cortez says she is not worried about this supposed "slippery slope" from socialism to communism, citing how we can't even get Congress to raise the minimum wage. Laura Ingraham lusts for AOC on Fox News, describing her as a "an attractive millennial with...raven hair and red lipstick". Mediaite posts a New York Post article from 1999 reporting a tongue-in-cheek story on Lindsey Graham and Laura Ingraham allegedly dating. Krystal Ball, co-host of Breaking Points and Krystal, Kyle and Friends, joins the program to recap the week's news. In the Fun Half: Elon Musk is interviewed by the Economist, and he folds like a cheap suit. Musk, the trillionaire, accuses the interviewer of living in a cloistered existence. Dave Rubin has a manic meltdown over Jon Stewart's interview with Zohran Mamdani in a rant that devolves into Rubin hurling antisemitic slurs at Stewart. Ben Shapiro begs for Trump to commit war crimes in Iran. all that and more. To connect and organize with your local ICE rapid response team visit ICERRT.com The Congress switchboard number is (202) 224-3121. You can use this number to connect with either the U.S. Senate or the House of Representatives. Follow us on TikTok here: https://www.tiktok.com/@majorityreportfm Check us out on Twitch here: https://www.twitch.tv/themajorityreport Find our Rumble stream here: https://rumble.com/user/majorityreport Check out our alt YouTube channel here: https://www.youtube.com/majorityreportlive Gift a Majority Report subscription here: https://fans.fm/majority/gift Subscribe to the AM Quickie newsletter here: https://am-quickie.ghost.io/ Join the Majority Report Discord! https://majoritydiscord.com/ Get all your MR merch at our store: https://shop.majorityreportradio.com/ Get the free Majority Report App!: https://majority.fm/app Go to https://JustCoffee.coop and use coupon code majority to get 10% off your purchase Check out today's sponsors: ONESKIN: Get 15% off OneSkin with the code MAJORITY at https://www.oneskin.co/majority SUNSET LAKE CBD: Use coupon code "Left Is Best" (all one word) for 20% off of your entire order at SunsetLakeCBD.com.  Follow the Majority Report crew on Twitter: @SamSeder @EmmaVigeland @MattLech On Instagram: @MrBryanVokey Check out Matt's show, Left Reckoning, on YouTube, and subscribe on Patreon! https://www.patreon.com/leftreckoning Check out Matt Binder's YouTube channel: https://www.youtube.com/mattbinder Subscribe to Brandon's show The Discourse on Patreon! https://www.patreon.com/ExpandTheDiscourse Check out Ava Raiza's music here! https://avaraiza.bandcamp.

Dave Rubin of "The Rubin Report" talks about Zohran Mamdani backing down on his plan to arrest Benjamin Netanyahu to hand him over to the International Criminal Court if he enters New York City, as Elon Musk endorses the idea of throwing Mamdani out of office and denaturalizing him; Palantir CEO Alex Karp telling Axios the real reason underemployed, overeducated Ivy League grads are supporting Democratic Socialist policies like those pushed by Zohran Mamdani; "The Daily Show's" Jon Stewart admitting to Ben Wikler that the Democratic Socialists do have some pretty insane ideas like abolishing prisons and open borders; NYC-DSA Co-Chair Grace Mausser getting cornered by NewsNation's Katie Pavlich when she refuses to denounce Hasan Piker's calls for political violence against capitalists and landlords as he continues to actively campaign for Democratic Socialists across America; Scott Jennings humiliating CNN's Abby Phillip for her pathetic attempts to deflect from New Jersey Governor Mikie Sherrill admitting that over 6,600 non-citizens were accidentally registered to vote due to a software error, giving Republicans more ammunition to push the SAVE Act to use voter ID to ensure election integrity; Rep. Brandon Gill grilling Smithsonian Director Anthea M. Hartig about some of the more radical ideas promoted by the Smithsonian under her leadership, such as Mickey Mouse being racist and politeness and objectivity being associated with white supremacy; and much more. Today's Sponsors: Kalshi - Download the Kalshi app and use code RUBINREPORT to get ten dollars when you trade ten or go to https://kalshi.com/r/RUBINREPORT DISCLAIMER: 18+ only. Restrictions and eligibility requirements apply. Event contract trading involves risk and may not be suitable for all investors. Prices, values, and available markets may differ from those mentioned. For more information see kalshi.com/regulatory Chapter - Optimize the one piece of your health most people never audit: the Medicare coverage that pays for all of it. For free and unbiased Medicare help: Dial (332) 867-0207 or Go to: https://askchapter.org/rubin Chapter and its affiliates are not connected with or endorsed by any government entity or the federal Medicare program. Chapter Advisory, LLC represents Medicare Advantage HMO, PPO, and PFFS organizations and stand alone prescription drug plans that have a Medicare contract. Enrollment depends on the plan's contract renewal. While we have a database of every Medicare plan nationwide and can help you to search among all plans, we have contracts with many but not all plans. As a result, we do not offer every plan available in your area. Currently we represent 50 organizations which offer 18,160 products nationwide. We search and recommend all plans, even those we don't directly offer. You can contact a licensed Chapter agent to find out the number of products available in your specific area. Please contact Medicare.gov, 1-800-Medicare, or your local State Health Insurance Program (SHIP) to get information on all of your options. Lean - A powerful weight loss supplement with remarkable results to help lower blood sugar, burn fat by converting it into energy, and curb your appetite. Rubin Report viewers get 20% off plus free rush shipping off their first order! Go to: https://TakeLean.com and enter promo code RUBIN for your discount.

Dave Rubin of "The Rubin Report" talks about CNN's Harry Enten sharing new polling data that should scare the Democratic establishment and Chuck Schumer as more incumbent Democrats face potential losses in their primaries to Democratic Socialists; CNN's Van Jones going viral for sounding the alarm about crazy Hamas-supporting Democratic Socialist radicals like Darializa Avila Chevalier taking over the Democratic Party; Nate Friedman breaking down the exact pitch that the Democratic Socialists are using to recruit young DSA members; Scott Jennings losing his cool when CNN's Sara Sidner tried to blame Andrew Arrabaca's anti-ICE incendiary device attack at a New York City federal immigration court on the tactics of ICE; new data showing how many excess deaths in Africa have resulted from Elon Musk's DOGE cuts to USAID and U.S. foreign aid; Zohran Mamdani doubling down on his plan to arrest Benjamin Netanyahu to hand him over to the International Criminal Court if he enters New York City; and much more. Today's Sponsors: Kalshi - Download the Kalshi app and use code RUBINREPORT to get ten dollars when you trade ten or go to https://kalshi.com/r/RUBINREPORT DISCLAIMER: 18+ only. Restrictions and eligibility requirements apply. Event contract trading involves risk and may not be suitable for all investors. Prices, values, and available markets may differ from those mentioned. For more information see kalshi.com/regulatory Plaud- Stay informed without drowning in content. Plaud helps turn long interviews, podcasts, and discussions into searchable notes and summaries in seconds. Use code RUBIN for up to 20% OFF Go to: https://Plaud.ai/rubin Prolon- Prolon's Fasting Mimicking Diet is a revolutionary, plant-based nutrition program that nourishes the body while keeping it in a fasting state. Prolon is offering 15% off plus a $40 bonus gift when you subscribe to the 5-Day Program. Go to: http://ProlonLife.com/DAVE

Josh opens the show by breaking down the failed domestic terror attack in New York City and why it serves as yet another stark reminder that the threat of terrorism—particularly homegrown extremism—remains very real. He explains why national security starts with strong immigration enforcement, argues that America must be far more selective about who is allowed into the country, and warns against complacency as new threats continue to emerge. Josh then turns to the latest developments in Iran, examining where the conflict stands, what the administration's next moves could be, and why Democrats continue to attack the White House's handling of the war. As the situation in the Middle East rapidly evolves, Josh separates political rhetoric from the strategic realities on the ground. Josh welcomes Joel Rubin, a former Bernie Sanders supporter, to discuss why he now believes Sen. Bernie Sanders has become one of the Democratic Party's leading anti-Israel voices. Rubin explains how the party's position on Israel has dramatically shifted, why anti-Israel sentiment has gained influence within Democratic circles, and what that transformation could mean for the future of U.S.-Israel relations.See omnystudio.com/listener for privacy information.

If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects.  In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge.  So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below.  Thank you and enjoy the episode!Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Substackhttps://substack.com/@theoccultrejects?r=7auau0&utm_campaign=profile&utm_medium=profile-pageCash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsPrimary Ancient SourcesAmbrose of Milan. De Virginibus / Concerning Virgins.Aristophanes. Lysistrata.Euripides. Iphigenia at Aulis.Euripides. Iphigenia among the Taurians.The Homeric Hymn to Demeter.Jerome. Against Helvidius: On the Perpetual Virginity of Blessed Mary.The Gospel According to Matthew, chapters 1–2.The Gospel According to Luke, chapters 1–2.Ovid. Fasti.Pausanias. Description of Greece.Philo of Alexandria. On the Contemplative Life.Plutarch. Life of Numa.Protoevangelium of James / Infancy Gospel of James.The Acts of Paul and Thecla.Core Academic SourcesBurkert, Walter. Greek Religion. Translated by John Raffan. Cambridge, MA: Harvard University Press, 1985.Castelli, Elizabeth A. “Virginity and Its Meaning for Women's Sexuality in Early Christianity.” Journal of Feminist Studies in Religion 2, no. 1 (1986).Cole, Susan Guettel. Landscapes, Gender, and Ritual Space: The Ancient Greek Experience. Berkeley: University of California Press, 2004.Foskett, Mary F. A Virgin Conceived: Mary and Classical Representations of Virginity. Bloomington: Indiana University Press, 2002.Harvey, Susan Ashbrook. Ministries of Song: Women's Voices in Ancient Syriac Christianity. Berkeley: University of California Press, 2025.Harvey, Susan Ashbrook. “Revisiting the Daughters of the Covenant: Women's Choirs and Sacred Song in Ancient Syriac Christianity.” Hugoye: Journal of Syriac Studies 8, no. 2 (2005).Lillis, Julia Kelto. Virgin Territory: Configuring Female Virginity in Early Christianity. Oakland: University of California Press, 2022.Parker, Robert. Miasma: Pollution and Purification in Early Greek Religion. Oxford: Clarendon Press, 1983.Sissa, Giulia. Greek Virginity. Cambridge, MA: Harvard University Press, 1990.Sourvinou-Inwood, Christiane. Studies in Girls' Transitions: Aspects of the Arkteia and Age Representation in Attic Iconography. Athens: Kardamitsa, 1988.Wildfang, Robin Lorsch. Rome's Vestal Virgins: A Study of Rome's Vestal Priestesses in the Late Republic and Early Empire. London and New York: Routledge, 2006.Greek Women, Ritual, and Sacred MaidensBeard, Mary. “The Sexual Status of Vestal Virgins.” Journal of Roman Studies 70 (1980).Beard, Mary, John North, and Simon Price. Religions of Rome. Vol. 1, A History. Cambridge: Cambridge University Press, 1998.Connelly, Joan Breton. Portrait of a Priestess: Women and Ritual in Ancient Greece. Princeton: Princeton University Press, 2007.Dillon, Matthew. Girls and Women in Classical Greek Religion. London and New York: Routledge, 2002.Pomeroy, Sarah B. Goddesses, Whores, Wives, and Slaves: Women in Classical Antiquity. New York: Schocken Books, 1975.Mary, Virginity, and Christian AsceticismBrown, Peter. The Body and Society: Men, Women, and Sexual Renunciation in Early Christianity. New York: Columbia University Press, 1988.Clark, Elizabeth A. Reading Renunciation: Asceticism and Scripture in Early Christianity. Princeton: Princeton University Press, 1999.Cooper, Kate. The Virgin and the Bride: Idealized Womanhood in Late Antiquity. Cambridge, MA: Harvard University Press, 1996.Elm, Susanna. Virgins of God: The Making of Asceticism in Late Antiquity. Oxford: Clarendon Press, 1994.Limberis, Vasiliki. Divine Heiress: The Virgin Mary and the Creation of Christian Constantinople. London and New York: Routledge, 1994.Rubin, Miri. Mother of God: A History of the Virgin Mary. New Haven: Yale University Press, 2009.Shoemaker, Stephen J. Mary in Early Christian Faith and Devotion. New Haven: Yale University Press, 2016.Also want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball. 

What if you could tell whether a rock exists on a planet 50 light-years away — without ever leaving Earth? This week Chris and Jesse welcome back friend-of-the-show Dr. Jackie Faherty (American Museum of Natural History) for her fourth appearance to talk about one of the wildest results in exoplanet science yet.The crew digs into a new Nature Astronomy paper on a rocky planet about 30% larger than Earth, orbiting a tiny star, whipping around fast with a scorching ~1,000 Kelvin surface and — surprisingly — no atmosphere. With no gas in the way, the James Webb Space Telescope was able to read the light coming off the planet itself and start distinguishing what the surface might be made of, right down to the debate geologists love: mafic vs. ultramafic, basalt vs. granite.Along the way, Jackie explains what makes JWST so special, why it sees in infrared, and how astronomers actually assign those famous colors to the images (spoiler: it's not random). They get into brown dwarfs and why she thinks one may be hiding in our own galactic neighborhood, the coming firehose of data from Rubin and NASA's Roman telescope, and why Jesse has to eat his old postdoc-era complaints that Webb was a waste of money.It's a mind-bending conversation about living in what feels like a science-fiction era — where a planet's light tells us its secrets.Download the CampGeo app now at this link. On the app you can get tons of free content, exclusive images, and access to our Geology of National Parks series. You can also learn the basics of geology at the college level in our FREE CampGeo content series - get learning now!Like, Subscribe, and leave us a Rating!——————————————————Instagram: @planetgeocastTwitter: @planetgeocastFacebook: @planetgeocastSupport us: https://planetgeocast.com/support-usEmail: planetgeocast@gmail.comWebsite: https://planetgeocast.com/

Dave Rubin of "The Rubin Report" talks about CNN's Harry Enten sharing new polling data that should scare the Democratic establishment about the upcoming midterm elections, as the rise of Democratic Socialists endangers Democrats' chances of winning; NYC-DSA Co-Chair Grace Mausser's disastrous interview with Fox News' Griff Jenkins, where she attempted to explain how socialism will solve America's biggest problems and why open borders are a good idea; CNN's Manu Raju exposing Democratic Socialist Abdul El-Sayed's hypocrisy live on-air after he falsely claimed that he had never said he wanted to defund the police; Elliot Page and Lupita Nyong'o continuing to bring controversy to Christopher Nolan's "The Odyssey" as Elon Musk doubles down on his criticism of Nolan for injecting wokeness into Homer's epic; Toronto Police going viral for their video promoting their 2SLGBTQIA+ Pride-themed cop car; Fox News' Gillian Turner exposing Ro Khanna's performative stunt in the West Bank, where he tried to frame Israel and the IDF for racism; and much more. Today's Sponsors: Sunday for Dogs - Give your dog the quality of a home-cooked meal with the scoop and serve ease of kibble. Make the switch to Sundays. Get 50% off your first order with link below or use code RUBIN at checkout. Go to: https://sundaysfordogs.com/RUBIN Lean - A powerful weight loss supplement with remarkable results to help lower blood sugar, burn fat by converting it into energy, and curb your appetite. Rubin Report viewers get 20% off plus free rush shipping off their first order! Go to: https://TakeLean.com and enter promo code RUBIN for your discount Shopify - Turn your big business idea into money with Shopify on your side. Go to Shopify and sign up for your one-dollar-per-month trial and start selling today at Go to: http://shopify.com/rubin Polymarket -Go to http://polymarket.com to trade on the outcomes of live events from politics, pop culture, to sports and more!

Dave Rubin of "The Rubin Report" talks about Marco Rubio shutting down Zohran Mamdani's Commissioner for International Affairs Ana María Archila's unauthorized meeting with Iran's UN Representative Amir-Saeid Iravani; Donald Trump telling CNN's Jake Tapper his honest and heartfelt reaction to the death of Lindsey Graham; Scott Jennings exposing Geoff Duncan live on CNN for making up facts about Donald Trump basing his decisions about the Iran War on the performance of the stock market; Border Czar Tom Homan refusing to tell Fox News' Will Cain what he really thinks of Tim Walz pardoning Tou Lue Vang and attempting to protect him from deportation; CNN's Ana Navarro immediately regretting naming the victims of ICE shootings after the death of Lorenzo Salgado Araujo when Caroline Sunshine quizzed her on how many victims of crimes committed by illegal immigrants she could name; "The View's" Joy Behar getting into a tense argument with Alyssa Farah Griffin over who the Democratic Party will nominate for the 2028 election and whether Kamala Harris could, in fact, be the nominee again; Elon Musk giving a dire warning that California elections could soon become the norm for the entire nation and guarantee one-party rule; and much more. Today's Sponsors: BUBS Naturals - BUBS helps restore collagen levels closer to what your body had in its youth—so your joints feel stronger, your hair and nails grow healthier, and your skin looks smoother. Live Better Longer with BUBS Naturals. For a limited time get 20% Off your entire order with code RUBIN at Bubsnaturals.com Angel Studios - Choose entertainment that is focused on stories about perseverance and real human experiences. If you go premium, you'll get 2 free tickets to see Young Washington in theaters this Independence Day, and be part of making this film the #1 movie in America for our nation's 250th birthday. Go to: http://Angel.com/rubin Balance of Nature - Make sure you are getting all the positive effects from a wide variety of fruits and vegetables. Get 10% OFF the Whole Health System™ supplements when you use Discount Code: RUBIN. Go to https://BalanceofNature.com

On this week's #guybensonshow Sunday Replay, we're looking back at our interview w/ Dave Rubin of The Rubin Report ! Rubin and Benson discussed Platner, the future of "classical liberalism," and why it is time for old-school Dems to leave the party. 

Dave Rubin of "The Rubin Report" talks about Graham Platner's career possibly being over after Jake Tapper's shocking interview with former girlfriend Jenny Racicot, who accused Platner of raping her while intoxicated; Donald Trump's warnings about Graham Platner coming true despite Democrats ignoring all the numerous warning signs; CNN's Anderson Cooper going silent as Scott Jennings rips into Democrats for only believing Jenny Racicot's accusations against Graham Platner because she is liberal; Bernie Sanders staunchly defending his hypocritical use of private jets to Fox News' Bret Baier for his Fight the Oligarchy rallies; Christopher Nolan's "The Odyssey" attracting more controversy as Lupita Nyong'o complains about Homer's depiction of Helen of Troy; "The View's" Sunny Hostin pushing the debunked fake history of the book "Black Athena" to explain why Helen of Troy should be played by a Black woman in Christopher Nolan's "The Odyssey"; Dave Chappelle's sarcastic response to CNN's Anderson Cooper asking him if he is proud to be an American; and much more. Today's Sponsors: Angel Studios - Choose entertainment that is focused on stories about perseverance and real human experiences. If you go premium, you'll get 2 free tickets to see Young Washington in theaters this Independence Day, and be part of making this film the #1 movie in America for our nation's 250th birthday. Go to: http://Angel.com/rubin Mars Men - A potent and natural testosterone stack that optimizes your body's ability to forge usable testosterone. For a limited time, our listeners get 50% off FOR LIFE AND 3 Free Gifts at Mars Men when you use code RUBIN . Go to: http://Mengotomars.com

Dave Rubin of "The Rubin Report" talks about Elon Musk's brutal response to Zohran Mamdani's 4th of July speech on America's 250th birthday, where he directly attacked America and Elon Musk; Argentina President Javier Milei revealing to Ciudad Magazine what he really thinks is behind leftists' denial of economic facts; Gavin Newsom telling Jack Cocchiarella why he welcomes the wave of Democratic Socialists coming into the Democratic Party, unaware that it will be establishment Democrats like himself who are targeted next; CNN's Dana Bash being surprised by Josh Shapiro being one of the few Democrats willing to mildly criticize Democratic Socialist Darializa Avila Chevalier; CNN's Harry Enten sharing new polling data that should scare the Democratic establishment about the upcoming midterm elections, as the rise of Democratic Socialists endangers Democrats' chances of retaking the House; Donald Trump telling Squawk Box's Joe Kernen why the SAVE Act and voter ID are the most important pieces of legislation in the country right now; and much more. Today's Sponsors: Plaud- Stay informed without drowning in content. Plaud helps turn long interviews, podcasts, and discussions into searchable notes and summaries in seconds. Use code RUBIN for up to 20% OFF Go to: https://Plaud.ai/rubin Shopify - Turn your big business idea into money with Shopify on your side. Go to Shopify and sign up for your one-dollar-per-month trial and start selling today at Go to: http://shopify.com/rubin Polymarket -Go to http://polymarket.com to trade on the outcomes of live events from politics, pop culture, to sports and more!

Dave Rubin of "The Rubin Report" talks about Stephen Miller and Ron DeSantis' brutal reaction to conservative Supreme Court Justices like Amy Coney Barrett ruling in favor of birthright citizenship and how conservatives may be able to get around the 14th Amendment; CNN's Jake Tapper being stopped in his tracks by Markwayne Mullin's blunt response to being lectured about the temporary protected status of Haitian migrants; the "Real Time with Bill Maher" crowd going quiet as JD Vance put Bill Maher in his place for thinking that ICE deportations and immigration enforcement can be done in a way that will look great on camera; Elon Musk's brutal response to a little-noticed detail in Dearborn, Michigan, Police Chief Issa Shahin's proudly proclaiming how Muslims are beginning to take over the Dearborn Police Department; Progressive Democrat Scott Wiener finally reacting to being targeted and harassed at a San Francisco trans march during Pride Month for not being anti-Israel enough and denouncing the perceived "genocide"; Tomi Lahren getting an unexpected answer from UFC head Dana White when she asked him if UFC would ever have a Pride Night for gay MMA fighters; and much more.   Today's Sponsors:  Sunday for Dogs - Give your dog the quality of a home-cooked meal with the scoop and serve ease of kibble. Make the switch to Sundays. Get 50% off your first order with link below or use code RUBIN at checkout. Go to: https://sundaysfordogs.com/RUBIN Angel Studios - Choose entertainment that is focused on stories about perseverance and real human experiences. If you go premium, you'll get 2 free tickets to see Young Washington in theaters this Independence Day, and be part of making this film the #1 movie in America for our nation's 250th birthday. Go to: http://Angel.com/rubin CBDistillery.com- Struggling with poor sleep or aches and pains? Take the advice of our over 2 million satisfied customers. Use CBD after physical activity for reductions in stress and pain. The 4th of July Sale is the perfect time to stock up and save up to 50% off with code RUMBLE. Go to http://cbdistillery.com and use code RUMBLE to save up to 50%!

Dave Rubin of "The Rubin Report" talks about Mark Twain Award winner Bill Maher telling the press at the Kennedy Center how the rise of Zohran Mamdani and the Democratic Socialists is likely to doom Democrats' midterm election chances; the "Real Time with Bill Maher" audience being shocked by Bill Maher admitting to JD Vance that he is open to voting for a Republican president in the 2028 election; CNN's Harry Enten sharing new polling data that should scare the Democratic establishment and Chuck Schumer as even more incumbent Democrats face potential losses in their primaries to Democratic Socialists; Barstool Sports' Dave Portnoy shocking Fox News' Jesse Watters by revealing that he is open to running against Zohran Mamdani to be the mayor of New York City; Zohran Mamdani telling ABC News' Jonathan Karl how the Democratic Socialists plan to use affordability to take over the Democratic Party and maybe even the presidency; Gavin Newsom immediately shifting to the left after the major wins of the Democratic Socialists by calling for a national billionaire tax to tax the rich and admitting to Brian Tyler Cohen that he's open to expanding the number of Supreme Court justices to 13; and much more. Today's Sponsors: BUBS Naturals - BUBS helps restore collagen levels closer to what your body had in its youth—so your joints feel stronger, your hair and nails grow healthier, and your skin looks smoother. Live Better Longer with BUBS Naturals. For a limited time get 20% Off your entire order with code RUBIN at Bubsnaturals.com Plaud- Stay informed without drowning in content. Plaud helps turn long interviews, podcasts, and discussions into searchable notes and summaries in seconds. Use code RUBIN for up to 20% OFF Go to: https://Plaud.ai/rubin Balance of Nature - Make sure you are getting all the positive effects from a wide variety of fruits and vegetables. Get 10% OFF the Whole Health System™ supplements when you use Discount Code: RUBIN. Go to https://BalanceofNature.com Rumble Wallet - Don't let the big banks freeze your accounts. Own Tether Gold - real gold, on the blockchain and get direct ownership of physical gold bars, each one fully allocated, verifiable by serial number, purity, and weight. Download Rumble Wallet - now with USA₮ - and step away from the big banks — for good! Go to: https://rumblewallet.onelink.me/bJsX/rubin

Dave Rubin of "The Rubin Report" talks about Scott Jennings surprising CNN's Anderson Cooper with his dire warning for Republicans and Democrats about the victories of Zohran Mamdani-endorsed candidates like Darializa Avila Chevalier, Claire Valdez, and Brad Lander, and how the Democratic Socialists are beginning to take over the Democratic Party; Scott Jennings explaining how Jewish Democrat Dan Goldman, who was banned from a coffee shop for supporting Israel, was defeated by Brad Lander, providing more evidence of the growing antisemitism within the Democratic Party; Democrat leader Hakeem Jeffries being caught not knowing how to handle the rising threat of socialism within the Democratic Party; Nick Shirley's tense confrontation with a criminal who had previously assaulted his cameraman, whom the NYPD didn't even bother to arrest; Joe Rogan pointing out the connection between huge anti-ICE protests and the Somali fraud scandal in Minnesota to Taylor Sheridan; Rand Paul explaining to Squawk Box's Joe Kernen why new evidence clearly shows that Dr. Anthony Fauci lied under oath to Congress; and much more. To celebrate Dave's birthday, we're giving you 50% OFF Copal from now through June 26. Don't miss this gift: https://drinkcopal.com/products/reposado WATCH the MEMBER-EXCLUSIVE segment of the show here: https://rubinreport.locals.com/ Check out the NEW RUBIN REPORT MERCH here: https://daverubin.store/ ---------- Today's Sponsors: Plaud- Stay informed without drowning in content. Plaud helps turn long interviews, podcasts, and discussions into searchable notes and summaries in seconds. Use code RUBIN for up to 20% OFF (Prime Day limited offer). Go to: https://Plaud.ai/rubin Gaia- Gaia is a streaming service dedicated to the evolution of consciousness, featuring videos on disclosure, ancient wisdom, and the nature of reality. Go to: https://www.gaia.com/lp/disclosure-tr...  Rumble Wallet - Don't let the big banks freeze your accounts. Own Tether Gold - real gold, on the blockchain and get direct ownership of physical gold bars, each one fully allocated, verifiable by serial number, purity, and weight. Download Rumble Wallet - now with USA₮ - and step away from the big banks — for good!  Go to: https://rumblewallet.onelink.me/bJsX/... CBDistillery.com- Struggling with poor sleep or aches and pains? Take the advice of our over 2 million satisfied customers. Use CBD after physical activity for reductions in stress and pain.  Go to http://cbdistillery.com and use code RUMBLE to save up to 25%!