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In the latest edition of Caught Offside, Andrew Gundling and JJ Devaney are joined by legendary Premier League fullback, Ashley Cole, talking Arsenal's dominance, the upcoming Premier League season as whole, his interest in coaching and much much more!Plus, we'll share our thoughts on a truly devastating loss for Celtic against LASK and their cast of American heroes. We'll also take a look at some of the wild financial figures being thrown around in the transfer window and try to make sense of it and we'll close things out with another edition of "This Week in Dutch Bluntness."For even more Caught Offside content, get on over to Caught Offside Plus right now! It's time for a Pep-talk! We've got a special interview for you with John Mackenzie to discuss his soon-to-be-released book about Pep Guardiola, "The Spectre of Pep: How Guardiola Haunts Modern Football Tactics."For all the latest merch, get over to https://caughtoffsidepod.com/ ---Reddit: https://www.reddit.com/r/CaughtOffsidePod/X: https://twitter.com/COsoccerpodInstagram: https://www.instagram.com/caughtoffsidepod/Email: CaughtOffsidePod@gmail.com Hosted on Acast. See acast.com/privacy for more information.
In the latest edition of Caught Offside, Andrew Gundling and JJ Devaney are looking back on the good and bad of the English Premier League's opening weekend. We'll discuss Manchester United's "passionless" defeat at Hull City as well as Tottenham's deja vu performance against Brentford. We'll also weigh in on Brighton's dismantling of Aston Villa and Arsenal looking very much like the defending champions that they are.And finally, we'll look at some of the situations that have caused fans to turn against their own players - Enzo Fernandez at Chelsea, Ollie Watkins at Villa and Julian Alvarez at Atletico Madrid.For even more Caught Offside content, get on over to Caught Offside Plus right now! Later this week, it's time for a Pep-talk! We'll have a special interview for you with John Mackenzie to discuss his soon-to-be-released book about Pep Guardiola, "The Spectre of Pep: How Guardiola Haunts Modern Football Tactics."For all the latest merch, get over to https://caughtoffsidepod.com/ ---Reddit: https://www.reddit.com/r/CaughtOffsidePod/X: https://twitter.com/COsoccerpodInstagram: https://www.instagram.com/caughtoffsidepod/Email: CaughtOffsidePod@gmail.com Hosted on Acast. See acast.com/privacy for more information.
Stamford Chidge interviews Jon McKenzie about his new book: 'The Spectre of Pep - How Guardiola Haunts Modern Football Tactics'.A natural successor to the brilliant 'Inverting the Pyramid' by Jonathan Wilson, Jon's new book put Guardiola in to a historical context before exploring his impact on football over the last 20 years and poses the question of what his legacy will be.Jon is a writer and presenter on The Athletic FC and Tifo and is a regular on the FanCast's Opppositon View.The book can be purchased here: https://geni.us/TheSpectreOfPep Hosted on Acast. See acast.com/privacy for more information.
What happens to football when the manager who shaped the modern game finally leaves? Jon MacKenzie, author of The Spectre of Pep: How Guardiola Haunts Modern Football, joins the Brazilian Shirt Name Podcast alongside Seb White to examine Pep Guardiola's extraordinary influence on the modern game and what happens after him. From playing out from the back and controlling space to high pressing, positional play and the growing tactical obsession with control, Guardiola's ideas have spread from the very top of the Premier League all the way down the football pyramid. But did that dominance eventually become a weakness? The conversation explores why Guardiola's later Manchester City sides became more risk-averse, whether his system restricted players such as Jack Grealish and Phil Foden, the criticism that Pep was simply a “chequebook manager”, and why football may now be moving away from the tactical consensus he created. Plus, what could Guardiola do next? International football? Another club? Or has one of football's great obsessives finally discovered that there is more to life than the game? And, naturally, there's some Michel Foucault, Martin Heidegger and Jacques Derrida along the way. Our good friends at Stanchion Books are putting on a second event with Jon. The first one sold out, some of us will be there so you should get your tickets before they sell out. You can pre-order the book from them as well.” https://www.eventbrite.co.uk/e/the-spectre-of-pep-guardiola-book-launch-with-jon-mackenzie-tickets-1997680941472? Join the Brazilian Shirt Name Whatsapp Channel: https://whatsapp.com/channel/0029VbBNgO58PgsAgQXRP32T
After the extraordinarily low lows of last week's Star Trek episodes, this week's batch feels like a welcome respite. Up to bat this week is previously-seen guest star Diana Muldaur in the psychedelic ugly alien freakout Is There in Truth No Beauty, followed by the good ol' fashioned wild west romp Spectre of the Gun!CHAPTERS:(00:00:00) - The Nextlander Watchcast Episode 187: Star Trek: Is There in Truth No Beauty and Spectre of the Gun(00:00:39) - Intro.(00:01:58) - Jumping right into Is There in Truth No Beauty, an impossible title to say correctly the first time.(00:15:26) - Don't look at the ugly alien. You'll go crazy.(00:17:57) - Pretty lady onboard, let's get weird about it.(00:29:13) - Goddammit, Larry.(00:37:49) - Show the pretty lady flowers while we step out with her ambassador.(00:44:11) - Piloting Spock around like it's the ambassador's vacation body.(00:47:57) - Spock goes crazy, and it barely matters.(00:51:41) - Spock's good, Dr. Jones got her mindlink, and Larry's dead, so I guess this is a happy ending.(00:56:37) - Break!(00:57:04) - We're back, and it's time to talk about Spectre of the Gun (and some OK Corral history)!(01:14:33) - A neon probe with a big booming snake head says to stay outta this space.(01:18:47) - A half-remembered idea of Tombstone, Arizona, where everybody is pointing you toward doom.(01:25:08) - A force field prevents escape, gas grenades are constructed, and Chekov "dies".(01:32:06) - Spock sees through the Matrix.(01:39:06) - Huh, you defeated our hastily assembled wild west trap. I guess you guys are cool.(01:44:21) - Final thoughts, and housekeeping for next week's episodes.(01:47:03) - Outro.
The Spectre of Pep: How Guardiola Haunts Modern Football Tactics (Seven Dials, 2026) by Jon Mackenzie A spectre is haunting European football. Watch almost any game, no matter the level, and you will notice it. From inverted full backs to extreme high lines, the tactical threads of the match will eventually converge onto a singular point: Pep Guardiola. For some though, his near total dominance has come at the cost of the game itself, with modern football's protagonists caught between acknowledging Guardiola's greatness and wanting to evolve the sport beyond him. In The Spectre of Pep, acclaimed tactics writer Jon Mackenzie explores this contradiction. But this is much more than the story of just one man. After tracing Guardiola's ascendancy, displacing giants like Jose Mourinho and Antonio Conte, it examines how other elite coaches have fought to establish their own legacies within this landscape - including the likes of Jurgen Klopp, Roberto De Zerbi, Luis Enrique and Mikel Arteta. And with his time at Manchester City coming to an end - in a world where his influence is arguably diminishing - it also speculates on what the next generation of tactics may look like, and how Guardiola's legacy will be viewed in the future. If you want to understand football today, you have to understand the tactical spectre of Pep Guardiola. To do that, you need this book. Jon Mackenzie is a writer and presenter working for The Athletic. Samee Siddiqui is Assistant Professor of World History at Drury University. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network
The Spectre of Pep: How Guardiola Haunts Modern Football Tactics (Seven Dials, 2026) by Jon Mackenzie A spectre is haunting European football. Watch almost any game, no matter the level, and you will notice it. From inverted full backs to extreme high lines, the tactical threads of the match will eventually converge onto a singular point: Pep Guardiola. For some though, his near total dominance has come at the cost of the game itself, with modern football's protagonists caught between acknowledging Guardiola's greatness and wanting to evolve the sport beyond him. In The Spectre of Pep, acclaimed tactics writer Jon Mackenzie explores this contradiction. But this is much more than the story of just one man. After tracing Guardiola's ascendancy, displacing giants like Jose Mourinho and Antonio Conte, it examines how other elite coaches have fought to establish their own legacies within this landscape - including the likes of Jurgen Klopp, Roberto De Zerbi, Luis Enrique and Mikel Arteta. And with his time at Manchester City coming to an end - in a world where his influence is arguably diminishing - it also speculates on what the next generation of tactics may look like, and how Guardiola's legacy will be viewed in the future. If you want to understand football today, you have to understand the tactical spectre of Pep Guardiola. To do that, you need this book. Jon Mackenzie is a writer and presenter working for The Athletic. Samee Siddiqui is Assistant Professor of World History at Drury University. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sports
The Spectre of Pep: How Guardiola Haunts Modern Football Tactics (Seven Dials, 2026) by Jon Mackenzie A spectre is haunting European football. Watch almost any game, no matter the level, and you will notice it. From inverted full backs to extreme high lines, the tactical threads of the match will eventually converge onto a singular point: Pep Guardiola. For some though, his near total dominance has come at the cost of the game itself, with modern football's protagonists caught between acknowledging Guardiola's greatness and wanting to evolve the sport beyond him. In The Spectre of Pep, acclaimed tactics writer Jon Mackenzie explores this contradiction. But this is much more than the story of just one man. After tracing Guardiola's ascendancy, displacing giants like Jose Mourinho and Antonio Conte, it examines how other elite coaches have fought to establish their own legacies within this landscape - including the likes of Jurgen Klopp, Roberto De Zerbi, Luis Enrique and Mikel Arteta. And with his time at Manchester City coming to an end - in a world where his influence is arguably diminishing - it also speculates on what the next generation of tactics may look like, and how Guardiola's legacy will be viewed in the future. If you want to understand football today, you have to understand the tactical spectre of Pep Guardiola. To do that, you need this book. Jon Mackenzie is a writer and presenter working for The Athletic. Samee Siddiqui is Assistant Professor of World History at Drury University. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/european-studies
In 1980, Vancouver Island mechanic Granger Taylor left his family a note saying he was leaving on a 42-month journey aboard an alien spacecraft. He then disappeared into a stormy night—along with his truck. This episode explores Taylor's remarkable mechanical talent, the homemade flying saucer he built in his family's yard, the note that made his disappearance legendary, and the evidence discovered years later at a nearby blast site. Paul and Michelle examine the difference between the documented record and the mythology surrounding the case, while remembering Granger as more than a UFO mystery: a gifted, complicated, and deeply human person. Episode Credits:Written, edited and produced by Paul & MichelleArtwork created by MIchelle & PaulMusic written and produced by I.C.D.LINKS: Listen to us here: SpookyCoop , find us @ spookycoop.com and follow us on: FaceBook | BlueSky | X (formerly Twitter) | Threads | Instagram | TikTok
The Blue Moon Podcast is back for YET another season... On today's show, David Mooney is joined by City fan Ciaran Murray and by the Daily Mail's Jack Gaughan to look back over the pre-season tour of Asia for Manchester City and to discuss what's new about the Enzo Maresca era at the club. The Athletic's Sam Lee has been speaking to the players on the tour, so he's here to look at what they've said about what's different tactically. Plus, we speak to Tifo's Jon Mackenzie, whose new book "The Spectre of Pep: How Guardiola Haunts Modern Football Tactics", looks at how much City's former manager has influenced how we all see the game. We take a look ahead to the Community Shield tie with Arsenal on Sunday, plus (and we make no apologies for this) we're starting off as weirdly as we mean to go on by taking a look at all the times that City have played on the same day as a partial or total solar eclipse. ========== To get more podcasts or to listen without the ads, join our Patreon. It's just £2 per month for all the extra content and you can get a 7-day free trial first: https://www.patreon.com/BlueMoonPodcast And why not gift a Patreon subscription to a friend or family member? More details: https://www.patreon.com/BlueMoonPodcast/gift Learn more about your ad choices. Visit podcastchoices.com/adchoices
In this episode: Eastern Air Lines Flight 401 — The 1972 Everglades crash and the reported appearances of Captain Bob Loft and flight engineer Don Repo aboard other Eastern L-1011 aircraft. The Ghost Blimp L-8 — In 1942, a Navy blimp landed near Daly City, California, with its two officers missing and no definitive explanation for their disappearance. Frederick Valentich — The young Australian pilot whose 1978 report of a strange aircraft over Bass Strait was followed by a mysterious radio sound and his disappearance. D.B. Cooper — The unidentified hijacker who parachuted from a Boeing 727 over the Pacific Northwest in 1971 and was never conclusively identified. Phantom Paratroopers — Folklore surrounding WWII-era spectral soldiers and parachutists near Salisbury Plain and former Operation Market Garden sites in the Netherlands. Michelle's UFO story — Michelle shares her own strange encounter from a flight home from Los Angeles. Sources & Further Reading NTSB report: Eastern Air Lines Flight 401 The Ghost Blimp L-8 — National Naval Aviation Museum “Lady Be Good” — National Museum of the U.S. Air Force FBI update on the D.B. Cooper investigation The Ghost of Flight 401 by John G. Fuller ABC Australia: The disappearance of Frederick Valentich Connect With SpookyCoop Have you experienced something strange in the sky—or on the ground beneath it? Send your haunted travel stories, UFO sightings, and suspiciously ghost-filled airport encounters to SpookyCoop. Until next time: keep your eyes on the skies, and your parachute packed… unless you have an alien pickup scheduled. Episode Credits:Written, edited and produced by Paul & MichelleArtwork created by MIchelle & PaulMusic written and produced by I.C.D.LINKS: Listen to us here: SpookyCoop , find us @ spookycoop.com and follow us on: FaceBook | BlueSky | X (formerly Twitter) | Threads | Instagram | TikTok
Set in medieval Conway, Wales, "The Castle Spectre" by Matthew Lewis is a captivating Gothic drama that showcases the author's early talent for blending romance, farce, and tragedy. The play features a menacing castle, a villainous nobleman with dark secrets, a damsel in distress, and an heroic lover striving to save her, all while interspersing comedic elements that keep the audience engaged. Originally staged in 1797, its themes of love, fear, and the supernatural continue to resonate today, reminding us of the timeless allure of Gothic storytelling and the human experience of confronting both light and darkness.
We started this set of broadcasts in trouble, and it's only getting worse! Why is it important to have conversations about subjects that make us uncomfortable? Tune in to The Public Square® today to hear more. Topic: Politics The Public Square® with host Dave Zanotti thepublicsquare.com Air Date: Friday, August 7, 2026
durée : 00:02:30 - Le 6/9 de l'été - L'inquiétude monte en Europe car les stocks de gaz sont à un niveau exceptionnellement faible. Comment l'expliquer ? - équipe : Romain Gueugneau Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France
Jace and Rocky break down all 10 DC releases for the week of August 5, 2026. In Absolute Superman #22, Superman risks being poisoned by Metallo's kryptonite blood because he knows reaching Christopher Smith emotionally is the only way to end the fight. In Batman #12, Verity is living up to the Pennyworth name as mention of her training and her quick thinking impresses Bruce and Damian while five highly trained killers each demonstrate their methods in a series of murders. Could their missions be tied to Gotham's newest threat? In Absolute Green Lantern #17, Jo confronts Mu as the issue adds more fascinating cosmic concepts, but seventeen issues of undefined terminology leave even the central conflict difficult to understand. But the issue does deliver the origin of Absolute Jessica Cruz and a great scene of Tomar-Re being fired like a bullet. In Adventures of Superman: Book of El #11, even though Superman has been reunited with Osul and Otho, the battle is not over. Kal-El is determined to stop Kryl-Ux even while trying to save him from himself and the grief of losing his family that has twisted his soul. In Deadman #3, Deadman travels through Hell to rescue a soul Rama Kushna says does not belong there, only for him to be shocked when Lucifer shows him proof the soul is where it belongs. The devil's got receipts. In Batgirl #22, Cassandra learns that Victoria was both her childhood friend and rival assassin. Both girls were manipulated by David Cain and Dr. Forget-Me-Not, respectively, in an experiment to build the perfect killer. The resolution for this one feels a little abrupt, but the possibility that Victoria may still be alive hints at more to come for this storyline in the future. In JSA #22, Jim Corrigan refuses to reclaim the Spectre, forcing the Spirit of Vengeance to seek a surprising new host from within the JSA. Kid Eternity fulfills her previously unknown mission to find that host, but turns down the chance to recover her memories and move on to the afterlife so she can remain with the team and continue to make a difference. In Supergirl: Survived #3, Kara's love for Kal drives her to attempt the impossible, using a Blue Lantern ring to recreate Krypton for both Kal-El and Hal Jordan. It's an emotionally powerful moment, but the issue takes an unexpected turn when an Elseworlds version of Mister Mxyzptlk arrives, shifting the story away from the hard sci-fi direction it had been building toward. In Gotham Academy: First Year #6, the final issue reveals more about Olive Silverlock, but the predetermined need to position every character for the beginning of Gotham Academy Vol. 1 leaves the story feeling paint-by-numbers. The rushed maneuvering from the end of issue #4 makes the conclusion feel both overstuffed and choppy, while surprisingly little of consequence actually happens. In 100 Bullets: The U.S. of Anger #2, the issue provides so little usable context that neither a new reader nor someone familiar with the original series can clearly explain who the characters are or what is happening. The series does seem to be appropriately named, though, as the anger the issue is steeped in feels all too real. While the story hasn't come together yet, the simmering anger of the U.S. reflected here feels authentic. They also give a rundown of this week's collected editions, reprints and facsimile releases. As always, all books are ranked from top to bottom and each host gives a Book of the Week pick. 00:00 — DC Week Overview, SDCC Aftermath & Comic Promotion 17:49 — Absolute Superman #22 35:27 — Deadman #3 43:03 — Absolute Green Lantern #17 57:44 — Supergirl: Survived #3 1:04:19 — Batman #12 1:13:51 — Batgirl #22 1:18:18 — Gotham Academy: First Year #6 1:21:19 — Adventures of Superman: Book of El #11 1:29:23 — 100 Bullets: The U.S. of Anger #2 1:37:20 — JSA #22 1:45:42 — Collected Editions, Reprints & Facsimiles 1:53:17 — Rankings & Books of the Week
Ecoutez RTL Matin avec Vincent Derosier du 05 août 2026.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.
Spooky Coop – Episode 46 Show Notes The Black Forest Haunting: The Lee Family Case : Join Paul and Michelle as they investigate one of Colorado's best-known modern paranormal cases—the reported haunting experienced by Steve and Beth Lee in Black Forest during the early 1990s. In this episode: The Lee family's move into their newly built log home in Black Forest, Colorado. The first unexplained events, including footsteps, opening doors, strange odors, disappearing objects, and unexplained sounds. Reports that the activity intensified over time with flickering lights, shadow figures, voices, cold spots, and alleged physical contact. Accounts involving the Lee children and why their experiences became one of the most compelling aspects of the case. Paranormal investigators' visits, reported EVP recordings, equipment malfunctions, and differing conclusions about the property. The visit from a respected Hopi elder, who approached the property from a Native spiritual perspective and reportedly advised respect for the land rather than confrontation with the phenomena. A discussion of conventional explanations, including environmental conditions, wildlife, home construction, stress, and the role of human perception. Why the Black Forest haunting remains one of Colorado's most discussed paranormal cases despite the lack of definitive proof. Steve Lee's later belief that his family may have been subjected to a covert government experiment involving nearby military facilities, altered perception, and the alleged influence of a powerful land vortex. What became of the property after the Lee family moved away and reports from subsequent owners that they experienced no unusual activity. Discussion Topics Can multiple eyewitness accounts strengthen extraordinary claims? Where is the line between personal experience and objective evidence? How do cultural and spiritual traditions differ in interpreting unexplained phenomena? Could environmental factors account for some reported hauntings? What role should skepticism play when examining paranormal claims? Key Locations Mentioned Black Forest, Colorado Colorado Springs, Colorado Schriever Space Force Base (formerly Schriever Air Force Base) Important Note The experiences discussed in this episode are based primarily on the reported accounts of Steve and Beth Lee and others connected to the case. Many claims remain unverified, and no scientific consensus exists regarding the reported paranormal events. The discussion is intended to explore the history, testimony, and differing interpretations surrounding this well-known case. Thank you for listening to Spooky Coop. If you enjoy exploring the strange, the mysterious, and the unexplained, be sure to follow the show, leave a review, and share this episode with a fellow paranormal enthusiast—or your favorite skeptic. You never know who might change their mind after spending a night in the Black Forest. Episode Credits:Written, edited and produced by Paul & MichelleArtwork created by MIchelle & PaulMusic written and produced by I.C.D.LINKS: Listen to us here: SpookyCoop , find us @ spookycoop.com and follow us on: FaceBook | BlueSky | X (formerly Twitter) | Threads | Instagram | TikTok
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
An engaging discussion and recounting alien contactee encounters. Michelle and Paul recount many of their experiences with ET's throughout their life. The discussion points toward more equable and amiable interactions with the grey aliens rather than accounts of fear and terror. Michelle goes into some depth about her experiences with aliens and fellow contactee's here on earth, also describing events with Dr. John Macks working group from the 1990's. Paul discusses experiences of seeing and interacting with various alien crafts along with a discussion about his 1996 experience with a family member while on a canoe trip on the Wahta Mohawk Reserve's Moon River. In closing they encourage listeners to share some of their own alien encounters, anonymously or otherwise. Episode Credits:Written, edited and produced by Paul & MichelleArtwork created by MIchelle & PaulMusic written and produced by I.C.D.LINKS: Listen to us here: SpookyCoop , find us @ spookycoop.com and follow us on: FaceBook | BlueSky | X (formerly Twitter) | Threads | Instagram | TikTok
Episode Notes: The Battle of Los Angeles On February 25, 1942, Los Angeles went dark. Air-raid sirens sounded, searchlights swept the sky, and U.S. anti-aircraft crews fired more than 1,400 rounds at reported unidentified aircraft. In this episode, Paul and Michelle explore the real story behind the Battle of Los Angeles: the fear gripping the West Coast after Pearl Harbor, eyewitness accounts from that night, conflicting Army and Navy statements, and the theories that still surround the incident. Was it a Japanese attack, a weather balloon, wartime panic—or an unexplained visitor in the sky? In this episode The Japanese submarine attack on Ellwood, California The 2:25 a.m. blackout across Los Angeles Witness reports of lights and aircraft The anti-aircraft barrage and its aftermath Newspaper coverage from the Los Angeles Times, Glendale News-Press, Evening Star, and Midland Reporter-Telegram The weather-balloon explanation Why the UFO theory persists Sources and further reading U.S. Army history: The Army Air Forces in World War II: Defense of the Western Hemisphere Los Angeles Air Force Base historical oral-history publication Los Angeles Times archive coverage Glendale News-Press, February 25, 1942 Western Defense Command statements reported by the Associated Press Spooky Coop discusses historical mysteries and paranormal theories for entertainment. The theories discussed in this episode are not presented as established fact. Episode Credits:Written, edited and produced by Paul & MichelleArtwork created by MIchelle & PaulMusic written and produced by I.C.D.LINKS: Listen to us here: SpookyCoop , find us @ spookycoop.com and follow us on: FaceBook | BlueSky | X (formerly Twitter) | Threads | Instagram | TikTok
What does it take to keep one of the most iconic names in premium cigars relevant while honoring its legacy? In Episode 172 of Deep Cuts Live, host Antoine Reid welcomes back Sean Williams, Cohiba's brand ambassador and master blender, for a wide-ranging conversation about luxury, innovation, and the evolving premium cigar industry. More than five years after his first appearance on the show, Sean reflects on how both he and the industry have changed—and why time, relationships, and experiences matter more than ever. From the philosophy behind Cohiba's luxury positioning to the stories behind releases like Riviera, Rubicon, Spectre, Serie M, and the Weller collaboration, Sean offers an inside look at the creative process that brings some of the industry's most anticipated cigars to life. He also discusses cigar blending, building memorable brand experiences, the rise of cigar bars, and why authentic relationships remain the foundation of long-term success. In this episode, you'll hear about: * How Cohiba continues to evolve while honoring its heritage * The inspiration behind Riviera, Rubicon, Spectre, Serie M, and Weller * What luxury really means in today's premium cigar market * The art and science of blending premium cigars * Why relationships still matter more than social media * The growth of cigar bars and changing consumer habits * Advice for boutique cigar companies trying to stand out * Sean's personal philosophy on time, purpose, and leaving a lasting legacy Whether you're a longtime Cohiba smoker, a cigar retailer, or simply fascinated by the business behind premium cigars, this conversation offers thoughtful insight into one of the industry's most recognizable brands—and the man helping shape its future. Subscribe to Deep Cuts Live for more conversations with the people behind the premium cigar industry. ============================================= Subscribe to Deep Cuts Live for more conversations with the people shaping the premium cigar industry. Visit deepcutslive.com for more episodes and updates. ============================================= Website: deepcutslive.com YouTube: youtube.com/deepcutslive Instagram: instagram.com/deep_cuts_live
Send us Fan MailToday's episode is my conversation about the 1931 film East Lynne. I'm joined by Lewis Beer from the Slow Movie Pictures newsletter and we talk about some drastic changes the film made from the source material, the outstanding performances from the female actors in the film, and several innovative camera techniques used to show the world from Isabel's point of view throughout the film. You can watch East Lynne on YouTube and be sure to check out Lewis's newsletter.Other films mentioned in this episode include:Vampyr directed by Carl Theodor DreyerDracula directed by Tod BrowningRebecca directed by Alfred HitchcockAll Quiet on the Western Front directed by Lewis MilestoneThelma & Louise directed by Ridley ScottThe Divine Lady directed by Frank LloydWeary River directed by Frank LloydThe Big House directed by George HillCondemned directed by Wesley RugglesHoliday directed by Edward H. GriffithUnderworld directed by Josef von SternbergShanghai Express directed by Josef von SternbergThe Divorcee directed by Clarence BrownDynamite directed by Cecil B. DeMilleThe Public Enemy directed by William A. WellmanThe Letter directed by Jean De LimurThe Vagabond King directed by Ludwig BergerThe Bride of Frankenstein directed by James WhaleEast Lynne on the Western Front directed by George PearsonCimarron directed by Wesley RugglesThe Sound of Music directed by Robert WiseDouble Indemnity directed by Billy WilderSunset Boulevard directed by Billy WilderOther referenced topics:"Mudd's Women" (Star Trek episode)Star Trek (original series)Mister Ed (series)The Fugitive (series)East Lynne (novel) by Ellen WoodRebecca (novel) by Daphne du MaurierAnn Harding - Cinema's Gallant Lady by Scott O'BrienFrank Lloyd: Master of Screen Melodrama by Anthony Slide"Bradley King" by Brian Taves on Women Film Pioneers ProjectAnna Karenina (novel) by Leo TolstoyUlysses (novel) by James JoyceThe New York Times reviewVariety reviewZoe K. on Hollywood GenesLisa Marie Bowman on Through the Shattered LensA Literature of Their Own: British Women Novelists from Bronte to Lessing by Elaine Showalter‘“Stepchildren of Nature”: East Lynne and the Spectre of Female Degeneracy, 1860-1861' in Victorian Crime, Madness and Sensation by Andrew MaunderSupport the show
Legendary comics artist Tom Mandrake joins Byron to discuss the release of one of the most iconic, atmospheric, and terrifying runs in DC Comics history, The Spectre (now collected in two Omnibus Editions,) and his new work as series artist finishing up Rick Veitch's run on the Swamp Thing from 1989, a project that has been shelved for decades due to controversy. We kick things off by celebrating the long-awaited hardcover treatment of Tom's masterpiece: The Spectre (with writer John Ostrander). Spanning 62 issues of unmatched supernatural horror, Tom shares behind-the-scenes stories, pushing the boundaries of mainstream DC editorial in the 90s, and utilizing the incredible visual malleability of The Spectre in the book. We also go back to the future to discuss Tom's return to the Swamp Thing. Learn what it was like to step back into his 80s artistic style; help finish Rick Veitch's legendary, once-cancelled run; and collaborate with creators to complete a historic piece of comic book legacy.
The One where we agree with Bendis on somehting and Daveis still confused about Spectre and the JSA. Please support the show on Patreon! Every dollar helps the show! https://www.patreon.com/SignalofDoom Follow us on Twitter: @signalofdoom Dredd or Dead: @OrDredd Legion Outpost: @legionoutpost
Watch the full video here: https://renderingunconscious.substack.com/p/ru409-david-renton-on-revolutionary Rendering Unconscious is now at Substack: https://renderingunconscious.substack.com RU409: DR DAVID RENTON ON REVOLUTIONARY FORGIVENESS: BEYOND MORALISM, TOWARDS LIBERATION Rendering Unconscious welcomes Dr. David Renton back to the podcast! He's here to talk about his new book Revolutionary Forgiveness: Beyond Moralism, Towards Liberation. https://amzn.to/3Tq6qNa Rendering Unconscious episode 409. D. K. Renton is a barrister and historian. His work has appeared in The London Review of Books, The Guardian, Jacobin, Spectre, and Tempest. Renton is a member of rs21. His books include The New Authoritarians: Convergence on the Right and Fascism: History and Theory. He is based in London. Follow him at Substack: https://davidrenton590934.substack.com And on social media: https://www.facebook.com/profile.php?id=100014210191305 https://www.instagram.com/david.renton1/ https://bsky.app/profile/davidkr.bsky.social Be sure to check out these related/mentioned episodes: RU165: BARRISTER DAVID RENTON ON NO FREE SPEECH FOR FASCISTS: EXPLORING ‘NO PLATFORM' IN HISTORY, LAW AND POLITICS RU403: ANTHEA LAWSON ON HOW NOT TO SAVE THE WORLD RU394: LARA SHEEHI & CARTER CARTER – FROM THE CLINIC TO THE STREETS: PSYCHOANALYSIS FOR REVOLUTIONARY FUTURES You may find your favorite Rendering Unconscious Podcast episodes and guests here: https://renderingunconscious.substack.com/p/episodes News & events: Rendering Unconscious Center for Psychoanalysis is celebrating 1 year! Join us THIS SATURDAY, July 11th for the next installment of my Introduction to Psychoanalysis course. we'll be going over the life and work of French psychoanalyst, Jacques Lacan, reviewing some of his main concepts. Following the lecture, we're going to have a party! We'll turn off the recording, hang out and celebrate. To join us, simply become a paid subscriber at RU Center for Psychoanalysis: https://rucenterforpsychoanalysis.substack.com Then Saturday, July 18th, I will be hosting a LIVE Rendering Unconscious Podcast event to celebrate the one year anniversary of RU Center with special guests Mary Wild, Emmalea Russo, and Mikita Brottman. More TBA! All paid subscribers at RU Podcast and RU Center Substacks will receive the ZOOM LINK to attend live. https://renderingunconscious.substack.com See you there! Rendering Unconscious is also a book: Rendering Unconscious: Psychoanalytic Perspectives, Politics & Poetry vols 1:1 & 1:2 (Trapart Books, 2024): https://amzn.to/4sOqSEu Thank you for being a paid subscriber to Rendering Unconscious Podcast. It makes my work possible. If you are so far a free subscriber, thanks to you too. Please consider becoming a paid subscriber to gain access to all the material on the site, including new, future, and archival podcast episodes. It's so important to maintain independent spaces free from censorship and corporate influence. If you are interested in pursuing psychoanalytic treatment or supervision with me, please feel free to contact me directly: www.drvanessasinclair.net/contact/ The song at the end of this episode is "Butterfly effect" from the album "All p0ets are p0rn0graphers" by Vanessa Sinclair and Pete Murphy: https://petemurphy.bandcamp.com/album/all-poets-are-pornographers-13 Check out our newest album "Sisters of the Apocalypse" just released on the summer solstice: https://petemurphy.bandcamp.com/album/sisters-of-the-apocalypse-22 Visit Pete's Bandcamp and support indie artists: https://petemurphy.bandcamp.com Our music is also available at Spotify and other streaming services. https://open.spotify.com/artist/3xKEE2NPGatImt46OgaemY Links to everything can be found at: https://linktr.ee/renderingunconscious Enjoy! Thank You.
here is a new Scream fan film and its pretty good!Watch it herehttps://youtu.be/oDJfe6iYXSA?si=oKIMk45m0aTkhgw6Find us on Patreon for early access, exclusives, and spoiler talk.https://www.patreon.com/c/scaretalk#scream #horror #horrorfilmreview #scarymoviereviews #scary
Legendary screenwriter, producer, and director John Logan (MICHAEL, SWEENEY TODD, GLADIATOR, THE AVIATOR, HUGO, ANY GIVEN SUNDAY, SKYFALL, SPECTRE, PENNY DREADFUL, RANGO, ALIEN: COVENANT, RED, THE LAST SAMURAI, STAR TREK: NEMESIS, THEY/THEM) returns to continue discussing his incredible career with Adam and Joe. Tired of commercials? Support THE MOVIE CRYPT for just $1 a month and start getting every episode commercial-free! Visit www.Patreon.com/TheMovieCrypt to sign-up today!
The One where Dave performs a Signal Salute for Leo Dorfman and Sammy Davis is BACK! Please support the show on Patreon! Every dollar helps the show! https://www.patreon.com/SignalofDoom Follow us on Twitter: @signalofdoom Dredd or Dead: @OrDredd Legion Outpost: @legionoutpost
Jace and Rocky break down all 11 DC releases for the week of July 1, 2026. Absolute Green Lantern #16 brings the Absolute Lantern spectrum closer together as Hal Jordan uses violet light to protect the group from the Black Star of Sinestro, Jo Mullein weaponizes the people of Evergreen inside the Green Lantern construct, and Jessica Cruz's connection to Mogo threatens to throw the fight sideways. Poison Ivy #46 moves the pieces into place for Bad Seeds without Pamela appearing in the issue, as Janet from HR explains why Harley may be the only person who can reach Ivy and why Pamela's love for Harley makes her feel vulnerable. Batman #11 brings Lady Ojo back into the story as Batman clashes with her, the Minotaur's hold on Gotham's criminal money comes back into focus, Dr. Zeller deals with the fallout of Bruce's almost-date, and Verity Pennyworth makes her first appearance at Wayne Manor. Adventures of Superman: Book of El #10 continues Philip Kennedy Johnson's House of El story as Superman battles for his adoptive children, the fight on Tamarian escalates, and Kryl-Ux proves how powerful he has become by taking on a Sun-Eater. Batgirl #21 brings Cassandra Cain back to Gotham as Doctor Forget-Me-Not forces her to confront memories of a young girl named Victoria who may have been killed by Cassandra when she was younger, while the issue continues building Batgirl's own supporting cast. 100 Bullets: The U.S. of Anger #1 returns to Brian Azzarello and Eduardo Risso's world in black and white, following Lono through a grim, angry story involving podcasters, gang violence, police violence and the fractured state of America. Deadman #2 improves on the first issue by bringing in Plastic Man, showing Deadman fail to take over Batman because of Bruce's psychological defenses, and expanding the villain's interference with souls transitioning into the afterlife. Supergirl: Survive #2 turns Kara and baby Kal-El's escape into a galaxy-hopping Elseworlds adventure as Lobo's gang hunts the last Kryptonians, the Lantern Corps appears across the emotional spectrum, and Kara receives a hope lantern ring after Saint Walker's death. Absolute Martian Manhunter #12 closes the series by focusing on John Jones' relationship with his son Tyler, how his inability to understand Tyler helped fracture his family, and how the Martian Manhunter experience lets him see others differently as he chooses a second chance with his wife and child. JSA #21 explores the Spectre's origin and has the spirit of vengeance jump between different JSA hosts, including Obsidian, Jade and Wildcat, while Metron suggests the mystery depends not only on the Spectre's beginning but also on Jim Corrigan's origin. Batman / Green Arrow / The Question: Arcadia #4 continues Gabriel Hardman's political mystery as Batman, Green Arrow and the Question push deeper into the conspiracy around Arcadia, with the book still functioning more as a dense ideological thriller than a traditional superhero team-up. They also give a rundown of this week's collected editions, reprints and facsimile releases. As always, all books are ranked from top to bottom and each host gives a Book of the Week pick.
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Really, 007! speak to the living legend Paul Weston about his 60 years performing and coordinating stunts - including his work on 10 Bond films from The Spy Who Loved Me right through to Spectre.Thanks for listening - we think you'll love it too! Disclaimer: Really, 007! is an unofficial entity and is not affiliated with EON Productions, Amazon Metro-Goldwyn-Mayer Studios Inc. and Danjaq, LLC. Hosted on Acast. See acast.com/privacy for more information.
PART 1: Three time Academy Award nominated and Tony Award winning screenwriter, producer, and director John Logan (SWEENEY TODD, GLADIATOR, THE AVIATOR, HUGO, ANY GIVEN SUNDAY, SKYFALL, SPECTRE, PENNY DREADFUL, RANGO, ALIEN: COVENANT, RED, THE LAST SAMURAI, STAR TREK: NEMESIS, THEY/THEM) joins Adam Green to discuss his incredible career and the crafting of his latest worldwide blockbuster- the Michael Jackson biopic MICHAEL. Look for Part 2 next week! Tired of commercials? Support THE MOVIE CRYPT for just $1 a month and start getting every episode commercial-free! Visit www.Patreon.com/TheMovieCrypt to sign-up today!
Will and H5T are joined by one of our favorites Jayden Mercer (Jarispectre) as we sat down and watched Best in the World for yet another epic watch along !!!!program note!!!!! as always, better to watchalong with us. We really enjoyed recording this one, and we hope you enjoy the episode. So Tune In, tune out and enjoy because you know we sure as shit did. The post ROH Revelry #235: The Best Spectre in the World appeared first on Shining Wizards Network.
"Hollywood Is CIA" LOOSE NUKES Cult Leaders """No Tomorrow"" + ""Last Fragment"" MEANWHILE A Conflict of Memory ""Propelling Inwards"" PHARMACIST Vertebrae After Vertebrae" ""Stay Alive"" + ""Hopes & Prayers"" CELL DETH Death Seller ""No Dawn"" TO THE DOGS Butchery" "Mystic Valley"" + ""Red, Gold and Green"" CAN'T LOSE Death Is Not the Worst of Evils ""Rip Snorter"" + ""Put the Gun In Your Mouth"" U.S. BASTARDS Negligent Discharge" ""Rule of Thumb"" + ""Kingdom"" BARRICADE No Spirit "" Thrash Full Blast"" T.F.B. Progress:Regress" ""Ministerraad"" + ""Warsystem"" OMTZIGT KOMMANDO S/T ""Oracle of Lies"" BONERIPPER Radiant In Ruin" ""Biale Rekawiczi"" WARA! Nie badz obojetny ""Corpse Burden"" + ""Siksa Abadi"" VICTIM OF ROT Durjana" ""Imondice"" BOMBARDEMENT Live ""Hermetik Axis"" PVRGATORII Profane Rites For Cursed Times" ""The Decline"" ESKRIMA An Evening Out With Eskrima ""Out of Control"" AUTOMATIC LOVERS S/T" ""United Front"" UNDERTAKERS Global Dominion ""Spectre"" + ""Meatgrinder"" CLOSETALKERS State of Nature" ""Die With Me"" LIFESICK Die With Me ""Wonderful World"" WOLFCHARGE Wonderful World" ""Deliberate Genocide"" + ""Bloodstained Profit"" CONVICTED Soaring Reality of Natures Demise ""War Never Changes"" DISAPPOINT Split w/DISEASE"
This week on ROH Revelry 234: Will and H5T are joined by one of our favorites Jayden Mercer (Jarispectre) as we sat down and watched Best in the World for yet another epic watch along !!!!program note!!!!! as always, better to watchalong with us. We really enjoyed recording this one, and we hope you enjoy the episode. So Tune In, tune out and enjoy because you know we sure as shit did. Follow Jayden at Facebook: https://www.facebook.com/JariSpectre Twitter: https://x.com/JariSpectre?s=20 Insta: https://www.instagram.com/jarispectre Watch Jarispectre on Youtube at https://www.youtube.com/@jarispectre Make sure to support the peeps that support us: https://www.youtube.com/@purevalunboxing Order Josh's book from Gimmick Press https://www.magicaljeep.com/product/existed/173? Follow Tom at https://twitter.com/High5Tom Follow WIll at https://twitter.com/Wmercierjr Follow Brundan at https://twitter.com/Irishmisfit Follow our Social Media Specialist SJ https://twitter.com/KarnivalofKhaos Follow VGM at https://twitter.com/VisGlobalMedia Follow the Shining Wizards at https://twitter.com/wizardspodcast ROH Revelry Logo by https://twitter.com/InBrightestDayX Follow ROH Revelry: https://twitter.com/ROHRevelry
**Jeep Talk Show: Anthony Dreyer - "Moonchild" | AC-130 Avaiator, Combat Missions, PTSD & Recovery** In this powerful episode, we sit down with Anthony Dreyer — former U.S. Air Force Special Missions Aviator and **AC-130 Gunship Avaitor ** with over a decade in special operations aviation. Anthony flew high-risk missions around the globe as a gunner on the legendary Spectre and Spooky gunships, earning multiple accolades including the Air Force's Jolly Green Rescue Mission of the Year in 2018. From growing up in the Appalachian Mountains of Sylva, North Carolina, to orbiting battlefields at night delivering devastating close air support, Anthony shares his raw and honest story of service, trauma, addiction, and ultimate recovery. **Topics Covered:** - Life as an AC-130 Avaitor– malfunctions, miniguns, 105mm howitzer, and combat operations - Combat search & rescue missions (including the intense June 8, 2018 mission) - PTSD, prolonged exposure therapy, and choosing better over bitter - The brotherhood of special operations and the real cost of service - Writing his memoir "Moonchild" – turning pain into purpose - Military humor, call signs, and why freedom isn't free Anthony's memoir **Moonchild** is a gripping, emotional look at war, family, loss, and healing — must-read for veterans, first responders, and anyone wanting to understand the invisible battles many service members face. **Grab the book here:** - Amazon → https://amzn.to/4gxVK8O - Barnes & Noble and major retailers **Connect with Anthony:** - Instagram: @marco_brolo21 - Facebook: Moonchild - Signed copies: anthonyp.direcjmo.com If you're a veteran or struggling, remember: It's okay not to be okay — but it's not okay to do nothing about it. Reach out and get help. Thanks for watching Jeep Talk Show! Drop a comment below — what part of Anthony's story hit you the hardest?
UK GOVERNMENT IN TALKS WITH NISSAN OVER SUNDERLANDOnce again the UK Government is in talks with Nissan about helping the company to secure the future of the Sunderland plant. At least they are demanding commitments to future production at the site in return for financial aide. Negotiations are still ongoing. If you wish to read more, click this Yahoo!Finance article link here.STELLANTIS TO BUILD FOR JLR IN THE USStellantis has reached an agreement with JLR to build their vehicles in the US. These will be from the Defender brand. Additionally both companies plan to share technology and car development with each other. To learn more, click this Autocar article link here.JLR EXPLAINS NEXT ‘REIMAGINE' PHASECombined with the Stellantis announcement, JLR also explained the next stage of their Reimagine strategy. They are aiming for £1.7 billion in savings, which they are calling Enterprise Missions, targeting material costs, warranty expenses and fixed costs. For more on this story, click this Autocar Professional article link here.EU NOTICES PHEV TARIFFS LOOPHOLEWhilst the EU has imposed tariffs on Chinese built EVs it seemed to forget they also sell plug-in hybrids. That is set to change as the European Commission intends to slap tariffs on PHEVs akin to the various levels imposed on individual Chinese brands for EVs, albeit at lower levels. Click this electrive article link here, to read more.UK GOVERNMENT LAUNCHES NEW AV CONSULTATIONThe Department for Transport has launched a new consultation about safety principles for automated vehicles. The draft principles in themselves are good and should be implemented. This will mean no AV will be on our roads as none can satisfy them all. Additionally, there were three principles that they chose to exclude, be able to drive without human monitoring, cyber resilience and explainability. These have to be included. Click this link to see the statement from the Transport Minister, Simon Lightwood MP announcing this matter.YET ANOTHER WAYMO RECALL FOR BASIC DRIVING ISSUEWaymo has recalled nearly 4,000 vehicles as it tries to update their software to stop them driving into freeway construction zones. To prevent any further issues until they can find and deploy a solution, freeway driving is being restricted. To read more, click this link from The Register here.If you like what we do, on this show, and think it is worth a £1.00, please consider supporting us via Patreon. Here is the link to that CLICK HERE TO SUPPORT THE PODCASTNEW NEW CAR NEWS -BMW i3BMW has revealed the details and specifications of the ‘First Edition' and 50 xDrive i3s and they are impressive. Prices start at £53,005, with the ‘First Edition' starting at £57,905. The car has a range of 563 miles, thanks to 108.7kW battery, front and rear electric motors which provide 463bhp, 476lb ft of torques enabling a 0-62mph time of 4.7 seconds. The design is based on the Neue Klasse design language. Click this Top Gear article link for more.Rolls Royce SpectreRolls Royce has upgraded the Spectre as it comes out in its ‘Series II' form, with an extended range and a cut to charging times. There is also more power, especially if you have a Black Badge edition and activate ‘Spirited' mode. Prices start from £300,000 but in reality, after personalisation, the costs are usually much, much higher. Click this Autocar article link here, for more.Peugeot E-208 GTIPeugeot are brining back the GTI, but this time it is electric. Leaning on their history, there will be plenty of visual reminders of their past glories, whilst underneath much engineering is taking place to move this apart from the standard E-208. Prices start at £34,995 and it will have a maximum range of 217 miles but expect closer to 160-170 in the real world. Click this EV Powered article link here, for more.LUNCHTIME READS: VAMOS A LA PLAYA PARTS 3 & 4Parts 3 and 4 are now out, from Driven To Write, on the theme of beach cars. You will learn about Italian offerings in the first link, which you can get to by clicking here. Then you will find out what Germany used to get their towels to the beach first, by clicking this link here.LIST OF THE WEEK: 1996 - A GOLDEN YEAR FOR NEW SPORTS CARSAntony Ingram once again has produced another cracking list for Hagerty. He selects a few of the more choice sports cars that you could've bought new in 1996. Click this link to view your options and see if you agree with Alan's choice.AND FINALLY: MOTORING ART BY VAL BIROVal Biro was prompted to start writing about his adventures in a 1926 Austin Heavy 12-4 Clifton tourer by those who heard him recount his stories. He did so, as children's books that many of you listeners have probably read when you were younger. But the art work is what we are focusing on as that helped make the words more vivid. Click this Classic & Sports Car article link here, to learn more.
Poland, which is regularly targeted by Russian hybrid attacks, takes the threat of war with Moscow very seriously. The country now spends nearly 5 percent of its GDP on defence, and aims to train its civilians too, so they will know how to react if war breaks out. To this end, it has introduced a programme of one-day training sessions with the army, entitled "Always Ready", which have turned out to be a runaway success. FRANCE 24's Adrien Sarlat and Jan Garstecki joined some of the participants at a session near Warsaw.
Talk the Talk - a podcast about linguistics, the science of language.
Australian magpies are even cleverer birds than we thought. New research from Dr Stephanie Mason shows that they do two language-like things we used to think only humans could do: learn their calls socially, and combine their calls in a way that looks a lot like syntax. So are we calling this language? If so, how are the linguists taking it? Stephanie joins us to talk about magpies, media, and the territoriality of linguists. Timestamps 00:00 Start 00:54 Intros: Your favourite bird 07:10 What's coming up: Magpies 09:34 Join us! Patreon spruikery 11:32 News: Jamaican MP shut down for speaking Jamaican in Parliament 19:35 News: Whale phonology 31:46 News: Unicode to include new genderless pronoun for Mandarin 36:37 News: China and the Rubio Workaround 38:16 Related or Not: New theme from Hugh! 40:05 Related or Not 1: SLAP, SMACK, and SWAT 45:45 Related or Not 2: SOUND 56:13 Related or Not 3: SPECK, SPECKLE, SPECTRE, and SPECTRUM 01:00:36 Talking about magpies with Stephanie Mason 01:03:38 About Australian magpies 01:06:17 The problem of anthropomorphism 01:15:21 What's the semantic content? 01:22:52 Linguists can be territorial about language 01:34:48 Social complexity drives new behaviours 01:45:19 Magpies learn their calls socially 01:49:42 Magpies combine their calls 01:58:44 Magpies learn calls across the lifespan 02:05:36 Finding those birds 02:08:10 Doing public engagement: Are metaphors actually helping? 02:17:26 Words of the Week: mog 02:24:54 Word of the Week: pied-à-terre 02:27:48 Word of the Week: dummymander 02:33:03 Word of the Week: Sooooo-ee! 02:39:22 Etymology of Guacamole 02:39:35 Comment: guacamole = testicle sauce? 02:41:28 The reads 02:46:28 Outtake
OUR MAN FLINT, the 1966 spy spoof that shook Hollywood, is back under the microscope. Dan and Tom from Cracking the Code of Spy Movies decode this James Coburn classic in their signature style. Released just weeks after THUNDERBALL, this movie punched well above its weight. It spoofed James Bond, "The Man from U.N.C.L.E.", and even drew from vaudeville comedy traditions. The movie's story follows Flint, a brilliant ex-agent pulled back to stop mad scientists bent on world domination through weather control. Highlighted themes in this episode: · James Coburn's portrayal of Derek Flint · Jerry Goldsmith's iconic score, which blends jazz, electronics, and exotica. He's ahead of his time. Some critics called this score better than anything from the Bond series. · ZOWIE, GALAXY, and SPECTRE: the movie directly mocks Bond's spy-agency formula with wit and precision. · Austin Powers' DNA traces directly back here — from the harem of women to the multifunction lighter. · Vaudeville roots: We uncover surprising comedy traditions hidden in the movie's structure Dan and Tom explore the movie's influences — from Fritz Lang's 1929 Spies to Sherlock Holmes — and trace its legacy through DIE HARD, KINGSMAN, AUSTIN POWERS, and beyond. Whether you're a die-hard spy movie fan or new to the genre, this episode will make you see Flint in a whole new way. This movie was the predecessor to IN LIKE FLINT. Tell us what you think of our decoding of OUR MAN FLINT Have you seen OUR MAN FLINT? If so, what are your thoughts? What spoofs did we miss? If you haven't seen it, does our decoding session make you want to watch it? Would you like us to decode the other "Flint" movie, IN LIKE FLINT? Let us know your thoughts, ideas for future episodes, and what you think of this episode. Just drop us a note at info@spymovienavigator.com. The more we hear from you, the better the show will surely be! We'll give you a shout-out in a future episode! You can check out all our CRACKING THE CODE OF SPY MOVIES podcast episodes on your favorite podcast app or our website. In addition, you can check out our YouTube channel as well. Episode Webpage: https://spymovienavigator.com/episode/our-man-flint-decoded
Somebody just paid the best part of eight grand for a James Bond watch that was designed for a video game before it was ever designed for a human wrist. And I'm genuinely tempted to be one of those somebodies.In this episode I read you my full, honest write-up of Omega's new Seamaster Diver 300M Chronograph “007 First Light”, the first-ever James Bond chronograph, and give you a take you won't get from any of the watch blogs, because I'm a tailor before I'm a watch person, and a lot of my clients are serious watch enthusiasts.I get into:•Why there's a Bond watch coming from a video game and not a film, and why that's cleverer than it sounds•The full specs, and why that 17.2mm thickness is the most controversial thing about it•Where it sits among the great Bond Omegas, from my own Casino Royale to the SPECTRE•The catch nobody mentions: it's not a limited edition•The tailor's angle: why this watch will not sit under a proper shirt cuff, and why that's actually perfect for the story it's telling•Whether it's a future classic or a future footnote, and whether you should buy oneRead the full written article on the blog, inked below.Got an opinion on the watch, the game, or whether Bond and gaming should mix? I want to hear it: tailoringtalkpodcast@gmail.com•Read the full article: https://www.robertorevillalondon.com/blog/omega-007-first-light-seamaster-tailor-review•Watch the YouTube version: https://youtube.com/@tailoringtalkmagazine•Omega 007 First Light official page: https://www.omegawatches.com/en-us/watches/seamaster/diver-300-m/007-first-light/productOmega, 007 First Light, James Bond, Seamaster, Bond watch, luxury watches, watch review, Casino Royale Omega, SPECTRE watch, IO Interactive, Bond game, bespoke tailoring, menswear, Tailoring Talk, future classic, video game watch Hosted on Acast. See acast.com/privacy for more information.
For everyone on the free feed, we have two sections taken from recent Patreon podcasts for you to check out!Up first, Jon Mackenzie from Tifo and the Athletic joins for a more tactical chat about this season and his forthcoming book, ‘The Spectre of Pep: How Guardiola Haunts Modern Football Tactics'. Recorded a week or so before Andoni Iraola was announced as Liverpool manager, the full episode chat's about the brilliance of his coaching and much more.Part two is a section where Neil Atkinson, from The Anfield Wrap, joins to chat about Liverpool's tricky season and the departure of Arne Slot (29:32). The full episode features more chat about Iraola's arrival and some general Premier League chat.Follow Jon on Instagram and Bluesky and you can pre-order the book here.Follow Neil on Bluesky, check out The Anfield Wrap here and his book is available here.Also, another shout for Musa's interview with Zohran Mamdani for GQ here. Tickets for our July live show, with Nish Kumar at the Southbank Centre, are available here.For more podcasts, ad-free and in full, plus access to the Stadio Social Club and much more, you can become a Stadio member by signing up at patreon.com/stadio. Hosted on Acast. See acast.com/privacy for more information.
Gene L. Coon had an incredibly influential run as a writer and producer during the first two seasons of Star Trek. This week, the EARTH STATION TREK crew will take a look at his pseudonymous contributions to Star Trek’s third season, including “Spock’s Brain,” “Spectre of the Gun,” “Wink of an Eye,” and “Let That […] The post Lee Cronin’s Star Trek – Earth Station Trek – Episode 266 appeared first on The ESO Network.
Air Date: 5–30-2026 Today we examine how Trump turned a sham lawsuit into a $1.8 billion reward fund for his political allies who attacked the Capitol on January 6th while implementing a counterterrorism strategy that erases right-wing extremism from the threat landscape, refocuses on left-wing violence that hardly exists and threatens to "find and kill" those they deem enemies. Full Show Notes Transcript Be part of the show! Leave a voice message, message us on Signal at the handle bestoftheleft.01, or email Jay@BestOfTheLeft.com BestOfTheLeft.com/Support (Members Get Bonus Shows + No Ads!) Use our links to shop Bookshop.org and Libro.fm for a non-evil book and audiobook purchasing experience! Join our Discord community! TOP TAKES KP 1: Acting U.S. Attorney General Todd Blanche Defends New $1.8B Anti-Weaponization Fund - Trump's Terms - Air Date 5-20-26 KP 2: Congress Strikes Back as Trump Rushes $1.8 Billion Scam - Legal AF by MeidasTouch - Air Date 5-18-26 KP 3: Dictatorship in Action David Cay Johnston on $1.8B Slush Fund Part 1 - Democracy Now! - Air Date 5-20-26 KP 4: Some Republicans in Congress Are Standing up to Trump - The NPR Politics Podcast - Air Date 5-22-26 KP 5: Why Jan. 6 Officers Are Suing to Stop Trump's $1.8 Billion Allies Fund - Here & Now Anytime - Air Date 5-21-26 KP 6: We Will Find You and We Will Kill You Part 1 - The Intercept Briefing - Air Date 5-15-26 KP 7: Daily Take Republicans Found $1.8 Billion Overnight for Trumps Thugs — So Why Are Seniors Choosing Between Food and Medicine - The Hartmann Report - Air Date 5-22-26 KP 8: Dictatorship in Action David Cay Johnston on $1.8B Slush Fund Part 2 - Democracy Now! - Air Date 5-20-26 (00:53:15) NOTE FROM THE EDITOR Trump's $1.8B Slush Fund & the Corruption We Saw Coming My commentaries on YouTube - Share them! DEEPER DIVES (01:11:48) SECTION A: ANATOMY OF THE HEIST A1: Andrew Weissman on Trumps $1.8 Billion Settlement with Himself Part 1 - Brian Lehrer: A Daily Podcast - Air Date 5-20-26 A2: Trump's $1.8 Billion DOJ-Facilitated Taxpayer Heist; Guest Robert Weissman of Public Citizen Part 1 - The BradCast - Air Date 5-18-26 A3: McConnell RIPS Trumps Jan. 6 Slush Fund as Utterly Stupid, Morally Wrong - All In W Chris Hayes - Air Date 5-21-26 (01:34:28) SECTION B: THE BIGGER PATTERN B1: Trump's $1.8 Billion DOJ-Facilitated Taxpayer Heist; Guest Robert Weissman of Public Citizen Part 2 - The BradCast - Air Date 5-18-26 B2: Andrew Weissman on Trumps $1.8 Billion Settlement with Himself Part 2 - Brian Lehrer A Daily Podcast - Air Date 5-20-26 B3: Craven Corruption - The Practivist Pod - Air Date 5-21-26 (01:57:06) SECTION C: ENEMIES OF THE STATE C1: Sources & Methods Trump's Counterterrorism Plan Part 1 - The NPR Politics Podcast - Air Date 5-25-26 C2: Trumps New Counterterrorism Strategy and the Spectre of Left-Wing Violence Part 1 - It Could Happen Here - Air Date 5-12-26 C3: Sources & Methods Trump's Counterterrorism Plan Part 2 - The NPR Politics Podcast - Air Date 5-25-26 C4: We Will Find You and We Will Kill You Part 2 - The Intercept Briefing - Air Date 5-15-26 C5: Trumps New Counterterrorism Strategy and the Spectre of Left-Wing Violence - It Could Happen Here - Air Date 5-12-26 Produced by Jay! Tomlinson Visit us at BestOfTheLeft.com Listen Anywhere! BestOfTheLeft.com/Listen Listen Anywhere! Follow BotL: Bluesky | Mastodon | Threads | X Like at Facebook.com/BestOfTheLeft Contact me directly at Jay@BestOfTheLeft.com
Send us a text or a voicemailThe host of a popular paranormal podcast becomes haunted by terrifying recordings from a defunct, vulgar, longform film discussion show sent to her by a loyal listener. On Episode 721 of Trick or Treat Radio we discuss Undertone, the film from director Ian Tuason! We also talk about low budget films that use their restrictions to create tense scenes, we dive into the legend of the Undertoker, and we react to trailers for the films; Her Private Hell the upcoming film from Nicolas Winding Refn, and the all AI feature Hell Grind. So grab your headphones and mic, create the perfect prompt to get yourself a 12 finger discount, and strap on for the world's most artificially intelligent podcast!Stuff we talk about: The Thing, Behind The Mask: The Rise of Leslie Vernon, The House By The Cemetery, Lucio Fulci, Run, Giovanni Frezza, Demons, The Undertoker, casket matches, Yokozuna, Roach on a Pole, Judy Bagwell, Orange Cassidy, Mr. Perfect, The Vampire's Ghost, Monster from the Ocean Floor, Gigantus: The Fire Monster, The Green Slime, Grizzly, Embryo, Spectre, Deadly Eyes, The Road Warrior, Crawlspace, Dark Age, Carnosaur, Terminator: Salvation, Fairuza Balk, Nick Cassavetes, The Craft, Judge Reinhold, Fast Times at Ridgemont Hight, Jonathan Hyde, Anaconda, Raymond Burr, Godzilla, Bride of the Gorilla, Transformers: The Movie, Rear Window, Dr. Teeth and the Electric Mayhem, Nicolas Winding Refn, dystopian films, Neon, NWR, Her Private Hell, Higgsfield AI, Hell Grind, all AI feature film, May 21th, horrible typos in your film trailer, Mohel Day, heavy trope action, Undertone, Ian Tuason, Nina Kiri, paranormal podcasts, folk tales, Guy Fieri's Flavortown, Root Beer Float seltzer, dutch angle, I speak New English, cautionary tales, Disney, Tobin Spirit Guide, A24, David Lowery, Mother Mary, Anne Hathaway, Hunter Schafer, The Green Knight, Faces of Death, Kyle Chandler, Lanterns, Aaron Pierre, Rebel Ridge, James Mason, Greg Travis, David Sleaze the Punk Rock Magician, Rodney's Place, HBO, Enbalmination, and the Legend of the Undertoker.Support us on Patreon: https://www.patreon.com/trickortreatradioJoin our Discord Community: discord.trickortreatradio.comSend Email/Voicemail: mailto:podcast@trickortreatradio.comVisit our website: http://trickortreatradio.comStart your own podcast: https://www.buzzsprout.com/?referrer_id=386Use our Amazon link: http://amzn.to/2CTdZzKFB Group: http://www.facebook.com/groups/trickortreatradioTwitter: http://twitter.com/TrickTreatRadioFacebook: http://facebook.com/TrickOrTreatRadioYouTube: http://youtube.com/TrickOrTreatRadioInstagram: http://instagram.com/TrickorTreatRadioSupport the show
Howie discusses the Spectre - or rather, spectacle - of Barney Frank. Visit the Howie Carr Radio Network website to access columns, podcasts, and other exclusive content.
Team Sneak up their sneak game by going into the Stalking to scout the danger of the Temple. Lafian intervenes, Rhal tries positivity with bad news, Squash scouts for names and Zaltanna learns a terrible possibility. Will their temporary companion be willing to put their life on the line in Empty Shallows? I guess we're about to find out… --- Get ad free episodes on Patreon! You can help support the show at http://www.Patreon.com/blighthouse Find us - Email: TheLuckyDiePodcast@gmail.com Website: www.TheLuckyDie.com Facebook: https://www.facebook.com/TLDPod Discord: https://discord.gg/vtgnVAZY44 This is a Blighthouse Studio production. --- Our Amazing Affliates You want TLD themed merch? Head over to our Teepublic store to get our Skulliver, The Key to Murder, Mirror and Hafling Girth designs! https://www.teepublic.com/stores/blight-house?ref_id=27307 Or if Displate is more your aesthetic, check out Kessir's incredible designs - www.displate.com/artist/BlighthouseStudio Use code BLIGHTHOUSE10 to get 10% off UrWizards dice - www.urwizards.com/?ref=BLIGHTHOUSESTUDIO --- Find and support our sponsors at: fableandfolly.com/partners Transcript - Apparently transcription services can't cope with our non US accents, so beware. Learn more about your ad choices. Visit megaphone.fm/adchoices
The Crown and Mitre Inn stands on the bones of an old abbey, and the man who bought the inn for what's buried under the cellar floor is about to find out he's not the only one who can call a ghost up the stairs.Look for this podcast on Apple Podcasts, Spotify, iHeart Radio, Amazon Music, Pandora, TuneIn Radio, and other podcast apps. Get a list of free listening apps here: https://weirddarkness.tiny.us/OTRCHAPTERS & TIME STAMPS (All Times Approximate)…00:00:00.000 = Show Open00:01:30.028 = CBS Radio Mystery Theater, “The House By The Seine” (October 27, 1977)00:48:21.264 = Sleep No More, “Waxwork Man and Snake” (January 09, 1957) ***WD01:16:28.954 = BBC Radio 4 Spinechillers, “Figures” (February 14, 1984)01:54:50.053 = Strange Wills, “One Shining Night” (July 20, 1946)02:24:24.864 = Strange, “Deadman's Reef” (1955) ***WD02:36:29.815 = Suspense, “Life Ends at Midnight” (February 17, 1944)03:07:07.268 = Tales of the Frightened, “Hands of Fate” (December 09, 1957) ***WD03:11:39.865 = The Creaking Door, “Inn Spectre” (July 27, 1964) ***WD03:37:49.809 = The Saint, “Nursemaid” (July 15, 1951)04:06:47.629 = Theater Five, “Deedle Deedle Dumpling My Son X-1” (November 13, 1964) ***WD04:28:40.805 = Tales From The Tomb, “A Ghost Or a Vampire” (1960s)04:34:07.376 = 2000 Plus, “Robot Killer” (August 30, 1950) ***WD (LQ)05:02:34.445 = Show Close(ADU) = Air Date Unknown(LQ) = Low Quality***WD = Remastered, edited, or cleaned up by Weird Darkness to make the episode more listenable. Audio may not be pristine, but it will be better than the original file which may have been unusable or more difficult to hear without editing.CUSTOM WEBPAGE: https://weirddarkness.com/WDRR0661
All of this week's episodes of It Could Happen Here put together in one large file. - Fighting Back Against the Surveillance State - Trump’s New Counterterrorism Strategy and the Spectre of Left-Wing Violence - Parasitism with Andrew - The Return of Jim Crow - Executive Disorder: Virginia Redistricting, Renaming the Iran War, TPUSA Event Cancelled by ANTIFA You can now listen to all Cool Zone Media shows, 100% ad-free through the Cooler Zone Media subscription, available exclusively on Apple Podcasts. So, open your Apple Podcasts app, search for “Cooler Zone Media” and subscribe today! http://apple.co/coolerzone Sources/Links: Fighting Back Against the Surveillance State https://www.eff.org/deeplinks/2025/03/meet-rayhunter-new-open-source-tool-eff-detect-cellular-spying https://www.supremecourt.gov/opinions/17pdf/16-402_h315.pdf https://citizenlab.ca/research/analysis-of-penlinks-ad-based-geolocation-surveillance-tech/ https://colonelpanic.tech/ SSD.eff.org Rayhunter.eff.org https://www.open-archive.org/save Trump’s New Counterterrorism Strategy and the Spectre of Left-Wing Violence https://www.whitehouse.gov/wp-content/uploads/2026/05/2026-USCT-Strategy-1.pdf https://trumpwhitehouse.archives.gov/wp-content/uploads/2018/10/NSCT.pdf https://icct.nl/sites/default/files/import/publication/NSC-1v2.pdf https://web.archive.org/web/20210615130908/https://www.whitehouse.gov/wp-content/uploads/2021/06/National-Strategy-for-Countering-Domestic-Terrorism.pdf https://www.fbi.gov/news/speeches-and-testimony/confronting-white-supremacy-examining-the-biden-administrations-counterterrorism-strategy-langan-092921 https://web.archive.org/web/20210615101231/https://www.whitehouse.gov/briefing-room/statements-releases/2021/06/15/fact-sheet-national-strategy-for-countering-domestic-terrorism/ https://www.gao.gov/blog/rising-threat-domestic-terrorism-u.s.-and-federal-efforts-combat-it https://uncoverdc.com/2023/02/08/the-fbi-doubles-down-on-christians-and-white-supremacy-in-2023/ https://angelusnews.com/news/nation/fbi-memo-investigation-update/ https://defendinged.org/press-releases/full-nsba-letter-to-biden-administration-and-department-of-justice-memo/ https://judiciary.house.gov/media/press-releases/us-house-judiciary-republicans-doj-labeled-dozens-of-parents-as-terrorist https://www.justice.gov/archives/ag/file/1170061-0/dl?inline= https://www.fbi.gov/investigate/terrorism Parasitism with Andrew Progress by Samuel Miller McDonald Worshiping Power by Peter Gelderloos The Return of Jim Crow https://www.naacpldf.org/case-issue/louisiana-v-callais/ https://constitution.congress.gov/browse/article-1/section-4/ https://www.scotusblog.com/2026/05/court-gives-immediate-effect-to-voting-rights-act-decision/ https://www.scotusblog.com/2026/04/after-major-voting-rights-ruling-parties-dispute-whether-the-court-should-finalize-decision-imme/ https://www.scotusblog.com/2026/05/court-clears-way-for-alabama-to-use-congressional-map-blocked-by-lower-court-as-racially-discrim/ https://thehill.com/opinion/judiciary/supreme-court/5872963-supreme-court-voting-rights/ https://www.ms.now/opinion/supreme-court-louisiana-callais-black-vote-warning https://www.democracynow.org/2026/5/12/voting_rights_scotus https://slate.com/news-and-politics/2026/05/supreme-court-alabama-voting-sotomayor-dissent-alito.html Executive Disorder: Virginia Redistricting, Renaming the Iran War, TPUSA Event Cancelled by ANTIFA https://www.who.int/emergencies/disease-outbreak-news/item/2026-DON600 https://www.bbc.com/news/articles/cvgzv77ldpdo https://www.calbee.co.jp/en/news/pdf/174-29160.pdf https://www.supremecourt.gov/orders/courtorders/051126zr_apl1.pdf https://x.com/joekent16jan19/status/2052477681036583183?s=20 https://x.com/pastormarkburns/status/2052227145921892710?s=20 ttps://www.newsguardrealitycheck.com/p/30-percent-of-americans-think-at-least-one-trump-assassination-attempt-was-staged https://x.com/i/status/2053865929633661046 https://x.com/diyarkurda/status/2054268681362804860?s=20 https://www.jpost.com/international/article-895828 https://x.com/mb_ghalibaf https://x.com/Reuters/status/2053897929174188187?s=20 https://www.cbsnews.com/news/pakistan-iran-military-aircraft-on-its-airfields-us-mediator-role/ https://www.c6f.navy.mil/Press-Room/News/Article/4482914/a-us-navy-ballistic-missile-submarine-arrived-in-gibraltar-may-10-2026/ https://www.them.us/story/uw-students-protest-turning-point-usa-after-trans-student-homicide https://x.com/MrAndyNgo/status/2054289485303525720 https://x.com/ChloeCole/status/2054365092054286605?s=20 https://www.vacourts.gov/static/opinions/opnscvwp/1260127.pdf https://www.supremecourt.gov/DocketPDF/25/25A1240/408563/20260511151941216_25A%20Application%20for%20Stay.pdf https://www.cnn.com/2026/05/11/politics/virginia-redistricting-us-supreme-court https://newrepublic.com/article/210250/trump-virginia-dems-redistricting-warSee omnystudio.com/listener for privacy information.
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