Podcasts about TPS

Immigration and Customs Enforcement (ICE) arrested nearly 50,000 people in July, marking the biggest month for ICE arrests of President Trump's second term. We talk to a researcher who thinks this increased ICE activity is creating an “economy of fear” in cities around the country — and has the data to prove it.Fact checking by Sierra Juarez.Your Next Listen — How ending TPS is squeezing workers, businesses and (soon) consumersConnect with The Indicator — Sign up for The Indicator's weekly newsletter! — Buy the Planet Money book — Find our socials, YouTube and more! — For sponsor-free episodes, subscribe to NPR+ Support public media with NPR+ and enjoy perks for over 25 podcasts like this one. This show's perks include sponsor-free listening. Learn more at plus.npr.org. See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

"They're eating the dogs, they're eating the cats!" That debate line from President Trump was one of the most famous of 2024. Now, Haitians in Springfield, Ohio are losing their TPS status and facing deportation. Joshua Lisec has now written the definitive book on how Haitians settled a struggling part of middle America and joins the show. Psychotherapist Jonathan Alpert weighs in on insanity pleas and when, if ever, they are a valid criminal defense. Danny joins for the weekly AMA, where the team takes questions on beef imports, punishing liberal judges, and whether Jack should be fitted with a shock collar for Thoughtcrime (seriously!). Watch every episode ad-free on members.charliekirk.com! Get new merch at charliekirkstore.com!Support the show: http://www.charliekirk.com/supportSee omnystudio.com/listener for privacy information.

Hawk walks through Adam Serwer's Atlantic piece, The See No Evil Supreme Court, and the framework Serwer names the neo Korematsu doctrine: if any non racial explanation for a policy can be constructed, however hypothetical, the racial animus behind it stops mattering legally. Serwer traces it from Trump v. Hawaii through Louisiana v. Callais and the TPS ruling, and contrasts it with Masterpiece Cakeshop, where hostile statements by officials counted for everything. Sotomayor's September dissent is the payoff, and Hawk reads from it. There is also footage circulating of agents in the back of a truck identifying people in traffic by appearance, using language that makes the criteria explicit. SUPPORT & CONNECT WITH HAWK- Support on Patreon: https://www.patreon.com/mdg650hawk - Hawk's Merch Store: https://hawkmerchstore.com - Connect on TikTok: https://www.tiktok.com/@mdg650hawk7thacct - Connect on TikTok: https://www.tiktok.com/@hawkeyewhackamole - Connect on BlueSky: https://bsky.app/profile/mdg650hawk.bsky.social - Connect on Substack: https://mdg650hawk.substack.com - Connect on Facebook: https://www.facebook.com/hawkpodcasts - Connect on Instagram: https://www.instagram.com/mdg650hawk - Connect on Twitch: https://www.twitch.tv/mdg650hawk ALL HAWK PODCASTS INFO- Additional Content Available Here: https://www.hawkpodcasts.comhttps://www.youtube.com/@hawkpodcasts- Listen to Hawk Podcasts On Your Favorite Platform:Spotify: https://spoti.fi/3RWeJfyApple Podcasts: https://apple.co/422GDuLYouTube: https://youtube.com/@hawkpodcastsiHeartRadio: https://ihr.fm/47vVBdPPandora: https://bit.ly/48COaTB

It's Free Speech Friday, and Joe Pags is going EVERYWHERE. He starts with Boston and Massachusetts politicians he says have completely lost the plot, including lawmakers proudly defending transgender athletes competing in women's sports. Then it's time for some incredible music history: Michael Jackson wanted Prince on "Bad," so why did Prince turn down what could've been one of the biggest duets EVER? Pags reveals the hilarious reason before flashing back to the glory days of MTV, talking Stevie Wonder, and revisiting Shaq's wild story that left him wondering whether Stevie can actually see. The phones light up before Pags turns to Vice President JD Vance campaigning in Ohio, playing his favorite moments and explaining why he loves seeing the VP get out and speak directly to Americans. Plus, the first deportation flight lands in Haiti following the TPS suspension—and somehow the crew ends up debating one of America's greatest mysteries: Why do we write dates backwards compared with England? Learn more about your ad choices. Visit megaphone.fm/adchoices

This is what I get for taking a month off because SO much has happened, and it's been nothing but twists and turns. We're talking Suffolk University letting students co-major in AI, ASU launching a degree for aspiring influencers while the U.S. loses 23,000 jobs, YouTube making it harder to get paid, and the latest mess surrounding Jools Lebron. We also have to talk about what's happening to Haitians as TPS ends, the questions surrounding Nolan Wells' death, Trump administration drama, NABJ handing out its Thumbs Down awards, Invest Fest backlash, Luigi Mangione, Flock cameras and the conversation around progressives and Black voters. Then somehow the billionaires went shopping for the Lakers and EA, FIFA switched things up, and pop culture gave us even MORE to discuss. Grab your drink because clearly, we have completely lost the plot. IG: Itswista IG/YouTube/Substack: wordswithwista

The first large U.S. deportation flight to Haiti since the end of temporary protected status arrived with more than 160 people. The flight landed in Cap-Haitien after officials avoided Port-au-Prince because of ongoing gang violence and security concerns. Subscribe to our newsletter to stay informed with the latest news from a leading Black-owned & controlled media company: https://aurn.com/newsletter Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Rommel H. Ojeda, senior community correspondent and engagement reporter for Documented, and Ralph Thomassaint Joseph, Caribbean communities correspondent for Documented, talk about how people, communities, and businesses across New York City are feeling the federal immigration crackdown. Photo: Haitians living in Brooklyn join local politicians and others at an Immigrant Resource Fair and Know Your Rights Event in an area of Brooklyn known as 'Little Haiti' on July 28, 2026, in New York City. (Photo by Spencer Platt/Getty Images)   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Primary election night delivers major wins, surprising losses, and plenty of controversy. We break down the Florida results, including Byron Donalds' victory, the DeSantis legacy, Randy Fine's race, Corey Mills' loss, and the upset involving Angie Nixon and Alexander Vindman.We also react to CNN's coverage of the results, the DSA's role in the Democratic primaries, Ashley Moody's numbers, the Michigan Senate race, Texas political developments, and the South Carolina runoff.Then we dig into a Minnesota poll-worker training video and the latest immigration and international headlines before turning to some of the internet's biggest controversies. That includes the Lindsay Clancy case and the online debate surrounding it, Candace Owens' commentary, viral TikTok trends, astrology conspiracy theories, and reactions from Phil Labonte, Jeremy Boring, and others.Plus: Scott Bessent assassination-attempt sentencing news, David Morens' guilty plea, ICE fines, TPS changes, Iran, China, MTG, the Pope, Sophie Cunningham's national anthem moment, and some of the strangest viral posts making the rounds.From election-night surprises to culture, politics, viral TikToks, and internet insanity, we're breaking down the stories everyone is talking about.SUPPORT OUR SPONSORS TO SUPPORT OUR SHOW!Be confident in your portfolio with Bulwark! Schedule your free Know Your Risk Portfolio review. Go to https://KnowYourRiskPodcast.com Ready to give MASA a try? Get 25% off your first order by going to https://MasaChips.com/CHICKS and using code CHICKS.Give $26 today to the Human Coalition. Be her lifeline. Create a life saving moment. Give today at https://HumanCoalition.org/ChicksSubscribe and stay tuned for new episodes every weekday!Follow us here for more daily clips, updates, and commentary:YoutubeFacebookInstagramTikTokXLocalsMore InfoWebsite

Labels are powerful. They can distill you down to a single word. They can get stuck on you without your permission. And sometimes, when they get taken away, they can upend your entire life. On this episode: labels and the havoc they wreak. Visit thisamericanlife.org/lifepartners to sign up for our premium subscription.(00:00:00) Prologue: Sara and Ethan have been dating for 10 months. They see each other at least once a week, text pretty much every day, and have been there for each other through hard times. But they refuse to have the “what are we?” conversation. So what gives? Guest host Tobin Low looks into it. (5 minutes)(00:05:14) Act One: Last week, when Temporary Protected Status, also known as TPS, was cut off for Haitian immigrants, some 300,000 people had their lives upended. Marie, a Certified Nursing Assistant in Boston, was one of them. Producer Chana Joffe-Walt spent time last week with Marie as she tried to figure out exactly when her very last day on the job would be. (21 minutes)(00:27:57) Act Two: Comedian Janet McNamara was walking off stage 15 years ago when the club owner asked, “Hey, have you ever been tested for autism?” No one had ever asked her this before, so Janet decided to look into it. (17 minutes)(00:44:01) Act Three: When you get down to it, labels are just our way of pointing out what we think is remarkable about other people. That's Alfred Jung Lee's argument. He's been thinking a lot about how we describe each other, especially as he tries to describe his wife, who passed away from cancer. (10 minutes)Transcripts are available at thisamericanlife.org / This American Life privacy policy. / Learn more about sponsor message choices.

Guest host Dean Karayanis steps in for Derek Hunter to break down current political debates, focusing on NYC's Mayor Zohran Mamdani's hypocrisy on landlords and Amazon delivery subcontractors, border enforcement, and TPS policies. Plus, the left's attacks — from Vice President Harris to Governor Kathy Hochul of New York — on constitutional structures like the Electoral College and Supreme Court because democracy means they don't get their way 100% of the time. Plus, Tom Homan as an eloquent spokesman for the dark side of choosing to encourage illegal entry into America.

Donate (no account necessary) | Subscribe (account required) Try Masa Chips: CLICK HERE - Use Code Wright for 25% Off Your First Order.  Join Bryan Dean Wright, former CIA Operations Officer, as he dives into today's top stories shaping America and the world. In this Monday Headline Brief of The Wright Report, Bryan breaks down a shocking new autopsy suggesting a Los Alamos National Lab employee found dead in the New Mexico wilderness was murdered, not a suicide as first believed, reviving fears about a mysterious string of dead American scientists. Bryan covers Senate candidate Abdul El-Sayed's plan to headline a Muslim conference alongside a Pakistani cleric who defends child marriage, a federal judge clearing the way for more Arizona border wall despite tribal objections, and a court order stripping protected status from over 1,100 Boston-area Somalis. He also breaks down new reporting on Texas school kids being bussed to mosques and a Connecticut library's "Hijab Story Time." Plus, Bryan covers the collapsed US-Iran ceasefire and the IRGC's new bounty on American troops, another blocked Moroccan migrant push into Spain's Ceuta enclave, Denmark's Prime Minister warning that Islam poses an existential threat to Europe, and a drought squeezing Europe's nuclear power and cheese industries. "And you shall know the truth, and the truth shall make you free." - John 8:32   Keywords: Wright Report, Bryan Dean Wright, Los Alamos, dead scientists, murder, New Mexico, Abdul El-Sayed, Michigan Senate, Pakistani cleric, child marriage, border wall, Arizona, Somalis, TPS, mosques, Texas schools, hijab, Iran ceasefire, IRGC, Ceuta, Morocco, Denmark, Islam, Europe drought

Fighting, you’re being used. Trump on high gas prices. Surrogate mother vs parents. Lindsay Clancy case commentary. Tim Berners-Lee. Keeping her last name. Spain, US migrants.

This Day in Legal History: Clinton Testifies Before the Grand JuryOn August 17, 1998, President Bill Clinton became the first sitting president to testify before a grand jury as the subject of its investigation. He gave his testimony via closed-circuit television from the White House to Independent Counsel Kenneth Starr's grand jury, concerning his relationship with a White House intern—and that same evening, he addressed the nation to admit he had misled the public about it.The legal machinery that brought a president to that moment is worth understanding. It began, improbably, with a civil lawsuit: Paula Jones's sexual-harassment suit, which produced the 1997 Supreme Court decision in Clinton v. Jones holding that a sitting president is not immune from civil litigation over unofficial conduct and can be deposed while in office. That deposition, and the questions in it, are what put Clinton's statements under oath—and when those statements collided with what Starr's investigation uncovered, the independent counsel built a case around perjury and obstruction of justice. Clinton, carefully, insisted his earlier answers had been “legally accurate,” a phrase that became emblematic of the entire episode.The significance of August 17, 1998 is layered. It led directly to Clinton's impeachment by the House on charges of perjury and obstruction—only the second presidential impeachment in American history—and his acquittal by the Senate. But its deeper legal legacies are the ones that still echo: Clinton v. Jones established that the presidency is not a shield against civil accountability for private conduct, a principle you can hear resonating in today's fights over presidential immunity, and the whole saga became a national seminar on perjury, executive privilege, and the limits of the independent-counsel model, which Congress let expire the following year. It's a fitting anniversary for a day when the relationship between political power and prosecutorial judgment is, once again, at the center of the news.Opening statements begin today in Las Vegas in the murder trial of Duane “Keffe D” Davis, nearly thirty years after the 1996 killing of rapper Tupac Shakur. Davis, 63, is charged with murder with a deadly weapon with intent to promote a criminal gang. He has pleaded not guilty and faces life in prison if convicted.A sixteen-person jury has been selected, and prosecutors are expected to call roughly forty witnesses. The witness list includes Suge Knight, who was driving the car in which Shakur was shot, and Nevada Governor Joe Lombardo, who responded to the shooting as a Las Vegas police sergeant in 1996.Prosecutors say Davis was the “shot caller” behind a quickly assembled plan to retaliate after Shakur and members of his entourage beat Davis's nephew at the MGM Grand earlier that evening. The government's theory places the killing within a larger conflict involving rival street gangs.The obvious problem for prosecutors is time. Trying a murder case three decades after the crime means dealing with faded memories, unavailable witnesses, and physical evidence that may have been lost or degraded. What eventually revived the case, however, was Davis himself.Over the years, Davis publicly discussed his involvement in Shakur's killing in interviews and in a memoir. Those statements now form an important part of the prosecution's case. They also give the defense an obvious line of attack: statements made years later for publicity, money, or street credibility are not necessarily reliable accounts of what actually happened.That makes Davis's own words one of the most important legal issues to watch. Prosecutors do not merely have to show that he repeatedly claimed involvement; they have to persuade jurors that those claims, considered alongside the remaining evidence, prove his guilt beyond a reasonable doubt. The trial is therefore as much about the reliability of decades-old admissions as it is about solving one of the most famous unsolved murders in American popular culture.Tupac shooting trial begins with opening statements | ReutersWashington Post · PBS NewsHourAttorney General Todd Blanche is publicly backing U.S. Attorney Jeanine Pirro after President Trump criticized her office for dropping a vandalism prosecution involving the Lincoln Memorial Reflecting Pool.Speaking on NBC's Meet the Press, Blanche said he “absolutely” supports Pirro, the top federal prosecutor in Washington. The comments came after Trump criticized her decision to abandon the prosecution of former Olympian David Hearn and others accused of damaging the Reflecting Pool.The case grew out of a roughly $15 million renovation project that the administration pushed to complete before July 4. After an algae bloom appeared and portions of the pool's lining began peeling, Pirro's office brought vandalism charges. Prosecutors later dropped the case after concluding that the damage resulted from problems with the renovation rather than deliberate sabotage.Trump was not happy with that conclusion. He publicly called on Pirro to revisit what he described as her “hastily made decision,” and the White House reportedly asked the Justice Department to consider whether additional charges were available.Blanche is now defending the prosecutor's decision. He said it was unfair to judge Pirro based on a single case when her office had made its decision based on the evidence available to prosecutors.The legal principle here is prosecutorial discretion. Prosecutors have substantial authority to decide whether the available evidence justifies bringing or continuing criminal charges, and those decisions are supposed to turn on the law and evidence rather than the political preferences of the president. That principle takes on additional importance because Blanche, who previously served as Trump's personal lawyer, faced questions during his confirmation about whether he could operate the Justice Department independently.There is an important qualification. Blanche also said Trump supports Pirro, despite the president's public criticism of her handling of this case. Still, an attorney general publicly defending a prosecutor's evidence-based decision against presidential criticism is a meaningful test of how much independence federal prosecutors will have when their charging decisions conflict with the White House.US Attorney General Blanche publicly backs Pirro after Trump criticism over Reflecting Pool | Reuters · US NewsAOLA federal judge has cleared the way for the Trump administration to end Temporary Protected Status for roughly 1,100 Somalis living in the United States.U.S. District Judge Allison Burroughs in Boston lifted a pause she had imposed in March on the Department of Homeland Security's termination of Somalia's TPS designation. The change largely reflects what has happened at the Supreme Court since Burroughs entered that earlier order.Temporary Protected Status allows people from designated countries experiencing armed conflict, natural disasters, or other extraordinary conditions to remain and work legally in the United States for a limited period. The protection does not itself provide permanent immigration status, and the executive branch periodically decides whether conditions in a particular country continue to justify the designation.The administration maintains that conditions in Somalia have improved enough to end TPS. Opponents point to continuing violence in the country, including fighting involving al-Shabaab militants, as evidence that returning people to Somalia remains dangerous.Four Somali plaintiffs and advocacy organizations also argued that the administration's decision was motivated by racial discrimination, citing President Trump's previous comments about Somalis. Burroughs rejected that discrimination claim at this stage, although the broader litigation continues.The most important legal development, though, happened above the district court. In June, the Supreme Court allowed the administration to terminate similar protections involving people from Haiti and Syria. Burroughs concluded that the Supreme Court's intervention changed the legal landscape and limited her ability to continue blocking the Somali termination.That illustrates how a Supreme Court ruling can affect considerably more than the people immediately involved in a particular case. Once the Court signaled that the executive branch has broad authority to terminate TPS designations, lower courts confronting similar challenges had less room to intervene. For the roughly 1,100 Somalis affected here, that means a temporary immigration protection that allowed them to remain legally in the United States can now be withdrawn while the underlying legal fight continues.US judge clears way for Trump to end Somalis' deportation protections | ReutersUS News · Fox News This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.minimumcomp.com/subscribe

We talk with immigration lawyer Emmanuel Olawale about the consequences facing immigrants after the Trump administration abruptly ended the Temporary Protected Status program. TPS was designed to provide temporary protection for people from countries experiencing disaster, conflict or severe instability, but it did not create a direct path to permanent residency or citizenship.The shortcomings of TPS are confronting some 250,000 to 300,000 immigrants in the U.S. whose lawful status has abruptly ended, including about 20,000 to 30,000 Haitians who have lived, worked, built businesses, and raised children in Springfield, Ohio, for years and are now at risk of being deportedThe discussion highlights the tension between federal immigration policy and the reality of conditions in Haiti, where political instability, gang violence, limited public services, and safety concerns remain severe. As Olawale puts it, TPS is “a temporary solution to a permanent problem,” emphasizing that Congress failed to create a durable legal pathway for long-term TPS holders. The conversation also explores the difference between TPS, asylum, refugee status, and family-based immigration relief.Olawale explains how immigration enforcement is unfolding locally, including ICE reporting requirements, ankle monitoring, expedited immigration court procedures, and pressure on immigration judges. He argues that shifting policies and administrative practices have made immigration law more difficult and unpredictable for attorneys and clients alike.The episode addresses the role of political rhetoric and misinformation in shaping public attitudes toward immigrant communities. The administration's use of dehumanizing language for immigrants attempts to make harsh enforcement policies easier to justify.The situation in Springfield is an example of how a temporary immigration policy like TPS leaves families vulnerable when political winds change.

Sekou Dukuly moonlights as a MN group home director, judge rules to end TPS, foot washing stations for Muslims in TX airports, Danno asked a couple of questions, Duluth has entered the Fraud Map, Sunshine Protection Act, the DSA strategy to infiltrate labor unions, and remember Code Pink...See omnystudio.com/listener for privacy information.

【聊了什么】 同样讨论总统罢免权，FTC 不是例外，美联储却是；同样声称尊重文本和原意，保守派大法官在出生公民权、投票权和跨性别权益案中，却一次次更换自己的法理工具。 本期我们与 Nancy、品达一起盘点美国最高法院 2025-2026 开庭期：特朗普的“单一行政权”走到了哪一步？谁仍然被视为美国人？《投票权法》还剩下多少效力？跨性别学生为什么被排除在女子校队之外？当法院的判决越来越只能用政治解释，偶尔对特朗普说“不”，究竟是制度制衡，还是“小骂大帮忙”？ 【支持我们】 如果喜欢这期节目并希望支持我们将节目继续做下去： 也欢迎加入我们的会员计划： https://theamericanroulette.com/paid-membership/ 会员可以收到每周2-5封newsletter，可以加入会员社群，参加会员活动，并享受更多福利。 合作投稿邮箱：american.roulette.pod@gmail.com 【时间轴】 01:39 FTC 与美联储：特朗普能解雇谁 08:02 “单一行政权”如何成为保守派共识 28:29 出生公民权与谁算“美国人” 43:31 TPS 终止：移民身份和司法审查 53:35 投票权法、黑人选区与政治划区 1:02:02 最高法院还有稳定法理吗 1:13:01 跨性别学生参加女子体育案 1:20:53 校园体育为什么不能套用精英体育逻辑 1:33:32 Alito 退休乌龙与大法官的个人政治 1:41:21 Barrett 为什么总成为右翼攻击目标 1:44:36 法院改革与对特朗普“小骂大帮忙” 【我们是谁】 美轮美换是一档深入探讨当今美国政治的中文播客。 本期的主播和嘉宾： Lokin：美国法学院毕业生，即将成为一名纽约诉讼律师 王浩岚：美国政治爱好者，岚目公众号主笔兼消息二道贩子 Nancy：普林斯顿大学政治学博士生，耶鲁法学院法律博士 品达：美国政治观察人士，《孤岛繁星》主播 【 What We Talked About】 When considering the same question of presidential removal power, the Court decided that the FTC was not an exception, but the Federal Reserve was. And while the conservative justices continue to profess their commitment to textualism and originalism, they repeatedly switched doctrinal tools in cases involving birthright citizenship, voting rights, and transgender rights. In this episode, Nancy and Pinda join us to review the U.S. Supreme Court's 2025-2026 term. How far has Trump's vision of the “unitary executive” advanced? Who still counts as American? How much remains of the Voting Rights Act? Why can transgender students be excluded from girls' school sports? As the Court's decisions become increasingly difficult to explain through legal doctrine alone, do its occasional rulings against Trump represent genuine institutional checks, or merely “small rebukes, big assists”? 【Support Us】 If you like our show and want to support us, please consider the following: Join our membership program: https://theamericanroulette.com/paid-membership/ Support us on Patreon: www.patreon.com/americanroulette Business Inquiries and fan mail: american.roulette.pod@gmail.com 【Timeline】 01:39 The FTC and the Federal Reserve: Whom can Trump fire? 08:02 How the “unitary executive” became a conservative consensus 28:29 Birthright citizenship and who counts as “American” 43:31 Ending TPS: Immigration status and judicial review 53:35 The Voting Rights Act, majority-Black districts, and partisan redistricting 1:02:02 Does the Supreme Court still have a coherent legal doctrine? 1:13:01 The case over transgender students participating in girls' sports 1:20:53 Why school sports cannot be judged by the logic of elite athletics 1:33:32 The false report of Alito's retirement and the personal politics of the justices 1:41:21 Why Barrett remains a favorite target of the right 1:44:36 Court reform and the Supreme Court's “small rebukes, big assists” approach to Trump 【Who We Are】 The American Roulette is a podcast dedicated to helping the Chinese-speaking community understand fast-changing U.S. politics. Our Hosts and Guests: Lokin: U.S. law school student, incoming NY litigation lawyer 王浩岚 (Haolan Wang): American political enthusiast, chief writer at Lán Mù WeChat Official Account, and peddler of information Nancy：Princeton Politics PhD student, Yale Law School graduate Pinda：American political enthusiast 【The Links】 Trump v. Slaughter：总统罢免 FTC 委员与“单一行政权” Trump v. Cook：总统罢免美联储理事与央行独立性 Trump v. Barbara：出生公民权 Mullin v. Doe：海地与叙利亚 TPS 终止及司法审查 Louisiana v. Callais：黑人多数选区与《投票权法》第二条 West Virginia v. B. P. J.：跨性别学生参加女子体育运动 《纽约时报》：TPS 被终止后，美国部分行业面临劳动力短缺 Harvard Kennedy School：Louisiana v. Callais 对《投票权法》意味着什么 《纽约时报》：最高法院跨性别学生体育案的当事人 Becky Pepper-Jackson Becky Pepper-Jackson 的母亲：这个母亲节，我感谢跨性别女儿教会我的事 ACLU：关于跨性别运动员的四个常见迷思 NPR Public Editor：Nina Totenberg 误报 Alito 退休事件的经过 《纽约时报》：Amy Coney Barrett 为何再次遭到共和党右翼攻击 《纽约时报》：Ketanji Brown Jackson 做客 Michelle Obama 播客 IMO with Michelle Obama and Craig Robinson：Ketanji Brown Jackson 访谈 Slow Burn: Becoming Justice Gorsuch Humphrey's Executor v. United States (1935)：FTC 独立性与正当理由解雇 Loper Bright Enterprises v. Raimondo (2024)：推翻 Chevron deference Seila Law v. CFPB (2020)：单一局长制与总统罢免权 United States v. Wong Kim Ark (1898)：“黄金德案”与出生公民权 Thornburg v. Gingles (1986)：《投票权法》第二条的 Gingles 标准 Shelby County v. Holder (2013)：削弱《投票权法》的关键判决

Un fuerte terremoto de 7.7 sacudió el este de Indonesia que provocó una alerta de Tsunami. Los residentes fueron evacuados a zonas altas y las autoridades evalúan los daños. El medio oeste del país sigue afectado por serias inundaciones y devastación producto del mal tiempo que acumula 6 días consecutivos de tormentas severas. Luigi Mangione se declaró culpable de cargos federales de acoso relacionados con el caso del asesinato del director ejectuvo del United Health Care.  

Ohio has an open race for Secretary of State for the first time since 2018, and almost nobody is watching it. Republican Treasurer Robert Sprague faces Democratic state Rep. Allison Russo, with Libertarian Tom Pruss also on the November 3 ballot. This episode breaks down what the office actually controls, where both candidates stand, and the Issue 3 disagreement that split the Democratic ticket in public. On August 7, Democratic governor nominee Dr. Amy Acton announced she supports Issue 3, the voter ID amendment. Allison Russo voted against the resolution that put it on the ballot and called it flawed and fast tracked. Robert Sprague supports it. If you are an Ohio Democrat, your nominee for governor and your nominee for Secretary of State are telling you opposite things about the same ballot question. Both arguments are laid out here at their strongest. Also covered: Ohio's kratom ban, and the company that told a Columbus judge it was selling roughly 59,000 bottles a day in this state. Gov. Mike DeWine's recovery housing executive order, and the jump from 356 recovery houses in 2022 to more than 1,700 today. The Ohio Supreme Court ruling that makes it harder for local councils to fast track a data center, and what it means in Ashville, Trenton, and Butler County. Springfield, temporary protected status, and a Republican governor asking a Republican president to change course. An Ohio 7 update, including the White House reversing course on Max Miller. And Ohio 6, where Rep. Michael Rulli filed 22 stock trades past the STOCK Act deadline. Every claim carries a named source, out loud, at the moment it is made. CHAPTERS 05:25 Cold open 06:46 Welcome and today's rundown 09:40 Ohio's kratom ban and 59,000 bottles a day 19:15 Recovery housing: DeWine's executive order 23:55 Ohio Supreme Court limits emergency resolutions on data centers 26:28 Trenton, Butler County, and the local data center fight 27:32 Where Ramaswamy and Acton stand on data centers 31:15 Springfield, TPS, and DeWine's break with the White House 36:05 Ohio State and the Department of Education letter 38:30 Ohio 7 update and how Trump got involved 43:20 Ohio 6: Michael Rulli and the STOCK Act 46:30 Main event: the Ohio Secretary of State race 48:33 Robert Sprague, Republican 50:23 Allison Russo, Democrat 54:36 Issue 3 and the split in the Democratic ticket 58:30 Trump's election order and where courts left it 1:04:05 Research on a Dime: emergency rules and the STOCK Act 1:07:20 Closing KEY DATES FOR OHIO VOTERS Voter registration deadline: Monday, October 5, 2026 Early in person and absentee voting begins: Tuesday, October 6, 2026 Election Day: Tuesday, November 3, 2026. Polls open 6:30 a.m. to 7:30 p.m. Check your registration and find your polling place at ohiosos.gov SOURCES CITED Signal Ohio, kratom sales and the ban, 8/13/26 Ideastream Public Media, recovery housing order, 8/11/26 Ohio Capital Journal, Ashville data center ruling, 8/14/26 Ohio Capital Journal, Springfield and ICE reporting, 8/14/26 Signal Ohio, Acton backs Issue 3, 8/7/26 Signal Ohio, Secretary of State primary results, 5/5/26 Politico, the White House and Max Miller, 8/10/26 NOTUS, Rulli's late stock disclosures, 8/11/26 Full source list with links is in the show notes on the site. PODCAST NETWORK ALIVE Podcast Network. Link: https://alivepodcastnetwork.com/ VOTING REFORM & DEMOCRACY Equal Vote Coalition & STAR Voting - Advocating for voting methods that ensure every vote counts equally, eliminating wasted votes and strategic voting. Link: https://www.equal.vote/star Future is Now Coalition (FiNC) - A grassroots movement working to restore democracy through transparency, accountability, and innovative technology while empowering citizens and transforming American political discourse. Link: https://futureis.org/ POLITICAL ENGAGEMENT Independent Center - Resources for independent political thinking and civic engagement. Link: https://www.independentcenter.org/ r/PolicySolutions - A subreddit where people can post and discuss political solutions. Link: https://www.reddit.com/r/policysolutions/ GET DAILY NEWS Text 844-406-INFO (844-406-4636) with code 'purple' to receive quick, unbiased, factual news delivered to your phone every morning via Informed (https://informed.now) Check Out the CIVICS App to Know More About Your Politicians: https://www.civicpolitics.com ALL LINKS https://linktr.ee/purplepoliticalbreakdown The Purple Political Breakdown is committed to fostering productive political dialogue that transcends partisan divides. We believe in the power of conversation, balanced information, and democratic participation to build a stronger society. Our mission: 'Political solutions without political bias.' Subscribe, rate, and share if you believe in purple politics - where we find common ground in the middle! Also if you want to be apart of the community and the conversation make sure to Join the Discord: https://discord.gg/ptPAsZtHC9

How long do people have to live in America before they're considered American? How long does it take to prove themselves as productive, peaceful members of society? For millions of immigrants who have lived in the United States for decades, the answer seems to be, “never.” Immigrants without documents or with temporary status are vulnerable to exploitation by their employers and have been made scapegoats by xenophobic politicians. Patricia Campos-Medina joins host Alex Lovit to discuss how problems with the United States' immigration, labor, and democratic systems are connected—and what it will take to fix them. Patricia Campos-Medina is a scholar of race, immigration, and labor. She's the executive director of the Worker Institute at the School of Industrial and Labor Relations at Cornell University. She coauthored the 2025 book Legalized Inequalities: Immigration and Race in the Low-Wage Workplace. https://www.ilr.cornell.edu/worker-institute https://www.russellsage.org/publications/book/legalized-inequalities Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

In a live conversation on YouTube, Lawfare Editor in Chief Benjamin Wittes sat down with Senior Editors Eric Columbus, Molly Roberts, and Roger Parloff to discuss the Trump administration requesting the Supreme Court stay the district court's order halting the enforcement of the mail-in voting executive order, updates on where TPS stands following the Supreme Court's order, developments in the Reflecting Pool prosecution, the D.C. Circuit blocking the ballroom construction and more.You can find information on legal challenges to Trump administration actions here. And check out Lawfare's new homepage on the litigation, new Bluesky account, and new WITOAD merch.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.

I met Yonatan Sompolinsky in Romania during his summer holiday, so we decided to do a roadtrip interview about covenants, the state of Kaspa development, why intents are more interesting than smart contracts & stag hunts. 00:00:54 – Intro: a road trip with Yonatan Sompolinsky 00:01:50 – Life since S16 E41: Oxford Union & the state of Kaspa 00:02:53 – Kaspa shipped covenants while Bitcoin debates OP_CAT 00:04:24 – Sequencing commitments & based apps explained 00:06:48 – Why OP_CAT alone can't do censorship-resistant based rollups 00:07:21 – BitVM & optimistic vs optimistically verified rollups 00:08:05 – ZK was born for privacy but lives for scaling 00:09:18 – "For the record, I'm very bearish on rollups" 00:12:27 – Ethereum quietly abandons the L2 roadmap 00:13:33 – What ZK is actually for: a thin L1 & separation of layers 00:15:05 – Kaspa follows Bitcoin's UTXO design principles 00:16:04 – Ephemeral logic: generalized atomic swaps 00:17:25 – Camera gymnastics & an Aston Martin 00:19:28 – What covenants won't give you: the DeFi Lego box 00:20:05 – One-time logic vs contract deployment 00:23:18 – The use case question & crypto's crisis of utility 00:26:17 – Bitcoin the turtle & the DAGKnight progress report 00:28:17 – DAGKnight: 49% Byzantine resilience, no latency parameter 00:29:50 – Why 100 BPS on GHOSTDAG would 8x confirmation times 00:30:21 – Sponsors: Cake Wallet, LayerTwo Labs, Braiins, Orange Rock & SideShift 00:34:33 – Zcash made a comeback 00:35:19 – The mental map: where Kaspa is unique 00:38:49 – Real-time decentralization: sampling the honest majority 00:39:47 – Solana & Hyperliquid: fast, but led by one 00:42:46 – Only proof of work bundles proposing & content 00:44:35 – A bear market in usage, not just price 00:45:53 – Old-world use case: bridges & oracles 00:47:22 – Tourism break: Bușteni, mascots & ski resorts 00:49:38 – Hit-and-run agents & the agentic economy 00:51:06 – Having your cake and eating it too 00:54:32 – Vlad on Ecash, miner fees & Satoshi's biggest bet 00:56:37 – Sinaia & the royal train station 00:57:38 – "Bitcoin is dead boring to me at this stage" 00:59:50 – Ironwood, verifiable supply & Tachyon 01:00:39 – Driving like six confirmations 01:01:59 – The tectonic shift nobody noticed: verifying the supply 01:03:43 – The bug & Zcash's unfixable history 01:06:04 – Kaspa's pruning vs historical correctness 01:08:12 – How Zcash's rise paves the way for Kaspa 01:09:17 – The Zooko sleeping-in-the-car story 01:10:32 – "Just launch": Zooko one day before the Sapling bug 01:12:05 – Downplaying the bug & the counterfeit scenario 01:14:34 – Peter Todd's demurrage & the Ironwood migration 01:17:25 – Would any other coin survive this? 01:18:55 – Ethereum's identity crisis 01:21:28 – Back to L1: too little, too late; Solana's cohesion 01:22:46 – Oxford Union & political ambitions 01:24:44 – Hayek: true vs false individualism 01:26:04 – The isolated individual: Lopp, Poelstra & early Bitcoiners 01:32:52 – The enthusiastic individualist 01:34:52 – Coordination markets & Project Stag Hunt 01:35:19 – Prisoner's dilemma vs stag hunt 01:36:24 – Moloch, the demon of defection 01:37:18 – Part 2: stag hunt as the optimistic game 01:42:43 – Intents & NEAR Intents 01:44:03 – Intents vs smart contracts 01:45:21 – Part 3: Rousseau's ape men 01:49:45 – Intendos: "if many sign this, consider mine signed" 01:51:44 – Example: migrating a liquidity pool together 01:53:34 – Pack formation & threshold homomorphic encryption 01:55:42 – Actionable but not committed 01:57:18 – The Netflix example: voice for the scattered majority 02:01:36 – Who are the stags we need to hunt? 02:02:11 – Minarchist, anarchist or libertarian? 02:03:32 – Stag = shared utility; escaping Ethereum's L2 mess 02:06:28 – Composable Intendos 02:07:58 – Exiting network effects 02:09:11 – The Kaspa-Ethereum overlap: GHOST & Casper 02:11:55 – "I don't like DAGs": it's just a data structure 02:12:51 – Why DAGKnight survives a world war 02:15:22 – Michael Sutton & the Argent language 02:16:58 – Bypassing the impossibility theorem 02:18:06 – Ahead of his time or poor communicator? 02:19:31 – BPS vs TPS: the shift already happened 02:21:05 – Falling between the cracks 02:23:17 – The next narrative: coordination markets 02:24:26 – Brașov to Bucharest: documenting the trip 02:25:31 – Roles reversed: Yonatan interviews Vlad 02:27:06 – ACTA protests, internet freedom & political science 02:28:53 – Sztorc's party metaphor 02:29:57 – Zcash at $30 & the Coldcard hack 02:31:40 – What Kaspa actually needs: wallets, not exchanges 02:33:56 – Broadening cypherpunk: from protective to positive 02:37:16 – What have we really accomplished? 02:39:33 – Arriving at the Palace of Parliament 02:43:57 – The secret handshake & farewell

Your favorite D&D Talk Show! This week's segments: GenCon Announcements Homebrewing "Pact of the Deck" Tier Ranking Fighting Styles Support us on Patreon https://www.patreon.com/c/TotalPartySkill/home to get access to PDFs of our homebrew and see uncut video from the podcast! Plus, bonus content exclusive only to patrons! Subscribe for more weekly Dungeons & Dragons content! And follow us on our socials for previous draft videos and to learn more about us: Gabe -- @gabespan (TikTok, Instagram) George -- @dmgeorge_primavera (Instagram, TikTok) Dylan -- @whatcha_mccollum (Instagram) MERCH ALERT! Perfect gifts for TPS listeners...Delightful almond-scented soap that contains a full set of dice! You can get yours here: https://fantasy-scents.com/products/total-party-skill-dice-soap-dungeons-bubbles

Hannah Davis of F.A.I.R. discusses Trump's EO on birthright citizenship as well as TPS in Ohio

This month on the Immigration Lawyers Toolbox Podcast, host John Q. Khosravi, Esq. skips the interview format for a rapid-fire rundown of the news, memos, and courtroom curveballs immigration attorneys need on their radar right now from USCIS quietly closing affirmative asylum cases without an interview and a judge denying a marriage-based green card under a new discretion memo, to the $100,000 H-1B fee proposal resurfacing for F-1 students, a biometrics-rescheduling trick that can save your client weeks, and a fresh court order pausing parts of USCIS's TPS and asylum-fee enforcement. It's the practice-management update that keeps you ahead of USCIS before your clients call you first. Timestamps: 00:00 Opening 00:33 Intro 02:57 my.USCIS.gov website glitch (false approvals/denials) 04:10 OPT denials tied to arrests & good moral character 05:24 FT report: 80% of US embassies lack an ambassador 06:07 Join the free private attorney community (Circle) 07:14 SB-1 returning resident visa risks 08:46 USCIS closing affirmative asylum cases without interview 10:49 Ciudad Juárez getting strict on affidavit of support 13:27 Sponsor: Constellation (websites/marketing for law firms) 15:07 TPS-to-marriage adjustment withdrawal trap 16:34 Immigration judge denies adjustment citing discretion memo 17:07 WebEx possibly leaving immigration court 17:39 $100K H-1B fee may extend to F-1 students 18:01 Biometrics rescheduling trick (switch ASC location) 19:14 DC court pushes back on 75-country travel pause (EB-5 case) 19:52 New civil penalties for contempt in immigration court 20:11 Court stays USCIS TPS EAD & asylum fee policies (Venez v. USCIS) 22:37 NVC shuffling Iranian cases between Embassies. 23:20 Lawsuit blocks travel ban on Afghan I-730 cases 23:50 Wrap-up & how to join the community/courses 24:11 Outro Spotify | iTunes | YouTube Music | YouTube Follow eimmigration by Cerenade: Facebook | Instagram | LinkedIn Start your Business Immigration Practice! (US LAWYERS ONLY - SCREENING REQUIRED): E-2 Course EB-1A Course Get the Toolbox Magazine!  Join our community (Lawyers Only) Get Started in Immigration Law! The Marriage/Family-Based Green Card course is for you Our Website: ImmigrationLawyersToolbox.com Not legal advice. Consult with an Attorney. Attorney Advertisement. #podcaster #Lawyer #ImmigrationLawyer #Interview #Immigration #ImmigrationAttorney #USImmigration #ImmigrationLaw #ImmigrationLawyersToolbox  

Is Abdul El-Sayed's primary victory a tipping point? Democrats are picking up the pieces after the DSA faction defeats the establishment in the swing state of Michigan. Non-socialist democrat voters in Michigan are planning on withholding their vote from El-Sayed in the general election. A counterintelligence expert is highlighting funding ties between El-Sayed and terror orgs like the Muslim Brotherhood. Haitians who had their "TPS" revoked are being fitted for ankle monitors by ICE.    Join UNGOVERNED on LFA TV LIVE every MONDAY - FRIDAY from 10am to 11am EASTERN!    www.FarashMedia.com www.LFATV.us www.OFPFarms.com https://www.SLNT.com/SHAWN  https://www.CovePure.com/SHAWN 

Immigration attorney Allen Orr Jr. unpacks ICE's targeting of Haitian families as TPS protections expire—plus the deportation of former NFL linebacker Daniel Adongo despite a suspected brain injury.

Le processus d'inscription sur les listes électorales a commencé depuis une semaine en Haïti. L'un des enjeux majeurs des élections dont le premier tour doit avoir lieu le 13 décembre 2026, c'est la participation ou non des figures politiques sous sanctions internationales. Les deux enjeux majeurs des élections à venir concerne la participation des Haïtiens au scrutin, avec un processus d'inscription particulièrement lent et long, et la validation des candidatures. Or, remarque Frantz Duval, rédacteur en chef du quotidien Le Nouvelliste, nombre de ces candidats sont aujourd'hui visés par des sanctions internationales en raison de liens présumés avec les gangs ou de soupçons de corruption.   Le Nouvelliste revient aussi sur la question du kidnapping. La PNH, la police nationale haïtienne, a annoncé le renforcement de son dispositif de sécurité à Delmas. Une annonce attendue, observe Frantz Duval, car la police était jusqu'ici aux abonnés absents. « La police a refait surface. C'est une bonne chose. Mais on va voir ce qu'elle va faire concrètement », dit-il. Le quotidien rapporte enfin qu'aux États-Unis, une juge fédérale a levé l'ordonnance qui empêchait encore l'administration Trump d'appliquer la fin du TPS, le statut de protection temporaire dont bénéficiait les quelque 330 000 Haïtiens résidents dans le pays. Ceux-ci ne pourront plus conduire ou travailler, et risqueront à tout moment d'être arrêtés par l'ICE, la police de l'immigration. « Aux États-Unis, quand vous ne travaillez pas et que vous ne pouvez pas conduire, vous n'existez pas », remarque Frantz Duval. À écouter aussiÉtats-Unis : pourquoi 350 000 Haïtiens perdent-ils leur protection?   Les influenceurs mexicains ciblés par les cartels Au Mexique, les cartels tuent aussi les influenceurs. Le dernier exemple en date est celui de César Gastélum, un créateur de contenu âgé de 25 ans, abattu en pleine rue à Culiacan, la capitale de l'État de Sinaloa. Il est le neuvième influenceur tué dans cet État de l'est du pays depuis septembre 2024. Les précisions de François-Damien Bourgery, du service International de RFI.   Au Salvador, quel avenir pour le bitcoin ? Le président Nayib Bukele avait fait du bitcoin une monnaie légale en 2021. Cinq ans plus tard, le bilan est contrasté. Si le gouvernement continue de promouvoir son image de pionnier des cryptomonnaies, l'usage du bitcoin reste très limité dans la vie quotidienne des Salvadoriens. Un reportage de notre correspondante régionale, Marie Griffon. À écouter aussiLe Salvador abandonne le bitcoin comme monnaie officielle Le journal d'Outre-mer la 1re Lentement mais sûrement, la rétrocession aux collectivités locales du foncier détenu par l'État en Guyane se poursuit…

Ucrania lleva semanas advirtiendo de que se está quedando sin el armamento necesario para interceptar los misiles que lanza Rusia prácticamente cada día. Ante esta situación el presidente del país Volodimir Zelenski ha vuelto a reclamar mayor rapidez a sus socios a la hora de entregar estas defensas antiaéreas.Tenemos resultados de las elecciones primarias del Partido Demócrata en algunos estados de los EEUU. Hablaremos de la victoria de Abdul El Sayed en Michigan. No abandonaremos EEUU y pondremos el foco en el TPS, el estatus de protección especial para ciudadanos de diferentes países afectados por guerras o desastres naturales, que ha sido suspendido con las consecuencias que eso tiene sobre cientos de miles de personas.Irán y Omán anuncian haber alcanzado un acuerdo sobre una ruta de navegación para los barcos que atraviesen el estrecho de Ormuz. Hablaremos del hambre que afecta a millones de personas en Afganistán, conoceremos por qué ha ido a más la tensión en la Cachemira paquistaní y estamos pendientes de la erupción del volcán del Fuego en Guatemala.Escuchar audio

Wednesday marks the official termination of temporary protected status (TPS) for Haitian migrants in the United States.A federal judge blocked the Trump administration from ending TPS in February. But after a Supreme Court ruling in June gave the federal government the green light to revoke the status, the judge determined that the February order was "no longer in effect."The decision leaves 350,000 Haitians at risk of losing their jobs and being deported.Wednesday on Midday Edition, we hear about how that is impacting Haitian migrants in San Diego and what is at stake.Guest:Guerline Jozef, executive director, Haitian Bridge AllianceResources:Temporary protected status updates - Haitian Bridge Alliance

Howie starts the show discussing the end of TPS and sending the Haitians back to Haiti. Then, Howie discusses the latest DSA candidate and her nutty old posts and recent comments. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The Haitian community across the United States is facing a critical moment of deep anxiety and urgency. Following the termination of Temporary Protected Status (TPS) for Haiti, hundreds of thousands of individuals who have lived, worked, and built families in the U.S. for years suddenly find themselves stripped of legal protections and work authorization. With the formal expiration taking effect and enforcement actions looming, communities are bracing for potential Immigration and Customs Enforcement (ICE) operations. In this episode, we unpack the reality behind the end of Haiti's TPS designation. Following the Supreme Court's decision clearing the way for the termination, federal guidance has invalidated employment authorization documents, leaving workers, small business owners, nurses, and caregivers vulnerable. We explore what this policy shift means on the ground, how advocacy groups and legal organizations are responding, and the practical steps families are taking to protect themselves. Whether you are directly affected by the expiration, an employer navigating compliance changes, or an ally wanting to understand the human impact of these immigration shifts, this episode provides essential context. We examine:The Timeline of the Termination: How the legal battles unfolded leading up to the final expiration date.The Threat of Enforcement: What community leaders and immigration advocates are saying about potential ICE actions and workplace audits.Know Your Rights: Crucial legal guidance on how individuals should handle interactions with law enforcement and immigration officials.Broader Community Impact: The economic and social fallout for industries heavily reliant on Haitian labor.We cut through the confusion to bring you clear, compassionate, and factual reporting on one of the most pressing civil rights and immigration stories of the year.Stay informed and support advocacy. If you value in-depth coverage of crucial social issues, please subscribe to the podcast and leave us a five-star review on Apple Podcasts or Spotify. Your ratings help amplify vital stories that need national attention. Share this episode with friends, family, and colleagues to spread awareness and support the Haitian community.Become a supporter of this podcast: https://www.spreaker.com/podcast/chaos-culture-radio--3078307/support.Follow Chaos Culture Radio for real conversations that move culture forward.New episodes every week.Share this episode with someone who needs to hear it.

We start the show by asking listeners how they're saving money in today's economyMichael Curry of the Mass League of Community Health Centers discusses impacts to healthcare due to TPS and Medicare changes.Corby Kummer, food policy analyst, discusses Mass' new chip-enabled EBT card rollout, in a Healey-endorsed effort to cut down on fraud ... Plus, the Taco Bell fans who just can't give up their chalupas.Carol Rose of the ACLUM discusses various policing/surveillance/data privacy stories — Xavier Bautista's death in Cambridge ... the backlash against Flock cameras ... plus a number of legislative actions on abortion access, media rights, and data privacy.We end the show by forcing listeners to make small talk with us

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

Your favorite D&D Talk Show! This week's segments: When the Campaign Ends Homebrewing "The Midnight Oil" Draft of CR 15 Monsters Support us on Patreon https://www.patreon.com/c/TotalPartySkill/home to get access to PDFs of our homebrew and see uncut video from the podcast! Plus, bonus content exclusive only to patrons! Subscribe for more weekly Dungeons & Dragons content! And follow us on our socials for previous draft videos and to learn more about us: Gabe -- @gabespan (TikTok, Instagram) George -- @dmgeorge_primavera (Instagram, TikTok) Dylan -- @whatcha_mccollum (Instagram) MERCH ALERT! Perfect gifts for TPS listeners...Delightful almond-scented soap that contains a full set of dice! You can get yours here: https://fantasy-scents.com/products/total-party-skill-dice-soap-dungeons-bubbles

Being Blunt Podcast – Episode Show Notes

Several new Minnesota laws about domestic violence are taking effect soon. The bills were passed with bipartisan support during the most recent legislative session. One gives police more time to arrest someone suspected of domestic violence without a warrant. Another clarifies the process for how offenders surrender their guns.Temporary Protected Status for people from Haiti ended this week, leaving hundreds of thousands of Haitians across the country facing uncertainty — including some in Minnesota. Immigration attorneys and business groups say the end of the program could also create challenges for employers that rely on TPS holders in industries like long-term care, construction and restaurants.More allegations emerged Thursday of bizarre behavior by St. Paul Mayor Kaohly Her. A former deputy police chief told investigators from an outside law firm that the mayor approached her in a locker room and asked if her own breasts were too big before complimenting the officer's breasts. A source with knowledge of the investigation tells MPR News that Her also allegedly made "orgasmic noises" while working out in the police gym.GOP U.S. Senate candidate Royce White's bid to undo an order for protection against him is outlined in a new legal filing. That order, granted by a district court judge, requires White to steer clear of his teen son for two years and his former partner for 50 years. Some GOP leaders urged him to drop out of the Senate race after judicial findings earlier this year that White engaged in bouts of aggression and other verbal threats.Minneapolis has approved the appointment of its next fire chief. Reginald Freeman will join the department after serving as fire chief in Hartford, Connecticut, Oakland, California and some jobs overseas.New Minnesota laws tackle domestic violence response and preventionAs Haiti TPS protections end, Minnesota immigrants and employers wonder what comes nextFormer St. Paul police official alleges Mayor Kaohly Her made inappropriate commentsMinneapolis City Council approves Reginald Freeman as new fire chief

Headlines for July 30, 2026; “Less Regulated Than Sandwiches”: MIT Prof. Calls for Oversight as OpenAI Agent Hacks Other Firms; Citizens Bank Cuts Ties to ICE Prison Companies GEO Group & CoreCivic After Grassroots De-ICE Campaign; Report from Springfield, Ohio: Haitians Brace for ICE Crackdown After TPS Protections End

Today on the show, Joe breaks down Dr. Anthony Fauci's explosive appearance on Capitol Hill, where visible nerves and shaking hands told the real story before he even uttered a word. From Senator Josh Hawley calling out systemic obstruction to Senator Rand Paul delivering a searing indictment on how arrogance, secrecy, and censorship destroyed public trust, we expose the deep state's crumbling defense. Plus, we dig into the bizarre paper trail surrounding Fauci's federal pardon—specifically why it traces back to 2014, the exact same year the NIH approved grant funding for bat coronavirus research at the Wuhan Institute of Virology. As questions mount, we ask the hard question: a federal pardon might shield him from Washington, but are state prosecutors ready to step up?Former FBI agent, whistleblower, and podcaster Kyle Seraphin joins Joe in the studio to pull back the curtain on bureaucratic warfare and institutional decay. Seraphin reacts to Fauci's testimony, dissects the lingering skepticism surrounding Senator Mitch McConnell's staged "proof of life" photo ops, and dives into shocking CIA whistleblower allegations revealing illegal surveillance against current leaders like Tulsi Gabbard. Furthermore, Kyle brings his tactical background to the table to analyze the Charlie Kirk assassination attempt, questioning official narratives and breaking down the physical real-world capabilities required for such an operation.Joe confronts the glaring double standards reshaping American life. While everyday families struggle to get basic life-saving medications approved by insurance, taxpayer dollars across dozens of states are quietly funding 100% free gender-affirming surgeries through Medicaid. Meanwhile in New York, socialist policy takes center stage as Mayor Zohran Mamdani pushes government-run grocery stores—threatening small business survival while imposing selective, government-controlled product restrictions. Finally, as TPS expires for Haitian migrants and ICE agents face daily hostility on the ground, Joe takes a hard look at the reality of federal deportations and where the country heads next.

Headlines for July 30, 2026; “Less Regulated Than Sandwiches”: MIT Prof. Calls for Oversight as OpenAI Agent Hacks Other Firms; Citizens Bank Cuts Ties to ICE Prison Companies GEO Group & CoreCivic After Grassroots De-ICE Campaign; Report from Springfield, Ohio: Haitians Brace for ICE Crackdown After TPS Protections End

A new Senate bill could open a green card pathway for 8 million people — Dreamers, TPS holders, farmworkers. But it's NOT law yet, and scammers are already circling. Here's the truth. Plus: schools are deploying pepper-spray drones instead of touching guns, and today I open 1-on-1 money calls. (Audio en español, subtítulos en inglés.) Hoy en Échale: el senador Padilla propone una green card para 8 millones de personas — pero es un PROYECTO, no ley, y ya hay quien te quiere cobrar por "aplicar." Te digo la verdad y cómo NO te estafan. Además: mandan drones con gas pimienta a las escuelas, Disneyland se quedó sin agua, y en el Concepto: deuda buena vs deuda mala. EN ESTE EPISODIO:

Haitians in Iowa on TPS are facing uncertainty as they lose that status this week. Former President Barack Obama has endorsed Democrat Josh Turek for Iowa's open U.S. Senate seat. And how are public camping bans being enforced in Midwest cities?

Dr. Anthony Fauci called to testify before a Senate committee under subpoena about his handling of the COVID-19 pandemic invokes his Fifth Amendment right against self-incrimination over 100 times. Committee Chair Sen. Rand Paul (R-KY) accused Dr. Fauci of covering up evidence of the "lab leak" theory for coronavirus. Dr. Fauci calls Sen. Paul "unhinged" and trying to trap him in any lie before Congress to start a prosecution. And Sen. Paul is threatening to hold Dr. Fauci in contempt of Congress; President Donald Trump says the U.S. would be hitting Iran hard and "smacking them a little bit" after an attempted Iranian surprise missile attack on American targets in the Middle East; Federal Reserve Chair Kevin Warsh announced the Fed is keeping interest rates steady for a fifth consecutive meeting, but the Fed is still determined to bring down inflation; Senate Republicans block Senate Democrats' attempt to pass a bill to create a legal pathway for immigrants with Temporary Protected Status, as TPS for 300,000 Haitian immigrants in the U.S. expired this week and the Trump Administration said it will move to start deportations; President Trump and Transportation Secretary Sean Duffy unveil a $22 billion renovation plan for Dulles International Airport; We talk to AP reporter Bill Barrow about the Joe Biden tapes, the conversations he had with a ghostwriter in 2016 & 2017, released after a conservative group filed a Freedom of Information Act request. Learn more about your ad choices. Visit megaphone.fm/adchoices

So, still dealing with the computer issues. Every time I think I have it fixed I'm wrong. So we have another raw episode, but we'll get through it. There is about a two minute dead spot where I had to mute to check what the boss was hollering about. For anyone who is curious, the dog was being a jerk. Anyway in this episode I talk about my thoughts on the termination of the TPS status of a bunch of Hatians, Fauci's diary, and Commie Monddami's brlliant idea for state run grocery stores. Then I give you a recipe for Puerco Pibil. It's good. As always go do the things at ihadtosayitpodcast.com so I don't feel like I am wasting my money on that website.

Trump's latest remarks reveal the GOP's real strategy for the midterms-- not winning more voters, but trying to block their opponents from voting. Plus, the latest on a crucial Senate race in Michigan. Vox's Zack Beauchamp, Puck's Abby Livingston, attorney Geoff Pipoly, and former Washington Governor Jay Inslee join The 11th Hour With Ali Velshi.  To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Five years ago this month, Haitian President Jovenel Moïse was assassinated inside his own home by a group of foreign mercenaries. His wife survived. The president's phone disappeared. Prosecutors were threatened. And, five years later, no one has been brought to trial in Haiti. In this conversation, journalist Macollvie J. Neel takes listeners back to the night of July 7, 2021, explains what investigators believe happened inside the president's home, and examines why the assassination remains one of the most consequential—and unresolved—political crimes in the Western Hemisphere. The conversation also explores the political vacuum left behind, the international investigation that continues in U.S. courts, and why understanding the events of that night is essential to understanding Haiti today. For more on this story, see Macollvie J. Neel's reporting for The Haitian Times, linked below. After midnight in Pèlerin —The killing of Haiti's president, the suspects scattered across borders, the convicted — and a justice system that still cannot find his phone. ‘Don't panic, mobilize instead,' Haitian leaders urge after TPS ruling Temporary Protected Status (TPS): What Haitians Need to KnowReporting, guidance and verified resources from The Haitian Times Learn more about your ad choices. Visit megaphone.fm/adchoices

La peor herencia que le puedes dejar a tus hijos NO es dinero — es tu miedo al dinero. Hoy te explico cómo se hereda ese miedo sin darte cuenta, y cómo romper el ciclo. Además: preparan un operativo contra los haitianos que pierden el TPS, quieren ponerle candado al voto por correo, y un caso del Buzón que te va a dejar con la boca abierta. EN ESTE EPISODIO:

The Pakistani court will reevaluate the case of a 13-year-old Christian girl forced to convert and marry. Meanwhile, the Archbishop of Miami pushes for urgent action from U.S. senators as TPS for Haitians in the U.S. has expired. And, Trump pays tribute to Blessed Stanley Rother, a martyred priest.

More than 330,000 Haitians lost their ability to legally live and work in the U.S. this week. A recent Supreme Court ruling allowed the Trump administration to end Temporary Protected Status for refugees from certain countries. In this episode, why the policy change will deliver a swift blow to the healthcare sector. Plus: Economists weigh the Fed's next interest rate move, businesses report cautious optimism in new survey, and a Baltimore club redistributes wealth to neighbors in need.Every story has an economic angle. Want some in your inbox? Subscribe to our daily or weekly newsletter.Marketplace is more than a radio show. Check out our original reporting and financial literacy content at marketplace.org — and consider making an investment in our future.Read the stories in today's episode:Economists share their Fed expectationsBusinesses report optimism, despite all the economic uncertaintyA new kind of club wants people to share the wealthWhat the end of TPS for Haitians could mean for the labor forceTo reduce their climate impact, airlines target contrailsCircus school balances world-class programs with small town values

More than 330,000 Haitians lost their ability to legally live and work in the U.S. this week. A recent Supreme Court ruling allowed the Trump administration to end Temporary Protected Status for refugees from certain countries. In this episode, why the policy change will deliver a swift blow to the healthcare sector. Plus: Economists weigh the Fed's next interest rate move, businesses report cautious optimism in new survey, and a Baltimore club redistributes wealth to neighbors in need.Every story has an economic angle. Want some in your inbox? Subscribe to our daily or weekly newsletter.Marketplace is more than a radio show. Check out our original reporting and financial literacy content at marketplace.org — and consider making an investment in our future.Read the stories in today's episode:Economists share their Fed expectationsBusinesses report optimism, despite all the economic uncertaintyA new kind of club wants people to share the wealthWhat the end of TPS for Haitians could mean for the labor forceTo reduce their climate impact, airlines target contrailsCircus school balances world-class programs with small town values