Podcasts about Expert

Posting more content on LinkedIn doesn't automatically lead to more business.The real question is, does your content have a purpose?In this week's podcast episode, I'm breaking down The LinkedIn Content Growth Formula and three principles that can completely change how you approach your content:Every post needs a job: Grow, Trust, or Sell.Specific content beats generic content.And relevance matters more than follower count.You don't need the biggest audience on LinkedIn. You need the right people paying attention.Don't forget to sign up for our exclusive 5-Day LinkedIn Workshop, called Amateur to Expert on LinkedIn in 5 Days here: https://www.thetimetogrow.com/AtoEonLinkedinWorkshop

Tom Clarkson, Jolyon Palmer and James Hinchcliffe turn their attention to the Temple Of Speed for this weekend's Italian Grand Prix. With championship leader Kimi Antonelli taking a grid penalty at his home race, can the Mercedes driver still fight his way through the field to win? How big an opportunity is this for George Russell? Can Lando Norris make it three wins in a row or will Monza expose McLaren's weaknesses?  And after going two races without a podium for the first time this season, can Ferrari find form again and give the Tifosi a race to remember? There's also reaction to Franco Colapinto extending his contract with Alpine for 2027 and the team remember when Jolyon upset Fernando Alonso at Monza.Listen to more Official F1 PodcastsIn-depth interviews on F1 Beyond The Grid - Charles Leclerc coming soon!Expert answers to your questions on F1 Explains

#517Rabbi Morris Raphall (1798 - 1868): Early American Rabbi (with Prof. Howard Rock)> To purchase Rabbi Raphall's sermon on slavery: https://amzn.to/45X03UL> To purchase Prof. Rock's book on the early Jews of New York: https://amzn.to/4gJhAF4> Rabbi Yaacov Haber opens up Rosh Hashanah and the Aseres Yemei Teshuvah through the deeper structure of the Sefiros, changing the way we understand these extraordinary days. Purchase "The First Ten Days" https://mosaicapress.com/product/the-first-ten-days/?sld=seforimchatter and use code CHATTER for 15% off> This episode is sponsored by KosherKlaf.com, your trusted source for mezuzos, tefillin, Sifrei Torah, and megillos. Expert guidance, transparent pricing, and nationwide shipping. Visit https://kosherklaf.com or call 516-737-5436> To join the SeforimChatter WhatsApp community: https://chat.whatsapp.com/DZ3C2CjUeD9AGJvXeEODtK> To join the SeforimChatter WhatsApp status: https://wa.me/message/TI343XQHHMHPN1>  To support the podcast or to sponsor an episode follow this link: https://seforimchatter.com/support-seforimchatter/or email seforimchatter@gmail.com (Zelle/QP this email address)Support the show

Text Me A Question!AI isn't your consistency problem—you're playing the wrong game. Stop copying influencer “attention” strategies and start creating conversion content using the get seen → get paid → get bigger roadmap. Set up a real AI thought partner with clear context, then answer three validated audience questions to get back in motion fast. Content Calendar Challenge Sign Up!Content To Customers Live Workshop Sign Up!Support the show ➡️ Are you a Coach, Expert, or Service Provider wanting to get more Customers from your Content?

Hello Brave Friends! Welcome to Season 11 of Brave Together Podcast!Brave Together Podcast is the podcast of We Are Brave Together, a nonprofit supporting moms of children with disabilities, neurodivergence, medical complexities, mental health struggles, and all forms of unique needs.Your hosts Jessica Patay, Susanna Peace Lovell, and Dr. Zoe bring their professional expertise, lived experiences, and years of caregiving motherhood to honest conversations created to help you feel seen, supported, encouraged, and less alone.This season, we're continuing the conversations you've come to know and love: Story episodes featuring the real experiences of caregiving moms; Expert episodes exploring topics that impact you and your family; Thriving Disabled and Neurodivergent Adult episodes offering invaluable insight into the lived experiences of disabled adults; and Ask Us Anything episodes, where Jessica, Susanna, and Dr. Zoe dig into the questions you send our way.Season 11 will explore everything from caregiver burnout, self-care, relationships, and advocacy to disability, mental health, navigating our kids' needs, and the complicated, beautiful, exhausting realities of caregiving.We are so honored that Brave Together Podcast continues to grow alongside this extraordinary community of brave moms around the world, including through UK Health Radio. Whether you've been with us since the beginning or you're just finding us now, welcome. We are so glad you're here.And we want YOU to be part of the conversation. Submit your stories through our website, call into our SpeakPipe through the link in the show notes and leave us your questions, or connect with us on Instagram and YouTube @wearebravetogether. Your questions and experiences help shape the conversations we have here.You can also find full episodes and more from Brave Together Podcast on YouTube. And if this podcast has made you feel seen, supported, or a little less alone, please rate and review the show and share it with your friends and fellow caregiving moms. Every share helps us reach another brave mom who may need this community.We are incredibly grateful to Rise Educational Advocacy for sponsoring Season 11 of Brave Together Podcast and for supporting our mission to strengthen and encourage caregiving families.At Brave Together Podcast, we offer you community and remind you every chance we get: you are strong, capable, and definitely not alone.We see you, and we love you.Welcome to Season 11!Find more information about Licensed Psychotherapist, Dr. Zoe here. Find Dr. Zoe's book, Stronger in the Difficult Places: Heal Your Relationship with Yourself by Untangling Complex Shame here.Find more information about Life Coach, Susanna Peace Lovell here.Find Susanna's book, Your True Self is Enough here.Find our first book from We Are Brave Together, Becoming Brave Together here.Find our second book from We Are Brave Together, Suddenly Brave Together here. Find FULL episodes and clips of our podcast on Youtube here.Brave Together is the podcast for We are Brave Together, a not-for-profit organization based in the USA. The heart of We Are Brave Together is to strengthen, encourage, inspire and validate all moms of children with disabilities and other needs in their unique journeys. JOIN the international community of We Are Brave Together here.Donate to support all of We Are Brave Together's programs and offerings here.Donate to keep this podcast going here.Can't get enough of the Brave Together Podcast?Follow us on Instagram or on Facebook.Feel free to contact Jessica Patay via email: jpatay@wearebravetogether.orgIf you have any topic requests or if you would like to share a story, leave us a message here.Please leave a review and rating today! We thank you in advance!DisclaimerBrave Together is the podcast for We are Brave Together, a not-for-profit organization based in the USA. The heart of We Are Brave Together is to strengthen, encourage, inspire and validate all moms of children with disabilities and other needs in their unique journeys. JOIN the international community of We Are Brave Together here. Donate to support all of We Are Brave Together's programs and offerings here. Can't get enough of the Brave Together Podcast? Follow us on Instagram , Facebook and Youtube. Feel free to contact Jessica Patay via email: jpatay@wearebravetogether.org If you have any topic requests or if you would like to share a story, leave us a message here.Please leave a review and rating today! We thank you in advance!Disclaimer

Jerica and Kayla are catching up on the gaming news they actually care about and video games they are playing right now.fyi Jerica has fallen hard for Splatoon Raiders and its dangerous...while Kayla shares her thoughts on Fishbowl, Beast of Reincarnation, and Leafy Corner!TIMECODES00:00 – Start10:45 – Housekeeping15:17 – Normal Mode: Gaming News15:32 – Hideo Kojima Turns 6322:54 – Is PlayStation 6 Going Handheld?34:24 – Can the PlayStation Handheld Run GTA 6?39:20 – Double Fine Is Independent Again41:13 – Amnesia Fortnight Returns47:53 – Expert Mode: What We've Been Playing48:00 – Splatoon Raiders57:10 – Fishbowl1:02:27 – Beast of Reincarnation1:06:13 – Leafy Corner1:10:40 – Getting Ready for Control ResonantSupport the showJK! Games! is a weekly gaming podcast where we bring you the news and reviews we actually care about.Our recurring play-along series — One More Game — is our version of a video game book club. We choose one title, set checkpoints, and break it down over multiple episodes. Currently Dave the Diver!You can:• Play at your own pace• Stay spoiler-light• Or dive deep with usWhether you're Easy Mode or Expert, you belong in the conversation.Join our Discord to play along and share your theories each week.Want to show us some love? Click Me!DiscordTwitch YoutubeInstaBsky

What happens when someone finally believes the story you've spent years carrying alone?Board-certified polygraph examiner David Goldberg joins Giving Pain Purpose for a powerful conversation about truth, trauma, validation, and the profound impact of finally feeling heard. With more than 30 years of experience investigating abuse, assault, and complex trauma, David shares how his work goes far beyond determining whether someone is telling the truth.Together, David and Lyndsay explore why trauma survivors can struggle to trust their own memories, the lasting damage of being dismissed or disbelieved, and how validation can become an unexpected part of the healing process. They also unpack common misconceptions about polygraph testing and its role in relationships, personal healing, and the pursuit of justice.At its heart, this conversation isn't really about a machine or a test. It's about what happens when someone is given the space to tell their truth—and someone is finally there to listen.For anyone who has ever questioned their own story or carried pain in silence, this episode is a reminder that being heard can be powerful—and your story matters.Episode Highlights:(00:02) Lyndsay opens up about lying, truth and self-protection(03:17) Introducing David Goldberg(04:19) What a polygraph actually is and how it works(06:04) How your body reveals the truth(07:27) What happens if you're nervous during a polygraph(08:09) How examiners reduce anxiety before testing(09:45) Who polygraphs are really for(16:00) Polygraphs for sexual assault survivors and victims(22:00) False accusations and how polygraphs protect the innocent(27:00) How polygraph results hold up legally(35:42) Advice for anyone who has been dismissed or not believed(36:05) What a professional examiner's job really is(37:45) Polygraphs for people in abusive relationships(38:28) How to find David and closing thoughtsFind David Goldberg Online Here:Website: www.executiveprotectiongrp.comInstagram: @thedavidgoldberg Facebook: Executive Protection Group Polygraph Service YouTube: @the_davidgoldbergPodcast: Inside The Polygraph with David GoldbergFind Giving Pain Purpose Online Here:Website: givingpainpurpose.comInstagram: @givingpainpurposeFacebook: The Giving Pain Purpose PodcastLinkedIn: Lyndsay SopranoYouTube: @givingpainpurposeShop: thegivingpainpurposeshop.comSubscribe on YouTube | Merch Shop is OPEN!! | COMING SOON: The Pain Hub - A Women's Healing Community. Subscribe Now!Unfiltered convos. Dark humor. Real healing. This is where pain meets purpose — and you're not doing it alone.++Want to be a guest on Giving Pain Purpose with Lyndsay Soprano? Send her a message on PodMatch, here: Be a Guest on The Show

What does it actually take to lead AI transformation inside a global industrial enterprise, and do it with both technical rigor and radical humanity?In this episode of B the Way Forward, host Brenda Darden Wilkerson sits down with Ambica Rajagopal, Group Chief Data and AI Officer at Michigan, where she leads enterprise-wide data strategy to optimize manufacturing, services, and industrial operations through advanced analytics and AI. Ambica has built her career transforming large-scale organizations across pharmaceuticals, energy, and manufacturing, and her through-line has never been the technology itself. It has always been the people.In this conversation, Brenda and Ambica dig into what happens when the urgency of AI meets the human cost of change, why empathy is the most underrated technical skill, what a brutal performance review 15 years ago taught Ambica about the difference between expertise and leadership, and why she believes the most courageous thing a woman in tech can do right now is invest in her own resilience.This is a conversation about clarity, transformation, and what it really means to make your path the evidence of the change you believe in.Connect with Ambica on LinkedInWe Learned About:Why empathy, not speed, is the real foundation of successful enterprise AI deploymentThe critical distinction between self-expression and authenticity, and why getting it wrong costs you influence in the roomAmbica's leadership mantra "Clarity wins the day" and what it actually looks like to apply it inside high-stakes, fast-moving organizationsHow a 15-year-old performance review that said "your team doesn't like you much" became one of the most transformative moments of her careerThe difference between being a journey person and a goal person, and why knowing which one you are changes everything about how you leadChapters:0:00 Introduction & Season Theme1:00 Meet Ambica Rajagopal, Group Chief Data and AI Officer2:30 Why Empathy Is the Only Way to Get AI Transformation Right5:30 Empathy as Active Common Ground, Not Passive Sympathy7:00 What Has Remained Constant Across Industries: Clarity and People10:00 How Ambica Stays Grounded When Performance Metrics Are Always On11:00 Meditation, Mental Space, and the "Room of One's Own" Framework13:30 Has Leading with Balance Ever Cost You Anything?15:00 The Shift from Showing Up as an Expert to Showing Up as a Leader17:30 Journey Person vs. Goal Person: How Ambica Defines Her Own Motivation19:30 When Personal Transformation Gets Tested the Most20:30 The Performance Review That Changed Everything23:00 Vulnerability in Leadership and Why It Takes More Courage Than Being Right24:30 Authenticity vs. Self-Expression: A Framework Worth Knowing27:00 Who Is Most at Risk of Burnout in the AI Era30:00 Advice for Early-Career Women in Data Science Who Are Already Exhausted32:00 Building Resilience and Finding Your Sources of Courage33:30 What Integrity in Leadership Actually Looks Like34:30 "Make Your Path the Evidence of the Change You Believe In"36:00 Closing Thoughts: People Always Come Before the Technology#BTheWayForward #WomenInAI #AITransformation #EmpathyInLeadership #WomenInTech #DataAndAI #TechLeadership #CourageToTransform

Judith hasst und liebt Social Media zugleich. Vor sieben Jahren hat sie auf Instagram ihren persönlichen Blog begonnen und zeigt dort offen und nahbar die Tiefen und Höhen ihres Lebens. Instagram als Therapieplattform? Nein – so einfach ist das natürlich nicht: Wie Judith auf Instagram, auf ihrer Webseite judithbestaendig.de und in ihrem neuen Buch "Nicht heil, aber doch ganz" ihren eigenen Entwicklungsprozess beschreibt und damit Möglichkeiten als Role Model für andere schafft, erzählt sie Lilith in dieser Folge des yeet-Podcasts. Social Media für Glaube und Kirche - das ist der yeet-Podcast: [yeet](https://www.yeet.de)-Redakteur* innen befragen Expert* innen und Influencer* innen und begeben sich auf die Suche nach den großen und kleinen Perspektiven auf die digitalen Kirchen-Räume und Welten in den Sozialen Medien.

Stell dir vor, dein Werk stößt an einem einzigen Standort so viel CO₂ aus wie ein Prozent von ganz Deutschland. Salzgitter will das ändern und investiert 2,7 Milliarden Euro in die Dekarbonisierung. Mitten im Prozess ändert die EU aber ihre Pläne für den Emissionshandel. In dieser Folge bekommst du ein Update was genau die EU-Kommission für den ETS 1 und CBAM vorschlägt. Und einen ehrlichen Einblick, wie sich das Hin und Her auf die Wettbewerbsfähigkeit deiner Dekarbonisierungsstrategie direkt auswirkt. Frank Best ist Professor für Betriebswirtschaftslehre an der HTWG Konstanz und forscht am Potsdam-Institut für Klimafolgenforschung genau dazu, wie der EU-Emissionshandel Unternehmen in ihrer Dekarbonisierung beeinflusst. Er räumt bei Podcast-Host Zackes gefährliches Halbwissen rund um den ETS auf und erklärt, warum wir die Dekarbonisierung in China massiv unterschätzen. Alexander Redenius von Salzgitter Mannesmann Forschung ist seit 2015 einer der Mitinitiatoren von SALCOS (Salzgitter Low CO₂ Steelmaking) - einem der vermutlich größten Dekarbonisierungsprojekte in Deutschland. Er gibt Zackes reale Einblicke in die Chancen und Herausforderungen der Dekarbonisierung deutscher Schwerindustrie und was es für unsere Wettbewerbsfähigkeit und funktionierende Geschäftsmodelle braucht. ✅ Was der neue EU-ETS-Vorschlag vom Juli 2026 konkret ändert: der lineare Reduktionsfaktor. ✅ Warum CBAM europäische Stahlhersteller schützt und wo er in der Praxis an Grenzen stößt. ✅ Wie Salzgitter mit SALCOS über 95 Prozent CO₂ einsparen könnte und wie sich ändernde Rahmenbedingungen darauf auswirken. ✅ Warum gerade jetzt Zauderer belohnt und Pioniere bestraft werden könnten. Eine wertvolle Folge für alle Sustainability Manager und Geschäftsführer, die verstehen wollen, wie sich der CO₂-Preis auf ihre Branche auswirkt. Auch außerhalb der Stahlindustrie.

Tom Clarkson is in the Zandvoort paddock with F1 correspondent Lawrence Barretto and F1TV expert Alex Brundle for reaction to a chaotic Dutch Grand Prix. Back-to-back wins for Lando Norris means he's now 83 points behind championship leader Kimi Antonelli with 11 races to go, so is the McLaren driver back in the title fight? Despite being behind him for most of the weekend, Kimi Antonelli got the better of his Mercedes teammate George Russell on race day. Why didn't George have the pace when it really mattered? Were Mercedes right to issue team orders to get George out of Kimi's way in the closing stages? On the topic of team orders, Lewis Hamilton was very frustrated that Ferrari didn't ask Charles Leclerc to let him past. Did that cost Hamilton a podium? And what does his fiery team radio and post-race reaction tell us about the seven-time World Champion's mindset? Plus, the guys discuss home race heartbreak for Max Verstappen after he crashed out on lap one, the impact Fernando Alonso's second points finish of the season will have on his future with Aston Martin, and a dramatic end to the weekend for Williams as Carlos Sainz crashed into teammate Alex Albon.Listen to more Official F1 PodcastsIn-depth interviews on F1 Beyond The Grid - Sergio Perez coming soon!Expert answers to your questions on F1 ExplainsThis episode is sponsored by:HexcladFind your forever cookware @hexclad and get 10% off at hexclad.co.uk/NATION! Offer excludes bundles and other items already on sale. #hexcladpartnerBetterhelpYou don't have to navigate life's changes alone.Sign up and get 10% off at BetterHelp.com/F1NATION 

In this episode, we sit down with real estate investor Dylan Osmon to talk about what scaling a portfolio looks like when the business starts demanding more than one person can handle. Dylan shares the reality of owning more than 200 units while managing rentals, vacancies, construction projects, and staffing problems at the same time. He explains why hiring has become one of his biggest challenges, why core values matter more to him than experience, and what he has learned from trying to grow without always having the right team in place.The conversation also explores how Dylan is building toward 1,000 units through a mix of new construction and value-add deals. He shares why simple, repeatable builds work well in his market, how doing more volume has helped him negotiate better construction costs, and why strong relationships with banks have become a major part of his strategy. Dylan also breaks down the decision between using in-house employees and subcontractors, including the hidden costs that can make the cheaper option more expensive once your own time and management are considered.Dylan also explains why he stopped flipping houses and shifted his attention toward rentals, equity, and long-term cash flow. He talks about growing in smaller markets, protecting cash while still moving forward, and learning from mentors who are operating at a much higher level. More than just growing the number of properties, Dylan shares how his long-term goal is to build the right team and create a real estate business that eventually gives him more freedom, not simply more work.===Key Topics:-Why core values matter more than experience when building a team-How new construction and value-add deals can work together to scale a portfolio-Why strong banking relationships can change the way you finance real estate-How to choose between in-house employees and subcontractors-Why Dylan moved away from flipping to focus on rentals and long-term equity-How mentorship can change the way you think about debt, growth, and investing===If you're selling land and still relying on Facebook messages, you're making it harder than it needs to be. Acrefy helps land investors create clean, professional dispo websites where buyers can see everything in one place. It saves time, looks legit, and helps you close faster.

#514> To purchase the Sefer (with free shipping): https://alehzayis.com/product/chilazon-hatcheiles/> This episode is sponsored by KosherKlaf.com, your trusted source for mezuzos, tefillin, Sifrei Torah, and megillos. Expert guidance, transparent pricing, and nationwide shipping. Visit https://kosherklaf.com or call 516-737-5436> To join the SeforimChatter WhatsApp community: https://chat.whatsapp.com/DZ3C2CjUeD9AGJvXeEODtK> To join the SeforimChatter WhatsApp status: https://wa.me/message/TI343XQHHMHPN1>  To support the podcast or to sponsor an episode follow this link: https://seforimchatter.com/support-seforimchatter/or email seforimchatter@gmail.com (Zelle/QP this email address)Support the show

In this episode, RFC - Grilling the Expert w/ Neahga Leonard (Cat Ba Langur Conservation)Follow Justin Julander @Australian Addiction Reptiles-http://www.australianaddiction.comIG https://www.instagram.com/jgjulander/Follow Rob @ https://www.instagram.com/highplainsherp/Follow MPR Network @FB: https://www.facebook.com/MoreliaPythonRadioIG: https://www.instagram.com/mpr_network/YouTube: https://www.youtube.com/channel/UCtrEaKcyN8KvC3pqaiYc0RQWebsite: https://mprnetwork.transistor.fm/

WHERE IS THE C*M!?! No seriously you guys that's the question we're asking today and also apparently…. men are BEGGING for this? News to me.On today's episode, I sit down with L.R. Fox, founder of NEXT Life Sciences - the man creating birth control for men among other science breakthroughs for both men and women. That's right - GET EXCITED because it's finally here. Ladies... we're almost free!!We break down Plan A—the non-hormonal male “IUD,” a hydrogel designed to prevent pregnancy for up to 10 years and be reversed whenever he's ready for a baby. Boom, bam—it's baby making time. We also get into the PausePill, an on-demand birth control pill for men. Guys in relationships could take it daily, while the single lads could pop one just two hours before sex for 24 hours of protection. INSANE, RIGHT?! But perhaps not as insane as one unexpected side effect.... it may help men last longer.Ladies, forget flowers. One day, the hottest anniversary gift might be your man looking you in the eyes and saying, “Babe, you can get off birth control. I've got this.”SEND THIS EPISODE TO YOUR MAN BECAUSE HIS TIME IS COMING... AND IT'S TIME TO STEP IT UP.Learn more about Plan A, the Pause Pill and everything NEXT Life Sciences is developing:Plan A for MenNEXT Life SciencesFollow L.R.Fox and PlanAForMen on Instagram.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Tasting blind is the only unbiased way to determine the best items on our shelves. This past spring, the Whiskey Hotline undertook a companywide blind bourbon tasting tournament to determine the best bourbon on our shelves without bias. Expert spirits staff from each of our 46 stores were brought together for a multiple round blind tasting that included 64 unique bourbons. We narrowed it down to 16, then tasted the top 4, determining the 4 best bourbons on our shelves tasted blind. This week on Barrel to Bottle we got to taste the top four. Angel's Envy Bourbon Port Barrel Finished Bourbon Noah's Mill Small Batch Bourbon Clark & Sheffield Original Barrel Proof Bourbon Maker's Mark Cask Strength 7-Year-Old Bourbon If you have a question for the Barrel to Bottle Crew, email us at comments@binnys.com, or reach out to us on Facebook, Twitter or Instagram. If we answer your question during a podcast, you'll get a $20 Binny's Gift Card! If you like our podcast, subscribe wherever you download podcasts. Rate and review us on Apple Podcasts.  

On this Thursday bonus episode we are going to play the interview from episode 490 from August 2024 featuring director Audrey Cummings who talks about establishing herself as a genre director. I thought Audrey was a good match for Sophia Banks from Monday because while they have very different careers they are both successfully working in the genre space as feature film directors. After that we play another round of You're the Expert, enjoy! Don't forget to support us on Patreon! www.patreon.com/mmihpodcast Leave us a Review on Apple Podcasts! https://podcasts.apple.com/us/podcast/making-movies-is-hard-the-struggles-of-indie-filmmaking/id1006416952 Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Inhaltswarnung: In dieser Folge geht es um Kannibalismus und Tierquälerei. Als die 22-jährige Cleo im Januar 2002 plötzlich verschwindet, macht sich ihre Familie große Sorgen. Ein Blick in ihr Zimmer verrät, dass bis auf ihre Schlafsachen keine Kleidung fehlt. Und auch die Tatsache, dass sie ihren geliebten Hund in ihrer Wohnung zurückgelassen hat, passt nicht zu ihr. Für Cleos Angehörige und die Polizei ist schnell klar: Irgendetwas muss ihr zugestoßen sein. Schließlich wendet sich ihr Cousin und Mitbewohner Marek an die Polizei. Er wisse, wo Cleo ist. Oder besser gesagt, das, was noch von ihr übrig geblieben ist … In dieser Folge von „Mordlust – Verbrechen und ihre Hintergründe“ sprechen wir über ein blutiges Verbrechen, nach dem ein kaum vorstellbarer Verdacht aufkommt. Einer, der die Frage aufwirft, ob der Täter Teile seines Opfers gegessen haben könnte. Außerdem erklären wir, warum ein juristisches Verbot am Ende dazu führt, dass eine einmal verhängte Strafe nicht mehr verschärft werden darf. Expert:innen in dieser Folge: Jürgen Johnen, Kriminalhauptkommissar a.D. Natalie Oesterlein, Fachpsychologin für Rechtspsychologie Prof. Dr. Thomas Rönnau, Inhaber des Lehrstuhls für Strafrecht,Wirtschaftsstrafrecht und Strafprozessrecht an der Bucerius Law School **Credit** Hosts: Paulina Krasa, Laura Wohlers Producer: Paulina Krasa, Laura Wohlers und Jon Handschin Redaktion: Paulina Krasa, Laura Wohlers, Jennifer Fahrenholz Schnitt: Pauline Korb Rechtliche Abnahme: „Abel und Kollegen“; Benedikt Müller **Quellen (Auswahl)** BGH - Beschluss vom 12.11.04 - 2 StR 367/04 Landgericht Koblenz – Urteil vom 10. April 2006 - 2030 Js 4046/02 – 2 Ks Spiegel: https://t1p.de/oo387 Stern: https://t1p.de/fhj0p Weißer Ring Magazin: https://t1p.de/d1q4o **Partner der Episode** Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte: https://linktr.ee/Mordlust Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

In this Ask the Expert episode, Lon Shaver, President of Silvercorp Metals, joins Craig Hemke for Sprott Money to discuss the silver price, gold price, mining stocks, supply constraints, production costs, and what could drive the next move in precious metals. Silver remains above historically significant levels, mining supply is difficult to expand, and industrial demand continues to support the long-term outlook. Lon explains why today's price of silver remains encouraging despite trading below earlier highs, why new mines can take many years to reach production, and how limited new supply could affect the silver market. He also discusses Silvercorp Metals' low-cost production, expansion plans in China, Ecuador and Kyrgyzstan, drilling programs, and the company's exposure to silver.

Part 8 - Trial Day 14 Lindsay Clancy, a labor and delivery nurse, murdered her three young children on January, 24th, 2023. In this episode, Nurse Erica breaks down day fourteen of the trial, which includes courtroom testimony from Lindsay's mother and sister and digital forensic health evidence extraction from Lindsay's Apple watch and iPhone. This episode includes the prosecution resting it's case and analysis of  courtroom dynamics and legal motions. The defense focuses on Lindsay Clancy's mental health, post partum depression, psychosis, and psychiatric medication history. The prosecution contends Clancy deliberately and meticulously planned this. This true crime trial series explores systemic issues in maternal mental health, the healthcare system and legal proceedings. Nurse Erica offers insights from a nurse's perspective to help listeners understand the complexities of the medical evidence and legal case. *Trigger Warning: this case discusses suicide and child death.   Advertise on the show! Email with the subject NURSES UNCORKED SPONSOR to: nursesuncorked@gmail.com   Become a Patron! Gain early access to episodes, ad-free episodes, exclusive bonus content, giveaways, Zoom parties, shout-outs, and much more. https://patron.podbean.com/nursesuncorkedpodcast ETSY Shop:  Stop Healthcare Worker Violence! https://www.etsy.com/shop/TheNurseErica   Chapters: 00:00 Intro to the Lindsay Clancy trial and episode   04:14 Patron Shoutouts 06:37 Apple Watch data and digital forensic evidence 14:46 Motion for Judgement of Acquittal  16:36 First defense witness: Margaret Hamp RN 19:45 Andrea Yates case 21:05 Witness: Allison Ozga, Lindsay's sister 23:26 Go Fund Me 25:23 Witness: Paula Musgrove, Lindsay's mother 31:40 Expert medical testimony on Lindsay's injuries 37:26 Judge's rulings and trial proceedings   Go Fund Me: https://www.gofundme.com/f/support-for-lindsay-clancys-parents   National Suicide Hotline: Call / Text 988   Send viewer questions to: thenurseericarn@gmail.com Help the podcast grow by giving episodes a like, download, follow and a 5 ️ star rating! Please follow Nurses Uncorked at: tiktok.com/nurses-uncorked https://youtube.com/@NursesUncorkedL   You can listen to the podcast at: podcasts.apple/nursesuncorked spotify.com/nursesuncorked podbean.com/nursesuncorked iheart.com/nurses-uncorked   Follow Nurse Erica:  @TheNurseErica on TikTok, Instagram, Facebook and YouTube! https://www.youtube.com/@thenurseerica9094 https://www.instagram.com/the.nurse.erica/   DISCLAIMER: This Podcast and all related content published or distributed by or on behalf of Nurse Erica or Nurses Uncorked Podcast is for informational, educational and entertainment purposes only and may include information that is general in nature and that is not specific to you. Any information or opinions expressed or contained herein are not intended to serve as legal advice, or replace medical advice, nor to diagnose, prescribe or treat any disease, condition, illness or injury, and you should consult your health care professional regarding all matters concerning your health, including before beginning any exercise, weight loss, or health care program. If you have, or suspect you may have, a health-care emergency, please contact a qualified health care professional for treatment. The views and opinions expressed on Nurses Uncorked do not reflect the views of our employers, professional organizations or affiliates. Any information or opinions provided by guests, experts or hosts featured within website or on Nurses Uncorked Podcast are their own; not those of Nurse Erica or Nurses Uncorked LLC. Accordingly, Nurse Erica and Nurses Uncorked cannot be responsible for any results or consequences or actions you may take based on such information or opinions. All content is the sole property of Nurses Uncorked, LLC. All copyrights are reserved and the exclusive property of Nurses Uncorked, LLC.

Podcast Chapters 00:00 – Welcome to the Real Boss Series with Tom Dheere Anne and Tom introduce the conversation and discuss how Anne's fashion interests have changed what she sees in her social media feed. 01:10 – How Social Media Algorithms Learn Your Interests Why the content you consume influences what platforms continue showing you—and what that means for voice actors. 02:40 – What Voice Actors Should Be Posting on Social Media Tom breaks down the two sides of social media: what you consume and what you create. 04:06 – Why Video Continues to Matter for Voice Actors Anne and Tom discuss video content, engagement, and creating content people actually find useful. 05:26 – The Two Audiences Voice Actors Need to Understand Why different voiceover genres may require different approaches to social media and why voice seekers aren't necessarily watching your content. 07:15 – "Charming the Humans While Feeding the Robots" Tom introduces one of the central ideas of the conversation: balancing human engagement with algorithmic visibility. 09:37 – Expertise, Experience, Authority, and Trust How E-E-A-T can help voice actors think more strategically about the content they publish. 11:08 – Stop Making Social Media a Hard Sell Why "I'm looking for work" and constant self-promotion can work against you. 12:06 – Your Comments Are Part of Your Professional Reputation How interactions, arguments, and behavior inside social media groups can affect how people perceive you. 14:47 – Tom's Failed 50-Video Social Media Campaign Tom shares the experiment that didn't work—and how turning the failure into content produced a much better result. 18:07 – Understanding Impressions and Social Media Reach Tom explains how LinkedIn impressions can expand the audience beyond your immediate connections. 19:44 – Why Vulnerability Creates Better Content Tom discusses his most successful blog posts and why people respond to mistakes, honesty, and lessons learned. 22:30 – You Don't Have to Be an Expert to Have Something to Say How sharing your own journey can become useful content even when you don't feel like you have anything important to contribute. 24:19 – Why Other Voice Actors Matter to Your Career Tom shares data from his business tracking and explains how fellow voice actors have contributed to his opportunities. 26:14 – Treat Your Social Media Like Your Voiceover Performance Why being friendly, conversational, authentic, and helpful applies both on the microphone and online. 27:50 – Your Passion Can Become Your Marketing Anne and Tom explore how hobbies and interests can attract people who eventually discover your voiceover work. 28:25 – Know, Like, and Trust: Becoming Memorable Why social media can be more effective when the goal is familiarity rather than a direct sale. 29:07 – Social Media Is Changing, So Your Strategy Has to Change Too Anne and Tom discuss the increasingly visible role of algorithms and why voice actors need to pay attention. 30:30 – The E-E-A-T Takeaway for Voice Actors Tom breaks down Expertise, Experience, Authority, and Trust as a framework for social media content. 31:12 – Closing Thoughts and IPDTL Top 10 Lessons From This Episode 1. Social media is not your digital business card. Simply announcing that you're a voice actor doesn't give people much reason to pay attention. Give them something useful, entertaining, interesting, or memorable instead. 2. Your audience may find you because of something completely unrelated to voiceover. A passion, hobby, personality trait, or unusual interest can become the doorway through which someone discovers your voiceover work. 3. A failed experiment can become successful content. Tom's 50-video campaign didn't produce the results he expected, but sharing what went wrong created a much stronger piece of content. 4. Vulnerability can demonstrate authority. Talking honestly about mistakes doesn't necessarily weaken your professional image. When you explain what you learned, it can demonstrate experience and credibility. 5. Different voiceover genres may require different social strategies. Tom's example of audiobook narration shows why understanding where your potential buyers spend time matters. 6. Don't assume going viral means you've built a business. A large number of views can create awareness, but attention only becomes useful when people have a reason to remember you and understand what you offer. 7. Your comments are part of your brand. The way you behave on someone else's post can tell potential clients just as much about you as the content you publish yourself. 8. Your fellow voice actors aren't simply your competition. Tom's tracking shows how much opportunity can originate from other voice actors. Community can become a meaningful source of referrals and professional growth. 9. Give people the answer instead of forcing them to click. Tom's LinkedIn example demonstrates why useful content should stand on its own. If people find the information valuable, they can choose to explore further. 10. Think about being remembered, not constantly being hired. The strongest social media strategy may be the one that makes people familiar with your personality, expertise, and approach so you're already on their radar when an opportunity appears. Listen to the Full Episode There is a lot more to this conversation than simply "post more on social media." Tom and I get into what happened when one of his carefully planned content campaigns completely missed the mark, why his most successful writing often comes from mistakes, how voice actors can use their interests to attract attention, and why your relationships with other voice actors may be more valuable to your career than you realize. We also talk about the increasingly important relationship between social media, search, AI, algorithms, and the way your professional reputation is discovered online. Listen to the full VO BOSS Podcast episode with Tom Dheere to hear the complete conversation.  

We break down the biggest gaming news from D23, including Kingdom Hearts IV, the new Kingdom Hearts animated series, and Disney is making a Fortnite ride.Then we discuss what are the best Disney video games of all time?Do you have a favorite? Let us know in the comments!Timecodes:00:00 – Start09:52 – Easy Mode: What We've Been Playing10:40 – Spiritstead & Slots and Diapers13:20 – Dimhaven18:00 – Big Walk26:43 – D23 Gaming News29:30 – Star Wars: Smugglers Run Comes to Fortnite35:00 – Kingdom Hearts55:21 – Expert Mode: Our Favorite Disney GamesSupport the showJK! Games! is a weekly gaming podcast where we bring you the news and reviews we actually care about.Our recurring play-along series — One More Game — is our version of a video game book club. We choose one title, set checkpoints, and break it down over multiple episodes. Currently Dave the Diver!You can:• Play at your own pace• Stay spoiler-light• Or dive deep with usWhether you're Easy Mode or Expert, you belong in the conversation.Join our Discord to play along and share your theories each week.Want to show us some love? Click Me!DiscordTwitch YoutubeInstaBsky

Part 8 - Trial Day 14 Lindsay Clancy, a labor and delivery nurse, murdered her three young children on January, 24th, 2023. In this episode, Nurse Erica breaks down day fourteen of the trial, which includes courtroom testimony from Lindsay's mother and sister and digital forensic health evidence extraction from Lindsay's Apple watch and iPhone. This episode includes the prosecution resting it's case and analysis of  courtroom dynamics and legal motions. The defense focuses on Lindsay Clancy's mental health, post partum depression, psychosis, and psychiatric medication history. The prosecution contends Clancy deliberately and meticulously planned this. This true crime trial series explores systemic issues in maternal mental health, the healthcare system and legal proceedings. Nurse Erica offers insights from a nurse's perspective to help listeners understand the complexities of the medical evidence and legal case. *Trigger Warning: this case discusses suicide and child death.   Advertise on the show! Email with the subject NURSES UNCORKED SPONSOR to: nursesuncorked@gmail.com   Become a Patron! Gain early access to episodes, ad-free episodes, exclusive bonus content, giveaways, Zoom parties, shout-outs, and much more. https://patron.podbean.com/nursesuncorkedpodcast ETSY Shop:  Stop Healthcare Worker Violence! https://www.etsy.com/shop/TheNurseErica   Chapters: 00:00 Intro to the Lindsay Clancy trial and episode   04:14 Patron Shoutouts 06:37 Apple Watch data and digital forensic evidence 14:46 Motion for Judgement of Acquittal  16:36 First defense witness: Margaret Hamp RN 19:45 Andrea Yates case 21:05 Witness: Allison Ozga, Lindsay's sister 23:26 Go Fund Me 25:23 Witness: Paula Musgrove, Lindsay's mother 31:40 Expert medical testimony on Lindsay's injuries 37:26 Judge's rulings and trial proceedings   Go Fund Me: https://www.gofundme.com/f/support-for-lindsay-clancys-parents   National Suicide Hotline: Call / Text 988   Send viewer questions to: thenurseericarn@gmail.com Help the podcast grow by giving episodes a like, download, follow and a 5 ️ star rating! Please follow Nurses Uncorked at: tiktok.com/nurses-uncorked https://youtube.com/@NursesUncorkedL   You can listen to the podcast at: podcasts.apple/nursesuncorked spotify.com/nursesuncorked podbean.com/nursesuncorked iheart.com/nurses-uncorked   Follow Nurse Erica:  @TheNurseErica on TikTok, Instagram, Facebook and YouTube! https://www.youtube.com/@thenurseerica9094 https://www.instagram.com/the.nurse.erica/   DISCLAIMER: This Podcast and all related content published or distributed by or on behalf of Nurse Erica or Nurses Uncorked Podcast is for informational, educational and entertainment purposes only and may include information that is general in nature and that is not specific to you. Any information or opinions expressed or contained herein are not intended to serve as legal advice, or replace medical advice, nor to diagnose, prescribe or treat any disease, condition, illness or injury, and you should consult your health care professional regarding all matters concerning your health, including before beginning any exercise, weight loss, or health care program. If you have, or suspect you may have, a health-care emergency, please contact a qualified health care professional for treatment. The views and opinions expressed on Nurses Uncorked do not reflect the views of our employers, professional organizations or affiliates. Any information or opinions provided by guests, experts or hosts featured within website or on Nurses Uncorked Podcast are their own; not those of Nurse Erica or Nurses Uncorked LLC. Accordingly, Nurse Erica and Nurses Uncorked cannot be responsible for any results or consequences or actions you may take based on such information or opinions. All content is the sole property of Nurses Uncorked, LLC. All copyrights are reserved and the exclusive property of Nurses Uncorked, LLC.

In this episode, we sit down with Dr. Amy Garza, an anesthesiologist turned longevity medicine physician and clinic owner, to talk about why traditional healthcare often waits until something is wrong instead of helping people stay healthy before problems happen. Amy shares how her own health changes and search for answers led her into age management and longevity medicine, where she now focuses on proactive, data-driven care for men, women, and couples in midlife. She explains how advanced labs, biomarkers, and personalized treatment can help identify potential health problems years before they become serious.The conversation also explores what Amy believes is missing from the traditional healthcare system and why she built AgeWell around a cash-pay and membership model. She explains the challenges of balancing patient care with running a growing business, why personalized medicine becomes harder to maintain as a clinic scales, and why she wants to learn more from the hospitality industry than from hospitals. Amy also shares her vision of building a healthcare experience where patients feel known, supported, and cared for rather than feeling like they are simply moving through a medical system.Amy also dives into hormones, sexual health, heart health, and the growing role of biomarkers and wearable technology. She explains why erectile function can be an early warning sign for cardiovascular problems, how sexual health can motivate men to take their long-term health more seriously, and why hormone and peptide treatments require a much more individualized approach than many people realize. ===Key Topics:-Why proactive care can catch health risks before they become serious-How hormones, biomarkers, and advanced testing create more personalized care-Why sexual health can reveal important signs about heart health-What traditional healthcare often misses about prevention and longevity-How hospitality can create a better experience for patients-How to scale a medical practice without losing personalized care===If you're selling land and still relying on Facebook messages, you're making it harder than it needs to be. Acrefy helps land investors create clean, professional dispo websites where buyers can see everything in one place. It saves time, looks legit, and helps you close faster.

This week on Expert, Sadaf and Tegan are putting their beauty budgets to the test. They reveal the categories they’re happy to splurge on, where they prefer to save, plus all the products they’re loving right now. Then, it’s time for Win, Bin or Recycle, and the team is divided over... voice notes. Are they a cute way to stay connected or just plain annoying? Plus, Tegan makes the case for giving your ‘neck and dec’, aka neck and décolletage, a little more love. These often-forgotten areas get plenty of sun exposure and can show signs of premature ageing, so are we really giving them the skincare attention they deserve? Products mentioned: Maybelline New York Surreal Extensions Mascara e.l.f. Cosmetics Halo Glow Liquid Filter Estée Lauder Double Wear Foundation Dior Backstage Face & Body Foundation Medik8 Crystal Retinal Anastasia Beverly Hills Brow Freeze Maybelline New York Super Lock Brow Glue Gel La Roche-Posay Cicaplast Baume B5+ Viviology Ceramide Moisturiser SkinCeuticals Triple Lipid Restore Moisturiser Huda Beauty Blush Filter Laneige Lip Sleeping Mask Davines Oi Shampoo Aspect Gentle Clean Maybelline Sky High Lash Sensational Mascara Givenchy Prisme Libre Glow Serum Foundation Cosmedix Define PCA Skin Hyaluronic Acid Lip Booster K18 Peptide Prep Detox Shampoo L'Oréal Professionnel Absolut Repair Molecular Mask PCA Body Therapy L’Occitane Almond Shower Oil PCA Skin Perfecting Neck & Décolleté BOOST LAB Edelweiss Neck Serum Send us your tips, tricks, questions and feedback at @adorebeauty on IG.Join the conversation in our Beauty IQ Facebook Group to discuss this episode, swap beauty tips, and submit your questions for future shows. Credits: Hosts: Sadaf Razi and Tegan MacDonald Producer: Melissa Mason For more beauty insights and exclusive offers, visit adorebeauty.com.auSee omnystudio.com/listener for privacy information.

Emma Vardy stands in for Naga Munchetty on Must Watch this week. She's joined by Scott Bryan and Hayley Campbell to review the week's biggest new TV and streaming releases.This week the trio reviewed ‘My Brilliant Career' the iconic Australian story that began as a 1901 semi-autobiographical novel by Miles Franklin. It follows Sybylla Melvyn (Phillipa Northeast), a headstrong and rebellious young farm girl in rural Australia who dreams of becoming a writer, without wanting to let romance get in the way.Next, it's 'Buried' with Michael Sheen, a new two-part documentary series with the well-known actor. Here he looks at allegations that a chemicals giant decades ago dumped huge amount of industrial chemicals all around Wales, resulting in contamination and concerns from locals that it could be responsible for an increase in cancer rates in the region.Finally, it's 'Race Against The Tide', a competitive sand sculpting competition on BBC Two. Expert professional sand sculptors are partnered with creative newbies and their sand sculptures are judged on technical prowess and detail. As the title suggests, they are racing against the tide.Remember you can email mustwatch@bbc.co.uk to have your say

#512The Unique Vision of Rav Kook (with Prof. Marc Shapiro)> To purchase the book: https://amzn.to/4qd5ubj> Podcast L'Ilui Nishmas Hindel Mirel bas Menachem Yosef> Podcast sponsored in honor of the 100th Yahrtzeit of Rav Meir Simcha of Dvinsk. Experience the Meshech Chochmah with the carefully edited, selected, and translated English collection of Rabbi Immanuel Bernstein. Purchase: https://mosaicapress.com/product/meshech-chochmah/?sld=seforimchatter> This episode is sponsored by KosherKlaf.com, your trusted source for mezuzos, tefillin, Sifrei Torah, and megillos. Expert guidance, transparent pricing, and nationwide shipping. Visit https://kosherklaf.com or call 516-737-5436> To join the SeforimChatter WhatsApp community: https://chat.whatsapp.com/DZ3C2CjUeD9AGJvXeEODtK> To join the SeforimChatter WhatsApp status: https://wa.me/message/TI343XQHHMHPN1>  To support the podcast or to sponsor an episode follow this link: https://seforimchatter.com/support-seforimchatter/or email seforimchatter@gmail.com (Zelle/QP this email address)Support the show

How do you protect your business's value when costs keep rising and uncertainty shows no sign of easing – and why does psychological safety matter in your workplace? In this week's episode, business sales and acquisitions expert Simon Bedard explains how rising costs, inflation and economic uncertainty are reshaping business value. Simon – who is Managing Director of Exit Advisory Group, and author of new book Exit Like an Expert - also unpacks why many owners are absorbing costs instead of passing them on, and where to focus to build a stronger, more resilient business. Then senior psychologist and managing director at Barrington Centre, Rhonda Andrews, explores why psychological safety is far more than a compliance issue. She explains how creating a workplace where people feel safe to speak up can improve performance, innovation and retention – and why doing nothing is now the biggest risk of all. If you’re considering launching a podcast to grow your authority and client base, reach out to our team to learn how we can support you. Business Essentials is produced by soundcartel.com.auSee omnystudio.com/listener for privacy information.

We're talking about City Council approving the Moda Center term sheet and what that means heading into negotiations — as well as the wildfire risk within Portland City limits. Plus, Oregon is now the home of the last outpost in the world for two American chain stores. Joining host Claudia Meza are Willamette Week City Hall reporter Sophie Peel and wildfire risk expert and principal of McCullough Research, Robert McCullough.  Become a member of City Cast Portland! ⁠Join today⁠, and we'll send you some exclusive City Cast Portland swag, while supplies last. Get all the details and sign up ⁠here⁠.⁠ ⁠ Discussed in today's episode: ⁠Council Approves Moda Center Term Sheet to Kick Off Negotiations With Blazers⁠ [Willamette Week] ⁠Could Portland handle an urban wildfire? Expert warns city isn't fully prepared⁠ [KGW8] Who would you like to hear on City Cast Portland? Shoot us an email at ⁠portland@citycast.fm⁠, or leave us a voicemail at ⁠503-208-5448⁠. Want more Portland news? Then make sure to sign up for our ⁠morning newsletter⁠ and be sure to follow us on ⁠Instagram⁠. Looking to advertise on City Cast Portland? Check out our options for podcast and newsletter ads at ⁠citycast.fm/advertise⁠. Learn more about the sponsors of this August 14th episode: ⁠Portland Spirit⁠ ⁠Rose City Comic Con⁠ ⁠Burger Week⁠ ⁠Stacked Piercing⁠ - take 20% off your first booking with code CITYCAST20 ⁠Grand Central Bakery⁠ ⁠pFriem Beer⁠

In this week's episode, Josh and Kayla discuss how narratives are portrayed in books vs games and their love for both. They also take you through a journey of a bit of their gaming history, their reading history, and Kayla gives you a few recommendations of "if you like this game, read this book". Timecodes:00:00 - Start3:37 - Housekeeping6:59 - Our Qualifications/Reading History25:19 - “This or That pt.1”41:50 - Our Favorite Narratives 1:08:55 - "This or That pt.2"1:15:35 - Agency (Books vs. Games)1:23:19 - Worldbuilding (Books vs. Games)Catch us live every Monday at 9 PM ET on Twitch!Support the showJK! Games! is a weekly gaming podcast where we bring you the news and reviews we actually care about.Our recurring play-along series — One More Game — is our version of a video game book club. We choose one title, set checkpoints, and break it down over multiple episodes. Currently Dave the Diver!You can:• Play at your own pace• Stay spoiler-light• Or dive deep with usWhether you're Easy Mode or Expert, you belong in the conversation.Join our Discord to play along and share your theories each week.Want to show us some love? Click Me!DiscordTwitch YoutubeInstaBsky

Sechs Wochen. Ein Thema: Wie du zum besten Interviewgast wirst. Denn das ist wohl der stärkste Hebel für Reichweite deines eigenen Podcasts. Heute zeige ich dir, wie du dich profesionell auf dein Podcastinterview als Gast und Expert:in vorbereitets. So nutzt du alle Chancen. Diesen Sommer nehme ich die Interviewhelden mit nach draußen. Raus aus dem Studio, rein in den Sommer. Rudernd auf dem See, bei fast 40 Grad an der Elbe, direkt aus dem Stadtpark, eine Gartenfolge gibt es auch und ganz viel Urlaubsvibes. Mit den tollen Kolleginnen und Kollegen: Katrin Hill, Jule Jankowski, Birgit Eschbach und Bertram Kaspar.

Expert veterinarians provide a granular look at the highly contagious bacteria Streptococcus equi, subspecies equi and the nuances of its prevention, detection and care.

Text Me A Question!Plot twist: after years of basically owning “Trello” on YouTube, we switched to ClickUp—and we're spilling why.The 2026 reality check: if your tools can't connect to your AI stack (thought partner + automations + agents), you're slowing yourself down.So, is ClickUp better than Trello? Stay Tuned!Content Calendar Challenge Sign Up!Content To Customers Live Workshop Sign Up!Support the show ➡️ Are you a Coach, Expert, or Service Provider wanting to get more Customers from your Content?

Navigating the complexities of medical malpractice cases demands a unique blend of clinical expertise and legal insight. This episode of the Legal Nurse Podcast welcomes a board-certified physician in emergency medicine, internal medicine, and neurocritical care, who brings more than 24 years of experience as an expert witness with over 300 trial testimonies. Together, they delve into the evolving role of medical experts, from pre-litigation consultations to providing critical insights on causation and damages in both medical malpractice and nursing home cases. Listeners will gain a behind-the-scenes look at how clinicians become involved in the legal world, how reviewing cases sharpens clinical practice, and the ever-changing nature of medical recordkeeping from handwritten notes to searchable digital PDFs and the emerging role of artificial intelligence in case review. Share practical advice for new experts, discusses the importance of honest, evidence-based opinions, and explore the challenges experts face when attorneys or clients may want to influence findings. Whether you're a legal professional, a clinician considering expert witness work, or simply interested in the intersection of medicine and law, this episode provides invaluable perspectives and actionable guidance for navigating tough cases and maintaining integrity in your practice. What You'll Learn in This Episode is The Intersection of Medicine and Law: Dr. Stein Shares Witness Strategies Here are 5 discussion questions answered by Pat Iyer and Kenny Stein in the podcast: How does a multi-specialty background inform his approach to reviewing medical malpractice cases? What are some of the key differences identified between being a testifying and a non-testifying expert? What potential and limitations does see in using AI to analyze medical records or assess standards of care? What are the biggest challenges and advantages that experience when working with electronic medical records compared to traditional paper records? How does recommend handling emotionally invested attorneys or families when a case does not have merit? Listen to our podcasts or watch them using our app, Expert.edu, available at legalnursebusiness.com/expertedu. Get the free transcripts and also learn about other ways to subscribe. Go to Legal Nurse Podcasts subscribe options by using this short link: http://LNC.tips/subscribepodcast. Your Presenters for The Intersection of Medicine and Law: Dr. Stein Shares Witness Strategies Pat Iyer Pat Iyer is a seasoned legal nurse consultant and business coach, renowned for her expertise in guiding new legal nurse consultants to successfully break into the field. As the host of the Legal Nurse Podcast, Pat addresses critical challenges that legal nurse consultants face, such as difficulty in landing clients and a lack of response from attorneys. Through her insightful episodes, she emphasizes the importance of effectively communicating one's value to potential clients. With a wealth of experience, Pat has empowered countless consultants to overcome these hurdles and thrive in their careers. Connect with Pat Iyer by email at patiyer@legalnusebusiness.com Kenny Stein Dr. Stein is board-certified in Emergency Medicine, Internal Medicine, and Subspecialty-certified in Neurocritical Care. Dr. Stein has been an Expert Witness for 24 years, reviewing over 800 cases for both plaintiff and defense. Dr. Stein has testified over 300 times at depositions and trials. Connect with Kenny Stein by email at kennystein1@gmail.com

Have you ever felt like you're getting things done, reaching your goals, and other people look at you thinking, "Wow, she's doing amazingly," and yet you still feel behind? Listen as our host, Diane Rolston, unpacks something she sees constantly in high-achieving women: being successful on paper but not feeling successful. She gets honest about why accomplishing a lot doesn't always feel like enough and what's really going on underneath.Listen to learn these key takeaways:The podcast award moment: being ranked number 17 out of 50 and what Diane's brain immediately jumped to instead of celebratingWhy high-achieving women reach one goal and almost immediately move the finish lineThe year Diane said, "I didn't accomplish much" and what the coaches around her said that stopped her coldThe client who kept collecting certifications and training before feeling ready to run workshops and what she actually needed that no certification could give herThe sprinter who was asked to run the 1,500 and why comparing your pace to someone else's race makes no senseWhat happened when a client and his two friends sold a company for 75 million dollars and why he felt badThe client who said her next step was a promotion at work and the real answer she gave when Diane asked what would actually make her happyWhy the problem isn't goals or achievement. It's the question we never stop long enough to askThe connection to Diane's one-woman show Chasing: what happens when success doesn't feel the way we thought it wouldWhat the gap between success and satisfaction is really telling you and why outrunning it doesn't workMaybe you aren't behind after all. Maybe you've just stopped noticing how far you've come. She's Goaled Coaching Mastermind doors are open now, closing September 4th at 11:59 pm Pacific. Apply here: https://shes-goaled-coaching-mastermind.dynamicwomen.biz/Learn more about Motivation Mountain: https://dynamic-women.captivate.fm/episode/steal-my-strategy-for-not-losing-momentum-with-diane-rolston-dw294Want to be invited to join Diane's NEW high-level, like-minded group of women? 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  Episode: 140 Taking Charge After Stroke: Self-Determination and Recovery with Vivian Fu   In this episode you will discover: ● The Conversation Is the Intervention — A structured, facilitated conversation that centers a person's identity, hopes, and vision for their best day produces measurable improvements in quality of life and independence a year after stroke. Connection isn't soft — it's evidence-based. ● Reframe the Expert in the Room — Take Charge asks clinicians to resist offering advice, validation, or direction — and to trust that the person with stroke already holds the wisdom they need. The hardest part of the facilitator role is staying out of the way. ● Self-Determination Is Not a Luxury — When people with stroke are supported to set their own direction, outcomes improve, costs decrease, and the effects last for years. Building systems that protect that autonomy isn't idealistic — it's what the data demands.   Welcome to the Aphasia Access Aphasia Conversations Podcast. I'm Katie Strong from Central Michigan University and a member of the Aphasia Access Podcast Working Group, a community dedicated to supporting better aphasia care.   Today I'm speaking with Dr. Vivian Fu, a stroke neurologist living and working in Kelowna, British Columbia, the unceded territories of the Syilx / Okanagan people. Vivian trained in Aotearoa New Zealand, where she completed her PhD running the Taking Charge After Stroke trial. Take Charge showed that people with stroke who were supported to follow their own self-determination had much better quality of life and independence a year after their stroke. The second Take Charge RCT showed that two sessions about six weeks apart produce better outcomes than one session. Vivian strives to embed the Take Charge philosophy in her daily practice, and is focused on improving access to high quality stroke care for rural, regional, and underrepresented populations.   I have been looking forward to this conversation. What drew me to Take Charge was how it reframes the question entirely — from what does the clinician do for this person to what does this person want for their own life. That shift is deceptively simple, and as you'll hear, the evidence behind it is anything but.   Let's get into it.   Katie Strong:  Welcome, Vivian. Vivian Fu: Thank you so much for having me, Katie. I'd like to start off with my Pepeha. This is an introduction in Te Reo Māori, which is the indigenous language of the people of Aotearoa New Zealand, and I'll just translate each line. Nō Hong Kong ōku tīpuna. My ancestors come from Hong Kong. I tipu ake au ki Aotearoa. I grew up in Aotearoa, New Zealand. E noho ana au ki Ki-Low-Na. I live in Kelowna, British Columbia. Ko tēnei taku mihi ki ngā tāngata whenua o te rohe nei. I like to pay my deepest respects to the first peoples of this land. I live on the unceded territories of the Syilx and Okanagan peoples. Ko tēnei taku mihi ki ngā maunga, ki ngā awa, ki ngā roto, ki ngā Papatuānuku, o te rohe nei. I'd like to pay my deepest respects to the mountains, rivers, and the lakes, and to Mother Earth. All these important landmarks that have been here for millennia. Nō reira, tēna koutou, tēna koutou, tēna koutou katoa. Therefore, hello, hello, hello, Ko Vivian Fu tōku ingoa. Lovely to be here. Katie Strong:  Well, I'm so glad you're here. And I wanted to start off today asking about the Take Charge program that you've been working with, and talk to me about how that began. What was the origin of that? Vivian Fu:  Sure, so really this work began in probably the late 90s early 2000s when clinicians in Aotearoa New Zealand realized that there were different outcomes for people who were Māori compared with non-Māori, and I guess an important point to illustrate is that in Aotearoa society we view things in a very bicultural lens, and by that I mean Tangata Whenua, who are the people of the land, so indigenous people of the Māori and Tangata Tiriti, so everyone else are people of the treaty. It doesn't really matter where you come from, but you are a person of the treaty if you live in Aotearoa, and so it's a bicultural lens, and so we always look at things in that way, and that's how our kind of entire society is grounded upon that. And so when we look at health outcomes, what we could see back then, and unfortunately what we, in a way, still see now is that Māori were experiencing a stroke at about 20 years younger than non-Māori, and they were more likely to die from their stroke, and also more likely to be severely disabled. And so there was a difference in life expectancy, a difference in overall rehabilitation access, difference in overall outcomes, and so it started off with Professor Matire Harwood's work. She is Tangata Whenua, and in her PhD, she was looking at why there were these differences in outcomes and wanting to address them. And so that started off with the Māori and Pacific Stroke Study, which was essentially conducted in just Māori and Pacific people in Aotearoa, New Zealand, out of many different centers around the country. It was a four arm study.. It was a randomized control trial, and it looked at a conversation, which was labeled as the "Take Charge" session, but it was, it was really a connection and a conversation versus a professionally made DVD from the New Zealand Stroke Foundation about people's experiences and stories after stroke. And then the fourth arm of the trial was getting both of those interventions, and there was a control group. So there were three active groups in one control group. And what the researchers basically found was that anybody who had received this session of discussion and connection did much better a year after stroke in terms of quality of life, independence, and caregiver strain. And so that was the first sort of indication that there was something in that conversation that was really important. Unfortunately, it didn't really take off in terms of being able to be implemented, and so then Dr. Harry McNaughton, who was Dr. Harwood's supervisor wrote multiple grants and tried to get this session into much more of a bigger trial with some some type of implementation, and that's where I came along and we essentially did two things. We operationalized the intervention into something that had a bit of a framework, so with a bit of a booklet and a bit of a structure to follow. Really looking into what was it about that conversation that was so powerful and made such a difference to people, and tried to put those things into practice. Then, secondly, to conduct a second trial in New Zealand out of seven centers for people with stroke who were non-Māori and non-Pacific, because the struggle that we came across was that it was only shown to be effective in a small group of people, but not in everybody, and so we had to do things backwards. And so that was really where it all began, from these principles of self-determination, so Tino Rangatiratanga, which is a really important principle in Te Ao Māori, and in the world of Māori, but also knowing that Māori and a lot of indigenous peoples, we think of health as this concept called Te Whare Tapa Whā, for example, which is the house with four walls. So a person is never just their body and their physical health, there are other walls that keep the house upright. So mental health, spiritual health, and family health, whānau is so important, and feeling as though you have strong foundations in where you belong is also really important. So it's that really holistic look at health of an individual and how we can address all of those things in an intervention is really where it all came from. Katie Strong:  Thank you for sharing. I appreciate the backstory, and also just the idea of what you're thinking about from a holistic health standpoint. I know our listeners are probably curious, some of them might not have heard about Take Charge before, so this is the first time of them hearing about this, and so I was hoping, Vivian, you could give us an overview of what the intervention is, and then maybe walk us through what a session actually looks like in practice. Vivian Fu: Yeah, absolutely. So I'll talk you through the session in the way that it was done in the larger Take Charge trial. So this was done in 400 non-Māori, non-Pacific people with stroke, and our trial there had three arms. So there was a control group, there was a group that received just one session, and then there was a group that received two sessions six weeks apart roughly. The sessions were provided by a trained facilitator, and they were timed roughly somewhere between three to 18 weeks after stroke. So quite a large window, really, depending on when the person with stroke was ready to receive it. So the short version of what it looks like is it's based off the booklet and it's done face to face. It's a conversation and the facilitator is trained at the beginning to really try and establish a relationship…to build a relationship. There's a concept in Te Ao Māori called Whakawhanaungatanga, which is really sort of seeking another person's identity, recognizing who they are as a person, and trying to build connections. And so I guess in English, we think of that as building rapport, building trust. And so it in our trial was done face to face. We have also looked at ways of doing this via telehealth as well, which is, you can imagine, is a little bit different. But that step is really important, and we knew that it was important also in the initial Māori and Pacific trial, because the facilitators who were trained were actually ethnicity matched. So that's something where I think it came quite naturally in that trial, but in the second trial we really wanted to ensure that a relationship was built, and then there are three pages, initial pages in the booklet that look at different things. So the first page is looking at how the person has been affected by the stroke, and then asking them to think about actually who they really are as a person. It's a very simple concept, and a very sort of simple question, but for many people that's the first time they've really been asked that and have had to think about that. So anything that comes to mind that they can relate to, you know, who they are. An individual, and what it is that they love, so for example, for me, I would say, "I'm a mum, and I'm, I'm a stroke neurologist, I am a painter, I'm a poet, I'm someone who loves going for walks in hikes," and, you know, so those kinds of things, and it, and it kind of really builds on on that person's identity and who they are in the world. And so that's that sort of page one. It's really about establishing identity and sense of self. And then the second page is talking then about my hopes and fears. And that might be a tricky one, really, for a lot of people to start talking about and thinking about. They may not want to express that, they may not have been asked that. Sometimes it brings out a lot of emotion, and that's why that initial beginning part of establishing trust and in a kind of a psychological safety space is really important. And then the third page is imagining or envisioning what my best day looks like. And you can be as wild and fantastical about that as you like. When we train our facilitators, we asked them to do these exercises as well themselves, so they can get a really good sense of what that's like, and I had a lovely training session with community health workers from Tanaha First Nation in Cranbrook, BC, and one of one of the attendees in the group said she'd love to have breakfast with a Sasquatch, and so that was absolutely, you know, it was so culturally relevant, so important. Katie Strong:  That makes my Pacific Northwest roots just smile there! Vivian Fu: Exactly! Just so unique to that individual and to where they are in the world. And it's not something you can, you can pluck out of a textbook or pluck out of anything, right. It's where they are. I love that activity. It's quite magical what comes out. And then after those three pages are done, then there are some pages that are specifically related to goal setting, which may or may not be relevant to the person in front of you, things like a physical page, emotional, social, financial, health management type pages. And that really is a way to think about or encourage the person to think about, or we know what are the things that do matter to you, and and what are the things you would like to achieve in the long run, and what are some ways you might be able to break that down into achievable steps that you would personally want to do. But the session in practice can look like anything out of that. It can look from a person at session one being completely clammed up about not wanting to dig deep or not not being ready to engage in that. Or just kind of being, you know, keeping it all to themselves, and thinking about it, and ruminating about it, and then session two, looking very different after they've had that six weeks to think about it themselves. It can look like blank pages, or it could look like a person coming up with all sorts of brilliant ideas. Hopefully, you know, we always encourage if they can write the person with stroke as the one who is writing in the notebook. They keep it. They stick it on their fridge. They do whatever they like with it. They write in it in their own time, but it's completely fine to have blank pages. It's completely fine to have nothing come out of that conversation, nothing verbal, but it's just a space to an invitation to dig deep, and if there's something to say to feel heard. And I guess there was another question. I think that's quite important with regards to what the facilitator is doing, what are they not doing, and the facilitator is trained specifically to listen and ask questions and reflect the ideas that are being expressed by the person with stroke, but what they're not doing is they're not offering any advice or suggestions or pathways forward or how abouts or what abouts. They are not, and this is probably the hardest part. They're not passing any judgment, and that includes good judgment. So, by saying something like, "oh, that sounds like a really good idea", which seems like a really normal response from most people, it kind of implies that there are other ideas that are less good. Or the person with stroke might feel as though they are needing to have that kind of external validation, that external approval, and, and what we don't want is to for them to feel like they're doing this or saying this or thinking these things for the facilitator. We want them to think about it for themselves. And so all we encourage people to respond, is "oh, so you'd like to ride your bike, that's really interesting. How, how do you think you'll, you'll go about doing that?" You know, it's, it's much more neutral, but reflective way of speaking and listening, I guess. Katie Strong:  I am curious, how long is the training, or you know, what kinds of.. what we didn't talk about this, but I know people are going to want to know. So, what does it take to be trained? Vivian Fu: This is quite funny. This is a thing that sort of been on my mind ever since we've started implementation around the world, and I have this ultimate goal of operate like actually making the training have some type of qualification and fidelity, and some structure. There is structure, but it's essentially myself or Harry doing a Zoom with people, and it takes maybe say four hours. But ideally, what we'd love to do is to have sort of recorded videos, and then we'd have live sessions, and then we'd have assessments, and then you get a certificate at the end. I have neither the budget or the… Katie Strong: There's always the next step, for sure. Vivian Fu: I'd love to be for people to be able to say, "Oh, I'm Take Charge trained" and for them to be able to like "Look, I've got this qualification, I'm Take Charge trained, I know how to do this," and especially for if we were thinking about doing it in more clinical trial settings, I think that's really important, but also, you know, for people, you know, to have on their CV, if they're moving between jobs and things, I think it's so useful. But yeah, basically people get in touch with myself or Harry and say, "Hi, we'd like to be trained, can you fit us in?" And we just do it for free. Katie Strong:  Lovely, I love it. And the other question I had as you were talking was how long is a typical session or or is there is there length? Vivian Fu: So in the trial what our facilitators did was they because it was face to face and they had to drive between people's houses, they booked in the session, usually at 10:30 in the morning, and then another session, I think, if I remember correctly, either maybe it was 1:30 in the afternoon. And so you can probably surmise that for people with stroke, usually the 10:30 slot is the most popular. And you know, some people would would prefer to wake up earlier and be ready earlier, so they might ask for something like 9 o'clock and then they might usually go for about an hour and a half to two hours if it's a good going session, and they're really digging deep, and there's a lot to say. And often you know that first half an hour is, "Here's a cup of tea and a biscuit. Let's get to know each other. Tell me all about what happened with your stroke." That kind of stuff, so that listening and connecting stuff actually takes takes a while to establish, and I think people just allowed for that time. Then they'd do a second session in the afternoon again. There are lots of things, you know, cognitive problems, fatigue is a big one. So, if people felt like they couldn't keep going, the facilitator would say , "That's totally fine, we'll book in for, you know, do the rest of this another time, is that okay?" It was fine to really just, you can truncate it and break it up as much as the person really wants. Katie Strong:  Thank you. I appreciate the extra information. You had a recent publication in 2025 with some colleagues that looked at this work from a qualitative lens.  You came up with some themes about doing things my way, coming to my own wisdom, and they're just so deeply related to identity, and what struck me was the contrast participants drew between Take Charge care and standard care, where they described being put in a box receiving scripted advice that had really nothing to do with who they were as a person. And when someone gets to tell the story of who they are to a genuinely good listener, something shifts. I was just curious, if you could think about what you think is happening there, or what you found in your study. Vivian Fu: Well, I think for the most part, as a, you know, as a clinician, if we think about from the moment the person has a stroke, they disempowered from that very moment.  Something happens where they just aren't themselves, and they are brought into a merge. A bunch of things happen to them that they can't really even speak up about. You know, they're popped in a scanner. They might get thrombolised, you know, all these things are happening. And the person doesn't really know what's going on. There are a lot of important qualitative studies that have been done, looking at that element of feeling disempowered, and what somebody in New Zealand study described as feeling gut-wrenchingly emotional. You know, experiences that that aren't heard and aren't ever expressed to to anyone in the healthcare side and aren't dealt with, and so, if you think about that, just that trauma of having a stroke and then going through all of that stuff, and you know, ending up in a bed somewhere on a ward with strangers on the other side of a curtain, and it's really pretty traumatic. And so I think what was powerful with Take Charge was it's an it's an opportunity for someone to finally tell their story and feel heard and for all that stuff to just come out. And a lot of the time you know people might say, "Oh, well, you could talk to your family, you can talk to your kids, or, you know, whatever. Once you get home" but actually, a lot of people don't feel like talking to those who are closest to them about how much it affected them, and there is still an element of stigma. There's still an element of, "Oh, you know, you look great physically, you to someone we know having had a big health scare, and then kind of coming back into that. But there's there's a real dissonance between what is going on inside a person and and how other people are reacting to them coming back into "normal spaces". And so I think the ability to tell your story from the very get go. You know, all of the messiness of it. All the things that went wrong, and then to have somebody listen to that really gives meaning to it and makes it real, but it also helps that person really reestablish their sense of power to regain some power from that that they had lost. And there's a lot of power in story, and I think the kind of, you know, lovely thing about it is that sort of all indigenous cultures sort of be like, you know, First Nations, Māori, Gaelic, a lot of cultures really put a lot of value in the power of storytelling. And that is how historically things were really passed down, and so I think it's through that ability to be able to feel heard and to tell your story that then the person who is speaking can actually hear themselves say things out loud for the first time. And then understand the power and the value and the worth in their words and their experience, and that is that process of coming to my own wisdom, is "Oh, actually, you know, I do know about my body. I am, I am the expert, not these people in their white coats, or you know, with their expertise, I am the expert. I know what I need. I can plan my rehabilitation. I can go back to the things that I want to do, and this is how I'm going to do it." And it's that, that kind of reestablishment of confidence and strength and hope that is so powerful. I think. Katie Strong: I agree. I agree, Vivian. As you know, our listeners are primarily speech language pathologists working with people with aphasia. We also have people with aphasia that are listening, and researchers as well. But you know, the capacity for language being disrupted by aphasia. I was just curious, with Take Charge, as you've studied it, is you know, really a talking therapy with writing, and, and those sorts of things, and so I was just curious, you know, what would it take to extend this kind of intervention to people with aphasia, and what principles would you want to most preserve in the adaptation. Vivian Fu: Yeah, thank you. That's such an important question. We certainly, in the Take Charge trial, included people with aphasia in the trial, and the way that we sort of just included them was if they could understand or the consent form and could mark an "x" on the form, we'd be happy to to have them included, and so we had people with mild to moderate and moderate to severe aphasia in the trial. And we hadn't made any specific changes or tweaks to the intervention, the facilitator just did what they could with what we had. But I think what has come out of reviews of the implementation of Take Charge now in New Zealand is that it is really important for us to look at how we can adapt the intervention for people with aphasia. I think for the facilitators in the trial, what they did was they allowed a lot more time, so the usual two and a half hours became three and a half hours, and that was fine if the person could continue. But also I think from the report we need to look at alternative ways of how we can complete some of these activities, whether it's providing images where people can point, whether it's a lot more inclusion of their family, their whānau important people to them who can help with, you know, subtle guidance, and, and, and having sort of lived with the person now, can read their non-verbal expressions a lot better than a stranger, a facilitator can and can help with the guidance of participating. And really, while Take Charge has been translated, I think, now into seven or eight different languages, we really need to adapt Take Charge to other communication needs and other languages, but what I.. so my role.. sorry, I didn't.. I'd actually say this, but my role in the in the large trial was as the blinded outcomes assessor, so I went around at one year after stroke. I traveled around to the 400 or so different other people, participants' homes around the country, and I sat in their living rooms, and I listened to their stories again. And then I did all of their outcome instruments and got all of their outcomes done, but I did not know which group they were allocated to. And then I locked all the data, and then I'd say, "Hey, so which group did you get Take Charge or not?" And it was really, it was good. I think out of the 400 there were only two people who, when I turned up, actually had the booklet on the table, and so you know, I just ignored it, and then, but yeah, it was, it was really good in terms of sort of blinding and masking, and then sort of having a guess as I was going through and checking where things were at. It was such a privilege for me to, as a stroke doctor, I think you know people don't usually get to do this as a physician, but to, to meet so many different people with so many different stories, and a number of people who still had moderate to severe aphasia, and and were telling me about the impacts of that on their life. But we were still able to communicate a year after stroke, and so I think it is so important that we don't exclude people with cognitive and communication difficulties. We have to adapt the things that we have to make it work for them. What I'd love is to be able to have some kind of focus group with people with aphasia, and, and show them Take Charge, and realize, oh, what can we do? How would this work better for you? Katie Strong:  I love that, and well. Well, and we haven't talked about the materials yet, but I do have to say they're so accessible, or they're very accessible from a visual standpoint as well. Vivian Fu: Thank you. Yes, we are actually modifying them and making the font bigger and having better graphics. We started off with stick figures that Harry drew, and then I think we're actually making them a lot nicer in terms of the graphics, but what we do have at the moment is actually available online for free, and you can just download them as a package. It's if you Google it, it's the Medical Research Institute of New Zealand, or www.mrinz.ac.nz  and then under programs, and we spell that as p r o g r a m m e s, and then under programs slash forward slash stroke, I think is where it lives, and at the bottom of the page you should be able to download, a training package and the booklet itself. Katie Strong: Yes, lovely, and we'll have the links on our show notes as well, so you can check those out, listeners, if you're interested. So, thank you. You have two randomized controlled trials that you've talked a little bit about, and a cost effectiveness analysis and qualitative work, all pointing in the same direction that the cost data suggests that Take Charge actually might save money, and that is remarkable, and lots of evidence showing that, but still, it's a challenge in implementing into standard practice, and I was curious if you could talk with us about what you think stands between what the evidence shows and what actually gets implemented. Vivian Fu: Sure, gosh, I love this question. It's it applies for so many interventions, I think, specifically for Take Charge, it's a number of things. So definitely the stuff that affects other interventions being implemented, but for Take Charge itself, it started off with a huge amount of disbelief bias, so people, you know, even after I presented the main results of the second trial at the European Stroke Conference in gosh, when was that? 2019 pre-COVID in Milan. So this is a huge international stroke conference, and this was a plenary session. People stood up and took photos of the results and went, you know, there was this collective gasp throughout the audience of 6000 odd people, but there's just this disbelief that something as simple as a conversation can make a difference to people's objective quality quantitative outcomes, like the, you know, Bartel or the FIM, what they, you know, what their physical outcomes are like at a year. And what their quality of life is like. I think for people who are stroke researchers, a lot of the focus is on that initial 24 to 48 hours after stroke. And you know, that that's kind of where that's kind of where all the funding goes, isn't it? So it's the pre-hospital stuff, and then the interventional things you can stick catheters into and thrombolysis, and you know that's, and that's all great. You know, I, as a stroke physician, I love that part of stroke as well, but to think that you could possibly do something at three to 18 weeks after stroke that could change a person's outcome by a year, and actually we've got a long-term follow-up study now that says that those those same magnitude of changes are still present at five years between the groups is, you know, kind of gobsmacking. And people just go, surely you've fudged this, or surely this can't be true. And so there's this huge disbelief bias that stopped us from being able to publish initially, and it was only after I presented the results in Milan that that we got accepted into a journal. And we'd been trying for about a year beforehand, and, and so there's, there's that disbelief. But also in the way that it has to be provided, you know, it's probably considered quite labor intensive. You know, one on one home visits, and that's why people of other researchers who do believe in Take Charge are now looking at providing take charge in different ways. Like I did a telehealth trial in Canada, in southern Alberta, when I was there as a fellow in Calgary, and there's potential other work that's being. Done in Australia with Take Charge, looking at providing it by computer avatars, by even maybe even AI, and maybe in a larger sort of telehealth format. And so you know other ways of doing it, but I think ultimately it's kind of cultural inertia, because you know, "We've never done this, this is not part of who we are. Why would we need to start something new?" There's probably kind of an established way of thinking about clinician and, and patient, rather than person with stroke, in still in how we practice, and this idea that the therapist and doctor are experts. And so I think it really challenges that dogma and challenges clinician's role, and therefore there's a resistance to accept that this is something that is useful and helpful, and actually doesn't, you know, you don't need to take it personally. You're still doing great work. Take Charge is just a tool that helps supplement everything else that's going on, and so there's a lack of time, and I think we also get quite, as practicing clinicians, we get quite tunnel visioned into this, you know, hamster wheel of go to work, treat all these people, go home. We just keep doing it. Hoping that things will get better for them, but, you know, we, it's only when we start looking at alternative interventions and alternative things that work and start trialing them and being open to that, that I think things will really start to change. We've certainly had interest from random little parts around the world. I think it's been translated into Latvian. I've got people from Sweden who are interested. A little hospital in Germany, and then parts of it's been trialed in pilot studies in the UK. And Harry and I last year trained a whole bunch of occupational therapists in Hong Kong, so you know it is, it is kind of picking up, slowly but surely. Katie Strong: Well to me, you know, most stroke survivors, or people with stroke, as you're referring to them, have chronic challenges, and so all of the early intervention, while important, doesn't necessarily help somebody navigate that longer term change. And so I love that this is just such an empowering way of putting that power back into the person's life, which it seems like it is showing up n in the results that you're sharing. Vivian Fu: Oh, absolutely. I mean, if we think about it, if we just think about thrombectomy and thrombolysis, somewhere between five to 15% of all people with stroke are eligible, and then receive the treatment. That leaves what?, 85 to 95% of people who don't get to receive that. And even after they receive the treatment, there are consequences of stroke that are beyond the physical that don't you know don't have any other thing to address them apart from our routine care, so I completely agree with you. Katie Strong: Well, thinking about what clinicians might be able to do tomorrow or you know, in the near future, for our speech-language pathologists or other practitioners who are listening today and are feeling the pull of this work, and you know, really encouraged by it, but are you know working in an embedded productivity driven impairment focused system, what's one thing that they could do differently in the very next clinical encounter that they have? Vivian Fu:  We have a paper, I think it's written. Oh gosh, where did it.. where did it get published? I think it was published in Practical Neurology. It's titled something, something intrinsic motivation. I should know better. Katie Strong: I'm going find out, and I don't think I read that one, so I'm gonna find it, and I'll put the link in the show notes for everybody. Vivian Fu: Sorry,Harry about the promo, but yeah, I'll send it to you, but essentially it's written to give some guidance on how you can embed Take Charge into your daily clinical practice. And it's written for neurologists, but honestly it applies to everybody.  One of the key things is when you have that next encounter, obviously you know therapists do this a lot better than doctors do, but they ask a lot about, you know, what's outside of the person's life and what's important to them, but maybe move away just, you know, from the very practical questions like "How many steps do you have going in and out of your house?, and How do you hang up your washing?", or whatever, but it's, you know, really much less functional, but more, "Who are you? Tell me a bit more about yourself. What do you love doing? What gets you out of bed?" You know, if I might, the one that I like to use a lot on my ward rounds is, "If you weren't in this hospital bed right now, where would you rather be? What would you rather be doing?", and I do that on my rounds, and it's incredible, because you know, I'll hear all sorts of things, "I'll, you know, be on my boat fishing out on the lake", or I think this lady was like 84 or something. "I'll be with my girlfriends, we'll be having coffee at Tim Hortons", you know, and it's just, I don't know, it's something so unique to that person that I could never, you know, they're in their hospital pajamas with a whole bunch of stuff stuck to them, and I can't envisage them doing that, and yet I'm like, I want you to imagine yourself there. Where would you rather be? Okay, so everybody, that's our goal. It's not to get her home or to get her walking again. Our goal is to get her back in Tim Hortons with her eight friends, having coffee, like that is what this person loves to do. And I think that you know that inquiry, that it shows you care, it shows you see them as an individual, and I think it completely shifts your rehab focus, and then you can ask more questions about that, and they, you know, then you have this whole conversation about about what their life is like. And I think that part of being seen, even if it's only within 60 seconds, makes such a difference to that person. So that's one thing. The second thing I'd be, you know, doing is I'm trying to involve family as much as possible, as much as the person wants, and basically, just seeing them as an individual makes a huge difference already to the way you practice, that would be what I'd focus on. Katie Strong: Agreed, agreed. Well, Vivian, is there something you wish people asked you about this work that they rarely do, something about Take Charge or stroke recovery more broadly that you think the field hasn't quite caught up to yet. Vivian Fu:  Oh gosh, this is a tricky one. I think one thing we ought to recognize is that everybody is doing the best that they can with what they have. And you know, we're not as clinicians on the ground on the front line, we're not involved with funding decisions, and what projects get funded and which ones don't. And there will be the ambitious amongst your listeners, who I really hope will be like, "Oh my gosh, I can apply for this little grant, and I'm going to pilot this, and I think we should give this a go with our people." and I think that is absolutely a great idea to have. And, and I would, myself and Harry will do everything that we possibly can to help support such projects from where we are. I think the important thing to think about is that every little bit that you do makes a difference, and to not feel as though, because you know your funding runs out, or you don't get it, or the world is such a bleak place that you know it's not worth continuing to try. Because people with stroke who see you do this work, they will be grateful for it. And also the patient partners I've met that I've spoken with, and all of the people whom I've interviewed with the qualitative work, they're also happy to be part of something like this. And so even if it's a pilot project. Even if it's, you know, something that may not last, you'd be so surprised at how much momentum you can build with a movement of people who see the value in this and then take it further and further, And that's what I've been really impressed by, and kind of stunned by in all these different locations around the world who have contacted me. I've just been like, "Wow, can't believe [this]." There's this wonderful group in Hunter Medical Research Institute down in Australia, who basically took Take Charge back to their own unique Aboriginal community, the Gamilaroi peoples of that particular area of Australia. They have, like Canada and like the States, they have 1000s of tribes and lots of different groups that all speak completely different languages, but they took it to their local group, and they broke down, take charge into little bits, and then rebuilt it into an intervention that just works for them, and it's called "Yarning Up after Stroke", because what they do is they have a yarn. They yarn, and that's the way they tell their stories. It's yarning, and so you know, I just think it's incredible. Like, they got funding for it, they did it, and now it's an ongoing project that just keeps on in the community being provided to their people, and it's fantastic. So, I think it'll evolve. I love to see how it evolves, and I certainly don't think of, you know, this isn't the kind of intervention that we go around patenting and making a ton of money out of. It's the kind of intervention that everybody makes their own, and hopefully with a lot of input by people with stroke, Katie Strong: I love it. Thank you. Thank you so much for being our guest today, and sharing about Take Charge, and your generosity in sharing about the intervention, and if people are interested in reaching out to contact you, so thank you so much, Vivian. Vivian Fu:  Thank you so much for having me. I hope I haven't spoken too long. Katie Strong: Oh no, it's perfect. Vivian Fu: I'm always happy to be contacted, and yeah, very happy to support anyone who'd like to explore this further. Katie Strong: Thanks so much. On behalf of Aphasia Access, thank you for listening. For references and resources mentioned in today's show, please see our show notes, available on our website at www.aphasiaaccess.org. There you can also become a member of our organization, browse our growing library of materials, and find out about the Aphasia Access Academy. If you have an idea for a future podcast episode, email us at info@aphasiaaccess.org. For Aphasia Access Conversations, here at Central Michigan University in the Strong Story Lab, I'm Katie Strong. Dr. Fu's Email  dr.vivianfu@gmail.com  Resources and Readings Fu, V. (2019). Taking Charge After Stroke: A novel, community-based intervention to improve the lives of people with stroke. https://www.semanticscholar.org/paper/Taking-Charge-After-Stroke:-A-novel,-intervention-Fu/3bc1dbb271f425c72e146510088856e3aad8683e  Fu, V., Fernando, K. M., Bright, F., Riley, J., McPherson, K., & McNaughton, H. (2025). Coming to my own wisdom: A qualitative study exploring the role of the Take Charge intervention in stroke recovery. Clinical Rehabilitation, 39(3), 377–387. https://doi.org/10.1177/02692155241310770 Fu, V., Weatherall, M., McPherson, K., Taylor, W., McRae, A., Thomson, T., Gommans, J., Green, G., Harwood, M., Ranta, A., Hanger, C., Riley, J., & McNaughton, H. (2020). Taking Charge after stroke: A randomized controlled trial of a person-centered, self-directed rehabilitation intervention. International Journal of Stroke, 15(9), 954–964. https://doi.org/10.1177/1747493020915144 Fu, V., Thompson, S., Kayes, N., & Bright, F. (2025). Supporting long-term meaningful outcomes in stroke rehabilitation. Current Neurology and Neuroscience Reports, 25, 17. https://doi.org/10.1007/s11910-025-01403-z Harwood, M., Weatherall, M., Talemaitoga, A., Barber, P. A., Gommans, J., Taylor, W., McPherson, K., & McNaughton, H. (2011). Taking charge after stroke: Promoting self-directed rehabilitation to improve quality of life - a randomized controlled trial. Clinical Rehabilitation, 26(6), 493-501. https://doi.org/10.1177/0269215511426017  Te Ao, B., Harwood, M., Fu, V., Weatherall, M., McPherson, K., Taylor, W. J., McRae, A., Thomson, T., Gommans, J., Green, G., Ranta, A., Hanger, C., Riley, J., & McNaughton, H. (2022). Economic analysis of the 'Take Charge' intervention for people following stroke: Results from a randomised trial. Clinical Rehabilitation, 36(2), 240–250. https://doi.org/10.1177/02692155211040727 McNaughton, H., & Fu, V. (2023). Intrinsic motivation. Practical Neurology, 23(6), 489-492.  https://pn.bmj.com/content/23/6/489  McNaughton, H., Gommans, J., McPherson, K., Harwood, M., & Fu, V. (2023). A cohesive, person-centric evidence-based model for successful rehabilitation after stroke and other disabling conditions. Clinical Rehabilitation, 37(7), 975-985. https://doi.org/10.1177/02692155221145433   Medical Research Institute of New Zealand. (n.d.). Take Charge rehabilitation resources. https://www.mrinz.ac.nz/take-charge-rehabilitation-resources World Stroke Organization. (n.d.). Taking Charge after stroke: A person-centred approach to life after stroke [Webinar]. https://www.world-stroke.org/what-we-do/education-and-research/education/webinars/taking-charge-after-stroke-a-person-centred-approach-to-life-after-stroke  

The medicines regulator says it has recorded just over 1500 adverse reactions to the anti-obesity injections known as GLP-1s. Expert in managing obesity, Professor Donal O'Shea talks about some of the side effects he's seen.

What if constantly trying to become your "best self" is actually making it harder to love the person you are right now?We're big believers in growth, healing, learning, therapy, and becoming more of who you want to be. But in this episode, we're asking a harder question: When does self-improvement stop supporting you and start becoming self-rejection?We talk about the endless hamster wheel of healing, self-help books, wellness advice, comparison, courses, experts, fitness goals, and the sneaky belief that happiness is waiting somewhere in the future."When I lose the weight...""When I heal this...""When my business reaches this level...""When I finally fix this part of myself..."But what happens to the version of you who's here right now?In this episode, we're talking about finding the middle ground between staying stuck and constantly treating yourself like a project that needs fixing. We unpack self-trust, personal mission statements, comparison, anxiety, listening to your own needs, and learning how to pursue growth without abandoning yourself in the process.We talk about:• Self-improvement vs. self-rejection• Why there's no finish line for healing• The "I'll be happy when..." trap• How comparison fuels self-abandonment• Knowing your WHY before chasing another goal• Why every expert doesn't have your answer• Trusting yourself again• Finding the healthy middle ground between growth and acceptance• Loving who you are while still wanting more for yourself

What if your karma could keep growing even when you weren't actively doing anything? In this episode of Living the Tao Shorts (2-28), Taoist Master Mikel Steenrod and Morgan explore the Taoist idea of neutrality—not as indifference or social disengagement, but as an internal position that can steadily build karma and strengthen one's ling. Using the metaphor of compound interest, Master Steenrod explains why the neutral path may produce small but continuous returns throughout everyday life. How you treat yourself, how you respond to your senses, and how quickly you form opinions can all affect that process. The episode also explores the relationship between neutrality, wu wei, karma, and ling, and why Taoist practice often favors establishing the right internal conditions rather than relying only on dramatic acts of effort. Sometimes the biggest gains come from simply being in the right place internally—and not interfering with the process. Prefer the video format?  That's here: https://youtu.be/AaJkSIMwL3A

In this new episode, Crawlspace Media's Tim Pilleri and Lance Reenstierna discuss the release of the ransom notes in the case of the abduction of Nancy Guthrie from her home in Catalina Foothills, Arizona on February 1st, 2026. Expert in forensic linguistics, Bootsie Martinez, joins the show to break down the letters. We touch on the writer's intentions and much more. Follow Bootsie at linkedin.com/in/lizcmartinez. Anyone with information concerning this case is asked to contact the FBI at 1-800-CALL-FBI (1-800-225-5324) or submit a tip online at ⁠⁠tips.fbi.gov⁠⁠. Check out Quince: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://quince.com/MISSING⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out Mint Mobile: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠mintmobile.com/missing⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out Kensington Publishing: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kensingtonbooks.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Main podcast theme by Kevin Macleod. Check out his work at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://incompetech.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Additional music by David Williams. See his work at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠http://williamsflutes.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Missing: IG: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/MissingCSM/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Youtube:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.youtube.com/missingcsm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. FB:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.facebook.com/MissingCSM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. X:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://twitter.com/MissingCSM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Spotify:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://open.spotify.com/show/0yRXkJrZC85otfT7oXMcri⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Apple:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://podcasts.apple.com/us/podcast/missing/id1006974447⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Crawlspace: IG:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/Crawlspacepodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. TT:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.tiktok.com/@crawlspacepodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. FB:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.facebook.com/Crawlspacepodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. X:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://twitter.com/crawlspacepod.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Spotify:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://open.spotify.com/show/7iSnqnCf27NODdz0pJ1GvJ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Youtube:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.youtube.com/crawlspace⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Apple:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://podcasts.apple.com/us/podcast/crawlspace-true-crime-mysteries/id1187326340⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out our entire network at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ http://crawlspace-media.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, we sit down with Jessey Kwong from Pebble to talk about what's changing in land investing and what the data is showing about direct mail. Jesse explains why the market has become tougher for people who treat land investing like a side hustle, while those who stay consistent and treat it like a real business are still finding opportunities. He also shares why follow-up and remarketing are working better than simply sending one campaign and moving on.The conversation breaks down what's working in direct mail today, from simple 4x6 postcards to personalized messaging and using satellite images of properties. Jesse explains why knowing your market, targeting the right sellers, and building trust matter more than relying on generic templates. As the market gets more competitive, strong marketing and sales skills are becoming harder to ignore.Jesse also shares where Pebble is headed as AI and automation continue to change the industry. While AI can make administrative work easier, he believes land deals will still depend heavily on real conversations, trust, negotiation, and relationships. The tools may change, but the human side of doing business isn't going away.===Key Topics:-What's changing in the land investing market and where opportunities still exist-Why consistent follow-up and remarketing are key to finding more deals-What's working in direct mail and why simple, personalized mailers perform better-Knowing your market, targeting the right sellers, and building trust-Why strong marketing and sales skills matter more in today's land business-How AI and automation are changing land investing without replacing human relationships===If you're selling land and still relying on Facebook messages, you're making it harder than it needs to be. Acrefy helps land investors create clean, professional dispo websites where buyers can see everything in one place. It saves time, looks legit, and helps you close faster.

#509> Isaac Leeser (1806 - 1868 ) And The Making OF American Judaism (with Prof. Lance Sussman)> Additional recording with Rabbi Elazar Meisels begins at 1:25:00> This episode is sponsored by KosherKlaf.com, your trusted source for mezuzos, tefillin, Sifrei Torah, and megillos. Expert guidance, transparent pricing, and nationwide shipping. Visit https://kosherklaf.com or call 516-737-5436> To purchase Profs Sussman's bio: https://amzn.to/4hQjzt4> To purxhase the new volume 1 of Isaac Leeser's "Discourses": https://amzn.to/4hPfSDZ> To purchase Isaac Leeser's "Jews and the Mosaic Law": https://amzn.to/4xpjSjb> To join the SeforimChatter WhatsApp community: https://chat.whatsapp.com/DZ3C2CjUeD9AGJvXeEODtK> To join the SeforimChatter WhatsApp status: https://wa.me/message/TI343XQHHMHPN1>  To support the podcast or to sponsor an episode follow this link: https://seforimchatter.com/support-seforimchatter/or email seforimchatter@gmail.com (Zelle/QP this email address)Support the show

QuickBooks Live is officially retired, and Alicia sits down with Dan DeLong and Matthew Fulton to unpack what replaces it: Intuit Experts, a narrower set of services built directly into QBO Simple Start and above. They walk through the new Books Check-In, Smart Expert Categorization, and Expert Books Upkeep offerings, how the accountant-attached toggle works, and why onboarding still applies to every new client regardless of status. They also dig into the Intuit ProPartner Accountants program launching in 2027 and what firm-billed versus client-billed accounts mean for who controls what.Sponsors:Intuit Accountants - http://uqb.promo/intuitPilot - http://uqb.promo/pilotKick.co - http://uqb.promo/kick(00:00) - Welcome and Setup (01:12) - QuickBooks Live Ends (02:47) - What Makes an Expert (07:08) - Firm of the Future Article (11:03) - Who Intuit Experts Serve (13:41) - Pro Partner Matching Network (15:35) - Services Built Into QBO (17:31) - Breaking Down New Services (21:36) - Consent and Default Off (25:57) - Intelligent Onboarding (28:50) - Cross Sell and Workforce (34:18) - Do They Compete With Firms (38:16) - Marketing Toggle Recap (38:52) - Unknown Accountant Status Rules (40:17) - Overlap Services Confusion (43:52) - Onboarding Versus Suppression (45:52) - Where Controls Live (46:28) - Client Versus Firm Billing (49:55) - Expert Hub Visibility (52:51) - QuickBooks Live Rebrand (56:39) - Why Intuit Changed Course (59:42) - What To Do Now (01:05:11) - ProPartner And Suite Updates (01:06:40) - Wrap Up And Community (01:07:32) - Hosts Updates And Outro LINKSIntuit Experts Shift: https://www.firmofthefuture.com/product-update/intuit-experts-and-accountant-controls/Intuit Experts Onboarding: https://quickbooks.intuit.com/r/bookkeeping/quickbooks-onboarding/Alicia's upcoming classes:3rd Party App Exploration, Aug 12: http://royl.ws/3rdpartyIntuit Enterprise Suite, Aug 19: http://royl.ws/intuit-enterprise-suiteBattle of the Books - QBO vs. Xero, sponsored by Xero: https://xero.zoom.us/webinar/register/4717843154915/WN_jWTalQhPSka8DGL2mQerIA#/registrationAI in QBO: http://royl.ws/AIIntuit Accountant Suite in Sept: http://royl.ws/IASDan's School of Bookkeeping blog on AI/Claude:Connect Claude to QuickBooks Online?: https://www.schoolofbookkeeping.com/blog/QBOClaude What Specifically can Claude do in QuickBooks Online: https://www.schoolofbookkeeping.com/blog/what-specifically-can-claude-do-in-quickbooks-online No, AI Isn't Coming for Your Bookkeeping Job: https://www.schoolofbookkeeping.com/blog/AIinAccounting1 The Blind Spots Automation Can't See (Yet): Where QuickBooks AI Still Needs a Human: https://www.schoolofbookkeeping.com/blog/AIinAccounting2 How to Become the Human Your Clients (and Their AI) Actually Need: https://www.schoolofbookkeeping.com/blog/AIinAccounting3 Schoolofbookkeeping YouTube: https://snip.ly/SOBYTFree Live Workshop Wednesdays: https://www.schoolofbookkeeping.com/workshop-wednesdayQB Power Hour: https://www.qbpowerhour.com/ We want to hear from you!Send your questions and comments to us at unofficialquickbookspodcast@gmail.com.Join our LinkedIn community at https://www.linkedin.com/groups/14630719/Visit our YouTube Channel at https://www.youtube.com/@UnofficialQBOPodcastSign up to Earmark to earn free CPE for listening to this podcasthttps://www.earmark.app/onboarding 

Simone Löffler ist ratlos, als sie erfährt, dass schon wieder eine Patientin ihres Palliativpflegeteams bei einem Wohnungsbrand ums Leben gekommen sein soll. Es ist bereits die vierte innerhalb weniger Wochen. Überzeugt davon, dass das kein Zufall sein kann, beginnt sie die Fälle zu vergleichen. Dabei fällt ihr auf, dass immer derselbe Arzt aus ihrem Team zuletzt versucht hatte, Kontakt mit den schwerkranken Frauen aufzunehmen: ihr Kollege Johannes M., der von allen geschätzt wird. Obwohl sie kaum glauben mag, dass ausgerechnet er mit den Vorfällen in Verbindung stehen könnte, wendet sie sich an die Polizei. Ohne es zu ahnen, setzt sie damit Ermittlungen gegen einen Mann in Gang, der später im Verdacht stehen wird, einer der mutmaßlich schlimmsten Serienmörder der deutschen Nachkriegsgeschichte zu sein. In dieser Folge „Mordlust – Verbrechen und ihre Hintergründe“ berichtet Paulina von einem Fall, den sie über Wochen vor dem Landgericht Berlin begleitet hat, und von einem Palliativarzt, der das Vertrauen von etlichen Menschen missbraucht, seinen Beruf in Verruf gebracht und sich selbst die Macht zugesprochen hat, über Leben und Sterben etlicher Patient:innen zu entscheiden. Expert:innen in dieser Folge: Alisha Jecke, Anästhesistin in Weiterbildung im dritten Jahr André Mors, Rechtsanwalt und Nebenklagevertreter Dr. Thomas Schindler, Palliativmediziner und Vorstandsvorsitzender von Home Care Berlin e. V. Philipp Meyhöfer, Staatsanwalt **Credit** Hosts: Paulina Krasa, Laura Wohlers Producer: Paulina Krasa, Laura Wohlers und Jon Handschin Redaktion: Paulina Krasa, Laura Wohlers Schnitt: Pauline Korb Rechtliche Abnahme: „Abel und Kollegen“; Benedikt Müller ** Weitere Berichterstattung (Auswahl)** Paulina war beim Prozess dabei. Weitere Artikel zum Fall findet ihr hier: rbb24: Landgericht Berlin: https://t1p.de/a2eci Zeit: Palliativarzt in Berlin: https://t1p.de/hujrg Spiegel: Berlin: https://t1p.de/l4sqe **Partner der Episode** Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte: https://linktr.ee/Mordlust Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

The trial comes to an end.Last week, the prosecution brought the first wave of evidence against Super Mario Sunshine, arguing that one of Nintendo's most beloved GameCube games is guilty of crimes against the player experience. This week, court is back in session as the prosecution presents new exhibits, surprise witness testimony, and one final argument before the jury reaches a verdict.Can the defense convince the court that Sunshine deserves its place among Nintendo's classics, or will it be found guilty once and for all?Along the way, we also talk about what we've been playing, including The Legend of Korra, The Quarry, Keyboard Warrior, Swan Song, and more.Timecodes:00:00 - Start12:20 - Housekeeping15:08 - Recap: The Trial So Far17:17 - Housekeeping19:26 - Easy Mode: What We've Been Playing20:00 - Jerica Plays The Legend of Korra24:15 - Josh's Indie Game Roundup28:01 - Kayla Reviews The Quarry34:27 - Why Swan Song Is So Underrated36:56 - Court Is Back In Session38:55 - The Charges Against Super Mario Sunshine40:05 - The Defense's Opening Statement42:45 - New Evidence Presented47:30 - Andy Cortez Testifies Against Sunshine52:30 - Witness Testimonies55:05 - Crimes Against Mario's Fists1:01:30 - The GameCube Controller Debate1:04:00 - Closing Statements and Final Verdicts Catch us live every Monday at 9 PM ET on Twitch!Support the showJK! Games! is a weekly gaming podcast where we bring you the news and reviews we actually care about.Our recurring play-along series — One More Game — is our version of a video game book club. We choose one title, set checkpoints, and break it down over multiple episodes. Currently Dave the Diver!You can:• Play at your own pace• Stay spoiler-light• Or dive deep with usWhether you're Easy Mode or Expert, you belong in the conversation.Join our Discord to play along and share your theories each week.Want to show us some love? Click Me!DiscordTwitch YoutubeInstaBsky

What if the reason you haven't reached your goal has nothing to do with motivation? Would that make you happy or annoyed? Listen as our host, Diane Rolston, gets honest about something most coaches won't admit and reframes the entire conversation around what's actually keeping you stuck. She makes the case that you don't have a motivation problem at all, and that could be a big relief.Listen to learn these key takeaways:Why motivation is a feeling, not a strategy and why you can't rely on it to carry a 6-month, 1-year, or 5-year goalThe book writing story: what Diane was missing that had nothing to do with motivation and what finally got it doneWhy losing motivation doesn't mean you picked the wrong goal, you're lazy, or you're failing. The first thing you're probably missing instead of motivation and why vague goals are impossible to act on no matter how inspired you feelThe comparison trap Diane fell into while managing a business with two kids under three and what her coach said that completely reframed itWhy we create plans for the fantasy version of our lives instead of our real ones and what it costs usWhy following through for someone else is so much easier than following through for yourself and how to use thatThe simple reframe: you don't have a motivation problem, you have a clarity, plan, or support problemComing up: She's Goaled Group Coaching Mastermind is opening in August 2026 for a September start. Learn more here: https://shes-goaled-coaching-mastermind.dynamicwomen.biz/Want to be invited to join Diane's NEW high-level, like-minded group of women? Email her at diane@dianerolston.com.Do you prefer reading blogs or watching videos?Read Diane's blogs here: https://www.dianerolston.com/blogWatch Diane's videos here: https://www.youtube.com/@CoachDianeRolstonThis show's host, Diane Rolston, is called THE Expert on Being Dynamic and living a Dynamic Life. She specializes in coaching high-achieving women who want to be successful AND satisfied. She is a Certified Professional Coach, International Speaker, 11-time Author, and host of the five-time award-winning Dynamic Women Podcast, ranked in the top 2.5% of podcasts.Diane has been recognized with multiple awards for her professional accomplishments and for the powerful impact she has on the women she inspires and empowers. Chicken Soup for the Soul co-creator Jack Canfield describes her as “an amazing woman” doing “incredible work helping women develop holistic lives of balance.”Through her program, VA Made Easy, she helps entrepreneurs go from task overwhelm to business ease by hiring and training Virtual Assistants for them while also providing proven systems, processes, and strategies for success.Outside of her work, Diane is a mother of two, a soccer player, and a stand-up comedy rookie, always embracing new challenges and personal growth.You're invited to reach out to Diane and visit her website: www.dianerolston.com Check out what Diane is up to and other opportunities here: linktr.ee/dianerolstonConnect with me on your favourite social platform:https://www.facebook.com/LifeCoachDianehttps://www.linkedin.com/in/dianercoaching/https://twitter.com/DianeRCoachinghttps://www.instagram.com/coachdianerolston/https://www.youtube.com/user/DianeRolstonCoachingPersonal Email: diane@dianerolston.comDiane believes we are not defined by our titles or our roles. Instead, we are more powerful and happy when we can be who we are. This brought out her book Dynamic You™, based on a successful program, where she reveals the secret code to confident, wealthy, and successful women and leads women to unleash the Dynamic Woman™ in them!Grab your copy of Diane's autographed Dynamic You™ Book at a special Discount:https://www.dianerolston.com/store/p3/Autographed_Dynamic_You%E2%84%A2_Book.htmlThanks for listening!It means so much to us that you listened to our podcast!With this podcast, we are building an international community of Dynamic Women®. We aim to inspire more women to unleash their dynamic selves and enhance their lives across all areas, particularly in business. If you know someone who would benefit from this message or would be an awesome addition to our community, please share it using the social media buttons on this page.Do you have some feedback or questions about this episode? Leave a note in the comment section below!Subscribe to the podcastIf you would like to get automatic updates of new podcast episodes, you can subscribe to the podcast app on your mobile device.Leave us a reviewWe appreciate every bit of feedback to make this a value-adding part of your day. Ratings and reviews from our listeners not only help us give you more of what you want, but also help others find us in their podcast app. If you have a minute, an honest review on Apple Podcasts and other apps goes a long way! If you do, send a screenshot along with your mailing address to our team team@dianerolston.com and you'll receive something in the mail!

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

Dois cariocas começaram montando uma bicicleta elétrica dentro da garagem de casa. Depois perderam praticamente tudo em um incêndio e tiveram que recomeçar. Hoje comandam uma rede de quase 50 lojas, uma fábrica em Manaus e hoje faturam quase R$300 milhões. Nesse episódio especial do Do Zero ao Topo, realizado no Expert 2026, Mariana Amaro conversa com os irmãos Bruno e Rodrigo Affonso, fundadores da Lev Nessa entrevista, eles contam como transformaram uma ideia trazida da China em uma das principais empresas de mobilidade elétrica do Brasil, os erros, as decisões e as estratégias que aceleraram o crescimento da Lev._________________________INFOMORNING | A newsletter com tudo o que você precisa para começar o dia Receba informações, análises e recomendações que valem dinheiro diretamente no seu e-mail: https://www.infomoney.com.br/newsletters/

#507Rabbi Abraham Rice (1800 - 182): America's First Rabbi (with Dr. Zev Eleff)> This episode sponsored by the Touro Graduate School of Jewish Studies. Are you ready to nurture your interest in Jewish studies or pursue your graduate degree in the field? Perhaps you're in need of a deeper understanding of Jewish history and thought to advance your career? At Touro's Graduate School of Jewish Studies, you can explore such topics as the history of Hasidism, studies in 19th-20th century biblical commentaries and much more. You'll learn from noted subject matter experts as you earn your master's in Jewish Education or Jewish History or audit any course that interests you. All courses are offered fully online via Zoom. For more information visit https://gsjs.touro.edu/history/> This episode is sponsored by KosherKlaf.com, your trusted source for mezuzos, tefillin, Sifrei Torah, and megillos. Expert guidance, transparent pricing, and nationwide shipping. Visit https://kosherklaf.com or call 516-737-5436> This episode is dedicated by Shoshana and Yosef Kassorla in honor of Morton and Anette Eleff, and Rabbi Dr. Zev Eleff > To join the SeforimChatter WhatsApp community: https://chat.whatsapp.com/DZ3C2CjUeD9AGJvXeEODtK> To join the SeforimChatter WhatsApp status: https://wa.me/message/TI343XQHHMHPN1>  To support the podcast or to sponsor an episode follow this link: https://seforimchatter.com/support-seforimchatter/or email seforimchatter@gmail.com (Zelle/QP this email address)Support the show

Expert witnesses often give key testimonies at criminal trials and are allowed to give their own opinions. But what happens when their opinions don't reflect the facts?  "48 Hours" correspondent Susan Spencer, Harold Dow and Peter Van Sant report. This classic "48 Hours" episode last aired on 8/23/1999. Watch all-new episodes of “48 Hours” on Saturdays and stream on demand on Paramount+.