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Bone loss doesn't start when fractures happen — it begins years earlier. In this episode, we explore how bone aging accelerates during menopause and the emerging role of the gut microbiome as a driver of skeletal decline. Dr. Kara Fitzgerald speaks with research scientists from Sōlaria biō, the company behind Bōndia, about their work studying plant-derived microbes, microbial synergies, and the connections between gut health, inflammation, and bone loss in peri- and postmenopausal women. We also review findings from a randomized, placebo-controlled clinical trial examining a microbiome-based intervention for bone health — and discuss why bone loss may need to be addressed earlier, systemically, and beyond hormones alone. Full show notes + references: https://www.drkarafitzgerald.com/fxmed-podcast/ GUEST DETAILS Alicia Ballok, Ph.D. is Director of Discovery at Sōlaria biō, leading research on plant-derived synbiotics for inflammatory and immune-mediated diseases. Trained in Microbiology and Immunology at Dartmouth, with postdoctoral work at Harvard Medical School and Massachusetts General Hospital, her work focuses on translating host–microbe science into therapeutic innovation. Mark Charbonneau, Ph.D. is Vice President of R&D at Sōlaria biō. He earned his Ph.D. in Computational and Systems Biology at Washington University in St. Louis, studying infant microbiome development and undernutrition. His work spans microbiome research, bioinformatics, and live biotherapeutic innovation. THANKS TO OUR SPONSOR Sōlaria biō: http://bit.ly/SolariaBio EXCLUSIVE OFFER FOR NEW FRONTIERS LISTENERS Looking for a clinically proven way to target bone loss? Bōndia by Sōlaria biō is a groundbreaking blend of plant-derived prebiotics and probiotics shown in a clinical trial to improve bone density outcomes by 85%. Try it for yourself at Sōlaria biō and use code Kara20 for 20% off your order. CONNECT with DrKF Want more? Join our newsletter here: https://www.drkarafitzgerald.com/newsletter/ Or take our pop quiz and test your BioAge! https://www.drkarafitzgerald.com/bioagequiz YouTube: https://tinyurl.com/hjpc8daz Instagram: https://www.instagram.com/drkarafitzgerald/ Facebook: https://www.facebook.com/DrKaraFitzgerald/ DrKF Clinic: Patient consults with DrKF physicians including Younger You Concierge: https://tinyurl.com/yx4fjhkb Younger You Practitioner Training Program: www.drkarafitzgerald.com/trainingyyi/ Younger You book: https://tinyurl.com/mr4d9tym Better Broths and Healing Tonics book: https://tinyurl.com/3644mrfw
Italian man is arrested for training his dog to illegally dump his rubbish on the street. Los Angeles Dept. of Transportation removed their PSA that told passengers not to poop on city buses. Man arrested after sneaking into woman's apartment with an axe, to axe her out on a date. Weird AF News is the only daily weird news podcast in the world. Weird news 5 days/week and on Friday it's only Floridaman. SUPPORT by joining the Weird AF News Patreon http://patreon.com/weirdafnews - OR buy Jonesy a coffee at http://buymeacoffee.com/funnyjones Buy MERCH: https://weirdafnews.merchmake.com/ - Check out the official website https://WeirdAFnews.com and FOLLOW host Jonesy at http://instagram.com/funnyjones - wants Jonesy to come perform standup comedy in your city? Fill out the form: https://docs.google.com/forms/d/e/1FAIpQLSfvYbm8Wgz3Oc2KSDg0-C6EtSlx369bvi7xdUpx_7UNGA_fIw/viewform
Your team keeps interrupting because you trained them to. How to build systems that let your team decide without you.
Dr Boyce discusses the clear paths to corporate slavery.
Dr. Marco Ventura is Professor of Law and Religion and Religious Diplomacy at the University of Siena in Italy. Trained in bioethics and biolaw at the University of Strasbourg, he has advised the European Parliament, the OSCE, and various governments on the intersection of religion and rights. He directed the Center for Religious Studies at the Fondazione Bruno Kessler in Trento and chairs the G20 Interfaith Working Group on Religion, Innovation, and Technology and Infrastructures.Marco is the author of numerous books, including From Your Gods to Our Gods and Nelle mani di Dio, la super religione del mondo che verrà. Over the past decade, he has helped shape the emerging field exploring the encounter between religion and innovation.In this episode, we explore Marco's work on bioethics and technoscience, their influential position paper mapping out this emerging field of religion and innovation, and what innovation really means in a religious context.In this first part of our conversation, we discuss:The balance between tradition and contemporary artThe story of St. Francis and “repair my church” as a metaphor for renewalCatholic Church's response to reproductive technologiesWhy “innovation” was chosen instead of simply “technology.”Distinction between technological innovation and social innovationTwo categories of innovationWhy religious actors want a voice in innovation-driven global agendasThe use of innovation in a religious contextTo learn more about Marco's work, you can find him at: https://credo.unisi.it/about/secretariat-and-experts/person/marcoLinks Mentioned:Religion, Innovation, Position paper, FBK 2019 - https://isr.fbk.eu/en/about-us/position-paper/ Fondazione Bruno Kessler – https://www.fbk.eu/ G20 Interfaith Forum – https://www.g20interfaith.org/ Organisation for Security and Cooperation in Europe (OSCE) – https://www.osce.org/This season of the podcast is sponsored by Templeton Religion Trust.Support the show
On this episode of The Money Mondays Podcast, Dan Fleyshman sits down with Jordan Belfort (The Wolf of Wall Street) and Hollywood super-connector Gavin Navarro for a rapid-fire masterclass on the three Money Mondays pillars: how to make money, how to invest it, and how to give it away.Jordan breaks down what really holds people back from earning more: it's not just “mindset”—it's missing skills, missing mentorship, and not putting in the reps. He shares why sales is the ultimate life skill, how to treat rejection as a numbers game, and why top performers don't waste time trying to “convert” hard no's.Then Gavin pulls back the curtain on turning relationships into real opportunities—without becoming the person who pitches everyone every day. He explains how to bring deals to high-profile people the right way, how to filter constant incoming pitches, and how he built a career by being the guy who can connect the right people in the right cities at the right time.On the investing side, Jordan shares his long-term approach (including S&P 500 and Bitcoin), while Dan lays out his 40/40/20 framework for balancing low-risk stability, medium-risk growth, and high-risk “shots at glory.” They also explain why businesses like beverages can require serious capital even when demand is booming.Finally, they talk about why charity matters—not just for optics, but for trust, culture, and legacy—and Dan closes with his signature question that always gets a different answer: what percentage of your net worth should go to your family?
Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Dr. Cameka Smith. Founder of The BOSS Network, from Money Making Conversations Masterclass: Purpose of the Interview The interview aimed to: Highlight The BOSS Network’s mission to empower women of color through entrepreneurship, career development, and community support. Share Dr. Smith’s personal journey from layoff to leadership, inspiring others to embrace entrepreneurship. Discuss strategies for business success, funding opportunities, and mentorship for Black female founders. Key Takeaways Origin of The BOSS Network Founded in 2009 during the recession after Dr. Smith was laid off from Chicago Public Schools. Initially started as local events in Chicago; now a digital community reaching 200,000 women nationwide. Mission: Bringing Out Successful Sisters (BOSS)—promoting small business spirit and career growth. Impact & Achievements Invested in 100 Black female founders through grants. Trained 50,000 women on business strategies. Coached 10,000 women on starting businesses. Created Boss Business University, offering mentorship and digital programs. Pivot During COVID Shifted from 35% event-based revenue to 75% digital. Launched Boss Impact Fund and Invest in Progress Grant: $10,000 grants + 4-year scholarships for recipients. Combined funding, mentorship, and marketing support for sustainability. Challenges & Mindset Entrepreneurship requires planning, resilience, and community support. Dr. Smith saved money before leaving her job and leveraged relationships for growth. Quote: “Entrepreneurs will work 80 hours for themselves but don’t want to work 40 hours for someone else.” Top 3 Mistakes Entrepreneurs Make Lack of research: Understand your industry, competitors, and market. No revenue model: If you’re not making money, it’s a hobby, not a business. Ignoring relationships: Networking and partnerships are key to success. Unique Marketing & Partnerships Dr. Smith built direct relationships with brands, bypassing agencies that offered “pennies on the dollar.” Created a dual revenue model: B2B (corporate partnerships) + B2C (community engagement). Core Philosophy Motto: Believe, Plan, Win. Quote: “Those that show up, go up.” Success is rooted in faith, persistence, and leveraging community. Notable Quotes “I was born to be an entrepreneur. My mother told me, until you become your own boss, you have to follow the rules.” “Less than 1% of Black women get VC funding—so we created our own fund.” “Relationships are your key to success. When social media goes away, your audience remains.” “If you have a business and you don’t have money, you’ve got a hobby.” “God will not birth anything inside of you that He will not give you the tools to deliver.” #SHMS #STRAW #BESTSupport the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.
Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Dr. Cameka Smith. Founder of The BOSS Network, from Money Making Conversations Masterclass: Purpose of the Interview The interview aimed to: Highlight The BOSS Network’s mission to empower women of color through entrepreneurship, career development, and community support. Share Dr. Smith’s personal journey from layoff to leadership, inspiring others to embrace entrepreneurship. Discuss strategies for business success, funding opportunities, and mentorship for Black female founders. Key Takeaways Origin of The BOSS Network Founded in 2009 during the recession after Dr. Smith was laid off from Chicago Public Schools. Initially started as local events in Chicago; now a digital community reaching 200,000 women nationwide. Mission: Bringing Out Successful Sisters (BOSS)—promoting small business spirit and career growth. Impact & Achievements Invested in 100 Black female founders through grants. Trained 50,000 women on business strategies. Coached 10,000 women on starting businesses. Created Boss Business University, offering mentorship and digital programs. Pivot During COVID Shifted from 35% event-based revenue to 75% digital. Launched Boss Impact Fund and Invest in Progress Grant: $10,000 grants + 4-year scholarships for recipients. Combined funding, mentorship, and marketing support for sustainability. Challenges & Mindset Entrepreneurship requires planning, resilience, and community support. Dr. Smith saved money before leaving her job and leveraged relationships for growth. Quote: “Entrepreneurs will work 80 hours for themselves but don’t want to work 40 hours for someone else.” Top 3 Mistakes Entrepreneurs Make Lack of research: Understand your industry, competitors, and market. No revenue model: If you’re not making money, it’s a hobby, not a business. Ignoring relationships: Networking and partnerships are key to success. Unique Marketing & Partnerships Dr. Smith built direct relationships with brands, bypassing agencies that offered “pennies on the dollar.” Created a dual revenue model: B2B (corporate partnerships) + B2C (community engagement). Core Philosophy Motto: Believe, Plan, Win. Quote: “Those that show up, go up.” Success is rooted in faith, persistence, and leveraging community. Notable Quotes “I was born to be an entrepreneur. My mother told me, until you become your own boss, you have to follow the rules.” “Less than 1% of Black women get VC funding—so we created our own fund.” “Relationships are your key to success. When social media goes away, your audience remains.” “If you have a business and you don’t have money, you’ve got a hobby.” “God will not birth anything inside of you that He will not give you the tools to deliver.” #SHMS #STRAW #BESTSee omnystudio.com/listener for privacy information.
Megan chats with Melodee from Pretty Focused about the Buyers Club hiring grant and why now is the time to stop doing everything yourself. Melodee is the creator and owner of Pretty Focused. She's a wife, homeschool mom and a second grade teacher turned food photographer. Melodee started working as a food photographer for food bloggers in 2016. In 2017, she started to have friends ask her to teach them how to do it too, so she did, and that's when Pretty Focused was born. Since then, she's had over 1,000 students join Pretty Focused to learn how to photograph food for food bloggers. Melodee connects them with potential clients inside their marketplace when they graduate. Over the last 3 years, 69% of our grads reported making $50,000+ working as food photographers for bloggers. If you are feeling stretched thin, navigating industry shifts, or walking through a hard season of life, this episode matters. Melodee shares a practical path to buying back your time, building real support into your business, and stepping into community instead of isolation. Key Topics Discussed: You do not have to do it all alone. Hiring support is often the move that unlocks momentum. Outsourcing buys back time. Time is the asset that allows you to grow and scale. Community creates resilience. The right ecosystem strengthens your business long term. Trained photographers matter. Working with people who understand food blogging saves frustration. The grant is based on need. Three bloggers will receive $500 to use inside Buyers Club. New seasons require new moves. Fresh strategies and support can redefine your year. The Pretty Focused Buyers Club Connect with Melodee Fiske Website | Instagram
Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Dr. Cameka Smith. Founder of The BOSS Network, from Money Making Conversations Masterclass: Purpose of the Interview The interview aimed to: Highlight The BOSS Network’s mission to empower women of color through entrepreneurship, career development, and community support. Share Dr. Smith’s personal journey from layoff to leadership, inspiring others to embrace entrepreneurship. Discuss strategies for business success, funding opportunities, and mentorship for Black female founders. Key Takeaways Origin of The BOSS Network Founded in 2009 during the recession after Dr. Smith was laid off from Chicago Public Schools. Initially started as local events in Chicago; now a digital community reaching 200,000 women nationwide. Mission: Bringing Out Successful Sisters (BOSS)—promoting small business spirit and career growth. Impact & Achievements Invested in 100 Black female founders through grants. Trained 50,000 women on business strategies. Coached 10,000 women on starting businesses. Created Boss Business University, offering mentorship and digital programs. Pivot During COVID Shifted from 35% event-based revenue to 75% digital. Launched Boss Impact Fund and Invest in Progress Grant: $10,000 grants + 4-year scholarships for recipients. Combined funding, mentorship, and marketing support for sustainability. Challenges & Mindset Entrepreneurship requires planning, resilience, and community support. Dr. Smith saved money before leaving her job and leveraged relationships for growth. Quote: “Entrepreneurs will work 80 hours for themselves but don’t want to work 40 hours for someone else.” Top 3 Mistakes Entrepreneurs Make Lack of research: Understand your industry, competitors, and market. No revenue model: If you’re not making money, it’s a hobby, not a business. Ignoring relationships: Networking and partnerships are key to success. Unique Marketing & Partnerships Dr. Smith built direct relationships with brands, bypassing agencies that offered “pennies on the dollar.” Created a dual revenue model: B2B (corporate partnerships) + B2C (community engagement). Core Philosophy Motto: Believe, Plan, Win. Quote: “Those that show up, go up.” Success is rooted in faith, persistence, and leveraging community. Notable Quotes “I was born to be an entrepreneur. My mother told me, until you become your own boss, you have to follow the rules.” “Less than 1% of Black women get VC funding—so we created our own fund.” “Relationships are your key to success. When social media goes away, your audience remains.” “If you have a business and you don’t have money, you’ve got a hobby.” “God will not birth anything inside of you that He will not give you the tools to deliver.” #SHMS #STRAW #BESTSteve Harvey Morning Show Online: http://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.
The Vital Veda Podcast: Ayurveda | Holistic Health | Cosmic and Natural Law
When an eclipse happens, something shifts. The light changes. The atmosphere feels different. Traditionally, these moments were never treated as ordinary.In this episode, Dylan is joined by Jyotishi and Ayurvedic practitioner Laura Plumb, Vaidya Dr Krishna Raju, Vaidya and medical astrologer Dr Harsha Raju, and mantra teacher Purnesh. Together they explore eclipses through the lenses of Ayurveda, Jyotish and mantra śāstra.They speak about Rahu and Ketu, the difference between solar and lunar eclipses, why digestion and prāṇa are considered more sensitive during these periods, and why eclipses have long been used as powerful windows for mantra and inner practice. Specific mantras and simple ritual guidelines are shared, along with practical recommendations around food, rest, meditation and how to orient the mind during these heightened times. The conversation moves between astronomy, subtle physiology and lived experience, offering a steady and grounded way to approach eclipse events.Rather than sensationalising eclipses, this episode invites a composed perspective. A reminder that moments of shadow can also be moments of alignment.IN THIS EPISODE WE DISCUSS:
Growing up in Crown Heights, Brooklyn as a member of the Chabad-Lubavitch Hasidic Orthodox Jewish community, Zalman Newfield was raised in an atmosphere of strict gender segregation, rigorous religious education, and nearly all-consuming ritual practices. Trained to be a Lubavitch emissary, he traveled around the world doing Jewish outreach to help usher in the messianic redemption. However, after exposure to the wider world, he abandoned the faith of his youth. Brooklyn Odyssey: My Journey Out of Hasidism (Temple University Press, 2026) is Newfield's poignant and hopeful memoir about exiting Orthodoxy. He recounts asserting his individuality and taking the radical step of shaving his beard. Reflective about his upbringing, Newfield is open to and curious about a world beyond Brooklyn while also maintaining his profound bond with his family and Jewish tradition. He writes candidly about his emotional, intellectual, and social experiences in and out of the Lubavitch community. From pivotal moments of devastation, including the illness and death of his younger brother and of his revered spiritual leader Rabbi Menachem Mendel Schneerson, to moments of joyful resolve, including the decision to pursue a doctorate and marry a non-Orthodox Jew, Newfield takes readers on his moving and impactful journey. Zalman Newfield is Associate Professor of Sociology and Jewish Studies at Hunter College, City University of New York and the author of Degrees of Separation: Identity Formation While Leaving Ultra-Orthodox Judaism (Temple). Visit him online at zalmannewfield.com. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network
Growing up in Crown Heights, Brooklyn as a member of the Chabad-Lubavitch Hasidic Orthodox Jewish community, Zalman Newfield was raised in an atmosphere of strict gender segregation, rigorous religious education, and nearly all-consuming ritual practices. Trained to be a Lubavitch emissary, he traveled around the world doing Jewish outreach to help usher in the messianic redemption. However, after exposure to the wider world, he abandoned the faith of his youth. Brooklyn Odyssey: My Journey Out of Hasidism (Temple University Press, 2026) is Newfield's poignant and hopeful memoir about exiting Orthodoxy. He recounts asserting his individuality and taking the radical step of shaving his beard. Reflective about his upbringing, Newfield is open to and curious about a world beyond Brooklyn while also maintaining his profound bond with his family and Jewish tradition. He writes candidly about his emotional, intellectual, and social experiences in and out of the Lubavitch community. From pivotal moments of devastation, including the illness and death of his younger brother and of his revered spiritual leader Rabbi Menachem Mendel Schneerson, to moments of joyful resolve, including the decision to pursue a doctorate and marry a non-Orthodox Jew, Newfield takes readers on his moving and impactful journey. Zalman Newfield is Associate Professor of Sociology and Jewish Studies at Hunter College, City University of New York and the author of Degrees of Separation: Identity Formation While Leaving Ultra-Orthodox Judaism (Temple). Visit him online at zalmannewfield.com. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/jewish-studies
Growing up in Crown Heights, Brooklyn as a member of the Chabad-Lubavitch Hasidic Orthodox Jewish community, Zalman Newfield was raised in an atmosphere of strict gender segregation, rigorous religious education, and nearly all-consuming ritual practices. Trained to be a Lubavitch emissary, he traveled around the world doing Jewish outreach to help usher in the messianic redemption. However, after exposure to the wider world, he abandoned the faith of his youth. Brooklyn Odyssey: My Journey Out of Hasidism (Temple University Press, 2026) is Newfield's poignant and hopeful memoir about exiting Orthodoxy. He recounts asserting his individuality and taking the radical step of shaving his beard. Reflective about his upbringing, Newfield is open to and curious about a world beyond Brooklyn while also maintaining his profound bond with his family and Jewish tradition. He writes candidly about his emotional, intellectual, and social experiences in and out of the Lubavitch community. From pivotal moments of devastation, including the illness and death of his younger brother and of his revered spiritual leader Rabbi Menachem Mendel Schneerson, to moments of joyful resolve, including the decision to pursue a doctorate and marry a non-Orthodox Jew, Newfield takes readers on his moving and impactful journey. Zalman Newfield is Associate Professor of Sociology and Jewish Studies at Hunter College, City University of New York and the author of Degrees of Separation: Identity Formation While Leaving Ultra-Orthodox Judaism (Temple). Visit him online at zalmannewfield.com. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/biography
Growing up in Crown Heights, Brooklyn as a member of the Chabad-Lubavitch Hasidic Orthodox Jewish community, Zalman Newfield was raised in an atmosphere of strict gender segregation, rigorous religious education, and nearly all-consuming ritual practices. Trained to be a Lubavitch emissary, he traveled around the world doing Jewish outreach to help usher in the messianic redemption. However, after exposure to the wider world, he abandoned the faith of his youth. Brooklyn Odyssey: My Journey Out of Hasidism (Temple University Press, 2026) is Newfield's poignant and hopeful memoir about exiting Orthodoxy. He recounts asserting his individuality and taking the radical step of shaving his beard. Reflective about his upbringing, Newfield is open to and curious about a world beyond Brooklyn while also maintaining his profound bond with his family and Jewish tradition. He writes candidly about his emotional, intellectual, and social experiences in and out of the Lubavitch community. From pivotal moments of devastation, including the illness and death of his younger brother and of his revered spiritual leader Rabbi Menachem Mendel Schneerson, to moments of joyful resolve, including the decision to pursue a doctorate and marry a non-Orthodox Jew, Newfield takes readers on his moving and impactful journey. Zalman Newfield is Associate Professor of Sociology and Jewish Studies at Hunter College, City University of New York and the author of Degrees of Separation: Identity Formation While Leaving Ultra-Orthodox Judaism (Temple). Visit him online at zalmannewfield.com. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sociology
Growing up in Crown Heights, Brooklyn as a member of the Chabad-Lubavitch Hasidic Orthodox Jewish community, Zalman Newfield was raised in an atmosphere of strict gender segregation, rigorous religious education, and nearly all-consuming ritual practices. Trained to be a Lubavitch emissary, he traveled around the world doing Jewish outreach to help usher in the messianic redemption. However, after exposure to the wider world, he abandoned the faith of his youth. Brooklyn Odyssey: My Journey Out of Hasidism (Temple University Press, 2026) is Newfield's poignant and hopeful memoir about exiting Orthodoxy. He recounts asserting his individuality and taking the radical step of shaving his beard. Reflective about his upbringing, Newfield is open to and curious about a world beyond Brooklyn while also maintaining his profound bond with his family and Jewish tradition. He writes candidly about his emotional, intellectual, and social experiences in and out of the Lubavitch community. From pivotal moments of devastation, including the illness and death of his younger brother and of his revered spiritual leader Rabbi Menachem Mendel Schneerson, to moments of joyful resolve, including the decision to pursue a doctorate and marry a non-Orthodox Jew, Newfield takes readers on his moving and impactful journey. Zalman Newfield is Associate Professor of Sociology and Jewish Studies at Hunter College, City University of New York and the author of Degrees of Separation: Identity Formation While Leaving Ultra-Orthodox Judaism (Temple). Visit him online at zalmannewfield.com. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/american-studies
Growing up in Crown Heights, Brooklyn as a member of the Chabad-Lubavitch Hasidic Orthodox Jewish community, Zalman Newfield was raised in an atmosphere of strict gender segregation, rigorous religious education, and nearly all-consuming ritual practices. Trained to be a Lubavitch emissary, he traveled around the world doing Jewish outreach to help usher in the messianic redemption. However, after exposure to the wider world, he abandoned the faith of his youth. Brooklyn Odyssey: My Journey Out of Hasidism (Temple University Press, 2026) is Newfield's poignant and hopeful memoir about exiting Orthodoxy. He recounts asserting his individuality and taking the radical step of shaving his beard. Reflective about his upbringing, Newfield is open to and curious about a world beyond Brooklyn while also maintaining his profound bond with his family and Jewish tradition. He writes candidly about his emotional, intellectual, and social experiences in and out of the Lubavitch community. From pivotal moments of devastation, including the illness and death of his younger brother and of his revered spiritual leader Rabbi Menachem Mendel Schneerson, to moments of joyful resolve, including the decision to pursue a doctorate and marry a non-Orthodox Jew, Newfield takes readers on his moving and impactful journey. Zalman Newfield is Associate Professor of Sociology and Jewish Studies at Hunter College, City University of New York and the author of Degrees of Separation: Identity Formation While Leaving Ultra-Orthodox Judaism (Temple). Visit him online at zalmannewfield.com. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/religion
Growing up in Crown Heights, Brooklyn as a member of the Chabad-Lubavitch Hasidic Orthodox Jewish community, Zalman Newfield was raised in an atmosphere of strict gender segregation, rigorous religious education, and nearly all-consuming ritual practices. Trained to be a Lubavitch emissary, he traveled around the world doing Jewish outreach to help usher in the messianic redemption. However, after exposure to the wider world, he abandoned the faith of his youth. Brooklyn Odyssey: My Journey Out of Hasidism (Temple University Press, 2026) is Newfield's poignant and hopeful memoir about exiting Orthodoxy. He recounts asserting his individuality and taking the radical step of shaving his beard. Reflective about his upbringing, Newfield is open to and curious about a world beyond Brooklyn while also maintaining his profound bond with his family and Jewish tradition. He writes candidly about his emotional, intellectual, and social experiences in and out of the Lubavitch community. From pivotal moments of devastation, including the illness and death of his younger brother and of his revered spiritual leader Rabbi Menachem Mendel Schneerson, to moments of joyful resolve, including the decision to pursue a doctorate and marry a non-Orthodox Jew, Newfield takes readers on his moving and impactful journey. Zalman Newfield is Associate Professor of Sociology and Jewish Studies at Hunter College, City University of New York and the author of Degrees of Separation: Identity Formation While Leaving Ultra-Orthodox Judaism (Temple). Visit him online at zalmannewfield.com. Caleb Zakarin is CEO and Publisher of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/secularism
Welcome to HBR News where we give the badger treatment to the news of the week! This week we will be talking about the upcoming documentary on the manosphere, Amanda Askell will be training the AI Claude, Spain will be making men pay for women's periods, and more!
To speak with an advisor and map out your student's next steps, book a Complimentary Strategy Call at admittedly.co/apply. In this episode of the Admittedly Podcast, Thomas sits down with Admittedly's Interim Director of College Counseling and former Senior Admissions Officer at Duke University, Sonam, for a candid look inside how highly selective admissions offices actually evaluate applications. Sonam reviewed more than 10,000 applications during her time in admissions. She holds degrees from Duke and an MBA from Rice, and she has worked across nearly every side of the process — inside a top university admissions office, in high schools, and in community-based organizations. In short: she understands both how decisions are made and how students should prepare. Together, Thomas and Sonam pull back the curtain on how admissions officers are trained, how institutional priorities shape decisions, and why the process is far more nuanced than most families realize. They discuss the return of standardized testing, what transcripts really signal, how committee rooms actually function, and why trying to "reverse engineer" a school's priorities is often a mistake. The conversation also dives deep into extracurricular strategy — what meaningful involvement looks like, how admissions officers spot inconsistencies, and why students don't need ten perfectly aligned activities to be compelling. From late bloomers to school list strategy to regional admissions nuances, this episode gives families a rare insider perspective grounded in real experience. This is especially valuable for parents and students aiming at highly selective colleges who want clarity about how decisions are made — and how to position themselves with intention rather than guesswork. Key Takeaways: • Admissions officers are trained — extensively — to evaluate applications within institutional priorities. • The supplemental essays often reveal more about what a school values than the personal statement. • Standardized testing is returning as a tool to combat grade inflation and assess academic readiness. • Admissions decisions are not pure meritocracies — they are shaped by institutional needs and shifting applicant pools. • Extracurriculars should demonstrate action and authenticity, not just alignment with a proposed major. • Changing direction mid-high school is acceptable — if it's explained thoughtfully and reflects genuine growth. • Students should build school lists based on fit, not assumptions about what a college "wants." Listeners can continue the conversation by following @admittedlyco on Instagram and TikTok, where Thomas and the Admittedly team answer real admissions questions weekly. Free resources, guides, and webinars are available at admittedly.co. If your family is ready for strategic, experience-driven guidance, book a Complimentary Strategy Call at admittedly.co/apply.
Show NotesWhat happens when anthropology turns its gaze on psychology and coaching?In this episode, Simon Western is joined by social anthropologist Dr Mikkel Kenni Bruun and social scientist Dr Rebecca Hutten to explore what sits beneath contemporary mental health, therapy, and coaching practices. Together, they discuss culture, power, and the often-invisible assumptions shaping therapeutic work.Rather than treating psychology as universal or value-neutral, Mikkel and Rebecca show how it is culturally produced, shaped by specific histories, institutions, and ways of making meaning. From this perspective, therapy and coaching are never neutral; they are embedded in social, political, and moral worlds.Ethnography is central to this conversation, not just as a research method, but as a way of listening and staying with complexity. Instead of forcing distress, healing, and care into predefined psychological categories, ethnography attends to how these experiences are actually lived across contexts.The discussion also challenges dominant Western ideas of the self. While psychology and coaching often centre the autonomous individual, anthropological perspectives highlight relational and socially embedded selves. This raises urgent questions about what happens when Western therapeutic models travel globally - and what they may erase or misunderstand.Cultural competence comes under scrutiny too. Often presented as a solution, it can risk flattening culture into tidy checklists rather than engaging with lived complexity and power. As psychological language increasingly shapes public policy, workplaces, and everyday life, anthropology helps reveal the cultural and political work happening beneath the surface.Key Takeaways Psychological and coaching practices are culturally produced, not universal Therapeutic cultures vary across histories, institutions, and contexts Ethnography reveals how mental health is actually lived The individual self is not a universal model Cultural competence can oversimplify difference Psychological practice is fundamentally relational Mental health discourse shapes ideas of the “good life” Anthropology makes the familiar strange - and visible again KeywordsAnthropology, psychology, coaching, mental health, therapeutic culture, ethnography, cultural competence, relationality, self, good lifeBrief BiosDr Mikkel Kenni Bruun is a social anthropologist at the University of Cambridge, Research Associate at the Healthcare Improvement Studies (THIS) Institute, and Affiliated Lecturer in the Department of History and Philosophy of Science. His ethnographic research includes NHS Talking Therapies (IAPT) services and community mental health initiatives in the UK. He is co-editor of Towards an Anthropology of Psychology (2025) and Rhythm and Vigilance (2025).Dr Rebecca Hutten is an independent researcher, social scientist, and Associate Lecturer at The Open University. Trained as an anthropologist, she has worked in government policy research and Public Health at the University of Sheffield, and brings extensive fieldwork and clinical experience within NHS psychological services. She is co-editor of Towards an Anthropology of Psychology (2025).
Send a textAnne Marie Gordon is a veteran yoga teacher and pioneering studio owner with over two decades devoted to spirituality and 15+ years immersed in yoga practice. Originally from New York, her path blends traditional yoga, Tai Chi, and the intensity of hot yoga into a deeply transformative approach. Now based in Sheffield, she founded and leads Soul Fire Studios — the city's first hot yoga studio. Trained in the lineage of Sri Dharma Mittra (500-hour certification), Anne Marie also developed the groundbreaking Hot Yoga Alchemy method, fusing trauma-informed principles with the traditional heat-based practice to create a powerful environment for healing, resilience, and personal transformation.Why listeners tune in: real-world wisdom, lineage-based teaching, trauma-aware insight, and the story of building a thriving yoga community from the ground up.Visit Anne Marie: https://www.soulfirestudios.co.uk/ & https://www.hotyogaalchemy.com/ Thanks for listening to this episode. Check out:
When Bad Dentistry Is a Cry for Help: Why Dental Professionals Need to Look DeeperA dentist posts about a colleague's terrible work on Facebook. Dozens of comments pile on. Dr. Allison House was the only one who asked a different question: What if that dentist is struggling?In this episode, Dr. House and Shawn Zajas confront one of the most uncomfortable truths in the dental profession. When you see consistently poor clinical work from a colleague, the default response is judgment and criticism. But what if bad dentistry is a symptom of something deeper? Substance abuse. Depression. Financial crisis. Personal tragedy. Burnout.Dr. House shares three real stories from her 26 years in practice that reframe everything:00:00 Intro 04:23 What community do you want to be part of? 17:50 Things aren't always as they seem 20:09 The Facebook post that started everything 22:18 Dentist Concern for Dentist: Arizona's intervention model 24:07 One bad crown is human. Twelve bad crowns is a signal. 26:55 The dentist who went to rehab (and never knew who called) 28:30 "We never give each other any space to be human" 29:08 When patients and team members act out of character 31:36 The care package that changed a team member's trajectory 33:46 When dementia explains the behavior 36:11 Byron Katie's "The Work" and how it applies to dental practice 38:30 Wrap-upOne story involves a colleague whose patients kept showing up with bad work. When a patient reported smelling alcohol on that dentist's breath, Dr. House called Arizona's Dentist Concern for Dentist program. Trained professionals visited the colleague and confirmed a serious addiction. He went to rehab. Dr. House says plainly: "I'm pretty sure that had he continued down that road, he would have died."Another story hits closer to home. Dr. House once told a patient that a famous colleague's work was terrible. Then she tried to redo it. Same result. The patient was nearly impossible to work on. The lesson: bad outcomes are not always bad dentistry.The conversation goes beyond dentists. Dr. House describes a team member whose personality changed overnight. Her daughter had entered a treatment facility, leaving her to raise her granddaughter while processing grief. Instead of termination, Dr. House responded with a care package. She talks about a 15-year patient whose inappropriate jokes turned out to be early-stage dementia, not character failure.Shawn and Dr. House also walk through Byron Katie's "Judge Your Neighbor" exercise, a practical tool for examining your assumptions before reacting. It is a method Dr. House uses regularly in her practice and personal life to see situations from the other person's perspective.The takeaway is clear: one bad day is human. A pattern of bad days is a signal. And the right response is care, not condemnation.ABOUT THE AUTHENTIC DENTIST PODCASTThe Authentic Dentist Podcast bridges the gap between clinical excellence and personal fulfillment in dentistry. Hosted by Dr. Allison House, a practicing dentist with over 26 years of experience, and Shawn Zajas, a dental marketing expert, this show tackles the profession's greatest challenges through candid conversations about ethical practice, authentic leadership, and sustainable success.Unlike typical dental podcasts focused solely on clinical techniques or practice management, The Authentic Dentist offers wisdom for the whole practitioner, addressing who you are and how you show up in your practice and life.ABOUT YOUR HOSTSDr. Allison House brings clinical expertise, ethical leadership, and organizational wisdom from over 26 years in practice. She has served as the youngest president of her local dental association and is a passionate advocate for ethical standards and dentist wellbeing across the profession.Shawn Zajas combines dental marketing expertise with authentic brilliance strategy, helping dental...
Thomas Bailey has been a force in the Michigan area independent scene. Trained at The House of Truth he's taking the lessons learned and been proving the dominance you'd expect from somebody named the Celtic Mercenary. As he gets ready for an excursion to Japan for FTO representing Fantastic League of Wrestling he stops through my door.Be sure to follow him on social media at…Facebook: Thomas BaileyInstagram: celtic_merc_tbBe sure to follow Drinkin at MO's on our social media accounts to stay up to date on the show..X(Twitter): Big_Mo83Instagram: drinkinatmosFacebook: Drinkin at MO's Threads: drinkinatmos Be sure to subscribe to the channel here on YouTube and all audio platforms…YouTube: https://youtube.com/@drinkinatmos338Spotify: https://open.spotify.com/show/6PqYhq9pQF21c5Hu01b23j?si=X8XLCOFZS_-qGBBzdYoD7AApple: https://podcasts.apple.com/us/podcast/drinkin-at-mos/id1617536259IHeartRadio: https://www.iheart.com/podcast/269-drinkin-at-mos-112523315?cmp=ios_share&sc=ios_social_share&pr=false&autoplay=trueAmazon: https://music.amazon.com/podcasts/5af99e6b-2c35-4f31-b8e4-5d8183216231/drinkin-at-mo%E2%80%99s?ref=dm_sh_pMALI1SeXwefTlaUdVRC9VIohSpotify for Podcasters: https://anchor.fm/drinkinatmosThank you to Prince Nana Coffee for sponsoring the podcast. Use the referral link below to order yourself some amazing premium coffee.Referral: https://princenanacoffee.com/?ref=BigMoThank you to Reaper Apparel for having Drinkin At MO's as a Brand Ambassador… be sure to use the code below for 10% off your order..https://www.reaperapparelco.com/discount/Drinkin?ref=ApFLTTMUPromo code:Drinkinatmos #prowrestling #independentwrestling #wwe #aew #ringofhonor #TNAwrestling #gcw #czw #ecw #letsfngo #drinkinatmos #njpw #nwa #flophousewrestling #socalprowrestling #luchaunderground #luchaundergroundtemple #pwrevolver #warriorwrestling #fantasticleagueofwrestling
A big thank you to Cisco for sponsoring this video. Kamal Hathi (GM at Splunk) reveals "Machine GPT" and explains why standard LLMs fail at processing machine data. Learn how Splunk's new open weights model helps you predict outages and secure networks before they happen. // Kamal Hathi's SOCIAL // LinkedIn: / kamal-hathi // Website REFERENCE // https://www.splunk.com/en_us/blog/lea... // David's SOCIAL // Discord: discord.com/invite/usKSyzb Twitter: www.twitter.com/davidbombal Instagram: www.instagram.com/davidbombal LinkedIn: www.linkedin.com/in/davidbombal Facebook: www.facebook.com/davidbombal.co TikTok: tiktok.com/@davidbombal YouTube: / @davidbombal Spotify: open.spotify.com/show/3f6k6gE... SoundCloud: / davidbombal Apple Podcast: podcasts.apple.com/us/podcast... // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com // MENU // 0:00 - Coming Up 00:48 - Intro 01:53 - How AI is Trained 03:10 - AI and Machine Data 04:02 - What is Machine GPT? 07:00 - How MachineGPT will help People 08:53 - What MachineGPT Could Tell Us 11:46 - MachineGPT Interface and Splunk 16:50 - About the Splunk Platform and Products 17:46 - The Future of AI and Machine Data 18:43 - AI as an Independent Creator 20:16 - AI and Security 21:51 - Outro Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! Disclaimer: This video is for educational purposes only. #cisco #machinelearning #sponsored
Join family therapists Ken Dolan-Del Vecchio and Nancy Saxton-Lopez as we share Niki's story about her beloved cat, Kizzie. .Reach Ken at kenddv@gmail.com, Nancy at nancysaxtonlopez@gmail.com.The Pet Loss Companion (book) on Amazon: https://www.amazon.com/Pet-Loss-Companion-Healing-Therapists/dp/1484918266/ref=sr_1_3?dchild=1&keywords=pet+loss+companion&qid=1612535894&sr=8-3mpa...The Pet Loss Companion (Audiobook) on Audible: https://www.audible.com/pd/The-Pet-Loss-Companion-Audiobook/B0FTPWPX8S?qid=1762457765&sr=1-1&ref_pageloadid=not_applicable&pf_rd_p=83218cca-c308-412f-bfcf-90198b687a2f&pf_rd_r=Y83TQXYM4VG4HKFZEX8X&plink=2mxV7mztbrGx4xEO&pageLoadId=v9F4M87SEHMsdyyw&creativeId=0d6f6720-f41c-457e-a42b-8c8dceb62f2c&ref=a_search_c3_lProduct_1_1To read our email correspondence with listeners and view photos of their beloved animal companions subscribe at https://petlosscompanionconversations.substack.com(A $5/month subscription fee applies.)To support our work on this podcast with a one-time gift: Venmo @Ken-Dolan-DelVecchio or PayPal (https://www.paypal.com/paypalme/kenddv?country.x=US&locale.x=en_US)To support this podcast with a monthly subscription: https://anchor.fm/kenneth-dolan-del-vecchio/supportWe are happy to announce our affiliation with Bereave, a company that offers beautifully crafted granite pet memorial plaques. When you purchase one of their plaques using the link that follows you are also supporting our podcast. https://shareasale.com/r.cfm?b=2399618&u=3798931&m=141340&urllink=&afftrack=To subscribe on YouTube: https://www.youtube.com/@thepetlosscompanion6602 (and hit the "subscribe" button)To RSVP for the next cost-free zoom pet loss support group facilitated by Ken: https://www.dakinhumane.org/petlossThis program is a friend of Dakin Humane Society in Springfield, Mass. Dakin is a 501 (c) (3) community-supported animal welfare organization that provides shelter, medical care, spay/neuter services, and behavioral rehabilitation for more than 20,000 animals and people each year. Since its inception in 1969, Dakin has become one of the most recognized nonprofit organizations in central Massachusetts and a national leader in animal welfare. You can learn more about Dakin and make a donation at dakinhumane.org.For a list of financial resources to help with payment for veterinary care visit the community tab on our YouTube channel.Additional resources/friends of the program:Kate LaSala, Multi-Credentialed Canine Behavior consultant and Companion Animal Death Doula, https://rescuedbytraining.comAngela Shook, End-of-Life Support, Companion Animal Doula Support, Pet Loss Grief Support, https://angelashook.com/Crystal Soucy, Pet Loss Grief Coach and Certified Grief Educator, https://www.getcrystalclear.com
The Department of Homeland Security says it has more than doubled the workforce of Immigration and Customs Enforcement under President Trump. Yet videos of immigration officers killing two U.S. citizens and using aggressive arrest tactics have left some politicians and community leaders rethinking the agency's approach. On today's show, law enforcement experts assess the training and culture at DHS. Related episodes: How ICE crackdowns are affecting the workforce For sponsor-free episodes of The Indicator from Planet Money, subscribe to Planet Money+ via Apple Podcasts or at plus.npr.org. Fact-checking by Sierra Juarez. Music by Drop Electric. Find us: TikTok, Instagram, Facebook, Newsletter. Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy
Body as Shadow: Jung's Method of Embodied Healing is Jungian analyst Erica Lorentz's passionate, clinically grounded argument that Jung's psychology was never meant to be “head-only.” It was always an embodied practice, one that asks us to meet psyche where it actually lives: in sensation, emotion, energy, imagination, and what Jung called the somatic unconscious or subtle body. At the heart of the book is Lorentz's central method: embodied active imagination, a way of working in which inward attention to a symptom, sensation, or emotion becomes a portal into imaginal material and archetypal depths, without forcing interpretation or prematurely translating experience into words. This approach is shaped by her long apprenticeship in Authentic Movement (also known as Movement as Active Imagination), where the psyche is allowed to emerge through the body in a protected relational container and a non-directive witnessing stance. Lorentz argues that many modern approaches to trauma and psychotherapy remain constrained by a left-brain bias: we attempt to heal through insight, narrative, and cognitive explanation, while the original wound and the original healing energy often sits below language. Drawing on Jung's own words from the Zarathustra Seminar, she emphasizes the mysterious interlocking place where body and psyche become indistinguishable: where we cannot know if we are in matter or in psyche, because we are in both. Throughout the book, Lorentz bridges what is too often split in Jungian circles: developmental work and archetypal work. She insists that when we work with complexes, we must come to terms not only with childhood roots, but with the archetypal core “on its own ground”, because the archetype is not a metaphor; it is a force, and one we encounter in a bodily way. Erica Lorentz, M.Ed., L.P.C., is a Jungian analyst (IAAP) and training analyst at the C. G. Jung Institute of New England. With early roots in dance and decades of experience in Authentic Movement (Movement as Active Imagination), she integrates depth psychology with embodied and imaginal approaches to healing. Trained in object relations and shaped by clinical work with autistic and psychotic youth, she has taught and lectured widely on Jung, the body, and embodied active imagination across the US, Canada, the UK, and internationally, including teaching in India in 2024. Helena Vissing, PsyD, SEP, PMH-C is a Licensed Psychologist practicing in California and Associate Professor at California Institute of Integral Studies. She can be reached at contact@helenavissing.com. She is the author of Somatic Maternal Healing: Psychodynamic and Somatic Treatment of Trauma in the Perinatal Period (Routledge, 2023). Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/psychoanalysis
THE HOMEOPATHY HEALTH SHOW Raising Standards, Protecting Patients – The IHA's Role in Modern Homeopathic Education With Dr Shamini Singh Sachdev In this important and profession-shaping episode of The Homeopathy Health Show, Atiq and Naila are joined by Dr Shamini Singh Sachdev, Chair of the Independent Homeopathy Association, to explore the vital role of educational standards, accreditation, and collaboration within UK homeopathy. At a time when credibility, transparency, and patient safety are essential, this conversation highlights how strong foundations in philosophy, principles, and clinical training safeguard both practitioners and the public - while strengthening homeopathy's position within modern healthcare. This episode reinforces a powerful message: professional standards are not restrictive - they are protective. In This Episode We Explore: From Educational Standards to Professional Integrity Dr Shamini explains how the IHA accredits colleges to ensure students receive comprehensive training grounded in philosophy, core principles, and supervised clinical practice. The aim is to raise standards and promote public confidence in qualified practitioners. Why Accreditation Matters Atiq and Naila emphasise the importance of sound homeopathic knowledge. When practitioners are deeply trained, patients receive safer, more effective, and ethically grounded care. Standards and Patient Safety High standards directly support patient wellbeing. The discussion explores regulatory oversight, structured education, and the necessity of continuing professional development across the profession. Professional Unity and Collaboration Dr Shamini highlights the importance of unified professional standards and collaboration between colleges - including engagement with non-member institutions - to strengthen the profession collectively and increase transparency. Homeopathy as Holistic Practice Beyond regulation, the episode explores homeopathy's holistic nature and the personal growth it offers practitioners. True education shapes not just skill, but insight and responsibility. About Our Guest Dr Shamini Singh Sachdev M.Tech (Hom) S.A. RSHom AFHom Dr Shamini Singh Sachdev is a London-based Integrative Homeopathic Doctor with over 25 years of experience. Trained in South Africa, she now runs a successful practice in the UK and currently serves as Chair of the IHA. Alongside her clinical work, Dr Singh Sachdev has lectured in medical sciences and pathology since 2006. She brings extensive experience from senior roles within the health and natural wellness sector, combining scientific understanding with a deep commitment to holistic care. Her passion for maintaining high standards in homeopathic practice underpins all her work. As an integrative practitioner, she blends homeopathy, herbal medicine, and nutritional therapy to support whole-person wellbeing. She believes true health is not merely the absence of illness, but a vibrant state of balance in body and mind - achieved by addressing the root causes behind symptoms, including stress, diet, and lifestyle. Website: https://i-h-a.org/ Social Media: Instagram (IHA): https://www.instagram.com/i.h.association/ Instagram (Dr Shamini): https://www.instagram.com/drshamini.homeopath/ Facebook: https://www.facebook.com/profile.php?id=61578846994198 About the Homeopathy Health Show The Homeopathy Health Show - co-hosted and produced by Atiq Ahmad Bhatti and Naila Cheema - is the world's #1 homeopathy talk show, reaching a global audience through the UK Health Radio Network and all major podcast platforms. Atiq Ahmad Bhatti, a 4th Generation Homeopath, Teacher, Educator, and Global Ambassador for Homeopathy, is joined by Naila Cheema, an experienced Homeopath and Nutritionist. Together, they bring thoughtful conversations, expert insights, and a shared passion for holistic healing to every episode. Connect with the Hosts Atiq Ahmad Bhatti - Homeopath, Educator, Broadcaster Online: www.liketreatslike.co.uk Instagram: @like_treatslike Facebook: @liketreatslike YouTube: like_treatslike Naila Cheema - Homeopath, Nutritionist, Educator Online: https://homeopathynaila.com Instagram: @homeopathnaila Facebook: @Neeli.KC Stream Now Across All Platforms UK Health Radio: https://ukhealthradio.com/program/homeopathy-health/ Podbean: https://homeopathyhealth.podbean.com/ Apple Podcasts: https://podcasts.apple.com/us/podcast/homeopathy-health-with-atiq-naila/id1715524908 YouTube: https://www.youtube.com/@like_treatslike/featured Spotify: https://open.spotify.com/show/17rSCmlPGDkiSCyHePLPFx?si=51c640498df84727 Join Our Global Community of Listeners Hosted by: Atiq & Naila Top 5% Podcast Worldwide (ListenNotes Global Ranking) #1 Global Talk Show on Homeopathy Audience in 60+ Countries Real conversations. Real stories. Real homeopathy. Unlock the power of natural remedies to restore balance and vitality. Inspiring guests, expert insights, and global voices shaping the future of holistic medicine. Tune in, stay inspired, and explore the world of homeopathy with us. Homeopathy in Practice Explore webinars, masterclasses, education, and practitioner resources at: https://homeopathyinpractice.co.uk Join our global Facebook community @homeopathyinpractice
Ja' Malik is just wrapping up his fourth year as the artistic director of Madison Ballet in Madison, WI, but his path to leadership has been shaped by decades inside the field. A former professional dancer with a 25-year performing career, Malik danced with companies including Cleveland Ballet, North Carolina Dance Theatre, BalletX and Ballet Hispánico, performing a wide range of classical, neoclassical and contemporary repertory. Trained at the Joffrey Ballet School and holding a BFA from The New School, his artistic voice draws equally on rigorous classical technique and socially engaged contemporary practice. He also continues to serve as the artistic director of Ballet Boy Productions, an organization he founded in 2007 that provides young men of color access to classical and contemporary ballet performing opportunities and that also offers training and mentoring.Since arriving in Madison, Ja' has led a period of significant artistic and organizational change, and the results are more than encouraging. At a moment when many ballet companies nationwide are grappling with shrinking audiences, Madison Ballet is growing its own, responding to programming that places contemporary work alongside the classics and reflects the community it serves. Six months into his tenure, Malik also stepped into the additional role of interim executive director, guiding the organization through a demanding transition with a small staff and limited resources.In this interview, Ja' reflects on the risks involved in reshaping a regional ballet company, from extending dancer contracts to rethinking programming and institutional structure. He also speaks candidly about leadership during the in-between phase of change and the emotional, physical and ethical demands placed on artists and arts leaders alike.https://www.madisonballet.org/about/staff/ja-malikHosted on Ausha. See ausha.co/privacy-policy for more information.
Podcast: Hack the Plant (LS 35 · TOP 3% what is this?)Episode: Systems Engineering for Survival: A Physician's Guide to Emergency ManagementPub date: 2026-02-17Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationOur host Bryson Bort welcomes Dr. Natalie Sullivan, Medical Director of the Emergency Response Medical Group and an emergency medicine physician at a D.C. area hospital. Trained in EMS and disaster and operational medicine, Natalie turned her attention to the critical intersection of clinical medicine, patient safety, and cybersecurity resilience after experiencing a prolonged ransomware attack on a major hospital. Dr. Sullivan lays out the disaster preparedness cycle, and the many vectors of risks for hospitals. How does a cyberattack on one hospital lead to increased cardiac arrest mortality at the hospital three blocks away? Why is a generation of "digital native" doctors a hidden vulnerability in an analog emergency? And what happens when a hospital's reliance on these "tightly coupled" systems—like water, power, and the Medical IoT—collapses during a ransomware event?“We are critical infrastructure, but we're deeply, deeply dependent on the surrounding critical infrastructure,” Dr. Sullivan said. Join us for this and more on this episode of Hack the Plan[e]t. The views and opinions expressed in this podcast represent those of the speaker, and do not necessarily represent the views and opinions of their employers. Hack the Plant is brought to you by ICS Village and the Institute for Security and Technology. The podcast and artwork embedded on this page are from Bryson Bort, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Ever catch yourself thinking, “I'm doing all the right things… so why do I still feel tired, foggy, or just off?” You're working out, trying to eat better, squeezing in sleep where you can, and yet your energy and longevity still feel like a question mark. If that sounds familiar, this episode of The Happy Hustle Podcast is going to land right where you need it.In this episode, I sit down with Dr. Joy Kong, a UCLA-trained, triple board-certified anti-aging physician, stem cell specialist, educator, and CEO. Dr. Joy is the founder of Chara Health and Chara Biologics, and she's deeply committed to advancing regenerative medicine in a way that is ethical, effective, and accessible. She also founded the American Academy of Integrative Cell Therapy, where she trains physicians around the world in stem cell therapies and cutting-edge regenerative practices.This conversation dives headfirst into stem cells, longevity, and what it actually means to optimize your health for the long game. Dr. Joy breaks down complex science in a way that feels grounded and practical. We explore how diet, exercise, sleep, and regenerative therapies can work together not just to help you live longer, but to live better. This episode matters because longevity isn't about chasing perfection or biohacking extremes. It's about understanding your body, making informed choices, and stacking small, intentional habits that compound over time.Here are a few powerful takeaways you'll walk away with.First, stem cells are not science fiction anymore. Dr. Joy explains what stem cell therapy actually is, how it works, and why it's becoming one of the most promising tools in regenerative medicine today. She also clears up common misconceptions and emphasizes the importance of quality, sourcing, and proper medical oversight.Second, longevity starts with the basics before the breakthroughs. While regenerative therapies are exciting, Dr. Joy reinforces that diet, movement, and sleep are still foundational. Stem cells and advanced treatments work best when your lifestyle is already supporting your body's natural healing processes.Third, education is the real power play in health. One of the most inspiring parts of Dr. Joy's journey is her commitment to teaching both patients and physicians. When you understand your options, you're no longer guessing or blindly outsourcing your health. You're making confident, informed decisions.Fourth, anti-aging is really about regeneration, not vanity. This episode reframes anti-aging as restoring function, reducing inflammation, and improving quality of life. It's not about looking younger. It's about feeling strong, clear, and capable for decades to come.Finally, serving others is the ultimate form of optimization. Dr. Joy's mission goes beyond medicine. Her work is rooted in service, integrity, and raising the standard of care across the industry. That alignment between purpose and profession is what truly defines a happy hustler.If you're curious about stem cells, longevity, or how to future-proof your health in a grounded, responsible way, this episode is absolutely worth your time. Do yourself a favor and listen to the full conversation. And if it resonates, share it with someone ready to take ownership of their health and hustle with intention.What does Happy Hustlin mean to you?Dr. Joy says if it's not fun, why are we doing this? So what's the whole point? You're spending eight hours a day at this place. I want you to have fun. So that's the happy hustling, but how to keep that state.Connect with Dr. JoyInstagramFacebookTiktokLinkedinTwitterYoutubeFind Dr. Joy on her website: https://joykongmd.com/ Connect with Cary!InstagramFacebookLinkedinTwitterYoutube Get a copy of his new book, The Happy Hustle, 10 Alignments to Avoid Burnout & Achieve Blissful BalanceSign up for The Journey: 10 Days To Become a Happy Hustler Online CourseApply to the Montana Mastermind Epic Camping Adventure“It's time to Happy Hustle, a blissfully balanced life you love, full of passion, purpose, and positive impact!”Episode Sponsors:If you're feeling stressed, not sleeping great, or your energy's been kinda meh lately—let me put you on to something that's been a total game-changer for me: Magnesium Breakthrough by BiOptimizers. This ain't your average magnesium—it's got all 7 essential forms that your body needs to chill out, sleep deeper, and feel more balanced. I take it every night and legit notice the difference the next day. No more waking up groggy or tossing and turning all nightIf you're ready to sleep like a baby, calm your nervous system, and optimize your recovery, go grab yours now at bioptimizers.com/happy and use code HAPPY10 for 10% OFF.
What do you think of the idea that you should "follow your heart"? It's time to lead with discernment, obedience, and purpose instead of listening to our feelings and the world. When it comes to fostering relationships, invite wisdom from God and trusted voices from friends and family (especially when it comes to finding a boo).Proverbs 16:9Jeremiah Lamentations#ChristianDating #FollowGodNotYourHeart #BiblicalDiscernment #MarriagePurpose #FaithOverFeelings #KingdomRelationships #TheNonMicrowavedTruth
A conversation with Sylvia Banda, Zambian business woman, restaurateur and social entrepreneur about her journey started when when she was 12. She opened her first food company, and she hasn't stopped since. She now runs a multi-million-dollar business with over 15 restaurants in Lusaka, Zambia, a food- processing company selling traditional Zambian food worldwide, and has trained over 60,000 smallholder farmers to produce higher-quality products and process them to receive better prices. We talk about why researchers should take a back seat and let farmers and entrepreneurs lead now; why the hand tools many farmers still use belong in a museum and why mechanisation is key, but with care; why processing and preserving are essential to ending hunger; and about nutrition, traditional food versus imported food, and how she taught urban people to re-appreciate what is often considered “food for the poor” that is traditional, nutrient-dense, and tasty food. To supply all of this, she set up two factories and trained over 60,000 smallholder farmers, changing many lives. Enjoy the story and the knowledge of a true Zambian and Southern African powerhouse.More about this episode.==========================In Investing in Regenerative Agriculture and Food podcast show we talk to the pioneers in the regenerative food and agriculture space to learn more on how to put our money to work to regenerate soil, people, local communities and ecosystems while making an appropriate and fair return. Hosted by Koen van Seijen.==========================
Training for Godliness - 2 Timothy 3.16-17Tyler WillisContinuing the series on Training in Godliness, we learn from 1 Timothy 4:6 that the Word of God is the essential "protein" for spiritual growth. Just as junk food harms the body, consuming the world's fear and slander trains us in anxiety.Holiness requires active training, not drifting. With the Holy Spirit as our helper, we must move from passivity to action—confessing our lack of desire and choosing to obey, trusting that delight will follow discipline.
Feeling capable, but still not signing the clients you expected?If you've finished your training, put real effort into your business, and still feel stuck or confused about why clients aren't coming consistently, this episode is for you.In this episode, the first in the Foundations Series, Candy Motzek breaks down the most common reason smart new coaches stay stuck. They spend time on tasks that feel productive, but do not lead to client conversations, clear offers, or actual coaching sessions.You'll learn the three activities that create paying clients, how to spot “busy work” that keeps you safe but stalled, and how to shift into simple business-building actions that generate real momentum. This episode includes a practical revenue lens exercise you can do today, plus a tiny weekly plan to start more conversations without feeling pushy or salesy.If you're a new coach, a newly certified coach, or an experienced professional building a coaching practice on the side, this will help you take action that actually leads to income. Without overwhelm. Without perfectionism.Get the free course and workbook: https://candymotzek.lpages.co/vfo/Book a call: https://candymotzek.as.me/breakthrough
This week, we discuss Wall Street's software-stock sell-off and a viral essay on X about the potential for widespread job displacement from A.I. Then, the New York Times reporter Alexandra Alter walks us through the process that a growing number of writers are adopting to churn out romance novels with help from A.I. chatbots. Finally, we each share one bit of good tech-related news — a new way to make playlists on Spotify and progress toward decoding whale sounds. Guest:Alexandra Alter, a New York Times reporter covering books and publishing. Additional Reading:The Dark Side of A.I. Weighs on Tech StocksMatt Shumer's essay “Something Big Is Happening”The New Fabio Is ClaudeHow a New A.I. Tool Fixed My Single Biggest Problem With SpotifyHow A.I. Trained on Birds Is Surfacing Underwater Mysteries We want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
In today's episode we will be talking about a Russian arrested for assault in Pattaya, a stolen pickup tracked by GPS, an illegal doctor exposed, a bizarre fermented-fish protest in Bangkok, Krabi's Valentine traffic lights, and a US$2.8 billion corporate shake-up in Malaysia.
From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:
Today's guest is Iantha Richardson, a talented American actress whose journey from Washington, D.C. to our screens is rooted in artistry, resilience, and intention. Trained as a dancer with a BFA from Fordham University: The Ailey School, Iantha began her creative path through movement before transitioning into acting. She landed her first major role on This Is Us and went on to captivate audiences as Tessa Lorraine in American Soul. Her breakout performance as Faith Mitchell on ABC's hit series Will Trent highlights her ability to bring depth, strength, and humanity to a character navigating loyalty, identity, and integrity within a male-dominated profession. Beyond acting, Iantha is also a writer, director, and producer, driven by a desire to “control the narrative” and elevate meaningful stories from underrepresented perspectives. In this conversation, we explore her creative evolution, the courage it takes to pivot careers, and how intention plays a central role in both her art and life. Whether she's stepping into complex roles on screen or building stories behind the camera, Iantha brings thoughtfulness, ambition, and vision to everything she creates. ✨ In this episode, we discuss: Transitioning from dance to acting Navigating the entertainment industry with purpose Representation and storytelling from underrepresented voices What it means to control your own narrative Creating art with intention and integrity This episode is a reminder that your path doesn't have to be linear, and that owning your story is a powerful act. Connect With Us: Iantha Richardson: @ianthasherii Ariel Travis: @wander_lounge
What if lasting energy and better health didn't require complicated routines or constant stress? In this episode, Dr. Debbie Ozment, DDS, shares her refreshingly simple approach to enhancing vitality, preventing disease, and creating sustainable wellness habits that truly work. As the host of the Vitality Made Simple podcast, Dr. Ozment focuses on early detection, prevention, and practical strategies that help people feel their best at every stage of life. With decades of experience in dentistry and integrative health, she highlights how oral health, inflammation, toxins, and emotional stress can quietly drain energy and impact long-term wellbeing — and what you can do about it. In this conversation, we explore: · How small, consistent lifestyle changes can extend your vitality span · The connection between oral health, inflammation, and chronic disease prevention · Simple, stress-free ways to support mental, emotional, and physical wellness Dr. Ozment has been in private dental practice since 1985 and is a graduate of the University of Oklahoma College of Dentistry. She later earned a Master's degree in Metabolic and Nutritional Medicine from the University of South Florida Morsani College of Medicine and is a Diplomate of the American Academy of Anti-Aging Medicine. Trained at the Mayo Clinic and certified as a National Board-Certified Health and Wellness Coach, she brings a truly integrative perspective to modern health. Follow Dr. Ozment on Instagram @drdebbieozment to stay up to date with her latest insights and resources. Episode also available on Apple Podcasts: https://apple.co/38oMlMr Keep up with Debbie Ozment socials here: Facebook: https://www.facebook.com/drdebbieozment/ Youtube: https://www.youtube.com/@drdebbieozment
Take2, an AI agents platform purpose-built for healthcare recruiting, today announced it has raised a $14 million Series A… The company's first agent, the AI Interviewer, conducts phone interviews with candidates 24/7, evaluates them, records calls, and syncs results directly into applicant tracking systems — all with no human-in-the-loop. Trained on healthcare-specific hiring data, the platform helps organizations assess candidates more accurately while generating predictive insights that improve hiring quality and long-term retention. “Healthcare systems are under enormous pressure, and hiring is one of their biggest hidden costs,” said Yaniv Shimoni and Kaushik Narasimhan, co-founders of Take2. “We're building AI agents that actually do the work — not just assist — so recruiting teams can focus on strategic decisions instead of time-consuming manual processes.” https://hrtechfeed.com/take2-raises-14m-series-a-to-automate-healthcare-recruiting/ “Help me find an engineering job in Chicago…” A simple prompt like that can open a world of opportunity for a job seeker. Says Indeed They just announced an expansion of their relationship with OpenAI, bringing Indeed's massive job marketplace directly into the ChatGPT experience. https://hrtechfeed.com/indeed-now-integrated-with-chatgpt/ Phenom, announced the acquisition of Be Applied, an AI‑driven cognitive assessment solution that validates candidate and employee capabilities at scale. By combining Phenom's AI with Be Applied's evidence‑based assessments, enterprises can confidently move to skills‑first hiring without sacrificing speed, quality or fairness. https://hrtechfeed.com/phenom-acquires-cognitive-assessment-platform/ Recruit Holdings just dropped its Q3 FY2025 results The U.S. labor market has been characterized as “stabilizing” or even “softer” lately, with job postings declining from their post-pandemic peaks. However, Recruit's U.S. revenue tells a different story: Revenue Surge: U.S. HR Technology revenue grew 10.1% year-over-year in dollar terms this quarter. https://hrtechfeed.com/high-tech-high-growth-recruit-holdings-u-s-engine-revs-up-in-q3/ Workday, Inc. announced that co-founder and current executive chair Aneel Bhusri is returning as chief executive officer as the company enters its next chapter, focused on leading in the rapidly evolving AI era. Carl Eschenbach is stepping down as CEO and as a member of the board after leading Workday through a period defined by global growth, an expanded industry focus, and strengthened operational discipline. He will continue to support Bhusri and the company as strategic advisor to the CEO. https://hrtechfeed.com/workday-announces-ceo-transition-as-co-founder-aneel-bhusri-returns-to-lead-the-companys-next-chapter/ Learn more about your ad choices. Visit megaphone.fm/adchoices
You're disciplined. You're committed. You show up every day and put in the work. But what happens when effort and motivation aren't delivering the results you know you're capable of? Santiago Brand is an international educator and consultant in brain mapping and neurofeedback who uses real brain data to reveal what's actually happening when people perform, stall, or burn out. Trained as both a sport and clinical psychologist, Santiago has spent over 17 years across more than 26 countries helping leaders and high performers improve focus, recover faster from stress, and perform with greater consistency—not by grinding harder, but by understanding the brain that's running the show. In this conversation, Santiago reveals why even the most driven individuals hit invisible walls. You'll discover how trauma markers and emotional dysregulation show up in brain maps, why high performers resist the truth about their own humanity, and how quantitative EEG technology turns invisible obstacles into something you can finally work with. Because once you see what your brain is doing, you can't unsee it—and that's when real transformation begins. If you've ever felt like you're doing all the right things but the breakthrough still hasn't happened, this episode shows you exactly where to look next.
Sworn to Justice (1996) was chosen by friend of the show and Patreon supporter Leigh, and is a prime example of mid-90s direct-to-video action thrillers built around martial arts credentials and late-night cable appeal. Produced by PM Entertainment — a studio known for churning out low-budget, high-concept action films — the movie was designed specifically for the booming VHS rental market rather than theatrical release. Director Paul Maslak leaned into the studio's house style: fast-paced action, neon-lit cityscapes, and a blend of crime, thriller, and exploitation elements. The film was shot quickly and economically, typical of PM's efficient production model, which prioritized practical stunts and tight schedules over polish or prestige.The production's biggest selling point was its lead, Cynthia Rothrock, already a well-established martial arts star with multiple Hong Kong and American action credits. Her real-life fighting background allowed the filmmakers to stage fight scenes with minimal doubles, keeping the choreography grounded and physical. Filming took place largely around Los Angeles, using recognizable streets and interiors to stretch the budget while maintaining a contemporary urban feel. Like many PM Entertainment titles, Sworn to Justice found its audience through home video, cable rotation, and word of mouth, eventually earning cult status among fans of 90s action cinema and martial arts B-movies. Today, it's remembered as a quintessential slice of direct-to-video action filmmaking — scrappy, stylish, and unapologetically of its era.Checkout Leigh on The Movie Vent.If you enjoy the show and would like to support us, we have a Patreon here.Referral links also help out the show if you were going to sign up:NordVPNNordPassTrailer Guy Plot SummaryA city drowning in crime… a system that's failed… and one woman who's had enough.When the law can't protect the innocent, justice goes underground. Trained to fight, driven by vengeance, and armed with nothing but her fists and her will, one relentless warrior takes the streets by storm — tearing through criminals, conspiracies, and anyone foolish enough to stand in her way.*Sworn to Justice* — no badge… no backup… no mercy.Fun FactsSworn to Justice is often categorized as an “erotic thriller meets martial arts action” hybrid, a niche genre that was surprisingly popular in the mid-1990s video market.The film was released during the peak VHS rental era, when action titles like this regularly outperformed small theatrical releases in video stores.Cynthia Rothrock performs nearly all of her own fight choreography, showcasing authentic Tang Soo Do and karate techniques rather than stylized wire work.The movie blends martial arts with noir-style detective elements, giving it a darker tone compared to Rothrock's earlier Hong Kong films.Several supporting cast members were real stunt performers, which helped make the fight scenes feel more physical and less choreographed.The film developed a late-night cable TV following on networks like USA Network and HBO Zone throughout the late 1990s and early 2000s.Rothrock fans often rank this among her most “adult-oriented” American roles, marking a tonal shift from her earlier PG-13 action vehicles.The movie features a synth-heavy 90s action score, typical of direct-to-video thrillers of the era.Collectors consider original VHS and DVD releases of the film minor cult items within martial arts movie circles.thevhsstrikesback@gmail.comhttps://linktr.ee/vhsstrikesback
Learn more about Michael Wenderoth, Executive Coach: www.changwenderoth.comMost leaders were taught to leave their emotions at the door. Today's guest says that advice isn't just outdated — it's costly. In this episode of 97% Effective, host Michael Wenderoth sits down with Dina Denham Smith, executive coach and bestselling author of Emotionally Charged, to unpack why emotional skill is now a core leadership capability, not a “soft” add-on. Drawing on behavioral science and her work as an executive coach and strategic advisor, Dina explains why emotions are data, how leaders unknowingly perform massive emotional labor, and what it really takes to manage triggers, prevent burnout, and unlock performance. As Dina puts it: “Emotions are money.” By the end of this conversation, you'll see why ignoring emotions is bad for you and bad for business – and what to do instead.SHOW NOTESDina's story — and why this work mattersOne surprising thing about Dina you won't find on the internetHow Emotionally Charged would have helped Dina earlier in her own careerWhat sparked Dina's interest in the science of emotionsHow the pandemic and technology shifts dramatically increased the emotional demands placed on leadersCore ideas from Emotionally ChargedThe key takeaway: Emotions are information“Emotions are money”: how feelings directly translate into performance, retention, and resultsThe biggest myth Dina wants to retire: that emotions get in the way of good business decisionsWhat “emotional labor” really means — and why research shows leaders perform as much of it as customer service professionals (and in more complex ways)The three layers of every emotion: physiology, cognition, and behaviorWhy suppressing emotions is like trying to hold beach balls underwater Practical tools you can use immediatelyBeach balls, masks, and “letting it all hang out”: finding the right balance at workWhy expanding your emotional vocabulary dramatically improves self-regulationDina's BRAVE framework for managing triggers in real time: Breathe, Refocus, Accept, Verbalize, Engage Restoration (not “self-care”): four evidence-based ways leaders recover from emotional strain: Detachment, Relaxation, Mastery, Control Power, leadership, and team cultureWhy leaders consistently underestimate their emotional impactHow power amplifies everything you feel and showWhy everyone cues off their leader's emotional signals (often unconsciously)How leaders can normalize emotional expression on their teams — without turning meetings into complaint sessionsSimple ways managers can reset emotional culture inside their own sphere of influenceDina's reminder: emotional skills are learnable — and improvable at any stage of your career. BIO AND LINKSDina Denham Smith is an executive coach and strategic advisor who helps senior leaders build their capacity, scale their impact, and thrive in complexity. For more than a decade, she has partnered with executives at some of the world's most successful companies, helping them navigate the demands of operating at the highest levels. Dina holds an MS in Industrial/Organizational Psychology and an MBA from the Ross School of Business at the University of Michigan, and she is credentialed by both the ICF and EMCC as an executive and team coach. A prolific thought leader, Dina has published more than 60 articles on leadership for Harvard Business Review, Fast Company, Forbes, and other premium outlets. She is the lead author of Emotionally Charged: How to Lead in the New World of Work (Oxford University Press, 2025).Connect with DinaWebsite: https://dinadsmith.comLinkedIn: https://www.linkedin.com/in/dina-denham-smith/Her book: https://dinadsmith.com/book/ People and Books ReferencedDr. Alicia Grandey — Dina's co-author https://psych.la.psu.edu/people/aag6/Why We Sleep by Matthew Walker https://a.co/d/07CbSJAYMore from 97% EffectiveMichael's Award-winning Book: Get Promoted: What You're Really Missing at Work That's Holding You Back: https://tinyurl.com/453txk74Watch this episode on YouTube: https://www.youtube.com/@97PercentEffectiveAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
Today’s Bible Verse: “For I myself am a man under authority, with soldiers under me. I tell this one, ‘Go,’ and he goes; and that one, ‘Come,’ and he comes. I say to my servant, ‘Do this,’ and he does it.” — Matthew 8:9 Matthew 8:9 highlights a powerful moment of faith from an unexpected source. A Roman centurion recognized something many others missed — Jesus’ authority didn’t depend on physical presence. He understood that if Jesus gave the word, it was as good as done. “Want to listen without ads? Become a BibleStudyTools.com PLUS Member today: https://www.biblestudytools.com/subscribe/ MEET YOUR HOST: Chaka Heinze at https://www.lifeaudio.com/your-daily-bible-verse/ Chaka Heinze is a writer, speaker, and lover of the Bible. She is actively involved in her local church on the Prayer and Healing team and mentors young women seeking deeper relationships with God.After personally experiencing God's love and compassion following the loss of her eleven-year-old son, Landen, Chaka delights in testifying to others about God's unfathomable and transformative love that permeates even the most difficult circumstances.Chaka and her husband of twenty-six years have five children ranging from adult age to preschool. Trained as an attorney, she’s had the privilege of mitigating sibling disputes for twenty-plus years.Follow her on Chakaheinze.com. This episode is sponsored by Trinity Debt Management. If you are struggling with debt call Trinity today. Trinity's counselors have the knowledge and resources to make a difference. Our intention is to help people become debt-free, and most importantly, remain debt-free for keeps!" If your debt has you down, we should talk. Call us at 1-800-793-8548 | https://trinitycredit.org TrinityCredit – Call us at 1-800-793-8548. Whether we're helping people pay off their unsecured debt or offering assistance to those behind in their mortgage payments. https://trinitycredit.org Discover more Christian podcasts at lifeaudio.com and inquire about advertising opportunities at lifeaudio.com/contact-us.
Have you been feeling an unexplained anxiety, a low-grade tension, or a sadness with no clear source? Do you feel perpetually “on alert,” emotionally raw, or disconnected from joy even when your personal life seems stable? You are not broken, and you are not alone.In this episode of Infinite Life, Infinite Wisdom, Susan Grau addresses the palpable yet often nameless weight so many are carrying. This isn't about politics or prediction. It's a compassionate exploration of what it means to be a sensitive, perceptive human when the collective consciousness itself feels dysregulated.Susan explains how widespread grief, fear, and unresolved trauma create an atmosphere of “ambient suffering” that our nervous systems, especially those of empaths, healers, and caregivers, cannot help but absorb. This leads to symptoms like chronic anxiety, irritability, emotional numbness, brain fog, and exhausting mental loops as our systems search for closure and safety that the external world cannot provide.Moving beyond spiritual bypassing, Susan offers a practical and somatic path back to yourself. She reframes anxiety not as a thought problem, but as a nervous system signal. She redefines confusion not as incompetence, but as the necessary “space between stories” when old maps no longer fit. The core of healing, she reveals, lies in one vital shift, moving from the question “Is this story true?” to “Is this story regulating my nervous system?”This episode is a gentle, firm guide to empowerment through inner authority. It's about learning to discern what energy is yours to carry and what belongs to the collective, and how to release the latter without guilt. Susan provides actionable anchors, like pausing your internal narrative, regulating your body, and shrinking your timeframe to the present moment, to help you reclaim your grounding, your peace, and your power.In This Episode:[00:00] Introduction [01:24] Collective emotional overwhelm[02:38] Dysregulation and self-regulation[03:38] Ambient suffering and emotional reactivity[06:12] Nervous system and collective stress[07:27] Symptoms of overload and disconnection[08:24] Anxiety: body vs. mind[10:30] Confusion as a developmental stage[12:40] The stories we tell ourselves[14:50] Survival stories and letting go[16:58] Regulating the nervous system[19:01] Empowerment without bypassing[20:17] Personal vs. collective anxiety[22:36] Grounding and sensation of safety[24:06] Returning to self: practical solutions[25:42] The limits of control and self-relationship[28:55] ConclusionNotable Quotes[01:19] "Nothing's wrong with you. And I don't mean that in a motivational way. I mean it in a psychological way. In an emotional way."[02:59] "We don't need to bury our heads in the sand... But how do we self-regulate so that we can handle what's going on?"[08:19] "Our nervous system needs closure, and when it doesn't have it, it plays a loop in our brains."[11:21] "Confusion is the space between the stories."[11:38] "The old map no longer fits, but the new map hasn't been drawn yet."[16:27] "A thought can feel absolutely true and still not be truth."[21:38] "You are not meant to metabolize the entire world. You are allowed to release what does not belong to you."[22:43] "Safety is not an idea. It's a sensation."[19:01] "Empowerment doesn't have to be loud.."[29:02] "The only control you have is over you. And that is the scariest comment, isn't it?"[30:08] "Don't run from fear. Shake hands with it, face it and look it in the eye."Susan GrauSusan Grau is an internationally celebrated intuitive life coach, a key opinion leader, author, medium and speaker, who discovered her ability to communicate with the spirit world after a near-death experience at age four. Trained by Dr. Raymond Moody, James Van Praagh, and Lisa Williams, Susan is a Reiki Master, hypnotherapist, and grief therapist. Her new book, "Infinite Life, Infinite Lessons," published by Hay House, explores healing from grief and the afterlife. With media coverage in GOOP, Elle, and The Hollywood Reporter, Susan's expertise extends to podcasts, radio shows, and documentaries. She offers private mediumship readings, life path guidance, reiki sessions, and hypnotherapy, aiding individuals in healing and finding spiritual guidance.Resources and LinksInfinite Life, Infinite Wisdom Podcast Infinite Life, Infinite WisdomSusan GrauWebsiteOrder FacebookInstagramYouTubeTikTokMentionedInfinite Life, Infinite Lessons Wisdom from the Spirit World on Living, Dying, and the In-Between by Susan GrauSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Most leaders strive for this praise but are thrown into the deep end - responsible for up to 260 meetings a year and expected to know how to motivate teams, navigate tough conversations, and drive results with little guidance. This episode is all about becoming leadership. So if you're trying to step into a new role or even just become a better boss, this episode is for you. Today's guest is Ashley Herd, former Head of HR North America at McKinsey, national keynote speaker, and LinkedIn Top Voice who has trained over 250,000 managers.She has a new book coming out tomorrow, February 6th entitled, The Manager Method: A Practical Framework to Lead, Support, and Get Results (February 10, 2026 // Hay House), she helps managers at every level lead with confidence, navigate challenges, build strong teams, and avoid burnout.Topics we explore in this episode: Major challenges that managers faceWhy you should challenge yourself to become a better leader. Shouldn't real world experience be enough?How a career quilt, a collection of career experiences, can shape you as a leader.How the “Pause–Consider–Act” framework helps managers lead with confidenceWhy leaders NEED to take time offWays a manager can stop micromanaging while keeping accountability highHow AI can make leaders more human by improving communication, time management, and connectionResources MentionedORDER ASHLEY'S BOOK, THE MANAGER METHOD: Managermethod.com/bookConnect on LinkedIn with Ashley Herd: https://www.linkedin.com/in/ashleyherd/Connect with Chris, the host: https://www.linkedin.com/in/chris-villanueva-cprw/Let's Eat, Grandma — Resume writing services to help you stand out with clarity and confidence. Hosted on Acast. See acast.com/privacy for more information.
Reese Jones is living every San Fransico tech guy's wet dream. Create a company, sell it to Motorola for $205 millions dollars, and meet a hot, blonde girlfriend who doesn't hold back in the bedroom. A lifestyle some would be jealous of even after Reese gets kidnapped. Three men jump out, blindfold him, force him into a car at gunpoint. Next thing he knows, Reese is being led through seven different rooms, representing the seven deadly sins. One is lust. Another is gluttony. Then, envy. Reese is bound to a chair while his girlfriend has intercourse with what is described as ‘a buffet of people.' After all seven rooms, all seven sins, Reese is reborn. Which just means he's now cloaked in white, standing on a rooftop deck while his blonde girlfriend waits for him in the distance: “Happy Birthday.” That's what you get as a present when you're worth $200 million dollars and your girlfriend is the founder of One Taste, a company that helps women meditate and reach an orgasm. Every tech guy's wet dream right? That's until Reese gets wrapped up in one of the strangest, potential trafficking cases, and his girlfriend, Nicole Daedone, wellness company CEO ends up in the same prison as none other than Ghislaine Maxwell. Full show notes available at RottenMangoPodcast.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
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