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Your periods are still regular and your labs may even look “normal.” So why aren't you sleeping? Why has your mood changed? And why do you suddenly feel like a different version of yourself? In this episode of the As a Woman podcast, Dr. Natalie Crawford talks with Dr. Alicia Robbins, a board-certified OB/GYN, certified Menopause Society Practitioner, and founder of The Elm. They discuss why sleep and mood can change before hot flashes or irregular periods begin, how cycle tracking may reveal patterns, and how progesterone, estrogen, and testosterone are considered in individualized perimenopause care. They also explain the connection between fertility and perimenopause, what else should be ruled out, and how to advocate for better care when your symptoms are dismissed. IN THIS EPISODE: 00:00 What Is Perimenopause and When Does It Start? 03:20 Why Dr. Alicia Robbins Changed Her Approach to Perimenopause 09:55 Perimenopause vs. Menopause: What's the Difference? 11:23 Fertility and Perimenopause 12:22 How Do You Know if You're in Perimenopause? 14:47 How to Track Perimenopause Symptoms 16:02 Hormone Therapy During Perimenopause 17:38 Symptoms of Low Progesterone 18:11 Hormone Therapy, Birth Control, and Pregnancy 20:17 Testosterone and Pregnancy 21:21 Testosterone for Women 23:43 Symptoms of Low Estrogen 25:12 When to Start Hormone Therapy 27:04 Estrogen Patches vs. Pills 27:40 Do You Need Hormone Testing for Perimenopause? 30:01 Is It Perimenopause or Something Else? 32:13 How to Advocate for Better Perimenopause Care GUEST RESOURCES: Instagram: https://www.instagram.com/aliciarobbinsmd/ Website: https://www.theelmgreenwich.com/ Podcast: https://www.draliciarobbins.com/podcast AS A WOMAN WITH DR. NATALIE CRAWFORD: YouTube: https://youtube.com/playlist?list=PLveZDgg3_HZ9m7Wy3C6Vvyrj_NtEOwj_l Apple Podcasts: https://podcasts.apple.com/us/podcast/as-a-woman/id1449553339 Spotify: https://open.spotify.com/show/12IYOLH9liu60gk5D0bRPk HOST RESOURCES: Order The Fertility Formula: https://www.nataliecrawfordmd.com/book Newsletter: https://www.nataliecrawfordmd.com/newsletter Instagram: https://www.instagram.com/nataliecrawfordmd YouTube: https://www.youtube.com/@NatalieCrawfordMD Become a patient: https://www.forafertility.com Earn FREE CE/CME: https://learnatpinnacle.com/education This episode is brought to you by The Pinnacle Podcast Network. ABOUT DR. NATALIE CRAWFORD: Natalie Crawford, MD, is a double board-certified OB/GYN and reproductive endocrinologist, co-founder of Fora Fertility, and author of The Fertility Formula. She helps women understand their hormones, fertility, and reproductive health so they can make informed decisions about their bodies. Disclaimer: Natalie Crawford, MD, is a doctor, but she is not your doctor. This content is for general education and is not a substitute for individualized medical care. Learn more about your ad choices. Visit megaphone.fm/adchoices
We asked and the Sporties DELIVERED! It's Sportish Question Time! Think of it like catching up for a coffee with us and you've brought along a sports issue you need nutted out. On today's agenda - trialling for rep even when you're not good enough, development pathways, managing kids who are showing early signs of being excellent and what if compared to your peers you're just, well, average? (Big thanks to all the Sporties that sent through questions - that fact that you're asking already means that you're doing great). Libby and Georgie x PS Sporties - don't forget about the Active Industries First Start Traineeship Program ($20k to bring on a staff member for your club? Yes please!) head to Active Queenslanders Industry Alliance. ++ Sportish is hosted by Libby and Georgie Trickett, and edited by Ally Rasmussen. Come follow us on Instagram @beingsportish! We acknowledge the Traditional Custodians of the land on which this podcast was made. We pay our respects to Elders past and present. We also recognise the outstanding contribution that Aboriginal and Torres Strait Islander peoples make to sport and celebrate the power of sport to tell stories and promote reconciliation. See omnystudio.com/listener for privacy information.
Twenty-five years ago, a single participant enrolled in what would become the Wisconsin Registry for Alzheimer's Prevention (WRAP), now one of the largest and longest-running studies of Alzheimer's risk in the world. To celebrate this milestone, we're joined by Dr. Mark Sager, who co-founded WRAP, along with current multi-principal investigators Dr. Sterling Johnson and Dr. Jessica Caldwell, to discuss how the study has evolved, the discoveries that have reshaped our understanding of Alzheimer's prevention and what the next 25 years may hold for the field. Guests: Mark Sager, MD, founder, Wisconsin Alzheimer's Institute (WAI), WRAP, Sterling Johnson, PhD, multi-principal investigator, WRAP, associate director, Wisconsin Alzheimer's Disease Research Center, WAI, Jessica Caldwell, multi-principal investigator, WRAP, visiting associate professor, department of neurology, University of Wisconsin-Madison Show Notes This episode was uploaded as an exclusive video episode on the Wisconsin ADRC YouTube page on August 12, 2026. Watch the video interview here. Listen to our episode with Dr. Gina Green-Harris, mentioned at 12:27, on our website. Learn more about WRAP on the WRAP website. Learn about Drs. Sager, Johnson and Caldwell at their profiles on the WAI website. Connect with us Find transcripts and more at our website. Email Dementia Matters: dementiamatters@medicine.wisc.edu Follow us on Facebook, Instagram and Twitter. Subscribe to the Wisconsin Alzheimer's Disease Research Center's e-newsletter. Enjoy Dementia Matters? Consider making a gift to the Dementia Matters fund through the UW Initiative to End Alzheimer's. All donations go toward outreach and production. Learn about Dr. Chin's book, When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia.
Spencer Gray and Griffin Haddad return with a data-packed episode digging into whether multifamily is truly turning a corner. They break down UDR's decision to exit the preferred equity business, raise guidance, and buy back its own stock — a move they read as one of the clearest bullish signals from a public REIT in some time — while weighing it against transaction volume and pricing data that hasn't fully confirmed a recovery yet. The conversation covers regional rent growth trends (with the Midwest leading and San Francisco posting standout numbers), the ongoing affordability dynamics pulling renters toward homeownership in Indianapolis, and why garden-style apartment sales volume dropped 21% year over year. Spencer and Griffin also unpack Fed Chair Kevin Warsh's departure from traditional forward guidance, the yen carry trade unwind and its ripple effects on US Treasury yields, and close with an update on the Grant Cardone class action lawsuit and what it reveals about investor fit and fee structures in real estate syndications. Follow The Gray Report for weekly conversations on multifamily investing, macro trends, and the forces shaping commercial real estate.
Women's health has long been underserved by research, leaving many women without the information they need to understand their own bodies. On this week's episode of the WHOOP Podcast, WHOOP Global Head of Human Performance and Principal Scientist Dr. Kristen Holmes sits down with Dr. Elina Berglund Scherwitzl, Co-Founder & CEO of Natural Cycles° and Nobel Prize-winning particle physicist. The conversation explores how data is transforming women's reproductive health.After helping discover the Higgs boson, Dr. Berglund Scherwitzl turned her expertise in physics and data science toward her own health journey by creating the world's first and only FDA-cleared birth control app. Together, Dr. Holmes and Dr. Berglund Scherwitzl discuss how wearable technology and physiological data are giving women unprecedented insight into fertility, pregnancy planning, menstrual health, and perimenopause.The conversation explores the new WHOOP and Natural Cycles° integration, designed to make women's health feel less overwhelming and more intuitive by combining continuous wearable data with advanced insights on fertility, hormonal health, and perimenopause. Dr. Holmes and Dr. Berglund Scherwitzl discuss how body temperature and other biomarkers reveal hormonal changes, why perimenopause often begins earlier than many women realize, and how passive, personalized insights can reduce uncertainty, ease mental load, and empower women to make informed decisions about their health at every stage of life.(00:00) Cold Open (00:37) Meet The Scientist Behind Natural Cycles°(01:00) Why Dr. Berglund Scherwitzl Started Natural Cycles°(04:35) The Only FDA-Cleared Birth Control App(05:54) The WHOOP x Natural Cycles° Partnership(07:51) The Early Signs of Perimenopause (And How To Track Them)(13:08) What Really Happens To Your Hormones Over Time(17:34) Why Understanding Your Body Changes Everything(19:09) Your Sleep Could Reveal Early Signs of Perimenopause(22:03) The Small Habits That Make A Big Difference(24:14) The Most Common Menstrual Cycle Myths(26:09) What Your Wearable Can Tell You About Your Hormones(27:07) The Truth About Fertility Timing(29:16) Knowing Which Symptoms Matter Most(31:36) HRT: What Women Should Know(34:10) What You Can Expect From The WHOOP x Natural Cycles° Partnership(37:05) The Future of Women's HealthFollow Dr. Elina Berglund Scherwitzl:LinkedInWebsiteFollow Natural Cycles°:InstagramYouTubeWebsiteSupport the showFollow WHOOP:Sign up for WHOOP Advanced LabsTrial WHOOP for Freewww.whoop.comInstagramTikTokYouTubeXFacebookLinkedInFollow Will Ahmed:InstagramXLinkedInFollow Kristen Holmes:InstagramLinkedInFollow Emily Capodilupo:LinkedIn
An autism diagnosis changed everything for Harry and Gus Psaros—but not in the way they expected. Together, this father and son share an honest conversation about fear, hope, parenting, and what happens when you stop focusing on limitations and start looking for possibility. ABOUT GUESTS Harry Psaros is the author of “From Struggle to Strength”, a memoir about raising his son Gus after he was diagnosed with autism at age 3 (https://www.harrypsaros.com/). Gus Psaros is a recent Kent State University graduate pursuing a career in physical therapy. CHAPTERS (0:00) Introduction (0:41) Diagnosis Drive Home (3:21) Early Signs and Denial (5:06) Gus on Being Different (6:49) Kindness as Superpower (7:55) Setbacks (9:24) Brain Balance Breakthrough (11:07) Finding Balance and Burnout (11:58) Senior Year and College Leap (14:38) Gus' Vision Board and Big Goals (15:24) Calling on Dads to Step Up (18:27) Conclusion
Burnout isn't just about working long hours—it's the result of hidden stressors that build up over time. In this episode, wellness and leadership expert Jasmin Dennis shares insights from her 30+ years in health and wellness and introduces her proprietary Burnout Pie Framework, a practical tool designed to help individuals and organizations recognize, understand, and prevent burnout before it reaches a breaking point.Together, we explore the difference between stress and burnout, why even people who love their jobs can experience burnout, the early warning signs leaders should never ignore, and how creating a culture of compassion can improve employee well-being while reducing turnover and healthcare costs.Whether you're a leader, entrepreneur, HR professional, or someone striving for a healthier work-life balance, this conversation offers practical strategies to build resilience, lead with empathy, and create workplaces where people can truly thrive.
Caring for those with dementia represents a major public health challenge, with caregivers, especially spouses, providing long-term, largely unpaid care. With caregiving stress linked to a decline in mental and physical health, it is crucial to understand the complex burden these care partners face in order to support them. Dr. Donna de Levante Raphael joins the podcast to shed light on the effects of caregiving on the spousal caregiver and share possible interventions and programs to support their needs. Guest: Donna de Levante Raphael, PhD, gerontologist, director of research, Alzheimer's Foundation of America Show Notes Read Dr. Donna de Levante Raphael's newly published review, “The Impact of Dementia Caregiving on the Health of the Spousal Caregiver,” on the MDPI website. Learn more about Dr. Donna de Levante Raphael at her profile on the Alzheimer's Foundation of America website. Learn more about ambiguous loss and anticipatory grief, mentioned by Dr. de Levante Raphael at 34:44, by listening to our past episode, “A Guide to Grief: Strategies for Navigating Loss and Dementia.” Connect with us Find transcripts and more at our website. Email Dementia Matters: dementiamatters@medicine.wisc.edu Follow us on Facebook and Twitter. Subscribe to the Wisconsin Alzheimer's Disease Research Center's e-newsletter. Enjoy Dementia Matters? Consider making a gift to the Dementia Matters fund through the UW Initiative to End Alzheimer's. All donations go toward outreach and production. Learn about Dr. Chin's book, When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia.
In this powerful international episode, Rashidah sits down with Dalisse — originally from Honduras, now living in Barcelona — who is raising two boys on the autism spectrum: Adrian and Guillermo. Both boys were diagnosed around 17 months old, but while Dalisse was learning to navigate autism evaluations, therapies, and advocacy for her children, she was also beginning a life-changing journey of her own—receiving her own autism diagnosis as an adult.Together, Rashidah and Dalisse discuss the emotional realities of parenting while discovering your own neurodivergence, the similarities and differences between autism support in Spain and the United States, and why community is essential for autism moms everywhere. Out of her family's journey came something powerful. Dalisse is the co-founder of Loving Pieces Books, a publishing company dedicated to creating inclusive children's books where autistic children can see themselves represented. What began as a way to support her own sons has become a mission to help families around the world embrace neurodiversity through meaningful storytelling.In this episode, we discuss:0:00 — Meet Dalisse from Barcelona, Spain1:09 — Early Signs & The Road to Diagnosis2:00 — "Wait and See" — A Universal Experience Across Borders5:43 — The 2 AM Google Spiral6:53 — Explaining the Diagnosis to Family & Grandparents9:19 — Processing the Guilt: "Am I a Bad Mom?"9:57 — Two Boys, Two Very Different Journeys21:05 — Autism Resources & The System in Spain vs. the US28:41 — From Digital Marketing to Children's Books35:07 — Loving Pieces Book42:47 — Traveling with Autistic Kids51:40 — Dalisse's Own Autism Diagnosis & Healing Her Inner Child54:32 — Advice for the New Autism Mom55:30 — What Makes Dalisse a Badass Mom?Connect with Dalisse: Instagram: https://www.instagram.com/lovingpiecesbooksFacebook: https://www.facebook.com/lovingpiecesbooks/LinkedIn: https://www.linkedin.com/company/loving-pieces-books-autism-childrens-books/posts/?feedView=allWebsite: lovingpiecesbooks.comAbout Loving Pieces BooksDalisse and her husband created Loving Pieces Books — a series of illustrated children's books written for autistic kids and their families, available in 4 languages (English, Spanish, Portuguese & Catalan). Books mentioned:Autism: My Invisible BackpackAutism: This is How I PartyAutism: A New School YearAutism: Calming the ChaosAutism: Confidence Starts Here(Coming soon) Autism: Our Travel AdventurePurchase on Amazon: https://a.co/d/0iRC3eRCAutism for Badass Moms is the award-winning podcast where no autism mom is left behind. Each week, host Rashidah shares honest conversations with mothers around the world about autism, advocacy, resilience, and the realities of raising neurodivergent children.Website: https://www.autismforbadassmoms.comFollow Autism for Badass Moms:Instagram: http://www.instagram.com/theabmpodcastFacebook: http://www.facebook.com/theabmpodcastIf this episode encouraged you, please subscribe, leave a review, and share it with another autism mom, caretaker, or provider. Together, we're building a global community where every mom feels seen, heard, and supported.
How to Recognize Autism: Early Signs and Treatment Trevor Hall, DO Autism spectrum disorder (ASD), commonly called autism, affects how people communicate, interact and experience the world. In this episode of San Diego Health, host Susan Taylor speaks with Trevor Hall, MD, a pediatrician at Scripps Coastal Medical Center Encinitas, about the early signs of autism, how it's diagnosed and treated and why early intervention can make a lasting difference. Dr. Hall also discusses common misconceptions, available therapies and practical guidance for parents and caregivers.
Most women know the warning signs of breast cancer. Far fewer know what ovarian cancer can actually look like—and that's a problem. In this episode, Danielle shares the seven early signs of ovarian cancer every woman should know, why they're so often mistaken for everyday health issues, and how to recognize when a symptom deserves more than a quick explanation or reassurance. She also discusses why ovarian cancer is notoriously difficult to detect early, what a proper medical evaluation may include, and why paying attention to changes in your body matters. This episode isn't about creating fear—it's about creating awareness. You'll also learn how to advocate for yourself, what questions to ask if your symptoms aren't improving, and how to have productive conversations with your healthcare provider. Because when it comes to your health, trusting your instincts and seeking answers can make all the difference. Disclaimer: This podcast is for educational and informational purposes only and is not intended as medical advice, diagnosis, or treatment. Listening does not establish a patient-provider relationship with Danielle Kepics, PA-C, or any of her businesses. Always consult your qualified healthcare provider before making changes to your medications, supplements, diet, exercise, or treatment plan. Never disregard or delay medical care based on information shared in this podcast. Learn more about working with Danielle Shop Danielle's masterclasses (learn more in 60-90 minutes than years of dr appointments) Follow Danielle on IG Follow Empowered Mind + Body on IG
In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan
Brain Talk | Being Patient for Alzheimer's & dementia patients & caregivers
Journey to Diagnosis is a Being Patient series sponsored by Eisai. As with all of our reporting, the sponsor has no role in choosing guests, shaping questions, or reviewing content before publication.George King's work spans film, theater, writing, and photography. With both parents having lived with dementia, King was already attuned to the possibility of cognitive change when he began noticing subtle shifts in his own memory. A word game on his phone eventually became an unexpected warning sign, prompting him to seek evaluation from a neurologist. Today, he documents his experience on Instagram at @dancing.with.delirium, using his platform to share what it is like to live with Alzheimer's.In this conversation with Being Patient founder, Deborah Kan, King reflects on the path to diagnosis, the role of Leqembi in his treatment and the importance of finding a neurologist who answers questions clearly. He also discusses the daily adaptations that help him manage frustration, stay independent and continue traveling, creating, and engaging with the world. For King, living with Alzheimer's is not about fighting the disease, but learning how to negotiate with it while continuing to live as fully as possible.----If you loved listening to this Live Talk, visit our website to find more of our Alzheimer's coverage and subscribe to our newsletter: https://www.beingpatient.com/Follow Being Patient: Twitter: https://twitter.com/Being_Patient_Instagram: https://www.instagram.com/beingpatientvoices/Facebook: https://www.facebook.com/beingpatientalzheimersLinkedIn: https://www.linkedin.com/company/being-patientBeing Patient is an editorially independent journalism outlet for news and reporting about brain health, cognitive science, and neurodegenerative diseases. In our Live Talk series on Facebook, former Wall Street Journal Editor and founder of Being Patient, Deborah Kan, interviews brain health experts and people living with dementia. Check out our latest Live Talks: https://beingpatient.com/live-talks/
Most businesses are built around one kind of brain. Usually yours as the founder, and then wonder why their top performers burn out, go silent, or check out while still technically doing their job.In this episode, we get into what happens when neurodivergent minds hit systems that were never built with them in mind, and what it takes to build a business that works for the humans inside it. We're joined by Alexis Frank, creator of Governance by Design, and Mark Sones, autistic ADHD leader turned neurodiversity consultant, to break down how hiring, leadership and processes change when you stop pretending everyone thinks the same.-Before repositioning, rebranding, or making ground-shaking changes with your business, if you don't understand what's performing well and what isn't, those changes won't work.Diagnose and fix the gaps in your brand today: http://badasserybyb.com/brand-health-audit-04:15 Where Systems Break Down07:17 Early Signs of Burnout21:45 Getting Honest Feedback27:42 Rethinking Hiring Interviews34:11 Designing Better Assessments38:48 Thinking Styles Fit40:21 Safe Screening Questions42:23 Gamify Applications46:03 Founder Bottleneck Signs51:23 Slow Down Handoffs-5 episodes to binge on:The no bullshit strategy with Alex SmithBrand strategy in action with Cam VarnerRepositioning to $10M with Nicole DownerYour Business Might Be Unsellable with Allie Beckmann and Alexandria SeydelRepositioning Without Losing Customers with Amy Heidersbach & Melissa Eaton-Reach out to guest speaker Alexis Frank: LinkedIn | Website | InstagramReach out to guest speaker Mark Sones:LinkedInFollow Beatrice Gutknecht:LinkedIn | YouTube | Instagram | Website
Prescription medications, over-the-counter drugs and supplements are incredibly common among older adults, yet many of these routine treatments can unexpectedly impact cognitive function and brain health. To help us safely navigate medication management, Dr. Lauren Welch, a clinical geriatric pharmacy practitioner, joins the podcast to discuss how common drugs affect cognition, the risks of drug-drug interactions, and how families can effectively partner with their health care team to review and de-prescribe medications. Guest: Lauren Welch, PharmD, BCGP, clinical geriatric pharmacy practitioner, Veterans Affair Geriatrics Research Education and Clinical Center Show Notes Learn more about the Wisconsin Pharmacy Quality Collaborative (WPQC) and how to get a physician referral at the Pharmacist Society of Wisconsin's website. Learn more about the effect of blood pressure control on dementia risk, mentioned by Dr. Welch at 22:50, by listening to our past episode “SPRINT to a Healthy Mind: How Blood Pressure Control Affects Brain Health and Dementia Risk” on our website. Connect with us Find transcripts and more at our website. Email Dementia Matters: dementiamatters@medicine.wisc.edu Follow us on Facebook and Twitter. Subscribe to the Wisconsin Alzheimer's Disease Research Center's e-newsletter. Enjoy Dementia Matters? Consider making a gift to the Dementia Matters fund through the UW Initiative to End Alzheimer's. All donations go toward outreach and production. Learn about Dr. Chin's book, When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia.
Rashidah sits down with Wendy Ernzen — mom, podcaster, and fierce advocate for families raising loved ones with intellectual and developmental disabilities. Wendy's daughter Nicola (Nic), 23, has profound autism and bipolar disorder. Wendy shares her decades-long journey through early diagnosis confusion, relentless school battles, a family crisis that spilled into the community, and ultimately finding Nic a safe, supported, and joyful home. This episode is raw, honest, and deeply moving — a must-listen for any parent in the thick of it. In this episode, we discuss:0:00 — Intro & Welcome 3:34 — Wendy Joins the Conversation 7:06 — Early Signs & The Road to Diagnosis 11:50 — School Battles & IEPs15:48 — Watching Peers Move On 19:50 — Bipolar Disorder Enters the Picture21:58 — First Crisis at 12: Psychiatric Units 26:40 — Planning for the Future 31:16 — Crisis Before Graduation 34:30 — Behaviors Spill into the Community37:00 — Crisis-Trained Officers 39:54 — Securing Housing: A Two-Month Fight 42:19 — Finding Strength44:59 — PTSD & Therapy47:22 — The Birth of Let's Plant Houses Podcast 50:36 — Advice for the Mom Who Feels Completely Alone 55:46 — What Makes Wendy Badass? 58:11 — How to Connect with Wendy Connect with Wendy: Let's Plant Houses Podcast — https://podcasts.apple.com/us/podcast/lets-plant-houses/id1724641933https://open.spotify.com/show/2J8JhQUrkKrXaYxs8JF4Zh?si=0dac5a63b4174d46 Website: www.letsplanthouses.comInstagram: www.instagram.com/letsplanthousesLinkedIn: Wendy Ernzen If this episode resonated with you:-Follow the Autism for Badass Moms Podcast-Leave a review to help other autism moms find thiscommunity-Share this episode with a parent who may feel unseen or misunderstood Follow Us: Instagram: www.instagram.com/theabmpodcastFacebook: www.facebook.com/theabmpodcast
Foundation Problems in Mississippi? Todd Sandridge of Superior Foundation Drops Truth & Advice In this must-listen interview, I sit down with Todd Sandridge of Superior Foundation Services to talk about the real foundation issues plaguing homes across Central Mississippi. From the relentless red clay soil and wild weather swings to hidden underground water and poor drainage — Todd explains why foundation problems are so common in areas like Madison, Brandon, Pearl, Clinton, Northeast Jackson, and around the Reservoir. He shares: Early warning signs homeowners should never ignore (doors sticking, cracks, uneven floors) Why it's smarter (and cheaper) to fix small issues before they become major ones What to do before selling your house Their transferable lifetime warranties and free estimates in the Jackson Metro area If you own a home in Mississippi, this conversation could save you thousands down the road. Superior Foundation Services – superior.ms | (601) 941-2125
Checkout ASUS ExpertBook P Series: https://www.flipkart.com/asus-expertbook-core-ultra-storeGet your hand-picked playbook here: https://www.figuringout.co/pdf/fo-534Guest Suggestion Form: https://forms.gle/bnaeY3FpoFU9ZjA47Disclaimer: This video is intended solely for educational purposes and opinions shared by the guest are his personal views. We do not intent to defame or harm any person/ brand/ product/ country/ profession mentioned in the video. Our goal is to provide information to help audience make informed choices. The media used in this video are solely for informational purposes and belongs to their respective owners.Order 'Build, Don't Talk' (in English) here: https://amzn.eu/d/eCfijRuOrder 'Build Don't Talk' (in Hindi) here: https://amzn.eu/d/4wZISO0Follow Our Whatsapp Channel: https://www.whatsapp.com/channel/0029VaokF5x0bIdi3Qn9ef2J(00:00) - Intro(03:45) - Who Is Dr. V. Mohan?(05:37) - What Is Diabetes & Why Is It So Hard to Control?(11:31) - Why Are Indians More Prone to Diabetes?(18:47) - Who Is More Likely to Get Diabetes: A Thin Indian or an Obese American?(24:51) - Plant Protein vs Animal Protein: What's the Difference?(27:25) - Early Signs and Symptoms of Prediabetes(29:33) - Type 1 vs Type 2 Diabetes Explained(33:41) - Can Type 2 Diabetes Be Reversed in 90 Days?(36:27) - Is Alzheimer's Disease Type 3 Diabetes?(50:06) - Brain Fog vs Dementia: How to Tell the Difference(53:28) - Which Organs Does Diabetes Damage First?(1:18:08) - Why Wounds Heal Slowly in People With Diabetes(1:20:51) - Best Sugar Substitutes Ranked(1:33:40) - Top 3 Healthy Breakfasts for Indians(1:45:55) - How Dr. Mohan Saved a 3-Month-Old Baby From 17,000 Injections(2:01:36) - Top 3 Takeaways From This Episode(2:04:31) - OutroIn today's episode, we sit down with Dr. V Mohan - Chairman, Dr. Mohan's Diabetes Specialities Centre & Madras Diabetes Research Foundation, Chennai, India's leading diabetologist, a researcher with over 800 publications, and the man behind Mohan's Criteria, a diagnostic framework used by doctors worldwide. About Raj ShamaniRaj Shamani is an Entrepreneur at heart that explains his expertise in Business Content Creation & Public Speaking. He has delivered 200+ speeches in 26+ countries. Besides that, Raj is also an Angel Investor interested in crazy minds who are creating a sensation in the Fintech, FMCG, & passion economy space.To Know More,Follow Raj Shamani On ⤵︎Instagram @RajShamani https://www.instagram.com/rajshamani/Twitter @RajShamani https://twitter.com/rajshamaniFacebook @ShamaniRaj https://www.facebook.com/shamanirajLinkedIn - Raj Shamani https://www.linkedin.com/in/rajshamani/About Figuring OutFiguring Out Podcast is a Candid Conversations University where Raj Shamani brings raw conversations with the Top 1% in India.
Could the changes you've been putting down to stress, ageing or a busy life actually be the early signs of perimenopause?Many women spend years wondering why they suddenly feel anxious, exhausted, forgetful or unlike themselves, never realising these can be some of the earliest signs of perimenopause. In this episode, Adele unpacks the symptoms that are so often overlooked, explains why hormonal changes can begin much earlier than many women expect, and shares practical guidance to help you better understand your body.The Link to the Masterclass: https://adelejohnstoncoaching.com/mscc-free-masterclass/Watch The Menopause Coach (and see extra content from Adele): https://www.youtube.com/@TheAdeleJohnston____________Check out Adele's FREE symptom assessment here:https://adelejohnstoncoaching.com/free-copy-of-our-symptom-assessment/ The Menopause Cheat Sheet: https://docs.google.com/document/d/1ka-fN6J5DJW2J3IE0Qa80zFCKFXmTs4srlnlXYBf-gA/edit?usp=sharingIf you want a chat for your future success, fuel yourself here: https://calendly.com/adelejohnston/successchatDownload Adele's Journey Journal here : https://adelejohnstoncoaching.com/my-journey-journal/Enquire about 121 coaching here : https://docs.google.com/forms/d/e/1FAIpQLSfw6vrmKPE7A1eYDKQJiR9No7ZDdpfq-grBdKYjZSR-vl0Qag/viewformFor extra support: Support@adelejohnstoncoaching.com____________From your host : Adele JohnstonI'm Adele Johnston, a certified nutritionist and positive psychology coach, passionate about helping women improve their menopause health and reclaim who you are without menopause taking over.This is a time in your life where you get to feel vibrant, sexy and reclaim you again!I'm proud to work with women like you and have created a very successful proven Reclaiming You 3 STEP PROCESS to help you take back control of your body during your menopause.For more details : https://adelejohnstoncoaching.com/To get Adele's FREE 3-step Menopause Weight Loss Guide: https://adelejohnstoncoaching.com/menopause-weight-loss-guide/
Dr. Quentin R. Youmans, Heart Failure and Heart Transplant Cardiologist, Northwestern Medicine, joins John Williams to talk about heart health, why he encourages patients to start small when on the path to better heart health, why it’s important to bring heart health screenings into the community, what you should know about the early signs of […]
Dr. Quentin R. Youmans, Heart Failure and Heart Transplant Cardiologist, Northwestern Medicine, joins John Williams to talk about heart health, why he encourages patients to start small when on the path to better heart health, why it’s important to bring heart health screenings into the community, what you should know about the early signs of […]
Dr. Quentin R. Youmans, Heart Failure and Heart Transplant Cardiologist, Northwestern Medicine, joins John Williams to talk about heart health, why he encourages patients to start small when on the path to better heart health, why it’s important to bring heart health screenings into the community, what you should know about the early signs of […]
Dr. Jessen returns to the podcast to take a deeper dive into subjective cognitive decline (SCD). Aiming to cover everything relating to SCD, Dr. Jessen shares insights in prognosis, risk factors and how SCD should be addressed in research and clinical practice. Guest: Frank Jessen, MD, professor of psychiatry and psychotherapy, director, department of psychiatry, University of Cologne, clinical board member, Clinical Alzheimer's Research, German Center of Neurodegenerative Disease (DZNE), chair, European Alzheimer's Disease Consortium (EADC), founder and past chairman, ISTARRT Professional Interest Area on SCD, SCD-initiative (SCD-I), founder, chair, German Memory Clinic Network (DNG), advisory board, German Alzheimer Association, Hirnliga Show Notes Read Dr. Jessen's study, “The characterisation of subjective cognitive decline,” on the National Library of Medicine website. Listen to our previous episode with Dr. Jessen, “Defining Subjective Cognitive Decline,” on the Dementia Matters website or wherever you get your podcasts. Read Dr. Jessen's study, “Longitudinal Cognitive Impairment and Dementia Study (DELCODE),” on the DZNE website. Learn more about Dr. Jessen at his profile on the DZNE website. Connect with us Find transcripts and more at our website. Email Dementia Matters: dementiamatters@medicine.wisc.edu Follow us on Facebook and Twitter. Subscribe to the Wisconsin Alzheimer's Disease Research Center's e-newsletter. Enjoy Dementia Matters? Consider making a gift to the Dementia Matters fund through the UW Initiative to End Alzheimer's. All donations go toward outreach and production. Learn about Dr. Chin's book, When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia.
There is only two weeks left in The Ultimate Custom Guided Hunt giveaway from Forged In The Backcountry. Head on over to https://forgedinthebackcountry.com/ and for every $5 you spend, you will get one entry to win an all-expenses-paid custom guided hunt with Mindful Hunter Outfitting In this final instalment of the Outdoor Vitals Mountain Pursuit series, I review the Hybrid Pursuit Fleece, a dynamic insulation piece designed for all-season use. How does it stack up against the Sitka Ambient—my long-time favourite? I dig deep into warmth, layering performance, sizing tips, and long-term durability. If you're serious about technical layering for backcountry hunts, you won't want to miss this.
Subjective cognitive decline (SCD) can be one of the earliest noticeable signs of Alzheimer's disease, yet SCD is not yet fully understood. To help us understand more, Dr. Frank Jessen, an expert in the field, joins the podcast to discuss how SCD is defined, classified, and evaluated in both clinical and research settings. Guest: Frank Jessen, MD, professor of psychiatry and psychotherapy, director, department of psychiatry, University of Cologne, clinical board member, Clinical Alzheimer's Research, German Center of Neurodegenerative Disease (DZNE), chair, European Alzheimer's Disease Consortium (EADC), founder and past chairman, ISTARRT Professional Interest Area on SCD, SCD-initiative (SCD-I), founder, chair, German Memory Clinic Network (DNG), advisory board, German Alzheimer Association, Hirnliga Show Notes Read Dr. Jessen's study, “The characterisation of subjective cognitive decline,” on the National Library of Medicine website. Learn more about Dr. Jessen at his profile on the DZNE website. Connect with us Find transcripts and more at our website. Email Dementia Matters: dementiamatters@medicine.wisc.edu Follow us on Facebook and Twitter. Subscribe to the Wisconsin Alzheimer's Disease Research Center's e-newsletter. Enjoy Dementia Matters? Consider making a gift to the Dementia Matters fund through the UW Initiative to End Alzheimer's. All donations go toward outreach and production. Learn about Dr. Chin's book, When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia.
Dementia doesn't show up with a warning label, and it definitely doesn't erase the person sitting right in front of you. We're back in Detroit to hear directly from people living with dementia and the care partners walking beside them and the honesty is refreshing, specific, and sometimes even funny in the middle of the hard stuff.First, we talk with Dick and John (with Gerry bringing the real talk as a spouse and support system) about what made them finally say, “Something isn't quite right.” They describe early signs like repeating themselves, word-finding issues, speech clarity problems, and the tension those changes can create at home. Then we dig into what happened after diagnosis: the relief of naming the problem, the power of telling family instead of keeping secrets, and how early testing can open doors to planning and medical options.We also get into Alzheimer's treatment realities, including infusion therapy and what it feels like to pursue treatment with a trusted care team. Just as important, we unpack why some people with dementia symptoms won't qualify for Alzheimer's infusions if the biomarkers aren't there, and what it's like to keep searching for answers. Then members of the National Council of Dementia Minds join virtually to share a wider view: executive function decline at work, REM sleep behavior disorder, getting lost close to home, misdiagnoses, and the exhausting maze of specialists and tests.You'll leave with practical strategies that actually help: whiteboards, calendars, lists, timers, note-taking, safety planning around driving, and staying social so life stays bigger than the diagnosis. If this conversation helps you, subscribe, share it with someone who's worried about cognitive changes, and leave a review so more families can find it.Many thanks to Council of Dementia Minds and Wayne State University. For more information, visit: https://agreed.wayne.edu/community-impactExecutive Producer/Host: J Smiles ComedyProducer: Mia HallVideographer/Editor: Annelise UdoyeSupport the show"Alzheimer's is heavy but we ain't gotta be!"IG: https://www.instagram.com/parentingupFB: https://www.facebook.com/parentingupYT: https://www.youtube.com/@parentingupTEXT 'PODCAST" to +1 404 737 1449 - to give J topic ideas, feedback, say hi!Be sure to leave us a review!
As a mom of three boys, one of the questions I think about most is: how do we raise healthy, resilient kids in a world that feels increasingly complicated? In this special compilation episode, I'm revisiting some of my favorite conversations about parenting, emotional health, food, independence, and resilience. These discussions have shaped the way I approach motherhood and have given me practical tools that I come back to again and again. You'll hear from Dr. Lauren Hartman on helping kids develop a healthy relationship with food and their bodies, Devon Kuntzman on understanding toddler behavior and responding with connection instead of reaction, Lenore Skenazy on why independence is one of the greatest gifts we can give our children, and Nicole Runyon on setting boundaries that foster confidence, resilience, and emotional strength. No matter what stage of parenting you're in, I hope these conversations remind you that raising healthy kids isn't about perfection, it's about showing up with consistency, presence, and trust. → Leave Us A Voice Message! Topics Discussed: → How can parents reduce childhood anxiety? → What are signs of resilient children? → How do you avoid parenting power struggles? → How do kids develop healthy self-esteem? → What creates emotional resilience in children? Sponsored By: → Function | Check your health the way I do. Function provides 160+ lab tests for $1/day and member pricing on MRI and CT scans. Join at https://functionhealth.com/bewellbykelly and use code BEWELL25 for a $25 credit. → Be Well By Kelly Protein Powder & Essentials | Get $10 off your order with PODCAST10 at https://bewellbykelly.com. → Timeline | Timeline's clinically proven formula is now more accessible. Mitopure starts at $99, and listeners can get 20% off at: https://timeline.com/KELLY Timestamps: → 00:00:00 - Introduction → 00:01:23 - Dr. Lauren Hartmen: Eating Disorder Warning Signs → 00:05:05 - The Physical Signs → 00:06:55 - Talking to your child → 00:10:01 - Eating Disorder Education → 00:14:22 - No One's To Blame → 00:17:26 - Personal Backstory → 00:22:01 - Finding The Balance + Rabbit Study → 00:26:37 - Devon Kuntzman: Toddler Brain 101 → 00:31:54 - Teaching Emotional Regulation → 00:37:17 - Knowing The Plan → 00:40:48 - Tantrums vs Meltdowns → 00:43:34 - Handling Tantrums → 00:47:21 - Setting Boundaries → 00:49:06 - Lenore Skenazy: Why I Let My 9-Year-Old Ride The Train Alone → 00:52:48 - Helicopter Parenting Fears → 00:58:52 - Stop “Worst Case” Thinking → 01:04:28 - Nicole Runyon: Middle School Mental Health → 01:08:12 - Building Independence → 01:10:59 - Development Milestones → 01:14:34 - Cultivating Bravery → 01:17:30 - Teen Driving Tips → 01:20:21 - Holding Kids' Boundaries Further Listening: → Teen Eating Disorders, Early Signs, + How Parents Can Help | Dr. Lauren Hartman → Toddler Tantrums, Boundaries + Raising Emotionally Safe Kids | Devon Kuntzman → Why Kids Are More Anxious Today and How to Fix It | Lenore Skenazy → How to Raise Resilient Kids in a Tech Obsessed World | Nicole Runyon Check Out Dr. Lauren: → Freeing Children and Young Adults From Shame, Skills, and Stigma (book) → Website → Instagram Check Out Devin: → Instagram → Website → Transforming Toddlerhood (Book) Check Out Lenore: → Let Grow Kids → Website → X ( Twitter) → Instagram → YouTube → Free Range Kids (Book) Check Out Nicole: → Website → Free To Fly (Book) → Instagram Check Out Kelly: → Instagram → Youtube → Facebook
Talk to KimIn this episode, Nandita Mahadevan shares her journey through midlife, emphasizing the importance of choosing oneself, understanding perimenopause, and embracing community and mindset shifts for a healthier, more fulfilling life.Chapters00:00 Introduction to Midlife with Courage00:49 Redefining Success in Midlife02:39 Cultural Influences on Success and Health04:14 The Neglect of Self-Care in Women06:31 Early Signs of Perimenopause09:03 Medical and Functional Approaches to Hormonal Balance10:28 The Wisdom and Power Gained in Midlife12:26 Creating a Roadmap for Hormonal Health14:52 The Role of Nervous System Regulation19:24 The Importance of Sisterhood and Community20:19 Connecting with Your Inner Power20:45 Addressing Libido and Sexual Wellness24:35 Breaking the Silence on Women's Sexual Health26:19 Resources and Support for Midlife Women28:06 Embracing the Exciting Phase of Midlife28:52 lifestyle-intro-low-short.wavResourcesSol Women - https://solwomen.comNandita Mahadevan on Instagram - https://www.instagram.com/solwomen/Looking for a health care provider who really listens and isn't constrained by insurance requirements? You need to check out the Centered Care Directory-a curated, national resource to help you find the right provider for you. Whether it's functional medicine, physical therapy or hormone help, you'll find it here.Check out how.healthcare to get started. Support the showKim Benoy is a retired RN, Certified Aromatherapist, wife and mom who is passionate about inspiring and encouraging women over 40. She wants you to see your own beauty, value and worth through sharing stories of other women just like you.****************************************************If you are looking for deeper connection, encouragement, and support, you should join my free online community. It's a safe, uplifting space to be inspired, share honestly, and grow alongside women who truly get this season of life.Midlife with Courage™ Community*****************************************************Want to be a guest on Midlife with Courage™-Bold Women Thriving After Forty with Kim Benoy? Send Kim Benoy a message on PodMatch, here: Podmatch Link NEWSLETTER WEBSITEFACEBOOK
Brain Talk | Being Patient for Alzheimer's & dementia patients & caregivers
An Alzheimer's disease or dementia diagnosis can leave families unsure of what comes next. In his new book, Dr. Nathaniel Chin offers families a guide to navigating Alzheimer's disease and other dementias, from the first signs of memory loss to the later stages of care.Chin, the medical director and clinical core co-lead of the Wisconsin Alzheimer's Disease Research Center and an associate professor in the University of Wisconsin-Madison's Department of Medicine, hosts the podcast “Dementia Matters.” `He wrote “When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia” after his father's diagnosis changed his career and he became one of his father's primary caregivers. The book offers practical guidance on recognizing early symptoms, navigating medical exams, holding family meetings, building routines and maintaining connection as the disease progresses.In the conversation with Being Patient founder, Deborah Kan, Chin shared guidance from “When Memory Fades,” including how families can approach early symptoms, why people should be prepared before pursuing biomarker testing, and how routines, habits and advance care planning can help people live with more stability and dignity. He also discussed the promise and limits of new Alzheimer's blood tests and treatments, the importance of building brain resilience in the early stages, and the need for honest conversations about what families may face as the disease progresses.---If you loved listening to this Live Talk, visit our website to find more of our Alzheimer's coverage and subscribe to our newsletter: https://www.beingpatient.com/Follow Being Patient: Twitter: https://twitter.com/Being_Patient_Instagram: https://www.instagram.com/beingpatientvoices/Facebook: https://www.facebook.com/beingpatientalzheimersLinkedIn: https://www.linkedin.com/company/being-patientBeing Patient is an editorially independent journalism outlet for news and reporting about brain health, cognitive science, and neurodegenerative diseases. In our Live Talk series on Facebook, former Wall Street Journal Editor and founder of Being Patient, Deborah Kan, interviews brain health experts and people living with dementia. Check out our latest Live Talks: https://beingpatient.com/live-talks/
Geriatrician Dr. Mark Supiano joins the podcast to discuss the connection between heart and brain health. Citing multiple clinical trials, he breaks down what these studies and their findings mean for blood pressure management's effect on cognitive decline and how they directly impact both patients and clinicians. Guest: Mark A. Supiano, MD, geriatrician, University Hospital Geriatrics Clinic, professor, Internal Medicine, Utah School of Medicine Show Notes Read about the Systolic Blood Pressure Intervention Trial (SPRINT) Study on the National Heart, Lung, and Blood Institute website. Learn about the SPRINT MIND study in the Journal of the American Medical Association. Read Dr. Supiano's study, “Hypertension in the Oldest Old,” published by the Journal of the American College of Cardiology: Advances on their website. Learn about the HYVET, STEP, SPRINT-HEART and China Rural Hypertension Control Project studies through their articles on the National Library of Medicine website. Learn about an ancillary study to SPRINT, “Changes in arterial stiffness under blood pressure control are independently associated with cognitive impairment,” on the National Library of Medicine website. Learn about the Systolic Hypertension in the Elderly Program (SHEP) study, published by Clinical and Experimental Hypertension, on the Taylor and Francis Online website. Learn about the ESPRIT study on The Lancet website. Learn more about Dr. Supiano on the University of Utah Health website. Connect with us Find transcripts and more at our website. Email Dementia Matters: dementiamatters@medicine.wisc.edu Follow us on Facebook and Twitter. Subscribe to the Wisconsin Alzheimer's Disease Research Center's e-newsletter. Enjoy Dementia Matters? Consider making a gift to the Dementia Matters fund through the UW Initiative to End Alzheimer's. All donations go toward outreach and production. Learn about Dr. Chin's book, When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia.
After decades as a surgeon, professor, and medical expert, Dr. Anthony Goodman found himself facing an unexpected diagnosis: early Alzheimer's disease. In this conversation, Dr. Goodman and his wife, Maribeth, share what it was like to recognize the signs, receive the diagnosis, navigate treatment, and redefine life as a family. They discuss the difficult moment their daughter urged Dr. Goodman to seek help, the emotional challenge of moving from doctor to patient, and the surprising hope they found through early intervention and new Alzheimer's treatments. They offer an honest look at caregiving, stigma, identity, and the importance of talking openly about dementia. ABOUT GUEST Dr. Anthony Goodman is a former surgeon and medical educator. His new memoir, “Great Saves and Terrible Losses: The Journeys of a Surgeon,” chronicles both his decades-long career in medicine and his journey after being diagnosed with early Alzheimer's disease. He is joined by his wife of over four decades, Maribeth. You can learn more about the book and find various Alzheimer's resources at his website: https://anthonyagoodman-author.com/. CHAPTERS 00:00 Introduction 00:46 Meet Dr Anthony Goodman and Maribeth 01:34 Early Signs and Family Concern 04:34 From Normal Aging to Dementia 05:58 Getting Alzheimer's Diagnosis 08:29 Treatments and Early Intervention 10:36 Sharing the News with Loved Ones 12:29 Still A Doctor in a Crisis 14:26 Writing to Destigmatize Dementia 15:56 Conclusion
Amyloid-targeting monoclonal antibody treatments have ushered in a new era of Alzheimer's disease therapies after decades of research and clinical trials. A recent review published by Cochrane, a global, independent, non-profit network of researchers, professionals, patients and carers regarded as a gold standard for producing and promoting trusted, high-quality health information, has a different perspective on these therapies. The review found these treatments produce “little to no difference” in cognition and offer few benefits while increasing risks for adverse effects. Drs. Cynthia Carlsson, a clinical trialist, David Wolk, a clinician, and Henrik Zetterberg, a biomarker and disease biology expert, join the podcast to break down the review and their concerns, as well as highlight how this review could impact clinical care, research and public policy. Guests: Cynthia Carlsson, MD, MS, director, Wisconsin Alzheimer's Institute, Clinical Core leader, Wisconsin Alzheimer's Disease Research Center (ADRC), Louis A. Holland, Sr., Professor in Alzheimer's Disease, geriatrician, University of Wisconsin (UW) School of Medicine and Public Health; David Wolk, MD, director, Penn ADRC, co-director, Penn Memory Center, co-director, Penn Institute on Aging, professor of neurology, University of Pennsylvania Perelman School of Medicine; and Henrik Zetterberg, MD, PhD, professor of neurochemistry, University of Gothenburg, visiting professor, UW–Madison and University College London, Biomarker Core co-leader, Wisconsin ADRC Show Notes Read Cochrane's review, “Amyloid‐beta‐targeting monoclonal antibodies for people with mild cognitive impairment or mild dementia due to Alzheimer's disease,” on the Cochrane Library website. Learn more about Dr. Carlsson and Dr. Zetterberg at their profiles on the Wisconsin ADRC website and about Dr. Wolk at his profile on the Penn Memory Center website. Watch and listen to Dr. Carlsson's past episode, “A Closer Look at the Lecanemab Clinical Trials,” on our YouTube channel or on our website. Listen to Dr. Wolk's past episode, “LATE, Explained,” on our website. Listen to Dr. Zetterberg's past episode, “The Future of Fluid Biomarkers for Alzheimer's Disease and Related Dementias,” on our website. Connect with us Find transcripts and more at our website. Email Dementia Matters: dementiamatters@medicine.wisc.edu Follow us on Facebook and Twitter. Subscribe to the Wisconsin Alzheimer's Disease Research Center's e-newsletter. Enjoy Dementia Matters? Consider making a gift to the Dementia Matters fund through the UW Initiative to End Alzheimer's. All donations go toward outreach and production. Learn about Dr. Chin's book, When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia, out June 2, 2026.
Curious about the early signs of labor and not sure what to watch for besides contractions? This episode of Pregnancy & Parenting Untangled with The Pregnancy Nurse® | Pulling Curls® dives into those subtle pre-labor symptoms that many parents-to-be miss. Learn about cramping, fatigue, mucus plug changes, shifts in discharge, stomach upset, and more—crucial indicators if you're wondering, "Am I about to go into labor?" Perfect for anyone searching for what early labor feels like, what the first signs before contractions are, and how to handle these changes at home before heading to the hospital. Watch the original episode on YouTube: Click here to watch on YouTube Disclaimer This podcast provides educational information only. It is not a substitute for medical advice. Always consult your own healthcare provider for guidance specific to your health and pregnancy. About Hilary: The Pregnancy Nurse® Hilary is an experienced labor and delivery nurse who has been a registered nurse since 1997. With 20 years dedicated to labor and delivery, she specializes in making the complex process of labor easy to understand for new parents. Her expertise ensures you're getting trusted, evidence-based information to make you feel confident and prepared as you approach labor and delivery. TIMESTAMPS: 00:00 Early signs of labor discussion 05:46 Signs labor might be starting 06:24 Early labor signs and tips
BrainStorm wants to hear from you! Send us a text.What if knowing sooner could change everything? In this episode of BrainStorm, host Meryl Comer sits down with Dr. Nathaniel Chin — geriatrician, Alzheimer's researcher, and author of When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia, to explore why early diagnosis of Alzheimer's and dementia is more empowering than it is frightening.Dr. Chin opens up about the fear, stigma, and denial that keep patients and families from seeking answers, and why those hesitations have a real cost. Drawing on his own experience watching his father's cognitive decline, he walks through the subtle early signs of memory change, the difference between normal aging and something more serious, and how new biomarker tests are reshaping the diagnostic conversation.From navigating family denial to balancing hope, Dr. Chin offers a rare combination of clinical expertise and deeply personal insight of the journey. He reminds us that behind every diagnosis is a family with their thoughts on what matters most. Support the show
On the surface, Victoria Cillier's life seemed to be picturesque. She had just given birth to her second child with her husband, Emile, and the couple had planned a skydiving excursion for Victoria. However, little did she know that her husband had been marred by debt and affairs, and that he could see only one way out. When Victoria's parachute malfunctioned, she plummeted 4,000 feet to the ground. Upon inspection, it quickly became clear that this was no freak accident; this was an attempted murder. And even worse, it was the second attempt that week.Butcher Box: Stock up on those proteins with https://butcherbox.com/MILEHIGHGER to receive sirloin tips, ground beef OR chicken wings!StitchFix: Take the stress out of shopping with https://stitchfix.com/milehigher for $20 OFF your first order!Casper: Start sleeping better with https://casper.com to take advantage of up to 30% OFF!Intro 0:00Victoria's Early Life 4:32Emile's Early Signs 7:50Mounting Problems 12:49Taking Advantage 19:34On the surface, Victoria Cillier's life seemed to be picaresque. She had just given birth to her second child with her husband, Emile, and the couple had planned a skydiving excursion for Victoria. However, little did she know that her husband had been marred by debt, affairs, and he could see only one way out. When Victoria's parachute malfunctioned, she plummeted 4,000 feet to the ground. Upon inspection, it quickly became clear that this was no freak accident; this was an attempted murder. And even worse, it was the second attempt that week.A Real Psychopath 24:01That Fateful Jump 28:15Managing to Survive... Falling From a Plane 35:03Investigating Emile 44:31A Strenge Trial, a Good Outcome 51:24Final Thoughts & Outro 59:06Mile Higher Media website: https://milehigher.com/ Higher Hope Foundation: https://www.higherhope.org/ Mile Higher Merch: milehighermerch.comCheck out our other podcasts!The Sesh https://bit.ly/3Mtoz4XLights Out https://bit.ly/3n3GaoePlanet Sleep https://linktr.ee/planetsleepJoin our official FB group! https://bit.ly/3kQbAxgMHP YouTube: http://bit.ly/2qaDWGfAre You Subscribed On Apple Podcast & Spotify?!Support MHP by leaving a rating or review on Apple Podcast :) https://apple.co/2H4kh58MHP Topic Request Form: https://forms.gle/gUeTEzL9QEh4Hqz88You can follow us on all the things: @milehigherpodInstagram: http://www.instagram.com/milehigherpodYouTube: https://www.youtube.com/@MileHigherHosts:Kendall: @kendallraeonytIG: http://instagram.com/kendallraeonytYT: https://www.youtube.com/c/kendallsplaceJosh: @milehigherjoshIG: http://www.instagram.com/milehigherjoshProducers:Janelle: @janelle_fields_IG: https://www.instagram.com/janelle_fields_/Ian: @ifarmeIG: https://www.instagram.com/ifarme/Tom: @cinematomgrapherIG: https://www.instagram.com/cinematomgrapher/Podcast sponsor inquiries: adops@audioboom.com✉ Send Us Mail ✉Kendall Rae & Josh Thomas 8547 E Arapahoe Rd Ste J # 233Greenwood Village, CO 80112Music By: Mile Higher BoysYT: https://bit.ly/2Q7N5QOSpotify: https://open.spotify.com/artist/0F4ik...Sources: https://pastebin.com/PEdYCb97The creator hosts a documentary series for educational purposes (EDSA). These include authoritative sources such as interviews, newspaper articles, and TV news reporting meant to educate and memorialize notable cases in our history. Videos come with an editorial and artistic value.
The Tenpenny Files – Tiffany Ashton Bramblett connects autism, Alzheimer's, seizures, and autoimmune dysfunction through measurable stress inside the brain. Her family's experience highlights oxidative damage, mold exposure, uric acid shifts, blood sugar instability, and inflammation as overlooked signals that may appear before diagnosis and cognitive decline or neurological regression emerges fully...
This episode is sponsored by Zoetis. Canine osteoarthritis is far more common, and far more underdiagnosed, than most veterinary professionals realize. In a recent episode of the Vet Blast Podcast presented by dvm360, Adam Christman, DVM, MBA, sat down with Mikayla Mayland, DVM, DACVS, to discuss the latest thinking on osteoarthritis in dogs and why early recognition matters more than ever.Throughout the episode, the duo challenges everything veterinary professionals thought they knew about canine osteoarthritis, from which dogs are actually at risk to what the real first step of OA management is.
Krissy Dilger of SRNA moderated an open Q&A on acute disseminated encephalomyelitis (ADEM) with pediatric neurologist Dr. Linda Nguyen of the University of Texas Southwestern Medical Center. Dr. Nguyen discussed how widespread MOG antibody testing has shifted many cases previously labeled ADEM to MOG antibody-associated disease, recommended MOG testing at onset, and reviewed relapse risk, mimics, and follow-up imaging [00:05:07]. Questions from the community covered acute treatments, recovery, guidance on pseudo-relapse, rehabilitation, and transition from pediatric to adult care [00:14:41].Dr. Linda Nguyen completed her MD, PhD training at West Virginia University in 2017, and then pediatric neurology residency at the University of California, San Diego in 2022. She then completed a combined pediatric and adult neuroimmunology fellowship at the University of Texas Southwestern in 2024, where she now serves as an Assistant Professor in the Department of Pediatrics. Dr. Nguyen currently sees patients in the Demyelinating Disease Clinic at Children's Medical Center Dallas.00:00 Welcome and Guest Intro01:08 What Is ADEM01:51 Causes and Who Receives This Diagnosis02:55 Early Signs and Onset04:20 Diagnosis and MRI Criteria05:07 MOG Antibodies and ADEM07:21 Testing and Relapse Risk09:06 Recurrent ADEM and Labels12:34 Mimics and Differential Diagnosis14:41 Acute Treatment Options15:38 Recovery Timeline and Rehab17:47 Long Term Effects and Seizures21:23 Family Support and Accommodations24:47 Follow Up Imaging and Relapse Signs27:25 Managing Fatigue and Headaches29:31 Supplements, Vaccines, and Genetics33:40 Pseudo Relapse and Exercise Balance37:41 Research and Predicting Outcomes43:14 Transition to Adult Care45:36 Weakness Sleep Issues and Final Thoughts
Guest host Dr. Art Walaszek takes over the podcast to interview Dr. Chin about his upcoming book, When Memory Fades. In addition to discussing the importance of dementia research and other clinical topics covered in the book, Dr. Chin shares what drew him to write When Memory Fades, what the writing process was like, and what he hopes readers take away from his story. Guest host: Art Walaszek, MD, geriatric psychiatrist, professor, vice chair for education and faculty development, Department of Psychiatry, University of Wisconsin School of Medicine and Public Health Show Notes Learn about and pre-order Dr. Chin's book, When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia, out June 2, 2026. Learn more about Dr. Chin's book at his upcoming event with Mystery to Me bookstore on June 3, 2026. RSVP to attend in person or watch via livestream. Learn more about Dr. Walaszek at his profile on the Wisconsin ADRC website. Find an Alzheimer's Disease Research Center near you on the National Institutes on Aging (NIA) website, and find clinical trials at clinicaltrials.gov. Connect with us Find transcripts and more at our website. Email Dementia Matters: dementiamatters@medicine.wisc.edu Follow us on Facebook and Twitter. Subscribe to the Wisconsin Alzheimer's Disease Research Center's e-newsletter. Enjoy Dementia Matters? Consider making a gift to the Dementia Matters fund through the UW Initiative to End Alzheimer's. All donations go toward outreach and production.
In this powerful episode of Autism for Badass Moms, host Rashidah speaks with Jessica Pereira from the Bronx, NY, about raising her daughter Ja'Niyah, who was diagnosed with autism at age 2.5. Jessica shares how her bond with her sister, Chasity, evolved into a deeply connected co-parenting partnership rooted in unconditional love, advocacy, and autism acceptance.Together, they have created a home where stimming is welcomed, communication in all forms is honored, and Ja'Niyah is embraced exactly as she is. This heartfelt conversation explores sisterhood, support, neurodiversity, and helping autistic children thrive without forcing them into conventional molds.In this episode, we discuss:00:00 Badass Moms Welcome00:55 Why Moms Need Support03:02 Meet Jessica Pereira04:52 Bronx Beginnings05:53 Early Signs and Referral08:27 Diagnosis Day and Next Steps10:44 Mom Guilt and Sister Steps Up14:38 Single Mom Survival Mode15:57 School Trauma and Homeschool Choice19:04 Aggression and Communication Tools21:51 Sister Co Parenting System28:05 Acceptance Stimming and Public Stares30:13 Future Fears and Big Love33:52 Routine Changes and Respite35:45 Sister as Lifeline38:28 Dating and Limited Support40:35 ADHD and Mental Health43:45 IEP and School Struggles46:38 Gratitude and Softening Up49:02 Mourning Expectations53:16 Badass Mom Reality57:08 Advice to Keep Going01:00:58 What Makes Her Badass01:03:38 Closing and Call to ActionConnect with Jessica:Instagram: www.instagram.com/crzyfantasy128Facebook: Jessica JanetteIf this episode resonated with you, don't forget to:-Follow the podcast-Subscribe on your favorite podcast platform-Leave a review to help us reach more autism moms across the globe-Share this episode with a mom who needs to hear thisInstagram: www.instagram.com/theabmpodcastFacebook: www.facebook.com/theabmpodcast
Brain Talk | Being Patient for Alzheimer's & dementia patients & caregivers
Journey to Diagnosis is a Being Patient series sponsored by Eisai. As with all of our reporting, the sponsor has no role in choosing guests, shaping questions, or reviewing content before publication.Eisai: https://www.eisai.com/index.htmlJourney to Diagnosis: https://beingpatient.com/journey-to-diagnosis/Juli Chenault first began noticing memory lapses in 2008 while working as a business representative and traveling regularly across Kentucky. One of the first warning signs came when she could not remember how to get to an apartment she had visited many times and had to call her supervisor for help. After raising concerns about her memory with her primary care doctor and undergoing further evaluation, Chenault was diagnosed with early-stage dementia in her early 50s. Her mother had also lived with Alzheimer's disease.Since her diagnosis, she has remained committed to managing her health, staying on medication, and keeping her mind active. The support of her family has played a central role, both in recognizing the early changes and in helping her move forward.In the conversation with Being Patient's Mark Niu, Chenault discusses the relief of finally having answers, the importance of staying mentally and socially active, and the role her family has played in supporting her day to day. She also speaks about the need for more awareness in Black communities, where stigma, denial, and lack of information may keep people from seeking help. Chenault encourages others to take memory changes seriously, see a doctor, follow treatment recommendations, and stay connected to the people and activities that help keep the brain active.-----If you loved listening to this Live Talk, visit our website to find more of our Alzheimer's coverage and subscribe to our newsletter: https://www.beingpatient.com/Follow Being Patient: Twitter: https://twitter.com/Being_Patient_Instagram: https://www.instagram.com/beingpatientvoices/Facebook: https://www.facebook.com/beingpatientalzheimersLinkedIn: https://www.linkedin.com/company/being-patientBeing Patient is an editorially independent journalism outlet for news and reporting about brain health, cognitive science, and neurodegenerative diseases. In our Live Talk series on Facebook, former Wall Street Journal Editor and founder of Being Patient, Deborah Kan, interviews brain health experts and people living with dementia. Check out our latest Live Talks: https://beingpatient.com/live-talks/
Over 40 Alpha Answers: What Are The Early Signs Of Hormonal Decline In Men?The early signs of hormonal decline in men often include fatigue, belly fat gain, muscle loss, low libido, poor sleep, brain fog, slower recovery, and reduced motivation. These symptoms are commonly linked to declining testosterone, stress, poor sleep, increased body fat, and lifestyle factors after age 40.In this episode, you'll learn:The early signs of low testosteroneWhy hormonal decline happens after 40The connection between belly fat and estrogenWhy sleep affects testosteroneHow stress impacts hormonesWhat you can do naturally to improve testosterone and hormone healthAre you waking up tired even after a full night of sleep?Struggling with stubborn belly fat, low motivation, poor recovery, brain fog, or low libido?In this episode of Over 40 Alpha Answers, Funk Roberts breaks down the 10 early signs of hormonal decline in men over 40 and explains why these symptoms are often connected to declining testosterone, chronic stress, poor sleep, increased body fat, and lifestyle habits that accelerate aging.You'll learn how hormonal decline affects:energymuscle massfat losssleeplibidoconfidencerecoverymoodmental sharpnessFunk also explains the science behind testosterone decline, aromatase, cortisol, sleep deprivation, and insulin resistance — and most importantly, what you can do naturally to take back control of your hormones and health.If you're a man in your 40s, 50s, 60s, or beyond and feel like your body has changed…This episode is for you.WHAT YOU'LL LEARN IN THIS EPISODE✔️ The most common early signs of low testosterone in men✔️ Why hormonal decline starts earlier than most men realize✔️ The connection between belly fat and estrogen dominance✔️ Why poor sleep crushes testosterone levels✔️ How stress and cortisol affect male hormones✔️ Why muscle loss accelerates after 40✔️ The truth about brain fog, low motivation, and mood changes✔️ How to naturally improve testosterone and hormone health✔️ The Over 40 Alpha strategy for rebuilding energy, strength, and confidence“What is hormonal decline?”“Hormonal decline refers to the gradual reduction of key male hormones, especially testosterone, that affects energy, muscle, fat loss, mood, libido, recovery, and cognitive function.”The early signs of hormonal decline in men often include low energy, belly fat gain, reduced muscle mass, poor sleep, low libido, brain fog, mood changes, slower recovery, decreased strength, and loss of confidence or drive. These symptoms are commonly linked to declining testosterone, stress, poor sleep, insulin resistance, excess body fat, and lifestyle factors after age 40.THE 10 EARLY SIGNS OF HORMONAL DECLINE IN MENLow Energy & FatigueStubborn Belly FatLoss of Muscle MassLow Libido & Sexual Performance ChangesBrain Fog & Poor FocusPoor Sleep QualitySlower RecoveryIrritability & Mood ChangesDecreased Strength & PerformanceLoss of Confidence & DriveSCIENCE & RESEARCH DISCUSSEDTestosterone levels naturally decline approximately 1–2% yearly after age 30Poor sleep significantly reduces testosterone productionBelly fat increases aromatase activity, converting testosterone into estrogenChronic stress elevates cortisol, which negatively impacts testosteroneStrength training improves insulin sensitivity, muscle mass, and hormone healthMuscle loss after 40 accelerates metabolic decline and fat gainKEY TAKEAWAYSHormonal decline is not just about aging — lifestyle plays a massive roleBelly fat is hormonally active tissue and directly impacts testosterone levelsSleep is one of the most powerful natural testosterone boostersBuilding muscle is critical for metabolism, hormone health, and longevityChronic stress and elevated cortisol can destroy testosterone over timeMost men ignore the warning signs until symptoms become severeYou can naturally improve your hormones through training, nutrition, recovery, and lifestyle changesTIMESTAMPS00:00 Introduction01:15 What Hormonal Decline Really Means03:00 Why Testosterone Levels Are Lower Than Ever04:40 Sign #1: Low Energy & Fatigue08:50 Sign #2: Stubborn Belly Fat11:40 Sign #3: Loss of Muscle Mass15:00 Sign #4: Low Libido & Sexual Health18:45 Sign #5: Brain Fog & Poor Focus22:20 Sign #6: Poor Sleep Quality25:20 Sign #7: Slower Recovery27:50 Sign #8: Mood Changes & Irritability29:50 Sign #9: Decreased Strength & Performance31:50 Sign #10: Loss of Confidence & Drive34:40 The Over 40 Alpha Hormone Solution39:20 Final Thoughts & Next StepsTHE OVER 40 ALPHA SOLUTIONTo naturally improve hormone health after 40, focus on:Building muscle through strength trainingLosing belly fat strategicallyPrioritizing deep sleep and recoveryManaging stress and cortisolEating hormone-supportive foodsReducing ultra-processed foods and alcoholImproving insulin sensitivityGetting proper blood work and hormone testingFAQAt what age does hormonal decline begin in men?Most men begin experiencing gradual testosterone decline after age 30, with levels decreasing approximately 1–2% per year.What are the first signs of low testosterone?Low energy, stubborn belly fat, poor sleep, low libido, reduced strength, and brain fog are often some of the earliest signs.Can hormonal decline be reversed naturally?In many cases, yes. Strength training, fat loss, sleep optimization, stress management, and proper nutrition can significantly improve hormone health.Does belly fat lower testosterone?Yes. Belly fat increases aromatase activity, which converts testosterone into estrogen.How does sleep affect testosterone?Most testosterone production occurs during deep sleep. Poor sleep can significantly reduce testosterone levels.RESOURCES & LINKS
Understand Loneliness & Create Fulfilling, Lasting Relationships https://offer.personaldevelopmentschool.com/loneliness-promo?utm_source=podcast&utm_campaign=overcoming-loneliness&utm_medium=organic&utm_content=pod-05-08-26&el=podcast Cheating is one of the most misunderstood dynamics in relationships. It's often seen as a problem of temptation or lack of discipline, but in reality, it usually stems from something much deeper: unmet needs and Attachment patterns. Understanding why cheating happens, and how it differs by Attachment Style can help you recognize early warning signs and build healthier, more secure relationships. Episode Summary In this episode, Thais Gibson breaks down the real psychology behind cheating and how it shows up across different Attachment Styles. You'll learn why Anxious Preoccupied, Dismissive Avoidant, Fearful Avoidant, and Securely Attached individuals experience and approach infidelity differently and how most cheating is driven by unmet emotional needs rather than purely physical desire. Thais also explains how secure relationships are built through open communication, needs fulfillment, and emotional awareness and why trust is something you actively create, not something you passively hope for. Key Takeaways ✔️ Cheating is often driven by unmet needs, not just physical attraction ✔️ Anxious Preoccupied individuals may seek emotional validation outside the relationship ✔️ Dismissive Avoidant individuals may cheat when feeling criticized or misunderstood ✔️ Fearful Avoidant patterns may lead to sabotage driven by fear and emotional instability ✔️ Emotional or physical affairs often reflect different Attachment Style patterns ✔️ Securely Attached individuals communicate needs instead of seeking them elsewhere ✔️ Healthy trust is built through communication, not assumed Meet the Host Thais Gibson is the founder of The Personal Development School and a world leader in attachment theory. With a Ph.D. and over a dozen certifications, she's helped more than 70,000 people reprogram their subconscious and build thriving relationships. Helpful Resources:
Send us Fan Mail You can hit every goal, ship every deliverable, smile in every meeting, and still feel like something is fraying underneath. This episode is for the high-achiever who has gotten good at masking, and is starting to notice it costs more than it earns. Sayan sits down with Dr. Gregory Obert, clinical psychologist and founder of the Royal Oasis Psychotherapy Institute, for a clear-eyed conversation about why driven people are particularly vulnerable to emotional overload. They unpack the Yerkes-Dodson Law, why ambition can be survival mode in a sharp suit, what unresolved trauma looks like at work, and the simplest mindfulness practice you can start today, while brushing your teeth. About the Guest: Dr. Gregory Obert is a clinical psychologist and the founder of the Royal Oasis Psychotherapy Institute. With over 15 years of experience, he helps people regulate stress, recover from trauma, and think more clearly under pressure. He hosts the Meditations by Gregory T. Obert podcast and The GOBERT Report, and is licensed in 43 states through PsyPact. Key Takeaways: High achievers don't break down loudly. They miss small details, sleep less, get irritable, feel stuck. Those are the signals worth listening to. The Yerkes-Dodson Law: anxiety in the 4-6 range can sharpen performance. Below or above that range, judgment and effectiveness fall off. Unresolved trauma at work shows up as black-and-white thinking, dominance, withdrawal, or burying yourself harder. Fight-or-flight in business clothes. Decision fatigue is real. By midday, a high-functioning leader may have already made hundreds of micro-decisions, leaving little resilience for home life. Mindfulness is not woo-woo. It is paying attention, on purpose, to the present moment, non-judgmentally. And you can train it while brushing your teeth. Healing begins where masking ends. Slowing down isn't falling behind, it's the doorway back to clarity. Connect With the Guest: Practice website: https://www.royaloasispi.com Free Autopilot Interrupt Protocol PDF: https://www.royaloasispi.com/autopilotinterruptprotocol Podcast Meditations by Gregory T. Obert: https://meditationsbygto.libsyn.com LinkedIn: https://www.linkedin.com/in/gregorytobert/ Instagram: https://www.instagram.com/drgtobert/ X / Twitter: https://x.com/DrGTObert Episode Chapters: [00:00] Cold Open: Looking Successful, Feeling Hollow [02:30] How Masking Hides the Early Signs of Burnout in High Achievers (approx.) [05:00] The Yerkes-Dodson Law: When Anxiety Helps, and When It Hurts (approx.) [08:30] Reclaiming Your Nervous System Through Mindfulness (approx.) [12:00] How Unresolved Trauma Quietly Shows Up in the Workplace (approx.) [16:30] Decision Fatigue: The Hidden Cost of Constant Output (approx.) [20:00] Practical Starting Points: Toothbrush Mindfulness and the Autopilot Interrupt Protocol (approx.) Want to be a guest on Healthy Mind, Healthy Life? DM on PM - Send me a message on PodMatch DM Me Here: https://www.podmatch.com/hostdetailpreview/avik Disclaimer: This episode is produced for educational and informational purposes only. All views expressed by the guest are their personal opinions alone and do not represent the views of the host or Healthy Mind by Avik™. The Network does not verify, endorse, or assume responsibility for any guest statements. Nothing in this episode constitutes medical, legal, financial, or professional advice, please consult a qualified professional before making any decisions. Listeners are encouraged to engage critically and independently with all content do not consume blindly. Use this content as a starting point for your own reflection and research, not as a substitute for professional guidance. Third-party content is referenced under fair use for informational purposes only. Guest speakers are solely responsible for their own statements. If you have concerns about any content, please contact us here. By listening, you acknowledge and accept this disclaimer in full. Read detailed disclaimer here. Healthy Mind By Avik™ is a global platform redefining mental health as a necessity, not a luxury. Born during the pandemic, it's become a sanctuary for healing, growth, and mindful living. Hosted by Avik Chakraborty, storyteller, survivor, and wellness advocate. With over 6500+ episodes and 200K+ global listeners, we unite voices, break stigma, and build a world where every story matters. Subscribe and be part of this healing journey. Brand: Healthy Mind By Avik™ | Email: https://www.podhub.club/contact | Website: https://www.podhub.club | Based in: India & USA Listen to all podcast shows: https://www.podhub.club/podcastnetwork | Be a guest: https://www.podhub.club/beaguest | Newsletter: https://healthymindbyavik.substack.com/ Support the show Want to Be a Guest on Healthy Mind, Healthy Life?
The latest Household Food Security report estimated that 13.7% of U.S. households experienced food insecurity in 2024. This lack of access to a nutritious diet can significantly affect health, so how does it then impact older adults' health and their risk for cognitive impairment? Dr. Heejin Lee joins the podcast to share what food insecurity means, how it can affect a person's overall health and what her study found about its effect on dementia and cognitive impairment, as well as resources for those experiencing food insecurity. Guest: Heejin Lee, PhD, postdoctoral research fellow, department of nutrition, Harvard T.H. Chan School of Public Health Show Notes Read Dr. Lee's article, “Food Insecurity and Risk of Dementia and Cognitive Impairment With No Dementia in US Older Adults,” published by JAMA Network Open online. Learn more about Dr. Lee at her profile on the Harvard T.H. Chan School of Public Health website. Read the Household Food Security report, "Household Food Security in the United States in 2024," on the U.S. Department of Agriculture's Economic Research Service website. Listen to our episode with Dr. Beth Olson, “How Ultra‑Processed Foods Impact Your Overall Health,” mentioned at 11:00, on our website, Spotify, Apple Podcasts or wherever you listen. Connect with us Find transcripts and more at our website. Email Dementia Matters: dementiamatters@medicine.wisc.edu Follow us on Facebook and Twitter. Subscribe to the Wisconsin Alzheimer's Disease Research Center's e-newsletter. Enjoy Dementia Matters? Consider making a gift to the Dementia Matters fund through the UW Initiative to End Alzheimer's. All donations go toward outreach and production. Learn about and pre-order Dr. Chin's book, When Memory Fades: What to Expect at Every Stage, from Early Signs to Full Support for Alzheimer's and Dementia, out June 2, 2026.
What if what we call Primary Ovarian Insufficiency isn't premature menopause at all, but "ovarian fatigue"? In this groundbreaking episode, Dr. Sarah Daccarett turns conventional hormone wisdom on its head, revealing how hormone depletion can start long before menopause and why diet and supplements alone often miss the mark. She shares her own powerful postpartum story, the science behind modern, safer hormone replacement therapy (HRT), and why optimizing your hormones isn't just about fertility, it's about protecting your heart, brain, and long-term vitality. If you've ever wondered whether HRT is right for you, this conversation will completely change the way you think about female hormone health, longevity, and what true balance really means. If you're LOVING this podcast, please follow and leave a rating and review below. PLUS FOLLOW MY INSTAGRAM PAGE HERE FOR BITE SIZED TTC TIPS! Need Nora's Support To Get Pregnant? Apply for Private Fertility Coaching with Nora here Click here for a collection of Nora's best self paced programs to get & stay pregnant For full show notes and guest related links: https://www.naturallynora.ca/blog/187 Grab Your FREE Resources: Just starting your TTC journey? Download my Eat To Get Pregnant Guide Having trouble getting and staying pregnant? Download my Top 3 Things To Do When You're Not Getting Pregnant Wondering what supplements to take to help you conceive? Download my Fertility Foundations Supplement Guide Please Note: The contents of this podcast are for educational and informational purposes only. The information is not to be interpreted as, or mistaken for, clinical advice. Please consult a medical professional or healthcare provider for medical advice, diagnoses, or treatment.
Emma Heming Willis and Helen Christoni, co-founders of Make Time Wellness, join Dr. Will Cole to talk about a conversation that's long overdue: women's brain health. They cover the striking statistics around women and Alzheimer's risk, what brain fog and memory lapses are really telling us, and why the standard of care has been failing women for decades. From the Mediterranean diet and the 3,000-step rule to sleep hygiene and breathwork, Emma and Helen share the practical pillars of brain health - and the science behind why starting now, not later, is what matters most. For all links mentioned in this episode, visit www.drwillcole.com/podcast.Please note that this episode may contain paid endorsements and advertisements for products and services. Individuals on the show may have a direct or indirect financial interest in products or services referred to in this episode.Sponsors: Go to DRINKAG1.COM/WILLCOLE to get an AG1 Flavor Sampler and a bottle of Vitamin D3+K2 for FREE in your AG1 Welcome Kit with your first AG1 subscription order!Use code BEINGWELL at Monarch.com to get your first year half off at just $50.Head to moshlife.com/AOBW and subscribe today to get 25% OFF your first Variety Pack and 20% off your monthly subscription with code AOBW.You can get an additional 15% off their 90-day subscription Starter Kit by going to fatty15.com/WILLCOLE and using code WILLCOLE at checkout.Refresh your wardrobe with Quince. Go to Quince.com/willcole for free shipping and 365-day returns. Now available in Canada, too.Produced by Dear Media.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Send us Fan MailLois Brady on Autism Digest, Real Stories, and Raising Expectations in Autism Support Host Tony Mantor welcomes Lois Brady, publisher of Autism Digest and a speech-language pathologist, to discuss how the magazine blends expert guidance with lived experience using multimedia features like QR-linked videos. Brady shares how writing Apps for Autism and contributing to Autism, Asperger's Digest led to her acquiring the publication nearly two years ago. As the mother of an autistic son, she describes early signs, being brushed off by providers, choosing not to label him in school, and the long-term effort that helped him become independent and employed, though social challenges remain. Brady emphasizes early identification, practical parent strategies, and avoiding the Rosenthal Effect of low expectations, including teaching non-speaking individuals to type. She describes a breakthrough with a non-speaking child using text-to-speech, addresses public misconceptions, contrasts meltdowns with tantrums, discusses Asperger's being folded into autism, highlights Temple Grandin's practical advice, and explores how AI can support early intervention and diagnostic cues. 00:00 Show Intro and Mission 01:21 Meet Lois Brady 02:18 How Autism Digest Began 04:28 Early Career and Inspiration 05:14 A Mom's Diagnosis Journey 07:39 Early Signs and Adulthood Today 09:16 How Autism Understanding Evolved 10:33 Raising Expectations and Communication 12:16 Typing Breakthrough Story 14:24 Lessons from Autistic Voices 16:06 Media Myths and Public Understanding 17:43 Aspergers vs Autism 18:17 AI for Early Support 21:10 From Cure to Support 22:51 Temple Grandin Wisdom 24:25 Meltdowns vs Tantrums 25:41 Stories and Rising Rates 28:52 Success and High Expectations 29:57 Transitions and Exposure 31:06 Ask Learn Connect INTRO/OUTRO Music: T. Wild Mantor Music BMI The content on Why Not Me: Embracing Autism amd Mental Health Worldwide, including discussions on mental health, autism, and related topics, is provided for informational and entertainment purposes only. The views and opinions expressed by guests are their own and do not reflect those of the podcast, its hosts, or affiliates.Why Not Me is not a medical or mental health professional and does not endorse or verify the accuracy, efficacy, safety of any treatments, programs, or advice discussed.Listeners should consult qualified healthcare professionals, such as licensed therapists, psychologists, or physicians, before making decisions about mental health or autism- related care.Reliance on this podcast's contents is at the listener's own risk. Why Not Me is not liable for any outcomes, financial or otherwise, resulting from actions taken based on the information provided.https://tonymantor.comhttps://Facebook.com/tonymantorhttps://instagram.com/tonymantorhttps://twitter.com/tonymantorhttps://youtube.com/tonymantormusicintro/outro music bed written by T. WildWhy Not Me the World music published by Mantor Music (BMI)
I've been thinking a lot about how we talk about food, bodies, and health with our kids…and with ourselves. In this episode, I sit down with Dr. Lauren Hartman to explore how parents can model kindness, trust their instincts, and help kids develop a healthy relationship with food without shame or rules. We talk about movement, social media, high sugar foods, and the small daily choices that shape how children experience their bodies. Whether you're navigating mealtimes, worried about eating habits, or just want practical ways to support your child's well-being, this conversation is full of insights you can actually use. We also touch on early intervention, balancing nutrition with life's realities, and why rest, self-compassion, and awareness can make all the difference. → Leave Us A Voice Message! Topics Discussed: → How do I spot teen eating disorders? → Picky eating vs ARFID → What causes eating disorders in teens? → How should parents talk about food? → When should I worry about eating? Sponsored By: → Function | Own your health for $365 a year. That's a dollar a day. Learn more and join using my link. Visit https://www.functionhealth.com/bewellbykelly and use gift code BEWELL25 for a $25 credit toward your membership → Be Well By Kelly Protein Powder & Essentials | Get $10 off your order with PODCAST10 at https://bewellbykelly.com. → Cozy Earth | Head to https://cozyearth.com and use code BEWELL for up to 20% off. And if you get a post-purchase survey, make sure you tell them you heard about Cozy Earth right here at the Be Well by Kelly podcast. → Timeline | Support your cells and how you age with Mitopure® Gummies from Timeline. Visit https://timeline.com/KELLY and save up to 39% off your Mitopure® Gummies. Timestamps: → 00:00:00 - Introduction → 00:02:04 - What are eating disorders → 00:04:32 - Eating disorders in boys → 00:06:22 - Why cases are rising → 00:09:40 - Dieting vs disordered eating → 00:12:59 - Early warning signs → 00:19:58 - Talking to your child → 00:24:10 - Eating disorder education → 00:29:28 - Personal backstory → 00:33:22 - Balance + rabbit study → 00:40:25 - Control and emotional eating → 00:44:26 - Eating disorder recovery → 00:50:07 - Preventing relapse → 00:52:09 - Sugar + processed foods → 00:55:55 - Talking to kids about food → 00:59:33 - Food freedom philosophy → 01:01:56 - Parenting legacy → 01:04:06 - Parenting lessons learned Show Links: → How to Nourish Your Child Through an Eating Disorder (book) Check Out Dr. Lauren: → Freeing Children and Young Adults From Shame, Skills, and Stigma (book) → Website → Instagram Check Out Kelly: → Instagram → Youtube → Facebook
When food starts to feel tense, restrictive, or obsessive at home, it can send a parent into panic fast.In observation of National Eating Disorder Awareness Week, Dr. Becky sits down with Dr. Erin Parks, Chief Clinical Officer at Equip Health, to talk about eating disorders and disordered eating in kids and teens—what the early signs look like, what's happening emotionally underneath, and how parents can respond without escalating shame or control struggles.Eating disorders affect an estimated 30 million Americans in their lifetime. They are common. They are serious. And they are not caused by “bad parenting.” In this episode, we cover: Early warning signs of eating disorders in kids and teens (including boys) When “healthy eating” becomes rigid or concerning The role of perfectionism and control How diet culture shows up in family language What shame sounds like inside a teen's head How to hold boundaries around health without turning food into a power struggle When it's time to seek professional help Disordered eating behaviors are not about vanity or defiance—they're often attempts to regulate overwhelming feelings. This conversation will help you feel more oriented, more compassionate, and clearer about your role.Equip Health is a sponsor of Good Inside's new Teen podcast. Thank you to our partners for making this episode of Good Inside possible! [Care.com](http://Care.comhttp://www.care.com/?utm_source=podcast&utm_medium=social&utm_campaign=GOOD35_INF00038): For a limited time, you can use the code GOOD35 to save 35% on a Care.com Premium Membership.* Once Upon a Farm: Use the code GOODINSIDE for 40% off your first subscription. -Hiya: Use the code DRBECKY for 50% off your first order. *Offer applies to initial term of Care.com membership subscriptions. Not applicable to add-on features or non-renewing access fees or services. Expires 4/26/26. Care.com does not employ or place any caregiver. Background checks are an important start, but they have limits. Visit www.care.com/safety. Order your copy of Leave Me Alone!, Dr. Becky's new picture book about Deeply Feeling Kids. 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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