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Most apple cider vinegar on supermarket shelves is dead. Pasteurized. Filtered. Stripped of the mother, the enzymes, and the polyphenols that made vinegar a medical remedy for over 2,000 years — sold back to you at a 33-fold markup for what's essentially acidified water. In this episode, we make genuine raw, unpasteurized apple cider vinegar from scratch — for about 30 cents a batch. You'll learn the two-stage fermentation process, how to avoid mold and spoilage, and why homemade ACV is more biologically active than anything you can buy. ⏱ Cost breakdown: $10 vs $0.30 11:00 — Safety rules you must follow
The ingredient people complain about the most is the single one that'll make the biggest difference in your skin — and no, it's not a retinoid or a fancy peptide. It's your sunscreen.This week we're getting into why SPF is the highest-leverage thing you can do for your skin (prevention is cheaper and easier than reversal), the difference between UVB and the UVA rays nobody talks about, chemical vs. mineral without the fear-mongering, what those "sunscreen in your bloodstream" studies actually found, and the new filter the FDA finally approved in June — what it is, and when you'll actually see it on shelves.No demonizing, no overclaiming. Just the nuance, from a friend who did the research so you don't have to.
Extrémna vlna horúčav ukázala reálny stav slovenských nemocníc. Kým pacienti mali v izbách vysoké teploty a nosili si vlastné ventilátory, minister Kamil Šaško vyhlásil, že klimatizácie nemajú prednosť pred obstaraním napríklad CT prístrojov. Mlčaniu štátu sa však mnohí ľudia nedokázali prizerať, a preto prevzali iniciatívu do vlastných rúk. Informovali sme o tom, že malá rodinná firma počas víkendu na vlastné náklady oblepila špeciálnymi fóliami celé oddelenie ružinovskej nemocnice, kde pacienti mali na izbách viac ako 42 stupňov Celzia, či o tom, že tri aktivistky sa rozhodli vyzbierať 380-tisíc eur na klimatizácie pre viaceré nemocnice.Po vlne kritiky ale aj solidarity pre nemocnice sa včera minister zdravotníctva za svoj výrok ospravedlnil a sľúbil nemocniciam 250 klimatizácií. Prečo by klimatizácia v nemocnici nemala byť nadštandardom a čo odhaľuje iniciatíva občanov, ktorí sa rozhodli nemocniciam pomôcť? Na podcaste spolupracovali Sophia Štefániková a Adam Obšitník.
Extrémna vlna horúčav ukázala reálny stav slovenských nemocníc. Kým pacienti mali v izbách vysoké teploty a nosili si vlastné ventilátory, minister Kamil Šaško vyhlásil, že klimatizácie nemajú prednosť pred obstaraním napríklad CT prístrojov. Mlčaniu štátu sa však mnohí ľudia nedokázali prizerať, a preto prevzali iniciatívu do vlastných rúk. Informovali sme o tom, že malá rodinná firma počas víkendu na vlastné náklady oblepila špeciálnymi fóliami celé oddelenie ružinovskej nemocnice, kde pacienti mali na izbách viac ako 42 stupňov Celzia, či o tom, že tri aktivistky sa rozhodli vyzbierať 380-tisíc eur na klimatizácie pre viaceré nemocnice.Po vlne kritiky ale aj solidarity pre nemocnice sa včera minister zdravotníctva za svoj výrok ospravedlnil a sľúbil nemocniciam 250 klimatizácií. Prečo by klimatizácia v nemocnici nemala byť nadštandardom a čo odhaľuje iniciatíva občanov, ktorí sa rozhodli nemocniciam pomôcť? Na podcaste spolupracovali Sophia Štefániková a Adam Obšitník.
„Vždy si predstavujem Hospodina pred sebou, lebo je po mojej pravici, aby som sa nepohnul.“ (Ž 16:8) Mladý kupec, kresťan, ostal niekoľko hodín sám v obchode, lebo jeho pán odišiel. Priateľ, zákazník, ktorý si kupoval súkno, povedal: „Teraz mi dobre odmeraj, keď tvoj pán tu nie je.“ Mládenec múdro odpovedal: „Môj Pán je tu vždy.“ Budeš aj ty dnes verný vo veciach, ktoré ti zverili? Pamätaj, že Pán všetko vidí a všetko vyjde skôr či neskôr na svetlo. Buď verný, ó, ľud Boží. Oddeľ sa od všetkého, čo ťa poškvrňuje, čo ťa s tvojím Ježišom, s tvojím nebeským svetlom vrúcne, pevne nespojuje! Buď verný, ľud Boží! PS 178, 2 MUDr. Viera Roháčková
Dvě první místa v nejobsazenější kategorii Mlékárenských dnů v Přibyslavi a dvě po sobě následující ocenění Regionální potravina se dvěma různými výrobky. Taková je vizitka Kateřiny Vackové z kozí farmy Vlčí Hory na Humpolecku, která na farmě zvládá téměř všechno sama – od péče o stádo přes dojení až po výrobu sýrů.
What does it actually take to keep your body healthy through rehearsals, performances, flights, hotels, and the demands of life on the road? Athletic trainer Adam Quigley has supported artists and tours including Post Malone, Lorde, Tyla, Karol G, the Jabbawockeez, 5 Seconds of Summer, plus broadway tours like MJ the Musical and Moulin Rouge, helping performers care for their bodies through the unique physical demands of touring. In this conversation, Adam shares practical strategies for staying healthy on the road while challenging dancers to think of themselves as athletes. We discuss how to build a warm-up that supports the demands of dance, what to pack on tour, navigating time zone changes and travel fatigue, fueling your body while traveling, and why recovery deserves just as much attention as performance. Adam also shares his philosophy of care, explains why movement is medicine, and offers practical advice for preventing small issues from becoming bigger ones. The conversation also explores the physical and mental comedown after tour - something many dancers experience but rarely discuss, despite how important it can be for long-term health and performance. Whether you're touring internationally, teaching on weekends, traveling for work, or simply want to build a longer, healthier dance career, this episode is packed with practical tools to help you take better care of your body. Follow Galit: Instagram - https://www.instagram.com/gogalit Website - https://www.gogalit.com/ Fit From Home - https://galit-s-school-0397.thinkific.com/courses/fit-from-home Connect with Adam Quigley on Instagram - www.instagram.com/adamquigley Explore Adam's company website - https://blvperformancetherapy.com/ Listen to DanceSpeak on Spotify and Apple Podcasts.
00:00-25:00: ML says it may be the year for the Bills to just air it out on offense and see what happens. Thanks to CH Insurance and Batavia Downs Gaming. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
Третий выпуск подкаста об образовании. На этот раз — про ШАД, Школу Анализа Данных Яндекса: бесплатную двухлетнюю программу, куда, по словам самих выпускников, поступить сложнее, чем её закончить. Разбираемся, чем ШАД отличается от вуза и курсов, как устроен отбор и почему компания вкладывается в образование, за которое не берёт денег. Гости: Анжелика Пуминова, куратор ШАДа в Новосибирске, организатор совместной магистерской программы ПМОБД на ММФ НГУ. Наталья Баданина, методист академических программ ML-направления Яндекс ШАД. В выпуске: Что такое ШАД на самом деле: не вуз, не курсы и не совсем магистратура Кто идёт в ШАД: студенты, разработчики, учёные — и один из выпускников этого года старше 60 лет Нагрузка 30+ часов в неделю: как совмещают ШАД с работой, вузом и личной жизнью Как устроен отбор: анкета, тестирование, творческий экзамен и собеседование — и почему порог такой высокий при бесплатном обучении Из чего состоит программа: ML, инфраструктура больших моделей и классические алгоритмы Новое направление для учёных: как в ШАДе учат применять ИИ в естественно-научных исследованиях Как программа не устаревает: гибкие занятия и обратная связь от студентов после каждого курса Первый опыт студенческих поездок на конференции ICML и ICLR ИИ внутри самого ШАДа: эксперимент с проверкой домашних заданий, где решение остаётся за человеком Зачем Яндексу бесплатно готовить специалистов — и куда потом идут выпускники Учиться можно и без поступления: открытые материалы ШАДа и открытые интенсивы Гитхаб ШАДа Оставайтесь на связи Пишите нам: info@linkmeup.ru Канал в телеграме: t.me/linkmeup_podcast Канал на youtube: youtube.com/c/linkmeup-podcast Подкаст доступен в iTunes, Google Подкастах, Яндекс Музыке, Castbox Сообщество в вк: vk.com/linkmeup Группа в фб: www.facebook.com/linkmeup.sdsm Добавить RSS в подкаст-плеер. Пообщаться в общем чате в тг: https://t.me/linkmeup_chat Поддержите проект:
VOV1 - Giữa những ngày tháng Bảy tri ân, trong căn nhà của nhiều Bà Mẹ Việt Nam anh hùng ở thành phố Cần Thơ rộn ràng tiếng cười, ấm lên bởi nghĩa tình từ "Mâm cơm bên Mẹ"."Bữa nay tụi con đến đây cúng mâm cơm các liệt sĩ ở gia đình mình, rồi dùng mâm cơm với Mẹ."- "Chà đông đảo vui vẻ quá hả, bữa nay đông đảo vui vẻ quá con ơi!" Không khí tại gia đình của Bà Mẹ Việt Nam anh hùng Nguyễn Thị Lành ở khu vực 5, phường Tân An (thành phố Cần Thơ), hôm nay thật đông vui, ấm cúng khi các ban, ngành, đoàn thể thành phố tụ họp về tổ chức "Mâm cơm bên Mẹ". Mẹ Lành có chồng và 3 con hy sinh trong kháng chiến. Năm nay Mẹ đã 96 tuổi và đang sống cùng người con gái thứ 5 là chị Trần Thị Kim Cương.Bên cạnh việc quây quần bên mâm cơm, thăm hỏi, động viên Mẹ Lành tiếp tục sống vui, sống khỏe; đồng thời bày tỏ lòng tri ân sâu sắc đối với những hy sinh to lớn của các Anh hùng liệt sĩ, đóng góp của Mẹ và gia đình trong sự nghiệp đấu tranh giải phóng dân tộc, xây dựng và bảo vệ Tổ quốc, các Sở, ngành, đoàn thể thành phố và địa phương còn tặng Mẹ và gia đình nhiều phần quà, trong đó có 60 triệu đồng để nâng cấp, sửa chữa lại căn nhà. Chị Trần Thị Kim Cương tâm sự: “Ngày thường thì chỉ có hai mẹ con thôi, mẹ thì bây giờ nói chung già quá rồi. Cơ quan ban ngành luôn luôn quan tâm tới gia đình, mỗi tháng đều xuống thăm, lễ lộc, Tết nhất vẫn xuống thăm. Bữa nay nói chung chị thấy rất là vui, rất xúc động khi được các tập thể, các ban ngành quan tâm đến mẹ tổ chức mâm cơm tri ân”.Lãnh đạo các Sở, ngành, đoàn thể thành phố Cần Thơ dâng hương tưởng nhớ các Anh hùng liệt sĩ tại nhà Bà Mẹ Việt Nam anh hùng Nguyễn Thị Lành.
00:00-15:00: ML likes the Orange's QB depth compared to last year. Thanks to CH Insurance and Batavia Downs Gaming. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
Robbie Deckard is a professional middle-distance triathlete and triathlon coach, and the founder of Freaky Fast Aero Accessories. In this episode, Robbie and Mikael dig into the real differences between American and European training methods, using lactate and VLaMax to optimise training and performance, and how age-group triathletes can apply lab-grade thinking to swim, bike and run training without actually going into a lab. HIGHLIGHTS AND KEY TOPICS: The real difference between FTP-centric American coaching and the German/Benelux style built around VLaMax, VO2max and physiological profiling. The evolution of VLaMax based training: from Alois Mader's 1970s model through Jan Olbrecht to current day coaches like Dan Lorang. Robbie's case for still doing dedicated VO2max work, including a case study that took an amateur cyclist's VO2max from 74 to 87 mL/kg/min over 58 weeks of block periodisation. Why building maximal aerobic power early is more valuable long-term than chasing threshold gains, and how that shapes the "durability" that separates junior and U23 cyclists from senior professional cyclists. How Robbie profiles athletes using LT1, LT2 and 5-minute power (or 500 yd swim time, or mile run time), and what typical percentage utilisation looks like for age-groupers versus elites. How he prescribes endurance, threshold and VO2max sessions in practice, including cues (power, pace, RPE), lactate targets, and why exact wattage precision on threshold work matters less than people think. Practical advice for age-groupers: open-water swim skills and ankle-band drills, running road races for pacing and durability, and hill running for strength and economy. Improving aerodynamics on a budget: reduce frontal area first, then refine airflow, and how to measure progress without a wind tunnel using Golden Cheetah or a simple speed sensor. SHOWNOTES, LINKS, RESOURCES AND RELATED EPISODES: Follow Robbie Deckard and Freaky Fast Aero Accessories on his website, blog, Instagram, and Freaky Fast Aero Accessories' Instagram. The shownotes for today's episode can be found here. The full podcast episode archives (including category filters) can be found here. A Scientific Approach to Improve Physiological Capacity of an Elite Cyclist - Rønnestad & Hansen 2018 FTP, VO2max and VLaMax with Sebastian Weber | EP#169 VLaMax, Polarised training, Fatigue and Complexity with Mark Burnley, PhD | EP#331 Training talk with Sebastian Zeller | EP#259 Training structure, periodisation and the science of winning with Jan Olbrecht, PhD | EP#198 Training, testing, metabolism and physiology with Björn Kafka | EP#286 Kolie Moore – Overrated and Underrated Factors for Cycling Performance The Science of Winning: Planning, Periodizing and Optimizing Swim Training - book by Jan Olbrecht The Triathlete's Training Bible - book by Joe Friel Software mentioned: Aerotune, Golden Cheetah, INSCYD, Sentiero SPONSORS: Precision Fuel & Hydration produce our favourite gels, sports drinks, and electrolyte and carbohydrate products here at That Triathlon Show and Scientific Triathlon. Use the free Fuel & Hydration Planner to get a personalised plan for your carbohydrate, sodium and fluid intake in your next event, and get 15% off your first 2026 order by using the code TTS2026 at checkout. Rouvy is hands down the most complete indoor cycling platform for triathletes. Among their thousands of beautiful bike courses from all around the world, all filmed in stunning quality, they have over 75 IRONMAN and IRONMAN 70.3 race courses plus 20+ Challenge Family courses, so you can pre-ride your race from home. Real gradients, real visuals, and real feel! Head to rouvy.com and use the code TTS to get your first month free on top of a 7-day free trial. Effortless Swimming produce the best swim goggles for triathletes and open water swimmers. Their NanoClear anti-fog lenses give you clear, fog-free vision that lasts and doesn't wear off. Don't let foggy or leaky goggles ruin another swim. Go to shop.effortlessswimming.com and use the code TTS15 to get 15% off your goggles, and get a free two-month Effortless Swimming course membership. ZenAI is the next frontier of podcasting. It lets you turn your ideas into a podcast without any knowledge of audio editing, video, or production. You do the talking, and the AI handles the production, editing, clipping, and the rest, and you can direct your AI producer in plain English. You can even record from your phone, so no expensive studio gear is needed to sound great. ZenAI is a new product from Zencastr, a platform I've been a paying customer of for over nine years here at That Triathlon Show. ZenAI is currently in invite-only beta. Join the waitlist today here.LEARN MORE ABOUT SCIENTIFIC TRIATHLON: The Scientific Triathlon website is the home of That Triathlon Show and everything else that we doContact us through our contact form or email me directly (note - email/contact form messages get responded to much more quickly than Instagram DMs)Subscribe to our NewsletterFollow us on InstagramLearn more about our coaching, training plans, and training camps. We have something to offer for everybody from beginners to professionals.HOW CAN I SUPPORT THAT TRIATHLON SHOW (FOR FREE)? I really appreciate you reading this and considering helping the show! If you love the show and want to support it to help ensure it sticks around, there are a few very simple things you can do, at no cost other than a minute of your time. Subscribe to the podcast in your podcast app to automatically get all new episodes as they are released.Tell your friends, internet and social media friends, acquaintances and triathlon frenemies about the podcast. Word of mouth is the best way to grow the podcast by far!Rate and review the podcast (ideally five stars of course!) in your podcast app of choice (Spotify and Apple Podcasts are the biggest and most important ones).Share episodes online and on social media. Share your favourite episodes in your Instagram stories, start a discussion about interesting episodes on forums, reference them in your blog or Substack. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
00:00-15:00: ML says it's time for Dalton Kincaid to finally break out and the Bills need big stuff from the tight end room overall in 2026. Thanks to CH Insurance and Batavia Downs Gaming. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
Disclaimer: This article is for educational purposes only and is not a substitute for individualized medical advice. Talk to your own physician before making decisions about cancer screening, testing, or treatment.TL;DR* A healthy 58-year-old gets a routine full-body scan, finds a “cancer” that would never have hurt him, and ends up with permanent incontinence from unnecessary treatment. This is more common than most people realize.* Dr. H. Gilbert Welch, a Dartmouth-trained cancer epidemiologist, spent 30 years documenting overdiagnosis — the discovery of cancers that meet the technical definition but would never have caused harm. His estimate: roughly 60% of PSA-detected prostate cancers and 25% of mammography-detected breast cancers fall into this category.* The “5-year survival rate” you hear cited as proof screening saves lives is often distorted by lead-time bias — finding a cancer earlier can make survival numbers look better without adding a single day to anyone's life.* Not all screening is suspect. Colonoscopy, low-dose CT for high-risk smokers, and cervical cancer screening (Pap/HPV) have strong randomized-trial evidence behind them.* The piece conventional screening misses: metabolic health. A 2026 Nature Communications study using machine learning on UK biobank data linked insulin resistance to increased risk across at least 12 cancer types — independent of body weight — and standard checkups rarely test for it.* Want a personalized look at your own metabolic terrain? Book a Metabolic Audit Call — link in show notes, spots limited weekly.The Test That Didn't Save His LifePicture a 58-year-old man. Healthy weight, active, doesn't smoke, feels completely fine. He goes in for a routine total-body scan — the kind now available at imaging centers with no doctor's referral required. Two hours later, a radiologist flags a small spot on his prostate.Six months, two biopsies, and one surgery later, he has a diagnosis: permanent incontinence. And the cancer itself? “Clinically insignificant.” It almost certainly would never have caused him harm. He would have lived out a full life and died of something else entirely, never knowing it was there.The test didn't save his life. It changed it — for the worse.This scenario happens thousands of times a year, and it's exactly what Dr. H. Gilbert Welch — a general internist, cancer epidemiologist, and senior researcher at Brigham and Women's Hospital — spent his career warning about. His book, Should I Be Tested for Cancer?, makes a case that runs against decades of public health messaging: more testing is not automatically better testing, and early detection does not automatically mean lives saved.This article unpacks what Welch got right, where his argument leaves a gap, and what a more complete, proactive approach to cancer risk actually looks like.The Cancer Reservoir: Why Finding More Doesn't Mean Saving MoreFor decades, the operating assumption in medicine has been simple: catch cancer early, save the life. No asterisk, no nuance.Welch's research complicates that. His central idea is the cancer reservoir — the observation that most people carry small clusters of abnormal cells somewhere in their bodies right now. In the prostate, thyroid, breast, or lung. Under a microscope, these cells look like cancer. But many of them will never grow, never spread, and never threaten a life. A person could carry one for thirty years and die at 87 of heart disease, never knowing it existed.The problem is that increasingly sensitive tools — full-body scans, PSA tests, low-dose CT — are very good at finding these dormant clusters. And once something is found and labeled “cancer,” the medical system is built to treat it.Welch's numbers, drawn from randomized trial data, are striking: approximately 60% of PSA-detected prostate cancers are overdiagnosed, meaning they meet the technical definition of cancer but would never have caused symptoms or death. For mammography-detected breast cancers, the estimate is around 25% — meaning roughly one in four women treated for a screen-detected breast cancer may never have needed that treatment: the chemotherapy, the radiation, the surgery, the fear, the financial cost.This isn't an anti-medicine argument. It's a call for a conversation that rarely happens: here's the case for this test, and here's the case against it — here's what we might find that helps you, and here's what we might find that sets off a chain reaction you'll spend years managing. For most patients, that conversation never occurs.The 5-Year Survival Stat Is Misleading YouFive-year survival rates for cancer are often cited as evidence that screening works — and they sound like exactly that. But Welch shows why the number can be deceptive, and it comes down to lead-time bias.Here's the mechanism. Imagine a woman whose cancer will kill her at 65, regardless of when it's found. If screening catches it at 62, she lives three years with the diagnosis before dying at 65 — a five-year survival rate under five years. But if that same cancer isn't found until symptoms appear at 64, she lives one year with the diagnosis and dies at 65 — a five-year survival rate of zero.Same woman. Same cancer. Same date of death. But the version of her found earlier through screening appears, statistically, to have “survived longer.” Screening didn't add a single day to her life — it just moved up the start date of her diagnosis. It's the equivalent of claiming a win in a race because someone moved your starting line 200 meters ahead of everyone else's: you didn't run faster, you just started earlier. The finish line never moved.Now layer in overdiagnosis. If 1,000 people are diagnosed with cancers that would never have hurt them, and all 1,000 are alive five years later — which they would have been regardless — the survival statistics look dramatically better without a single life actually being saved. Welch's research shows that 5-year survival rates can climb while actual cancer death rates stay flat. More survivors on paper. Same number of people dying.None of this means medicine isn't making genuine progress in some cancers — colon cancer being a clear example, discussed below. It does mean that 5-year survival statistics, on their own, are not proof that a screening program is saving lives.Where the Evidence for Screening Is Actually StrongIt would be a mistake to leave this discussion thinking all screening is suspect. Welch himself is careful to draw a distinction, and there are tests with solid, randomized-trial evidence behind them.Colonoscopy for colorectal cancer is arguably the strongest case for screening that exists. It's unique because it doesn't just detect cancer — it can prevent it, by removing precancerous polyps before they ever become malignant. Colon cancer incidence and mortality have both dropped measurably in populations with high screening rates. If you're 45 or older, or have a family history, this is worth a serious conversation with your doctor.Low-dose CT for lung cancer, in high-risk individuals specifically, showed a 15–20% reduction in lung cancer deaths in the National Lung Screening Trial — but only among heavy smokers (roughly a pack a day for 20+ years). The risk-benefit math works because the baseline risk in that population is high.Cervical cancer screening — Pap smears and HPV testing — is a genuine public health success story. Rates have dropped dramatically since routine screening began, because cervical cancer has a long, slow, detectable precancerous stage that can be caught before it turns invasive.The common thread: these screenings either catch a long, slow precancerous process, or they target a population where the risk is already high enough that the math clearly favors testing. That's the question worth bringing to your doctor: given my specific risk factors, does the math on this test work in my favor?By contrast, the evidence is much weaker for consumer-marketed total-body scans, full-body MRI as a general “optimization” tool, universal PSA screening in all men over 50, and mammography in average-risk women in their 40s. These aren't mandates — they're conversations, and informed consent means understanding both sides before deciding.The Harms Nobody Talks AboutHealthcare marketing tends to present testing as one-sided: test early, catch it early, save your life. Welch's research catalogs the costs that rarely make it into that pitch.False positives. A mammogram flags a shadow. It isn't cancer — but you don't know that yet. Six weeks of follow-up imaging, maybe a biopsy, and the stress hormones flooding your body during that stretch are a real physiological cost, even when the final answer is “you're fine.”Unnecessary treatment. When a cancer that would never have caused harm is treated anyway — with surgery, radiation, or chemotherapy — the harm is real and the benefit is zero.The cancer label itself. Research shows that being labeled a cancer patient, even for a cancer that's never actively treated, changes a person's psychology, relationships, insurability, and life trajectory. Welch identifies this as a form of harm medicine rarely accounts for.Radiation exposure. Repeated CT scans carry cumulative radiation risk. A full-body scan can expose a person to the radiation equivalent of hundreds of chest X-rays — a real risk added to the body in pursuit of a cancer that may never develop.Welch's central reframe: the question isn't “should I get tested,” it's “given my risk factors, my age, my family history, and my values, does the math on this specific test work in my favor?” That's informed consent — and most people never get that conversation.The Missing Piece: Your Metabolism Is an Early Warning SystemWelch's work is thorough on what not to do. Where it leaves a gap is the proactive question: if blanket screening of healthy people isn't the answer, what is?The answer lies in the years — sometimes decades — before a tumor ever forms. Cancer doesn't appear overnight. The cellular environment that allows it to take root and grow develops gradually, and it leaves metabolic fingerprints long before any scan could detect a tumor.The clearest evidence for this comes from a 2026 study published in Nature Communications, which used machine learning on a massive UK database and linked insulin resistance to a significantly increased risk of at least 12 types of cancer. Pancreatic cancer risk was elevated by roughly 29%, colon cancer by 18%, and breast cancer by 13% — and critically, this risk showed up independent of body weight. A person at a healthy weight can still be carrying the metabolic dysfunction that drives cancer risk, and a standard annual physical would miss it entirely, because most doctors check fasting glucose, not fasting insulin. By the time glucose is elevated, insulin regulation has often been off for years.Layer in chronic inflammation (measured by hs-CRP), elevated ferritin, low vitamin D, rising homocysteine, and a poor triglyceride-to-HDL ratio, and what emerges is a picture of a metabolic environment that is increasingly hospitable to cancer. Think of it as soil: a healthy garden doesn't grow weeds easily, but depleted, imbalanced soil invites them. Cancer is the weed. Metabolic dysfunction is the depleted soil. The strategy, then, is to work on the soil rather than wait to spot the weed.What to Actually Do About ItPath A: Testing to ask your provider forThese tests build a real metabolic picture — the kind that shows soil quality before any weed appears.* Fasting insulin + HOMA-IR — not just fasting glucose. This is likely the single most important test most doctors aren't ordering.* Hemoglobin A1c — your 3-month blood sugar average.* hs-CRP — a high-sensitivity marker of systemic inflammation.* Full lipid panel, including TG/HDL ratio — a ratio above 3 is a strong metabolic red flag.* Ferritin — elevated levels are increasingly linked to inflammatory cancer environments.* Vitamin D (25-OH) — low levels are associated with higher cancer risk across multiple types; optimal is 60–80 ng/mL, not just “in range.”* Homocysteine — a methylation marker that, when elevated, signals oxidative stress.* LDH (Lactate Dehydrogenase) — rises when cells are under metabolic stress.For a deeper look, consider a comprehensive nutrient and organic acids panel (NutrEval), a gut microbiome panel (GI-MAP) — the gut-cancer connection is real — and a full hormone panel including cortisol, estrogen, testosterone, and SHBG.Path B: Lifestyle changes to start today* Eat in this order: protein and fat first, vegetables second, starches last. This alone can meaningfully blunt post-meal blood sugar spikes.* Cut refined sugars and seed oils — the two most direct dietary drivers of insulin resistance and inflammation.* Move daily. At minimum, 150 minutes of moderate activity per week, resistance training twice a week, and even a 10-minute walk after meals to improve glucose metabolism.* Prioritize sleep. Poor sleep disrupts glucose metabolism after a single bad night. Seven to nine hours is non-negotiable for metabolic health.* Manage stress. Chronic cortisol elevation drives insulin resistance — this is biochemistry, not soft advice.You don't need to do all of this at once. Pick one test to ask for at your next appointment, and one lifestyle change to start this week.Summary & Next StepDr. Welch's research makes an uncomfortable but important case: early detection is not automatically synonymous with lives saved, the 5-year survival statistic can be misleading, and testing healthy people carries real costs — false positives, unnecessary treatment, radiation exposure, and the psychological weight of a cancer label. At the same time, some screenings — colonoscopy, cervical cancer screening, low-dose CT for high-risk smokers — have strong evidence behind them and are worth pursuing for the right person.What's missing from that picture is a proactive strategy, and that's where metabolic health comes in. Insulin resistance, chronic inflammation, and blood sugar dysregulation show up years before cancer does, and unlike a full-body scan, they're both measurable and fixable.If you want a clear picture of where your own metabolic terrain stands — and what your highest-leverage next steps are — book a Metabolic Audit Call. It's a complementary 45-minute session where we review your current labs, symptoms, health history, and goals together. Spots are limited each week; the link is in the show notes.References* Welch, H.G. Should I Be Tested for Cancer? Maybe Not and Here's Why. University of California Press.* National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening.* Nature Communications (2026). Machine learning analysis of UK biobank data linking insulin resistance to increased risk across 12 cancer types, independent of body weight.* Thrive 120 Podcast, Episode 126: “Should I Be Tested for Cancer? What Dr. Welch Got Right — And What He Missed,” This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit tripleplaydoc.substack.com/subscribe
00:00-25:00: Pinstripe People Crossover. Yanks' 2nd Half Preview. Sal and ML break it down. Thanks to CH Insurance and Marz Motors. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
In this talk, Maryam Ramezani-Bartsch, Data and AI Leader with over 20 years of experience at companies like adidas and Zalando, shares her extensive career journey from building foundational ML systems at adidas to coaching data experts through the modern AI landscape. We explore the critical intersection of technical strategy and the essential human skills needed to thrive in the AI era.LINKS:- https://maryamramezani.com/designyourdatacareerYou will learn about:- The surprising similarities and critical differences between the current Generative AI boom and the previous Big Data era.- Why the traditional boundaries between data roles are disappearing and the specific T shaped profile companies are actually hiring for today.- The hidden danger of perfectionism in corporate tech, and what a healthy margin of failure actually looks like in practice.- How to stop leaving your career trajectory to chance by applying product Design Thinking to your own life.- A practical framework for navigating industry uncertainty and tech career anxiety without burning out.- Battle tested strategies for regaining your footing and standing out in a highly competitive job market after a layoff.- The specific non technical human skills that will become your ultimate career moat against automation.TIMECODES:00:00 Human Skills in the AI Era07:07 Building ML Systems at Adidas12:49 Generative AI vs Big Data Era18:39 T-Shaped Data Engineering Roles24:04 Overcoming Perfectionism in Tech30:34 Design Thinking for Data Careers38:59 Managing Tech Career Anxiety45:07 Aligning Passion with Tech Skills50:18 Job Search Strategies After Layoffs56:03 Essential Soft Skills for JuniorsThis talk is essential for data professionals, software engineers, and tech leaders looking to future proof their careers in an increasingly automated world. Whether you are a junior developer navigating a tough job market, an engineer bouncing back from layoffs, or a senior professional looking to strategically design your next pivot, this session provides the tools to build a highly resilient career.Connect with Maryam- Linkedin - https://www.linkedin.com/in/maryam-ramezani-bartsch/- Website - https://maryamramezani.com/- Substack - https://maryamramezani.substack.com/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/
Owner of one of the effortlessly sexy voices among male seiyuu, Takuya Sato is no stranger to singing in 2D music projects and even had his own solo career (and it was genuinely solid). However, something was missing in his singing that acappella music - and namely, the Aoppella franchise - unlocked for him in 2019.Since then, he has been a show stealer in all 2D music projects he is a part of.Music suggestions:- FYA'M "Think about U"- TRIGGER "Triple Down"- TRIGGER "SOL"Thanks to M L for inspiring this series of episodes!
Lieve Freundinnen (m/v/x), niet schrikken maar we hebben een MAN te gast in de nieuwste aflevering van DAMN, HONEY de podcast *gasp* Het gebeurt niet vaak, maar soms komt er eentje op je pad die Echt Wat Te Zeggen heeft. En tja, dan moet je! Dan ga je! Het is historicus en schrijver Alex Bakker, gespecialiseerd in transgendergeschiedenis. Zijn nieuwste boek heet Transgender pioniers en speelt zich af in het roerige Berlijn, in de jaren tussen de 1e en 2e Wereldoorlog aka het Interbellum. De levensreddende medische zorg voor trans mensen begon toen en daar. Alex deed voor zijn boek uitvoerig onderzoek naar een revolutionair seksuologisch instituut waar oprichter en arts Magnus Hirschfeld probeerde te begrijpen wat trans mensen nodig hebben. Twee van zijn cliënten waren Dora Richter (een trans vrouw van in de 30, opgegroeid op het platteland en diepgelovig katholiek) en Gerd Katter (een jonge trans man van 18 uit de stad, die in de leer was voor timmerman). Dankzij bewaard gebleven bronnen wist Alex hun levens (grotendeels) in kaart te brengen. We hameren altijd erg op historisch besef en als het gaat om transgendergeschiedenis weten we lang niet genoeg. Wat kunnen we leren van de verhalen van Dora en Gerd? Waarom is het zo belangrijk dit soort verhalen te kennen? En hoe staat de transgenderzorg er nu voor? Verder: Wat zien we daar bewegen daar op de bovenlip van ML? Is het een... snor? Ga voor de shownotes en het transcript naar damnhoney.nl/aflevering-291DAMN, HONEY wordt gemaakt door Marie Lotte Hagen en Nydia van VoorthuizenIn deze aflevering hoor je een advertentie voor ONSZELF! Steun ons maandelijks vanaf 1 euro en krijg onze eeuwige dank én toegang tot onze tweewekelijkse bonuspodcast ‘Je doet het er maar mee’ die je gewoon kunt luisteren via je favoriete podcastapp. Bonusbal word je via petjeaf.com/damnhoney.editwerk: Daniël van de Poppejingles: Lucas de Gier website: Liesbeth Smit DAMN, HONEY is onderdeel van Dag & Nacht Media. Heb je interesse om te adverteren in deze podcast? Neem dan contact op met Dag en Nacht Media via adverteren@dagennacht.nlSee omnystudio.com/listener for privacy information.
Imagine a dark warehouse. Racks and racks of devices with wires, tubes, and electronics sticking out. The next AI data center? No. This is Lila Sciences‘ dream for the future of science. A dark warehouse full of AI-guided robotics and lab equipment, cranking out new experiments 24/7, building toward a scientific superintelligence.Their automated lab is almost hypnotizing to watch. They have floating plates zipping around on Wall-E-esque tracks, used vision-language models to control Windows 95 boxes, and created the world's largest collection of voided warranties. In the process they've built a massive library of scientific reasoning tokens. Over 10 trillion of them, all experimentally validated.No warranties were voided in the making of this videoTo say Lila is ambitious is an understatement. Their goal is a scientific superintelligence wired directly into the wet lab. They are all in on the bitter lesson, and the thesis follows from it: a lab is an infinite token generator. Produce data at scale, and the synergies give you a general reasoner that can tackle any scientific problem. They are committing hard. Biology, chemistry, drug discovery, and materials science, all at the same time. Time will tell if it works, but it is an exciting hypothesis.In our latest episode we sat down with Lila's very own Andy Beam (CTO) and Rafa Gómez-Bombarelli (CSO, physical sciences) and went on a journey through the possibilities of AI-run science, almost as wide-ranging as Lila's goals.Did we mention they do both materials science and biology? In the same AI science factory? Same time, same lab, same AI. Finally a guest who can settle a long-running debate we've had amongst ourselves: is biology or materials science harder?Watch to find out!We discuss:* The internet is spent, science is next. Why Lila thinks the scientific method is the last untapped internet-scale dataset, and why they treat RL as a data generation mechanism with nature as the verifier.* The lab as a data center. Instruments as nodes on a graph, a magnetically levitating “PCI bus” transport layer between them, orchestration as a slurm queue. Andy is not short on analogies.* Why Lila insists it is not an automation company. They optimize for flexibility and generalizability over raw throughput, which means humans stay below the API line wherever automating does not pay.* Your experiment has a runtime. We put Escalante Bio's question to Andy: if science is the token generator, what is the runtime of your data collection? His answer, in short, is that you cannot make the ribosome go faster. Why Lila bets on fast round-over-round iteration rather than big noisy multiplexed screens, and how Rafa's team rebuilt a gas sorption measurement to run roughly 2,500x faster.* What is actually in 10 trillion scientific tokens. Not sequences. Experimentally verified reasoning traces, a kind of data that Andy argues exists on the internet in quantities that round to zero.* Breadth as a path to depth. Small molecule chemistry priors transferring to metal organic frameworks for carbon capture, and the claim that the general model beats domain-specific models sample for sample.* If you have the data, what do you need the model for? Sri Kosuri's koan about the ML-for-drug-discovery business model, and Andy's answer: the coding model got better because it also read Shakespeare and carnitas recipes.* The serendipity they want to automate. Emily Whitehead survived the first pediatric CAR-T cure only because the doctor treating her happened to know, from pediatric arthritis, which antibody would blunt her IL-6 response. Roll that dice again and you probably lose her. Breadth is how you stop depending on luck.* Move 37 for catalysts. Model suggestions for platinum-group-free electrocatalysts that went from boring, to what a 40-paper expert called stupid, to the best performers they have made.* Six months to in vivo CAR-T data in non-human primates, and the zero-FTE virtual startup commercial model that fell out of it. For context on why that number is startling, AbbVie paid $2.1B for Capstan on the strength of preclinical in vivo CAR-T data.* You cannot have scientific superintelligence if you are just a good test taker. Ken Stanley, who wrote Why Greatness Cannot Be Planned, runs open-endedness at Lila. RL at scale gives you a ruthlessly Vulcan problem solver. Machine creativity is a different thing, and it is the part nobody has solved.* The chain of thought is an unreliable narrator. The model reasons in latent space and only emits tokens. Sometimes it skips the experiment entirely and is still right. So how much do you trust the reasoning versus the verifier?* Reward hacking when the rollout is physical. Chains of thought that collapse into repetition, and a model that got annoyed and swore at the scientist who kept asking it to redo a plate map. What happens when a pathological loop has a wet lab inside it?* The bittersweet lesson. Rafa's inversion of the bitter lesson: in AI, scaling is a roadmap. In materials, scaling is a filter, because only the things that scale end up mattering.* Not your typical Flagship company. Why a famously single-asset biotech incubator spun out a platform bet, and Andy's line that if Lila called itself a biopharma it would have a top-three GPU cluster.* Bottlenecks they would remove by fiat. Sim-to-real for physics-based simulation, and the fact that RL training runs at roughly 5% mean FLOP utilization.Watch on YouTube: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe
In this episode of the Prolonged Field Care Podcast, Dennis sits down with Alex to break down a hot-off-the-press retrospective study from the Journal of the American College of Surgeons titled “Challenging Legacy Burn Resuscitation Paradigms with Fluid Restriction and Early Plasma.”They dismantle the decades-old “swell to get well” mentality and the classic Parkland formula that has led to dangerous fluid overload, massive edema, and compartment syndromes in burn patients. Instead, they explore a more physiologic approach using lower crystalloid volumes (starting at 2 mL/kg adjusted body weight) plus early fresh frozen plasma (FFP) for patients with larger burns.Key Takeaways:The Parkland formula (4 mL/kg/%TBSA) frequently causes massive over-resuscitation; the new restrictive approach delivered significantly less fluid while maintaining (and often improving) urine output.Capillary leak from glycocalyx damage is the real enemy in burn shock — plasma helps restore oncotic pressure and may reduce third-spacing.Titrate everything to urine output (target 0.3–0.5 mL/kg/hr). Formulas are only a starting point.Use adjusted body weight (ideal body weight + 0.4 × [actual – ideal]) instead of actual body weight for fluid calculations.Early plasma (1–2 units for >30% TBSA) showed a strong signal toward lower mortality, less ventilator days, and reduced renal failure in this study.The Joint Trauma System (JTS) Burn Care CPG still emphasizes early consultation with a burn center — phone a friend early.This approach has direct application for prolonged field care and austere environments, though the study is retrospective and should be implemented thoughtfully.Whether you're a special operations medic, flight paramedic, or managing burns in a resource-limited setting, this conversation will fundamentally change how you think about burn shock resuscitation.Resources:prolongedfieldcare.org (free downloads, worksheets & more)Follow @prolonged_field_care on InstagramJTS Burn Care CPG (CPG #12) – includes the excellent burn resuscitation worksheetChapters: 00:00 – Introduction: Why Burn Care Still Terrifies Experienced Medics03:09 – The Horrifying Reality of Over-Resuscitation (Edema Photos & Leaky Pipe Analogy)05:30 – Understanding the Glycocalyx and Why Crystalloid Leaks So Fast09:05 – The One-Third Rule Myth & Why Fluids Disappear in Sick Burn Patients11:14 – Parkland Formula Breakdown: History, Math & Its Biggest Flaw13:00 – The New Study: PICO, Methods & the Shift to 2 mL/kg + Early Plasma16:54 – Elevator Pitch: What This Paper Actually Found20:06 – Primary Results: Dramatically Less Fluid with the Restrictive Protocol21:24 – Urine Output Reality Check: Why the “Less Fluid” Group Still Hit Targets24:23 – Practical Protocol Breakdown: Who Gets 2 mL vs 3 mL + When to Give Plasma25:30 – Adjusted Body Weight Calculation Explained (and Why It Matters)27:26 – Titration to Urine Output is King – Stop Chasing Vitals29:55 – Dennis Rates the Evidence on the PFC Gestalt Scale30:38 – Why Plasma Makes Physiologic Sense (and Whole Blood May Be Next)35:30 – Study Limitations & Provider Bias Discussion37:30 – Can We Implement This in Prolonged Field Care Right Now?38:38 – JTS Burn Care CPG: The Burn Center Contact You Need to Save42:53 – Final Advice: Titrate Aggressively, Phone a Friend Early, Close the GapFor more content, go to www.prolongedfieldcare.orgConsider supporting us: patreon.com/ProlongedFieldCareCollective or www.lobocoffeeco.com/product-page/prolonged-field-care
00:00-30:00: Sal and ML break down the Yanks' sweep of the Nats, chat Jazz gonna Jazz, ASG and Derby with break and more. Plus, Ben Rice rocks. Pinstripe People crossover. Thanks to CH Insurance and Marz Motors. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
Small teams with smart AI can solve problems no one imagined before. In this episode, I spoke with Zhen Lu, CEO of Runpod, about the rapidly evolving AI landscape and the future of software development. Zhen Lu shared how Runpod is empowering engineers to train AI models tailored to specific business needs, improve efficiency, and reduce waste. We also discussed his founder journey, the power of co-founder alignment, global innovation outside traditional tech hubs, and the importance of human accountability in AI-driven organizations. Here are the highlights: ● Runpod builds AI developer infrastructure. The platform supports tailored AI workloads, enabling businesses to efficiently train models specific to their needs. ● AI is changing how software is built. Engineers are challenged to rethink software design, not just accelerate existing processes, creating new opportunities for innovation. ● Efficiency and sustainability matter. Fine-tuning smaller, focused models reduces resource waste, energy consumption, and operational costs compared to massive off-the-shelf models. ● Co-founder alignment drives success. Implicit trust, complementary skills, and low ego between Zhen and his co-founder have accelerated decision-making and execution. ● Global networks and bootstrapping foster innovation. Scarcity encourages creativity, and building strong relationships outside Silicon Valley has enabled Runpod to grow and support AI developers worldwide. About the guest: Zhen Lu and co-founder Pardeep Singh started by running crypto mining rigs out of their New Jersey basements. When Ethereum's "The Merge" threatened to make that obsolete, they pivoted, converting the rigs into AI servers. As corporate developers at Comcast building ML projects, they saw firsthand that the GPU developer experience was, in Zhen's words, "just hot garbage." That insight became Runpod. Launched in early 2022, Runpod offers fast, developer-friendly GPU infrastructure: clean APIs, CLI tools, serverless options, and easy configuration. Rather than the traditional VC route, they debuted on Reddit with "Hey Reddit, give us your worst" — and got "please take my money" in return. They went on to earn validation from Hugging Face co-founder Julien Chaumond and a seed round led by Dell Technologies Capital, hitting $120M ARR, 10 billion serverless requests, and serves ~900,000 developers across 31 global regions. Connect with Zhen Lu: LinkedIn: https://www.linkedin.com/in/zeen/ Website: https://www.runpod.io/ Connect with Allison: Feedspot has named Disruptive CEO Nation as one of the Top 25 CEO Podcasts on the web. LinkedIn: https://www.linkedin.com/in/allisonsummerschicago/ Website: https://www.disruptiveceonation.com/ #CEO #leadership #startup #founder #business #businesspodcast Learn more about your ad choices. Visit megaphone.fm/adchoices
Small teams with smart AI can solve problems no one imagined before. In this episode, I spoke with Zhen Lu, CEO of Runpod, about the rapidly evolving AI landscape and the future of software development. Zhen Lu shared how Runpod is empowering engineers to train AI models tailored to specific business needs, improve efficiency, and reduce waste. We also discussed his founder journey, the power of co-founder alignment, global innovation outside traditional tech hubs, and the importance of human accountability in AI-driven organizations. Here are the highlights: ● Runpod builds AI developer infrastructure. The platform supports tailored AI workloads, enabling businesses to efficiently train models specific to their needs. ● AI is changing how software is built. Engineers are challenged to rethink software design, not just accelerate existing processes, creating new opportunities for innovation. ● Efficiency and sustainability matter. Fine-tuning smaller, focused models reduces resource waste, energy consumption, and operational costs compared to massive off-the-shelf models. ● Co-founder alignment drives success. Implicit trust, complementary skills, and low ego between Zhen and his co-founder have accelerated decision-making and execution. ● Global networks and bootstrapping foster innovation. Scarcity encourages creativity, and building strong relationships outside Silicon Valley has enabled Runpod to grow and support AI developers worldwide. About the guest: Zhen Lu and co-founder Pardeep Singh started by running crypto mining rigs out of their New Jersey basements. When Ethereum's "The Merge" threatened to make that obsolete, they pivoted, converting the rigs into AI servers. As corporate developers at Comcast building ML projects, they saw firsthand that the GPU developer experience was, in Zhen's words, "just hot garbage." That insight became Runpod. Launched in early 2022, Runpod offers fast, developer-friendly GPU infrastructure: clean APIs, CLI tools, serverless options, and easy configuration. Rather than the traditional VC route, they debuted on Reddit with "Hey Reddit, give us your worst" — and got "please take my money" in return. They went on to earn validation from Hugging Face co-founder Julien Chaumond and a seed round led by Dell Technologies Capital, hitting $120M ARR, 10 billion serverless requests, and serves ~900,000 developers across 31 global regions. Connect with Zhen Lu: LinkedIn: https://www.linkedin.com/in/zeen/ Website: https://www.runpod.io/ Connect with Allison: Feedspot has named Disruptive CEO Nation as one of the Top 25 CEO Podcasts on the web. LinkedIn: https://www.linkedin.com/in/allisonsummerschicago/ Website: https://www.disruptiveceonation.com/ #CEO #leadership #startup #founder #business #businesspodcast Learn more about your ad choices. Visit megaphone.fm/adchoices
Idaho National Laboratory (INL) was born in 1949 as the National Reactor Testing Station after the Atomic Energy Commission decided they needed a place to test controlled nuclear fission power systems. Before being taken over by the AEC, the land was controlled and used by the U.S. Navy to test refurbished weapons, up to and including the 16″ guns carried by battleships. After a five decade long hiatus in building new reactors while also implementing a significant level of diversification into other research areas, the INL is regaining strength in its original purpose as a place to test and demonstrate complete nuclear reactor power plants. (The term “plant” is not really applicable to micro-reactors, but it will serve as a general term for now.) This time through, the design, approval, construction and testing programs are not being led by a federal monopoly called the Atomic Energy Commission. It is not focused on developing reactors that can be used to test the ideas of scientists who don’t really care if anyone wants to buy the system they are developing. Instead, the reactor development efforts underway and in planning for the future are more cooperative, distributed and commercially driven. Josh Gillespie is the Chief Operating Officer of the National Reactor Innovation Center (NRIC). He visited the Atomic Show to talk about NRIC and its role in helping the Nuclear Renaissance gain traction and success. NRIC was created in 2019 as a result of directives and authorizations contained in the Nuclear Energy Innovation Capabilities Act (NEICA). Its purpose is to build bridges that enable private sector organizations to work with national laboratory scientists and physical resources to cross the “valley of death” between good ideas and commercially viable products. It is tasked with preparing facilities to serve as test beds for new reactor development and testing and to develop sites where new facilities can be built. Though it can be a challenge for any government organization – like a national lab – NRIC has been tasked to be able to operate at the speed of a start-up. It is taking strides in that direction, though it is still limited in speed by the federal government budget cycle. Josh described how NRIC is working closely with the Department of Energy Idaho Operations Office which is directly responsible for the DOE 1271 authorization process for both reactors and supporting facilities – like those that are in the fuel supply chain or involved in radioactive materials testing and evaluation. NRIC helps private sector companies develop their plans, find suitable facilities, prepare required submittals and engage in readiness reviews. NRIC’s reach extends beyond the boundaries of the Idaho National Laboratory; has been contracted to support several of the Reactor Pilot Program developers that have built or are building their reactors in Texas or Utah. Josh described DOME as the crowning jewel of NRICs facilities. It once served as the containment dome for the highly successful but prematurely retired Experimental Breeder Reactor II, the remains of which are encased in concrete and grout in the basement and foundations of the existing facility. DOME is designed to be able to host a test reactor that might be exercised to its limits while still preventing any release of radioactive materials. Radiant Nuclear was selected as the first tenant of the DOME. It is scheduled to complete its operational testing and to remove its equipment from the facility in a year to make room for the next tenant. Top shield covering Antares Mark-0 in RACE facility NRIC played an important role in the success of the Reactor Pilot Program. It helped to find and repurpose facilities for Antares (RACE – Reactor and Criticality Experiment), Deployable Energy (NRAD – Neutron Radiography Reactor) Aside: Bit of INL Trivia – The building that is now RACE housed the Army’s ML-1 reactor development program from the late 1950s until the program was ended in 1964. End Aside. Josh described NRIC’s role in the Nuclear Energy Launch Pad as similar to that of a subdivision developer. A 2,000 acre plot has been allocated. NRIC is responsible for developing basic infrastructure, including roads and common utility systems. It will arrange for site characterization studies in preparation for environmental assessments. Private sector developers will lease their sites and contract for any additional services desired from an available menu. These include additional security and fire services. There will be similar services offered to developers who are building on sites that are not INL; that part of the program is called Launch Pad – USA. Josh is an Idaho native who is happy to be involved in creating a winning partnership between private sector companies and government/national lab organizations. He believes that the new developments will help Idaho National Lab continue to thrive and lead in nuclear energy development. He is excited about helping to deploy abundant sources of clean electricity and heat that will contribute to a bright future for Idaho, the U.S. and the rest of the world. You’ll enjoy this show. Listen carefully and comment via X if desired.
This article is for educational purposes only and is not a substitute for individualized medical advice. Always talk to your own healthcare provider before changing your diet, supplements, or medications.Unlocking the Secrets of Ferritin: What Your Iron Levels Are Telling YouYour “normal” bloodwork might be hiding the real reason you're exhausted, foggy, and losing hairTL;DR: * Ferritin is your iron savings account — and most labs only flag it as “abnormal” once it's nearly empty. * A level of 14 or 22 ng/mL might get a “you're fine” from your doctor, but optimal energy, mood, cognition, and hair growth usually need ferritin closer to 70–100 ng/mL. * Low ferritin can come from menstrual blood loss, poor absorption (celiac disease, low stomach acid, H. pylori), or inflammation-driven hepcidin blocking iron uptake.* If you're fatigued, foggy, cold, or shedding hair, ask for a full iron panel — not just a ferritin number — and talk through the results with your doctor.There's an old Japanese proverb: “When the body speaks, the wise person listens. When the body whispers, the fool waits for it to scream.” In health diagnostics, one of the quietest whispers is your ferritin level. It's often overlooked, yet it can be the missing link behind exhaustion, hair loss, brain fog, or the frustrating experience of bloodwork that comes back “normal” while you still feel terrible.What Is Ferritin?Ferritin is your body's iron storage protein. Think of your iron levels like a financial setup: hemoglobin is your checking account, drawn on daily. Ferritin is your savings account, tapped only when things get tight. Under stress, your body will drain the savings account long before it lets the checking account — hemoglobin — run low. That's why you can have “normal” hemoglobin and still be iron-depleted. A low ferritin level means your reserves are running out, and that shows up as fatigue, brain fog, mood changes, and thinning hair.Normal vs. OptimalMost labs flag ferritin as “normal” above roughly 10–20 ng/mL. That threshold mostly means you're not in immediate danger — not that you're thriving. Levels associated with feeling genuinely well tend to run from 70 to 100 ng/mL. So if you've been told your ferritin of 14 or 22 is fine, but you still feel wiped out, you're not imagining it — you're just being measured against a bar set for avoiding crisis, not for feeling good.Why Your Ferritin Might Be Low* Menstrual blood loss. For many women, the cumulative loss over months and years outpaces dietary iron intake, slowly draining reserves.* Absorption issues. Even a solid iron intake doesn't help if it isn't absorbed. Silent celiac disease, low stomach acid, or an H. pylori infection can quietly block uptake for years.* Inhibitors and hepcidin. Coffee, tea, and dairy consumed close to meals can inhibit iron absorption. Separately, inflammation can push your liver to produce hepcidin, a hormone that shuts down iron uptake even when you're eating enough.Symptoms to WatchPersistent fatigue, thinning hair, feeling cold more easily than others, and brain fog are the classic signs. If two or more of these sound familiar, it's worth getting your ferritin checked specifically — not just assumed to be fine because your CBC looked normal.The Bigger PictureIron does far more than carry oxygen. It's involved in thyroid hormone conversion, dopamine production, mitochondrial energy synthesis, and hair follicle health. That means low ferritin can produce symptoms that look a lot like depression or hypothyroidism — even when your thyroid panel and mood screening come back clean.Taking Action* Review your bloodwork. Look specifically at ferritin. Anything under 70 ng/mL is worth a conversation with your doctor.* Ask for a full iron panel. Ferritin alone isn't the whole story — request serum iron, total iron binding capacity (TIBC), transferrin saturation, and CRP (to rule out inflammation skewing the picture).* Adjust absorption habits. Space coffee and tea away from meals, lean into iron-rich foods, and avoid taking calcium and iron supplements together.* Choose the right supplement, if needed. Ferrous sulfate is harsh on the gut for many people. Iron bisglycinate is gentler and pairs well with vitamin C for better absorption — but check with your provider before starting, especially if high ferritin is a concern.* Loop in a professional. This is especially important before making changes if you suspect elevated ferritin, since iron overload carries its own risks.Listening to Your Body's WhisperYour body is constantly sending signals. Ignored long enough, whispers become screams. Taking ferritin seriously — not just as a checkbox on a lab report, but as a meaningful marker — is one concrete way to catch a problem while it's still easy to fix.Final ThoughtsMonitoring and optimizing ferritin can meaningfully change how you feel day to day. It starts with a simple ask: get the right test, read the number in context, and act on what it's telling you. If you want a more personalized look at your own levels and symptoms, consider scheduling a comprehensive health session.Stay informed, stay healthy, and listen closely to what your body is telling you. Until next time, take care.References* Camaschella, C. (2015). Iron-deficiency anemia. New England Journal of Medicine.* WHO guidance on serum ferritin concentrations for the assessment of iron status.* Clinical literature on ferritin thresholds and symptomatic iron deficiency without anemia.* Hepcidin and inflammation's role in iron regulation — recent reviews in Blood and Haematologica. This is a public episode. 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July 14, 2026 ~ Chris Renwick and Lloyd Jackson check in with Detroit Free Press 'On Guard' Reporter, M.L. Elrick. ML watched every debate by candidate in Michigan, what did he take away from each of them? Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
How do you build the career you actually want—inside an industry that was not exactly built for you—without softening who you are to fit inside it? Chee Smalls has spent fifteen years answering that question. She is Senior Director of Product Development at Coach, a style voice, and a content creator. She is also a Harlem girl who has never—not once, not in a single meeting, not in a single boardroom—code-switched a day of her career.That refusal is the whole conversation.In this episode, we get into how she got into fashion without the resume the industry usually demands, and how curiosity became her actual professional currency. We talk about the moments when she could have softened her voice to fit a room and didn't, and what that cost her at first, and what it eventually earned her. Her personal philosophy on luxury and why she refuses to follow trends—including the very sharp question she asks about whether the algorithm is quietly killing our individuality. The features she was insecure about for years and what changed when she stopped shrinking from them. Her marriage as a real partnership—the "we take turns climbing" model she and her husband built, where one goes head-down while the other holds it together, then they switch. And how she is thinking about ambition, identity, and the life she is building on her own terms.She closes the episode with an "I am" affirmation you'll want to write down: "I am beauty. I am woman. I am ambition. I am God's favored and blessed. I am me."This is also the first episode with our new behind-the-scenes intro. We shot Chee arriving at the door, the two of us mixing a rosé lemonade in my kitchen, and the moment before we settled onto the couch. The full recipe is below, make it while you listen: Meanwhile, She Rosé LemonadeMakes 4 cocktails (about 5 oz each)Ingredients1 bottle (750 mL) chilled rosé (The Beach Ros√©)1/2 cup (4 oz) freshly squeezed lemon juice2 oz Grand Marnier1 1/2 oz simple syrup1 oz agave nectarIce Optional GarnishesLemon wheelsFresh mintEdible rose petals (if you're feeling fancy)Frozen raspberries or strawberries InstructionsIn a pitcher, combine the lemon juice, Grand Marnier, simple syrup, and agave. Stir until fully combinedPour in the chilled rose and stir gently so you don't lose too much of its freshness.Taste.Want it brighter? Add another splash of lemon.Want it softer? Add another ½ oz simple syrup.Fill glasses with ice and pour over.Garnish with a lemon wheel and mint.
In this episode, Mark Russinovich, CTO of Microsoft Azure revealed Brain, the AI-powered AIOps system that continuously monitors Azure's health, detects incidents, identifies root causes, and increasingly automates responses such as pausing problematic deployments and notifying affected customers. Built on Azure Resource Graph, Brain creates a real-time digital twin of Azure, mapping dependencies across hundreds of services, data centers, and regions. Although Brain predates the generative AI boom, years of data engineering, standardized service-level indicators (SLIs), and machine learning laid the foundation for today's capabilities. Brain combines standardized SLIs, service-specific monitoring, and third-party signals to detect anomalies, while ML models dynamically establish service baselines and correlate outages with software rollouts. Microsoft says automated notifications have reduced customer support tickets by four to six times, with 80–90% of Brain-covered services receiving notifications within 15 minutes, often in under five. The company is also layering LLM-powered agents, called Triangle, on top of Brain to streamline incident routing and eventually enable AI agents to autonomously troubleshoot and remediate outages. Learn more from The New Stack around the latest in Microsoft Azure: Meet Brain, the AI that decides when Azure is officially down Microsoft's pitch to enterprises: Ditch Azure Repos for GitHub, despite its rocky reliability record Join our community of newsletter subscribers to stay on top of the news and at the top of your game.
00:00-15:00: ML recaps another blown chance for Team USA men's soccer. Thanks to CH Insurance and Marz Motors. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
VOV1 - Nửa đầu năm 2026, HĐND TP.HCM đã ban hành 2 nghị quyết quan trọng liên quan đến các dự án treo hàng chục năm ngay trên các khu “đất vàng” trung tâm Thành phố. Cuộc đời những người dân tại đây sẽ sang trang, mang theo nhiều phấn khởi lẫn kỳ vọng.Người dân đồng tình, phấn khởi Khu Mả Lạng (phường Cầu Ông Lãnh, thuộc Quận 1 cũ) tồn tại từ trước năm 1968, nhiều gia đình sinh sống qua 2-3 thế hệ. Điều kiện sống tại khu vực này rất nhếch nhác nên TP.HCM có chủ trương giải tỏa để chỉnh trang đô thị vào năm 2000.Sau hàng chục năm không triển khai, đến năm 2023, UBND TP.HCM chấm dứt chủ trương đầu tư khu Mả Lạng. Tháng 6/2026, HĐND TP.HCM thông qua chủ trương chi hơn 16.300 tỷ đồng chỉnh trang khu Mả Lạng và khu chợ Gà – chợ Gạo (phường Bến Thành, thuộc Quận 1 cũ).Người dân Mả Lạng sắp thoát cảnh ở chật chội, nhếch nhác (Ảnh: Duy Phương)
What is AI, really? And how much of what you think you know is actually true? In this episode, we break down how AI actually works (ML vs. AI), debunk common misconceptions, and dig into why AI hallucinates and why it doesn't actually "think." Joined by Alex Oldemeier, AI enthusiast and CAIO of Formove, a Physical AI startup.Follow us on our LinkedIn page here: LinkedIn and on Instagram here: InstagramLearn more about what we do at Processand here: Processand
Makoto Furukawa has impressed since the start with his powerful vibrato and unique, fancy brand of jazz-pop. However, since his solo debut, he has been perfecting his control and bringing new tools to his arsenal that have elevated his singing from "good" to "stellar", making Makoto Furukawa one of the best singers among male seiyuu.Music suggestions:- BAD SKUNK "Violet Love"- Makoto Furukawa "Uso to Gekko"- RUBIA Leopard "Prisoner"Thanks to M L for inspiring this series of episodes!
Welcome to the Personal Development Trailblazers Podcast! In today's episode, we're talking about how to restore your joy after loss by embracing healing, finding hope, and discovering that joy is possible again. Monique Lynelle Gray was born in Hartford, CT, and grew up in Philadelphia, PA. Monique Lynelle is a Transformational Joy Restoration Coach and Speaker/Entrepreneur, with experience in various backgrounds and has the spirit that just doesn't quit at the first sign of adversity. Having certifications as a Joy Restoration Coach/Grief Support Specialist, Stress Relief Coach, and Christian Counseling. A Doctoral student at Capella University, Master's Degree in Entertainment Business from Full Sail University graduating with a 3.67 GPA, and a Bachelor's in Strategic Communications from Temple University, she knows about finishing a few things. Continuing education after leaving the US Army, and completing her Cosmetology Instructor's simultaneously while completing her Bachelor's degree, then completing her 1st book in 2012, "My Journey at 30," and "A Teen's Guide to Succeed in High School," and in 2015 completing and publishing, "Blessed in the Midst of the Storm." Monique Lynelle went on to complete her 1st studio album entitled "Love of the Music," in 2013. And in 2016 and 2017 released the EP's "Get Lifted Up," and "I'm Still Blessed." And in 2021, "The Love of the Music-Compilation Album"ML produced shows for the Public Access Television Station DCTV in 2016 through 2018, "The Monique Lynelle Show," and "Monique Lynelle's Journey - The Documentary," She also wrote, produced and directed, and starred in her first film, "The PK's."As a Joy Restoration Coach, Coach ML is here to assist others in navigating through life issues to become the best versions of themselves, reaching towards their highest potential. She is also here to help people process their grief and manage their stress to achieve their goals with joy on their journey.Connect with Monique Here: https://instagram.com/MoniqueLyn_ellehttps://YouTube.com/MoniqueLyn_ellehttps://MoniqueLyn-elle.com===================================If you enjoyed this episode, remember to hit the like button and subscribe. Then share this episode with your friends.Thanks for watching the Personal Development Trailblazers Podcast. This podcast is part of the Digital Trailblazer family of podcasts. To learn more about Digital Trailblazer and what we do to help entrepreneurs, go to DigitalTrailblazer.com.Are you a coach, consultant, expert, or online course creator? Then we'd love to invite you to our FREE Facebook Group where you can learn the best strategies to land more high-ticket clients and customers. QUICK LINKS: APPLY TO BE FEATURED: https://app.digitaltrailblazer.com/podcast-guest-applicationDIGITAL TRAILBLAZER: https://digitaltrailblazer.com/
Het was me het weekje wel. Een vrouw kan niet eens meer normaal een lekker banaantje eten / zichzelf uit het water hijsen of ze wordt alweer belaagd. Pierre van Hooijdonk zei dingen waar we hartelijk om gelachen hebben. Maar meiden, het is toch godgeklaagd dat zo'n man zendtijd krijgt. Verder: de overheid maakte excuses aan de afstandsmoeders, helaas bestonden die geheel uit gebakken lucht. ML pluist even helemaal uit waarom dat zo is. En Nydia heeft goed nieuws over een medische doorbraak waarbij niemand doodging. Ga voor de shownotes en het transcript naar damnhoney.nl/aflevering-290DAMN, HONEY wordt gemaakt door Marie Lotte Hagen en Nydia van VoorthuizenIn deze aflevering hoor je advertenties voor NordVPN en onszelf:- Profiteer NU van de exclusieve NordVPN-deal op nordvpn.com/damnhoney. Probeer zonder risico met de 30 dagen geld-terug-garantie! - Steun ons via PetjeAf.com/damnhoney. Vanaf 1 euro per maand help je ons blijven bestaan én krijg je toegang tot onze bonuspodcast 'Je doet het er maar mee' editwerk: Daniël van de Poppe jingles: Lucas de Gier website: Liesbeth Smit DAMN, HONEY is onderdeel van Dag & Nacht Media. Heb je interesse om te adverteren in deze podcast? Neem dan contact op met Dag en Nacht Media via adverteren@dagennacht.nlSee omnystudio.com/listener for privacy information.
In this episode, Professor Paola Cinnella - Professor of Fluid Mechanics at Sorbonne University and Director of the Sorbonne Cluster for Artificial Intelligence (SCAI) - joins Neil to discuss her path from classical fluid mechanics and high-order numerical methods into uncertainty quantification, Bayesian methods, data-driven turbulence modeling and AI for Science.Paola has built a career at the intersection of CFD, compressible and turbulent flows, dense gas dynamics, uncertainty quantification, robust optimization and machine learning. We discuss academic careers, dense gases, RANS uncertainty, AirfRANS, surrogate modeling, scientific publishing, education in the age of AI, and the idea of the "centaur scientist".Key topicsFluid mechanics, CFD and high-order schemesDense gases, real-gas effects and expansion shockwavesUncertainty quantification and Bayesian methodsRANS turbulence-model uncertaintyAirfRANS and CFD datasets for machine learningTurbulence modeling vs surrogate modelingScientific publishing and ML-for-CFD standardsSCAI and AI for ScienceEducation, ChatGPT and centaur scientistsPapersQuantification of model uncertainty in RANS simulations: A review - Heng Xiao, Paola Cinnellahttps://doi.org/10.1016/j.paerosci.2018.10.001Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression - Martin Schmelzer, Richard P. Dwight, Paola Cinnellahttps://doi.org/10.1007/s10494-019-00089-xBayesian estimates of parameter variability in the k-epsilon turbulence model - W.N. Edeling, P. Cinnella, R.P. Dwight, H. Bijlhttps://doi.org/10.1016/j.jcp.2013.10.027AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutionshttps://arxiv.org/abs/2212.07564Data-driven turbulence modeling - Paola Cinnellahttps://arxiv.org/abs/2404.09074Direct numerical simulations of supersonic turbulent channel flows of dense gases - Luca Sciacovelli, Paola Cinnella, Xavier Gloerfelthttps://doi.org/10.1017/jfm.2017.237LinksPaola Cinnella named Director of SCAIhttps://scai.sorbonne-universite.fr/news/paola-cinnella-new-directorSCAIhttps://scai.sorbonne-universite.fr/Paola Cinnella - HAL publicationshttps://cv.hal.science/paola-cinnellaPaola Cinnella - Google Scholarhttps://scholar.google.com/citations?hl=fr&user=wBRA0JAAAAAJERCOFTAC SIG 54 - Machine Learning for Fluid Dynamicshttps://www.ercoftac.org/special_interest_groups/54-machine-learning-for-fluid-dynamics/master-of-science-internships/Chapters00:00 Podcast intro00:39 Introducing Prof. Paola Cinnella03:28 Conversation begins03:56 How Paola found fluid mechanics07:09 Moving from Italy to France08:37 High-order schemes and compressible flows09:30 Building an academic career12:06 Dense gases and uncertainty quantification15:16 Expansion shockwaves and real-gas effects19:17 Returning to Paris and academic mobility24:52 Academia, passion and persistence27:51 Bayesian methods and turbulence uncertainty30:47 Learning statistics across disciplines33:07 LearnFluidS, AirfRANS and CFD datasets36:33 Skepticism and physics in ML turbulence modeling40:41 Could ML lead to a universal turbulence model?42:59 Turbulence models, surrogate models and RANS45:03 Why LES alone cannot solve optimization47:15 Multi-fidelity modeling49:08 What Computers & Fluids looks for in ML-for-CFD papers54:05 CFD metrics vs machine-learning metrics57:13 Overselling, publication pressure and quality62:22 SCAI and AI for Science66:07 Cross-disciplinary AI for Science69:26 Education in the AI era72:44 Critical thinking and AI outputs78:15 AI as a companion, not a replacement81:42 AlphaFold and the future of discovery83:43 Training centaur scientists85:11 Closing thoughts
We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li
One of Detroit's most notorious crimes inspires a murder mystery set Up North. Author Bryan Gruley joins ML and Marc […]
00:00-30:00: Sal and ML break down a critical series against the Rays, another bad one against the Twins, just how bad this lineup is and more. The Twins, really? Thanks to CH Insurance and Batavia Downs Gaming. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
This month we speak with Demetrios for the second time about: what is ML and MLops, narrow Machine Learning being still relevant, vibe coding, working with agents, talking to your computer, unknown productivity gains of LLMs, the chat interface as a bad interface for all knowledge, AIs that know when they're wrong, the lack of ground truth, and more...
You were told your AMH is low, and maybe that IVF was your only option. Maybe you went through it, and it did not work. Maybe you already have one child and have been told this time is different. And some part of you has been wondering whether that number is really the whole story. Here is what most women are never told. AMH reflects egg quantity, not egg quality, and not your ability to conceive. It predicts how your ovaries respond to medication. It does not explain why a cycle failed, why embryos stopped developing, or why outcomes have not changed despite every protocol adjustment. In this episode, I walk through a real case. Her AMH was 0.27 ng/mL. She had a failed IVF cycle behind her and was navigating secondary infertility. One pattern that stood out was inflammation. Her hs-CRP was 1.3 mg/L, read as normal by conventional ranges but not optimal, and something was driving it. She conceived naturally, not because the number changed, but because the systems shaping egg development were finally looked at. This is not about avoiding IVF or chasing a better lab value. It is about reading what your body has already shown you, so your next decision is an informed one. CHAPTERS 00:00 What your AMH actually tells you, and what it does not 01:00 Why a failed IVF cycle can be more informative than the number 02:00 This was not unexplained, age, or bad luck 03:00 The patterns we see, inflammation, nutrient absorption, brain, and hormone signaling 04:00 Addressing systems in sequence, not a random checklist 05:00 What it looked like when the body started responding 06:00 Natural conception at AMH 0.27, and why the number was not the point 07:00 Pausing before the next cycle, readiness over urgency 07:30 The Functional Fertility Second Opinion WHAT YOUR CLINIC MISSED The companion guide walks through the patterns a standard fertility workup tends to miss, with the markers behind each one, so you can take it to your next appointment and ask the questions. Email hello@fabfertile.ca, subject line MISSED, and we will send you the guide. FUNCTIONAL FERTILITY SECOND OPINION A free 45-minute call where I review your bloodwork, your history, and your partner's results with you. You leave knowing what your biology has been telling you and what your next decision could be. Email hello@fabfertile.ca, subject line FERTILE, or book here: https://fabfertile.com/pages/book ABOUT THE HOST I'm Sarah Clark, founder of Fab Fertile and host of Get Pregnant Naturally, a podcast with over one million downloads. My functional fertility team works with couples navigating low AMH and failed IVF, reviewing functional lab results, gut microbiome, food sensitivity, vaginal microbiome, nutrigenomics, HTMA, DUTCH, toxin testing, and bloodwork alongside nervous system work, to help identify patterns that may not have been considered. We work alongside your medical team, not instead of them. Sarah Clark, founder of Fab Fertile, host of Get Pregnant Naturally (1M+ downloads), and author of Fabulously Fertile. If this episode helped, leave a review on Apple Podcasts. It is how other women find this work.
Jim McDonald takes the Identity at the Center podcast on the road to Rome, Italy, for a special two-part episode. The first segment is an IdentiBeer roundup where Jim gathers quick-fire takes from practitioners in the Italian IAM community, including Andrea Rossi and Alessandro Piscopo of IAMONES and Marco Venuti of Thales on the biggest trends shaping identity today. The second segment is a three-course meal where Jim sits down with Alessandro Piscopo, Head of AI and Co-founder at IAMONES, to discuss AI and identity over food and wine.Across a seafood starter, scialatielli alla pescatora, and tiramisu, the conversation covers the history of AI in identity, why LLMs represent a revolution rather than an evolution, the AI-first product philosophy versus retrofitting AI onto legacy systems, compute and architecture constraints facing large language models, and what life looks like for the IAM practitioner in 2030. Alessandro envisions an identity equivalent of Claude Code, a specialized AI tool that democratizes identity expertise the way coding assistants have transformed software development.0:00 Intro and IdentiBeer Rome roundup7:01 Alessandro on AI for IAM vs. IAM for AI12:00 Three-course dinner begins - Course 1: Seafood starter14:09 History of AI in identity, from ML models to LLMs17:51 Course 2: Scialatielli alla pescatora and Falanghina wine19:56 AI-first products vs. AI layered onto legacy systems22:00 Transition period and the new world of identity24:04 The ChatGPT moment vs. the iPhone moment27:05 Compute constraints, energy costs, and architecture breakthroughs30:46 Smaller models and cost-efficiency tradeoffs32:35 Course 3: Tiramisu, baba, and espresso33:00 Life as an IAM practitioner in 203035:19 Claude Code for IAM and democratizing identity tools37:24 App store ecosystem analogy for AI platforms43:07 Closing thoughtsKeywords: IAM, identity and access management, AI for IAM, IAM for AI, agentic AI, non-human identity, IGA, LLMs, large language models, AI-first, machine learning, Alessandro Piscopo, IAMONES, Jim McDonald, Jeff Steadman, Identity at the Center, IDAC, IdentiBeer, Rome, Italy, Marco Venuti, Thales, Andrea Rossi, agentic identity, transformer architecture, compute efficiency, identity practitioner 2030, Claude Code for IAM, identity democratization, Identiverse, European Identity Conference
Hosts: Ed Jones (Owner – Nutrition World) & Clint Powell A variety of topics all about a healthy life Presented by: Nutrition World www.nutritionw.com Broadcasting from the Nooga Dentistry Studio www.noogadentistry.com Production of: Whitfield Media Group www.vitalhealthradio.com High baseline of general anxiety in pets; fireworks can escalate it dramatically Rough estimates from Dr. Smith: ~30% with noticeable anxiety ~10% with severe reactions: Destruction of property Defecating/urinating in house Extreme escape behaviors (e.g., dog through plate glass window) Natural vs Pharmaceutical Support & Timing Severe cases: Trazodone often used; Dr. Smith avoids drugs that completely knock pets out Mentions older drugs (e.g., acepromazine) that overly sedate animals She prefers starting treatment the day before fireworks: Anxiety and pain wind up; if you get behind, it's hard to control Natural options she likes: Melatonin, tryptophan, theanine, GABA Pet products that combine several of these Start 1–2 days before fireworks because neighbors often start early [0:18:58] Melatonin Dosing & CBD for Pets + Environmental Concerns Melatonin for dogs: Start around 3 mg, can go up, even up to ~10 mg in some cases Must be given at bedtime to preserve serotonin/melatonin rhythm Human reference: some serious disease protocols use up to 50 mg CBD: She likes CBD: generally very safe, large margin before toxicity Important: oil directly in mouth, not hidden in food (stomach acid breaks it down) Treats are OK, but observe individual response Ed notes TN hemp rule change (July 1) hurting many businesses; pet CBD appears less restricted for now Environmental side of fireworks: Harm to birds and nocturnal wildlife Startled flocks flying at night, running into obstacles [0:21:57] About CHAI: Services & Ozone Therapy Chai = Chattanooga Holistic Animal Institute On Main Street, Southside Open ~13–14 years Services: Conventional: surgery, X-rays Integrative: herbal medicine, nutrition-first approach, acupuncture, chiropractic Heavy focus on nutrition as foundation Ozone therapy: Used for cancer (mixing with blood + UVB), ear infections (ear cups), GI issues (ozone enema + fecal transplant) Antiviral, antifungal, antibacterial; research supports it but not mainstream due to lack of patentability Ed shares parallel human ozone experiences and enthusiasm [0:24:27] Regulatory Limits on CBD Advice & Practical Dosing Forms California example: Vets cannot legally discuss CBD with clients, while retail hemp shops can freely advise For cats: Liquids in vegetable glycerin are best Alcohol-based tinctures: cats won't like them (foaming, spitting) Liquids can go in food or directly in mouth For dogs: Easier to hide products, but Dr. Smith dislikes many chews: Often have rice flour, tapioca starch, molasses, smoke flavor (potential carcinogen) Prefers powders and liquids [0:26:16] Why Kibble Is Harmful & Heat Safety for Pets Core problems with kibble: Ultraprocessing damages proteins and fats Produces advanced glycation end products (AGEs)—carcinogenic High carbohydrate; not species-appropriate for carnivorous animals Expensive kibble = “expensive Skittles” – processing is still the issue Better options: Raw, dehydrated, freeze-dried Balanced homemade diets Light cooking under ~200°F for seniors to aid digestibility without denaturing nutrients Heat and Summer Safety for Pets Hot ground + hot air → limit daytime walks, adjust exercise Brachycephalic (“smushy-faced”) dogs at special risk: Pugs, Boston Terriers, bulldogs, etc. Shortened face doesn't reduce internal soft tissue; narrow airways = breathing through a straw Heat + humidity = much higher risk of heat exhaustion; many just lie on A/C vents Cats handle heat better (tend to stay inside), but should still be kept cool and supervised Pool safety: pets often don't know how to get out, so human supervision is essential [0:30:41] Independent Practice vs Corporate Vet Medicine CHAI is one of the last independent practices in town Independence allows: Thinking outside the box and the standard “cookbook” Corporate practices: Strict protocols, less flexibility Vets can't always practice as they'd like [0:36:09] Chemical Aging, Peakspan, and Electrolytes Ed quotes Dr. Keith Scott-Mumby (81-year-old MD) on “chemical aging”: Modern environment “poisons” us: Plastics, can linings, pesticides (glyphosate), microplastics Addictive refined carbs, seed oils Many of these mimic hormones and drive accelerated aging “Chemical aging” shows up as: Hair thinning, dry/crepey skin, age spots Persistent belly fat, “man boobs,” fragile bones, poorly fitting clothes Ed's own book: “Are You Sick and Tired of Being Sick and Tired?” He believes it lays out an A–Z game plan for aging better / “peakspan” Available as an ebook on TheHolisticNavigator.com Electrolytes vs Gatorade; Critique of Mainstream Sports Drinks Context: intense summer heat and need for electrolytes Our bodies run on electrical currents (heart, brain, nervous system) regulated by electrolytes Daily potassium need ≈ 3,400 mg Comparison: 20 oz Gatorade: ~75 mg potassium (very low) 0 mg magnesium ~270 mg sodium ~34–36 g carbohydrates/sugar Gatorade Zero: no sugar but uses sucralose, which Ed says can disrupt the gut microbiome Ed's personal take: Would rather “spend” that sugar on a cheesecake dessert than on Gatorade Example True Grace electrolyte formula (carried at Nutrition World): ~750 mg sodium ~250 mg potassium ~100 mg magnesium ~100 mg cordyceps (supportive for lungs/endurance) Great Naturally has ~700 mg potassium per serving Conclusion: many store-brand sports drinks are nutritionally weak and sugar-heavy compared to targeted electrolyte blends [0:44:27] Pepcid (Famotidine), Serotonin, and Essential Oils for Sore Throat Ed introduces Pepcid (famotidine): H2 blocker commonly used OTC for heartburn Prefers it over long-term proton pump inhibitors (e.g., Nexium) Key claim from research Ed cites: Famotidine uniquely blocks certain serotonin activity Can sometimes help with: Chronic pain Inflammation Fatigue Case example: life-threatening serotonin syndrome reversed in 15 minutes with famotidine Elevated serotonin may: Impair mitochondrial energy production Promote chronic inflammation and, paradoxically, some depression and pain Ed has bought a box himself; recommends it as the safer short-term choice for bad heartburn Essential Oils vs Antibiotics for Sore Throats Study on sore throat treatment: 97 adults with clinically diagnosed sore throat Group 1: penicillin twice daily Group 2: oral essential oils capsule 3× daily Group 3: both Outcomes: 100% improvement in antibiotic group 88% improvement in essential oil–only group 100% improvement in combined group Essential oil blend ingredients: Oregano, eucalyptus, lemon, cinnamon, pine oils Clint raises important question: absent a no-treatment control, some percentage may have improved naturally) Quick guide to buying quality beef: Prefer “100% grass-fed” over just “grass-fed” Look for or confirm grass-finished (often not on labels due to cost; best to know your farmer) Beware empty buzzwords: “natural,” “farm raised,” “pasture inspired” Real grass-fed/finished usually costs more due to land/time inputs Fat color: Slight yellow hue suggests carotenoids from real forage [0:53:35] Strength, Independence, and Vitamin D Ed references recent high-production video interview in Atlanta Draws inspiration from Jack LaLanne: Early television fitness and vitamin pioneer Nutrition World once helped bring him to Chattanooga; he lived to ~95 Paraphrased LaLanne theme: Strength gives you options: Carry your own groceries Climb stairs confidently Travel, explore, stay active Play with grandchildren, work in the yard, maintain independence Without strength: Tasks become difficult Confidence drops Independence shrinks; world gets smalle Ed reiterates: muscle is the organ of longevity and needs: Regular weight training Adequate protein Targeted supplementation Discussion of vitamin D: Ed's recent lab: ~54 ng/mL despite summer tan Wants to remain above 50 ng/mL year-round; may increase winter dosing Clint mentions his last check (~3 years ago) was ~70 ng/mL, even before supplementation The post Radio Show / Podcast – July 5, 2026 first appeared on Vital Health Radio.
Seth Woolcock and Andrew Erickson break down their favorite early NFL Week 1 bets, including spreads, totals, moneyline underdogs, and where they believe sportsbooks have already made mistakes. The duo discusses why the Cowboys could continue their dominance over the Giants, whether the Steelers are being undervalued against Atlanta, why several Week 1 unders stand out, and which underdogs offer intriguing value before kickoff. If you're betting NFL futures, building your Week 1 card, or simply looking for the sharpest early betting angles, this episode has you covered. Timestamps: (May be off due to ads) Intro - 0:00:00 Dallas Cowboys -1.5 vs. New York Giants - 0:04:04 Pittsburgh Steelers -2.5 vs. Atlanta Falcons - 0:06:43 Tennessee Titans -2.5 vs. New York Jets - 0:10:43 BettingPros App - 0:12:38 New York Jets vs. Tennessee Titans Under 39.5 - 0:13:12 San Francisco 49ers vs. Los Angeles Rams Under 49.5 -0:15:44 Baltimore Ravens vs. Indianapolis Colts Under 49.5 - 0:18:58 Hard Rock Bet - 0:22:02 Houston Texans (-104) ML vs. Buffalo Bills - 0:24:15 Miami Dolphins ML (+176) vs. Las Vegas Raiders - 0:28:03 Minnesota Vikings ML (+100) vs. Green Bay Packers - 0:30:54 Helpful Links: Hard Rock Bet - Sign up for Hard Rock Bet and make a $5 bet and you'll get $150 in bonus bets if you win. Head over to Hard Rock Bet, sign up and make your first deposit today. Payable in bonus bet(s). Not a cash offer. Offered by the Seminole Tribe of Florida in FL. Offered by Seminole Hard Rock Digital, LLC, in all other states. Must be 21+ and physically present in AZ, CO, FL, IL, IN, NJ, OH, TN or VA to play. Terms and conditions apply. Concerned about gambling? In FL, call 1-888-ADMIT-IT. In IN, if you or someone you know has a gambling problem and wants help, call 1-800-9-WITH-IT. GAMBLING PROBLEM? CALL 1-800-GAMBLER (AZ, CO, IL, NJ, OH, TN, VA) BettingPros App - Make winning bets with advice and picks from top sports betting experts. The BettingPros app puts consensus and expert-driven sports betting advice at your fingertips to help you pinpoint the best odds and make winning bets. Download it today on the App Store or Google Play. BettingPros Discord - Looking to up your game in sports betting? Join our exclusive sports betting Discord community at bettingpros.com/chat! Not only can you connect with expert handicappers who provide free picks for NBA, NFL, MLB, NHL, player props, live betting, and more, but now you can also participate in our weekly community picks. Cast your vote, see how your picks stack up against the experts, and track your success! BettingPros Pick Tracker – Want to track all of your wagers in one place? Check out the BettingPros Pick Tracker. It syncs up with your sportsbooks to tally which picks hit, and which miss AND gives you a live look at what the public is doing so you can use real-time tracking to determine which plays to make, and which to fade: bettingpros.com/pick-trackingSee omnystudio.com/listener for privacy information.
Fluent Fiction - Mandarin Chinese: Finding Home: Jiā Hào's Journey Through Miao Traditions Find the full episode transcript, vocabulary words, and more:fluentfiction.com/zh/episode/2026-07-02-07-38-20-zh Story Transcript:Zh: 在贵州的夏天，山间的雾气轻柔地笼罩着苗族小村。En: In the summer of Guizhou, the mist gently envelops the Miao village nestled in the mountains.Zh: 村子里，五彩斑斓的传统苗族建筑在绿色的群山背景下显得格外明亮。En: In the village, the colorful traditional Miao buildings stand out brightly against the lush green mountainous backdrop.Zh: 空气中弥漫着准备节日的声音，许多人忙碌着装饰，欢声笑语不断。En: The air is filled with the sounds of festival preparations, with many people busy decorating, and laughter constantly echoing.Zh: 贾豪，自小在城市长大，对于自己的故乡记忆模糊。En: Jiā Hào, who grew up in the city, has a blurry memory of his hometown.Zh: 他站在村子的入口，感到陌生，却充满期待。En: He stands at the entrance of the village, feeling unfamiliar yet full of anticipation.Zh: 他的目标是了解自己的文化根源，找到归属感。En: His goal is to understand his cultural roots and find a sense of belonging.Zh: 村里的人都友善地欢迎贾豪，但他心里仍有些不安。En: The villagers warmly welcome Jiā Hào, but he still feels a bit uneasy inside.Zh: 他望向繁忙的村落，看到他的堂兄强正在大树下协助其他人准备节日用的长桌。En: He gazes at the bustling village and sees his cousin Qiáng assisting others in preparing the long tables for the festival under a large tree.Zh: 强从未离开过村子，他熟悉这片土地的每一个角落。En: Qiáng has never left the village; he knows every corner of this land.Zh: 贾豪上前，微笑着向他打招呼。En: Jiā Hào approaches with a smile to greet him.Zh: “贾豪，你终于来了！En: "Jiā Hào, you finally came!"Zh: ”强热情地说道，“准备帮忙吗？En: Qiáng says enthusiastically, "Ready to help?"Zh: ”贾豪点头，心下决定参与到节日的准备中去，也许这样能让他更快融入这个他几乎已遗忘的世界。En: Jiā Hào nods, deciding inwardly to participate in the festival preparations.Zh: 米琳是一位充满智慧的长者，也是村里的文化传承者。En: Perhaps this will help him integrate into this world he has almost forgotten.Zh: 她看着贾豪参与进来，满意地点了点头。En: Mǐ Lín is a wise elder and the cultural inheritor of the village.Zh: 米琳耐心地教贾豪如何制作特殊的节日糕点。En: She watches Jiā Hào joining in and nods with satisfaction.Zh: 贾豪学习得很快，尽管刚开始有些生疏，但他努力模仿村民们的每一个动作。En: Mǐ Lín patiently teaches Jiā Hào how to make special festival pastries.Zh: 节日的高潮来到了，村子中央搭起舞台，锣鼓声敲响，村民们开始聚集。En: Jiā Hào learns quickly; although he feels a bit clumsy at first, he diligently imitates every move of the villagers.Zh: 突然，米琳走到贾豪面前，微笑着示意他加入跳舞。En: The climax of the festival arrives, with a stage set up in the village center, drums sounding, and villagers gathering.Zh: 贾豪犹豫片刻，但在众人的鼓励下，他走上了舞台。En: Suddenly, Mǐ Lín approaches Jiā Hào, smiling and signaling for him to join the dance.Zh: 强上前与他并肩共舞，让贾豪感受到无比温暖。En: Jiā Hào hesitates for a moment, but with everyone's encouragement, he steps onto the stage.Zh: 随着音乐的节奏，贾豪找到了舞步的节拍，他心中的不安渐渐消退。En: Qiáng comes up to dance alongside him, making Jiā Hào feel incredibly warm.Zh: 村民们的欢呼声盖过了他的紧张，他终于感受到了一种从未有过的归属感。En: As the music's rhythm guides him, Jiā Hào finds the beat of the dance steps, and his inner unease gradually fades away.Zh: 当夜幕降临，灯火通明的舞台上，跳舞的人们渐渐散去。En: The cheers of the villagers overshadow his nervousness, and he finally experiences a sense of belonging he never felt before.Zh: 贾豪坐在长桌旁，心中萌发了一个新的决心。En: As night falls, and the dancers gradually disperse from the brightly lit stage, Jiā Hào sits by the long table, a new determination taking root in his heart.Zh: 他要更深刻地了解自己的文化根源，把这些美好分享给更多的人。En: He wants to understand his cultural roots more deeply and share these beautiful experiences with more people.Zh: 在这个夏夜，星空下，贾豪明白了自己的选择。En: On this summer night, under the starry sky, Jiā Hào understood his choice.Zh: 在这片苗族故土，他第一次真正找到了自己的位置，亦如绿意盎然的山间溪流，融入了他生命的背景中。En: In this Miao homeland, he truly found his place for the first time, like the flowing stream in the emerald mountains, blending into the backdrop of his life. Vocabulary Words:mist: 雾气envelops: 笼罩nestled: 坐落backdrop: 背景anticipation: 期待belonging: 归属感uneasy: 不安bustling: 繁忙integrate: 融入clumsy: 生疏diligently: 努力imitates: 模仿climax: 高潮rhythm: 节奏overshadow: 盖过nervousness: 紧张disperse: 散去determination: 决心inheritor: 传承者pastries: 糕点echoing: 回响villagers: 村民preparations: 准备hometown: 故乡gazes: 望向signal: 示意hesitate: 犹豫encouragement: 鼓励feasible: 可行的stream: 溪流
Seth Woolcock and Andrew Erickson break down their favorite early NFL Week 1 bets, including spreads, totals, moneyline underdogs, and where they believe sportsbooks have already made mistakes. The duo discusses why the Cowboys could continue their dominance over the Giants, whether the Steelers are being undervalued against Atlanta, why several Week 1 unders stand out, and which underdogs offer intriguing value before kickoff. If you're betting NFL futures, building your Week 1 card, or simply looking for the sharpest early betting angles, this episode has you covered. Timestamps: (May be off due to ads) Intro - 0:00:00 Dallas Cowboys -1.5 vs. New York Giants - 0:04:04 Pittsburgh Steelers -2.5 vs. Atlanta Falcons - 0:06:43 Tennessee Titans -2.5 vs. New York Jets - 0:10:43 BettingPros App - 0:12:38 New York Jets vs. Tennessee Titans Under 39.5 - 0:13:12 San Francisco 49ers vs. Los Angeles Rams Under 49.5 -0:15:44 Baltimore Ravens vs. Indianapolis Colts Under 49.5 - 0:18:58 Hard Rock Bet - 0:22:02 Houston Texans (-104) ML vs. Buffalo Bills - 0:24:15 Miami Dolphins ML (+176) vs. Las Vegas Raiders - 0:28:03 Minnesota Vikings ML (+100) vs. Green Bay Packers - 0:30:54 Helpful Links: Hard Rock Bet - Sign up for Hard Rock Bet and make a $5 bet and you'll get $150 in bonus bets if you win. Head over to Hard Rock Bet, sign up and make your first deposit today. Payable in bonus bet(s). Not a cash offer. Offered by the Seminole Tribe of Florida in FL. Offered by Seminole Hard Rock Digital, LLC, in all other states. Must be 21+ and physically present in AZ, CO, FL, IL, IN, NJ, OH, TN or VA to play. Terms and conditions apply. Concerned about gambling? In FL, call 1-888-ADMIT-IT. In IN, if you or someone you know has a gambling problem and wants help, call 1-800-9-WITH-IT. GAMBLING PROBLEM? CALL 1-800-GAMBLER (AZ, CO, IL, NJ, OH, TN, VA) BettingPros App - Make winning bets with advice and picks from top sports betting experts. The BettingPros app puts consensus and expert-driven sports betting advice at your fingertips to help you pinpoint the best odds and make winning bets. Download it today on the App Store or Google Play. BettingPros Discord - Looking to up your game in sports betting? Join our exclusive sports betting Discord community at bettingpros.com/chat! Not only can you connect with expert handicappers who provide free picks for NBA, NFL, MLB, NHL, player props, live betting, and more, but now you can also participate in our weekly community picks. Cast your vote, see how your picks stack up against the experts, and track your success! BettingPros Pick Tracker – Want to track all of your wagers in one place? Check out the BettingPros Pick Tracker. It syncs up with your sportsbooks to tally which picks hit, and which miss AND gives you a live look at what the public is doing so you can use real-time tracking to determine which plays to make, and which to fade: bettingpros.com/pick-trackingSee omnystudio.com/listener for privacy information.
This episode has a fun personal twist: There's a counterfactual world where I was employee #1 at Genesis Molecular AI, the company behind today's episode. A certain introduction happened a few weeks too late and I had already happily signed at Atomwise, another ML-for-drug-discovery startup. Same problem, different company. I was certain ML was going to transform small molecule drug discovery. Early results were underwhelming. Useful at times, but nowhere near revolutionary. In the last year I've seen signs that ML is finally ready to deliver on my convictions from a decade ago. Genesis is one of the places that might have finally cracked this problem. I was super excited to come full circle and catch up with co-founder Evan Feinberg and CTO Sergey Edunov.If you are at all interested in small molecule drug discovery, we think you will find this fascinating!In our nearly two hour chat we cover:* What is small molecule drug discovery, and why is it hard* Structure prediction as a hotbed of innovation in AI algorithms* How advances in AI elsewhere have enabled stepwise improvements in predictive power* How the community benchmarks are essentially calling AI slop good enough* The Genesis flagship model (PEARL) can routinely hit a threshold that is necessary for real-world applications* New agentic workflows enabled by these highly accurate modelsRead on for more, and also some personal thoughts on the future at the end.The coolest diffusion research is happening at GenesisSergey Edunov came to Genesis from Meta where he led Llama 2 training and Llama 3 pretraining. Sergey was a former physicist who thought he was done with physics after many years of training LLMs. Then, he discovered Genesis, and was blown away with all the novel architecture work they've been developing.It probably surprises no one that modern LLM research has not resulted in fundamentally novel or exciting updates in architectures since almost the advent of the transformer — the entire field is using variants on the same idea that came out in the original “Attention is all you need” paper. Sure, some were quite useful (mixture-of-experts in particular allowed for the massive model paradigm we're at today), but there was very little conceptually exciting.“We sort of had to wait for the right primitive to get created, and that turned out to be diffusion… Actually, some of the most innovative diffusion research that's happening in our field is happening in 3D structure prediction right now.” — Evan FeinbergThe field of 3D structure prediction on the other hand has been a hotbed of research. Genesis' recent model PEARL (Place Every Atom at the Right Location) is able to understand protein flexibility, and model not just where the ligand goes, but also make small adjustments of the protein so that the two fit better than either alone. The field knew this was missing for a long time, but it was really hard to model until now.Agentic DiscoveryWhat makes this problem so hard? As Sergey points out, there are 10^60 possible drug-like small molecules. You'll never be able to search them all, and trying to find the good ones is something like finding a needle in a haystack — except everything except your needle is dangerous.“There are 10 to the 60 drug-like small molecules in the universe… it's like finding a needle in a haystack, where everything except your needle is very, very dangerous.” — Sergey Edunov“Or finding hay in a needle stack might be a more apt analogy.” — Evan FeinbergTrying to solve the multi-parameter optimization problem is even worse. What makes a strong binder and a molecule with good “ADMET Properties” are oftentimes at tension with each other. For example, a good binder is likely greasy, but a greasy molecule is likely insoluble so it won't enter the bloodstream and get to where it needs to go!Genesis' advances in generative AI have now pushed them beyond the threshold where they believe agentic drug discovery loops are finally possible. We all remember the early days of LLMs. They were great chatbots but terrible agents, as small errors compounded rapidly into uselessness. As LLMs got better, the usefulness of agents rapidly improved. Evan and Sergey argue that their models at Genesis recently passed a similar threshold. Their internal agentic drug-discovery system (code named SAPPHIRE) can now iterate like a chemist: look at and reason about poses, form hypotheses, read literature, use internal tools, create candidates for the next iteration. Combining this with automated lab partnerships like the one Genesis has with Incyte, we're rapidly approaching a time of drug discovery agents running 24/7 making/testing new molecules. Exciting times!Benchmark crisis: Everyone's favorite benchmark is slopOne surprising point that isn't talked enough about: the academic field of “co-folding” has settled on a benchmark value of “2 Angstrom RMSD” as a metric for a “good pose”. Evan does not mince words: this threshold is just bad. Perhaps even deceptively bad. For many strong binders, there's a very clear pose, one that you can even directly resolve in the PDB electron density! And yet, with a 2Å RMSD threshold, you can get the pose quite wrong in ways that might even mislead a medicinal chemist. For example, flip around an aromatic ring, and everything looks reasonable, but you're no longer modeling the right interactions.Evan makes the strong claim that 1Å RMSD is really the threshold necessary to ensure the core of the molecule is sitting where it needs to be, and models all interactions.“If your model is sitting at 1.8, 1.9 Angstrom RMSD, that's slop, most likely.” — Evan FeinbergAs a simple example, he points out hydrogen bonds which are responsible for many of the most important interactions in protein-ligand systems. Hydrogen bonds only have a 0.6Å range to be valid! Clearly if you're accurately resolving all H-bonds, you generally have to be doing much better than the 2Å threshold.This is clearly a hard-fought lesson for Evan and Genesis. In their opinion, the community is stuck on these benchmarks because academics developing methods were not users. Evan does see signs of life, with the use of new metrics such as lDDT for co-folding. Hopefully soon the community can agree that “1.8Å RMSD is slop”, and start hill climbing on this much harder task.For a more thorough exploration of the weaknesses in conventional benchmarks, see the PEARL technical report.PEARL tops OpenBindWhich makes what happened next all the more striking. Near the end of the podcast, we talked about a recent “proof-is-in-the-pudding” moment for Genesis — evaluating their PEARL model on a recently released OpenBind benchmark. This benchmark featured 802 never before seen co-complexes on a target protein EV-A71. This target seems almost custom-chosen to give most classical docking methods a problem. When a ligand binds to the main binding site, the protein moves around to close off the path the ligand used to enter the binding pocket. This process, known as “induced fit” is notoriously hard for traditional methods to model. The tradeoff is easy to understand: treating the protein as a static structure, it becomes difficult to place a ligand in a binding pocket. Treat the protein as dynamic, and now you have to simulate complicated processes that take a long time to resolve.PEARL was able to model the induced fit of the ligand without running long MD simulations. Across the different evaluation metrics, PEARL came out not just ahead, but oftentimes well ahead of any public model. A truly impressive result.“Where PEARL was exceptionally good is figuring out how to move this loop. We are basically correct for every single pose.” — Sergey EdunovEven more exciting, this was done without any fine-tuning, or using any data on the target or homologous targets — the template PDB was released after PEARL's training cutoff.Where does co-folding go now?As someone who has followed or participated in ML techniques for protein-ligand interactions for almost a decade, I was genuinely impressed with the results that Genesis has released recently. This has been many years in development, and I'm sure Evan and the team had many sleepless nights trying to get to this point. I also think other teams are making similar progress — both Isomorphic and Deep Origin have released results that seem spiritually similar and combine computation, wetlab data, ML, to achieve genuine predictive power that seemed impossible a decade ago. Sadly, all of the above are closed source so there's no way to honestly compare them. Looking at the results I think there might be a time in the not so distant future where we can consider protein-ligand binding “solved”.I sincerely hope that the academic community can take inspiration from these developments. Once you know something can be done, it's much easier to execute. Still, I believe that the key enabler in all of the above was the tight integration of ML, large-scale computation, and real-world drug discovery applications. Sadly academia is just not structured in a way that makes such a development easy.With those parting thoughts, we hope you give the podcast a listen! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe
Welcome to RetroLogic! I'm Sam Wagers here with Shannon Eno and John Cummins! Intros But RetroLogic isn't just a podcast. It's a community of retro gamers! - We've got an active, friendly, and free discord. - Giveaways - Contests - AND Dive into our family of Retro podcasts! Like RetroGroove, a music history podcast, and On Topic Retro, a podcast dedicated to 1 video game per episode hosted by our very own John Cummins. - you can find everything at our website retrologic.games Housekeeping John - New retrologic content coming! Articles in substack On Topic Retro - Mario Galaxy The Price Is Retro If this is your first time playing Price Is Retro, here's how we play. I'm going to list off 4 or 5 games and everyone has to guess how much the lot is worth in total. Whoever is closest to the actual value wins that round! Everyone has a list and everyone guesses on each other's list. At the end, the player that won the most rounds wins the episode! But watch out for the robot Deus Guess Machina! He averages all of our guesses together for his own guess Dinosaur - Leonard - adds up original costs, retail value Ghost - Polterguest - always guesses 300 RetroLogic Ep150 Shannon's List Sam's list John's List Flightsy's Live List Show Topic - What We're Playing-New Games, Old Games, New hardware? Sam- Shannon- John- Community Couch Savannah the Hyena Queen — 6/28/2026 1:17 PM Been playing Mario Galaxy for the challenge, but after that I've gotta get back to the backlog: Mario Galaxy 2, Mario & Luigi Brothership, Hyrule Warriors: Age of Imprisonment, Xenoblade Chronicles Definitive Edition, Xenoblade Chronicles X Definitive Edition, Persona 3 Portable, Omori, Sea of Stars, etc etc etc. I'm a busy gal Add Deltarune chapter 5 to the list as well, though hopefully that won't take too long Games with Coffee — 6/28/2026 1:40 PM Working on Final Fantasy VII Rebirth, but took a break to play Star Fox. After Rebirth, I'm gonna switch to Clair Obscur: Expedition 33, followed by Mina the Hollower and then Hades and Chained Echoes OctoRock_1982 — 6/28/2026 2:51 PM I finally got to dig into Mina The Hollower. I'm about 15 hours in and having the time of my life. The game scratches such a specific type of itch for me, and I'm sure would do the same for veteran gamers of a certain era. It somehow feels authentically retro and yet wholly unique at the same time. As much as the game is touted as a top down Zelda-like, it borrows just as much DNA from the likes of NES games like Dragon Warrior, StarTropics, Castlevania and Ninja Gaiden. So, as you might expect, I'm in heaven playing this game. Trey — 6/28/2026 3:02 PM I bounced onto starfox and castlevania harmony of dissonance since getting the gba castlevania double pack last week. It's my first castlevania and my first starfox 64 playthrough! I hope to return to Mina the hollower after but rhythm heaven is coming soon! BeerBierCerveza [nDAD], — 6/28/2026 3:58 PM I've been playing through the games in the Resident Evil bundle. Finished Requiem, then played through biohazard, and I just rolled credits on village this weekend. All of these games were amazing! I was certain at least one of the games in the bundle was going to be a dud, but I was wrong. Requiem is definitely a contender for GOTY! Drex1981 [ARC], — 6/28/2026 4:39 PM I've been playing the New Star Fox remake which has been really good. Also Marathon, Dispatch, Mario Kart world, and Final Fantasy 6 for my snes. And also Street Fighter 6 I've been competing in Tournaments Your Wallet's Ambassador — 6/28/2026 6:46 PM I've been playing Super Mario Galaxy, trying to get to 242 stars as quickly as possible for Retro Rewind. I've also been playing an Archipelago run with some college friends, where I'm playing Mario & Luigi: Superstar Saga. My backlog includes Deltarune Chapter 5, Rhythm Heaven Groove, The rest of the M&L games after Bowser's Inside Story, and kinda every game I own... Thanks for listening to the RetroLogic Podcast! We are proudly part of the Nintendo Dads family of podcasts. If you like what you hear, check me out on Bluesky at @retrologicgames.bsky.social. You're also welcome to jump into our friendly and 100% non-toxic Discord Community! The link to that is in my Blusky bio. You can also find everything on our website Retrologic.games
In episode 226, host Galit Friedlander and guest Link (dancer, educator, and one of the most respected voices on hip-hop and house culture) discuss what it means to truly understand hip-hop and house, not just as dance styles, but as cultures shaped by people, places, music, and community. Link shares stories from the clubs that influenced generations of dancers, the mentors who shaped his perspective, and why he never thought of himself as someone who "trained." He simply loved to dance. Together, we explore how understanding who you're learning from can deepen your understanding of the movement itself, why dancing and teaching require different skill sets, and how the social spaces that gave birth to these dances continue to shape the way we move today. Whether you're a street dancer, commercial dancer, educator, or simply someone who wants to deepen your understanding of hip-hop and house, this conversation offers a thoughtful perspective that may change the way you approach both the dance and the culture. Follow Galit: Instagram - https://www.instagram.com/galitfriedlander Website - https://www.gogalit.com/ Fit From Home - https://galit-s-school-0397.thinkific.com/courses/fit-from-home You can connect with Link on Instagram https://www.instagram.com/link.efc Herbert Holler LPR Party https://www.instagram.com/herbertholler
00:00-10:00: ML breaks down how offense impacts interest or lack of in soccer in America and Internationally. Thanks to Marz Motors and Batavia Downs Gaming. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
00:00-15:00: ML explains why Aaron Judge never winning a World Series ring is very possible. Thanks to CH Insurance and Batavia Downs Gaming. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
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