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Vitamin D misinformation is everywhere! Learn why the vitamin D recommended dosage doesn't align with actual science, how this vitamin D controversy started, and how much vitamin D you really need.
Send us a textWe look back at 2025's privacy and security reality: useful AI where data was ready, repeating breach patterns, and infrastructure limits that slowed the hype. We call out backdoors, weak 2FA, and the shift toward passkeys, decentralization, and owning more of our stack.• AI succeeds when data, process and governance are mature• Power, chips and cost constraints limit AI growth• SALT Typhoon shows backdoor risk and patching failures• SMS 2FA remains weak while passkeys gain ground• Data hoarding expands breach blast radius• Streaming consolidation drives algorithm control and piracy's return• Decentralization and self‑hosting rebuild trust with users• 2026 outlook: AI contraction, ML pragmatism, fewer but stronger toolsCheck out our website: the problemlounge.comIf you have episode guest ideas or topics you want us to talk about, please send them our wayGo check out YouTube channel, Privacy Please PodcastIn 2026, would you like to see us do live streams? Everyday AI: Your daily guide to grown with Generative AICan't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.Listen on: Apple Podcasts SpotifySupport the show
00:00-15:00: Dodgers sign Edwin Diaz. ML breaks 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.
Episode 210: Heat Stroke BasicsWritten by Jacob Dunn, MS4, American University of the Caribbean. Edits and comments by Hector Arreaza, MD.You are listening to Rio Bravo qWeek Podcast, your weekly dose of knowledge brought to you by the Rio Bravo Family Medicine Residency Program from Bakersfield, California, a UCLA-affiliated program sponsored by Clinica Sierra Vista, Let Us Be Your Healthcare Home. This podcast was created for educational purposes only. Visit your primary care provider for additional medical advice. Definition:Heat stroke represents the most severe form of heat-related illness, characterized by a core body temperature exceeding 40°C (104°F) accompanied by central nervous system (CNS) dysfunction. Arreaza: Key element is the body temperature and altered mental status. Jacob: This life-threatening condition arises from the body's failure to dissipate heat effectively, often in the context of excessive environmental heat load or strenuous physical activity. Arreaza: You mentioned, it is a spectrum. What is the difference between heat exhaustion and heat stroke? Jacob: Unlike milder heat illnesses such as heat exhaustion, heat stroke involves multisystem organ dysfunction driven by direct thermal injury, systemic inflammation, and cytokine release. You can think of it as the body's thermostat breaking under extreme stress — leading to rapid, cascading failures if not addressed immediately. Arreaza: Tell us what you found out about the pathophysiology of heat stroke?Jacob: Pathophysiology: Under normal conditions, the body keeps its core temperature tightly controlled through sweating, vasodilation of skin blood vessels, and behavioral responses like seeking shade or drinking water. But in extreme heat or prolonged exertion, those mechanisms get overwhelmed.Once core temperature rises above about 40°C (104°F), the hypothalamus—the brain's thermostat—can't keep up. The body shifts from controlled thermoregulation to uncontrolled, passive heating. Heat stroke isn't just someone getting too hot—it's a full-blown failure of the body's heat-regulating system. Arreaza: So, it's interesting. the cell functions get affected at this point, several dangerous processes start happening at the same time.Jacob: Yes: Cellular Heat InjuryHigh temperatures disrupt proteins, enzymes, and cell membranes. Mitochondria start to fail, ATP production drops, and cells become leaky. This leads to direct tissue injury in vital organs like the brain, liver, kidneys, and heart.Arreaza: Yikes. Cytokines play a big role in the pathophysiology of heat stroke too. Jacob: Systemic Inflammatory ResponseHeat damages the gut barrier, allowing endotoxins to enter the bloodstream. This triggers a massive cytokine release—similar to sepsis. The result is widespread inflammation, endothelial injury, and microvascular collapse.Arreaza: What other systems are affected?Coagulation AbnormalitiesEndothelial damage activates the clotting cascade. Patients may develop a DIC-like picture: microthrombi forming in some areas while clotting factors get consumed in others. This contributes to organ dysfunction and bleeding.Circulatory CollapseAs the body shunts blood to the skin for cooling, perfusion to vital organs drops. Combine that with dehydration from sweating and fluid loss, and you get hypotension, decreased cardiac output, and worsening ischemia.Arreaza: And one of the key features is neurologic dysfunction.Jacob: Neurologic DysfunctionThe brain is extremely sensitive to heat. Encephalopathy, confusion, seizures, and coma occur because neurons malfunction at high temperatures. This is why altered mental status is the hallmark of true heat stroke.Arreaza: Cell injury, inflammation, coagulopathy, circulatory collapse and neurologic dysfunction. Jacob: Ultimately, heat stroke is a multisystem catastrophic event—a combination of thermal injury, inflammatory storm, coagulopathy, and circulatory collapse. Without rapid cooling and aggressive supportive care, these processes spiral into irreversible organ failure.Background and Types:Arreaza: Heat stroke is part of a spectrum of heat-related disorders—it is a true medical emergency. Mortality rate reaches 30%, even with optimal treatment. This mortality correlates directly with the duration of core hyperthermia. I'm reminded of the first time I heard about heat stroke in a baby who was left inside a car in the summer 2005. Jacob: There are two primary types: -nonexertional (classic) heat stroke, which develops insidiously over days and predominantly affects vulnerable populations like children, the elderly, and those with chronic illnesses during heat waves; -exertional heat stroke, which strikes rapidly in young, otherwise healthy individuals, often during intense exercise in hot, humid conditions. Arreaza: In our community, farm workers are especially at risk of heat stroke, but any person living in the Central Valley is basically at risk.Jacob: Risk factors amplify vulnerability across both types, including dehydration, cardiovascular disease, medications that impair sweating (e.g., anticholinergics), and acclimatization deficits. Notably, anhidrosis (lack of sweating) is common but not required for diagnosis. Hot, dry skin can signal the shift from heat exhaustion to stroke. Arreaza: What other conditions look like heat stroke?Differential Diagnosis:Jacob: Presenting with altered mental status and hyperthermia, heat stroke demands a broad differential to avoid missing mimics. -Environmental: heat exhaustion, syncope, or cramps. -Infectious etiologies like sepsis or meningitis must be ruled out. -Endocrine emergencies such as thyroid storm, pheochromocytoma, or diabetic ketoacidosis (DKA) can overlap. -Neurologic insults include cerebrovascular accident (CVA), hypothalamic lesions (bleeding or infarct), or status epilepticus. -Toxicologic culprits are plentiful—sympathomimetic or anticholinergic toxidromes, salicylate poisoning, serotonin syndrome, malignant hyperthermia, neuroleptic malignant syndrome (NMS), or even alcohol/benzodiazepine withdrawal. When it comes to differentials, it is always best to cast a wide net and think about what we could be missing if this is not heat stroke. Arreaza: Let's say we have a patient with hyperthermia and we have to assess him in the ER. What should we do to diagnose it?Jacob: Workup:Diagnosis is primarily clinical, hinging on documented hyperthermia (>40°C) plus CNS changes (e.g., confusion, delirium, seizures, coma) in a hot environment. Arreaza: No single lab confirms it, but targeted testing allows us to detect complications and rule out alternative diagnosis. Jacob: -Start with ECG to assess for dysrhythmias or ischemic changes (sinus tachycardia is classic; ST depressions or T-wave inversions may hint at myocardial strain). -Labs include complete blood count (CBC), comprehensive metabolic panel (electrolytes, renal function, liver enzymes), glucose, arterial blood gas, lactate (elevated in shock), coagulation studies (for disseminated intravascular coagulation, or DIC), creatine kinase (CK) and myoglobin (for rhabdomyolysis), and urinalysis. Toxicology screen if history suggests. Arreaza: I can imagine doing all this while trying to cool down the patient. What about imaging?-Imaging: chest X-ray for pulmonary issues, non-contrast head CT if neurologic concerns suggest edema or bleed (consider lumbar puncture if infection suspected). It is important to note that continuous core temperature monitoring—via rectal, esophageal, or bladder probe—is essential, not just peripheral skin checks. Arreaza: TreatmentManagement:Time is tissue here—initiate cooling en route, if possible, as delays skyrocket morbidity. ABCs first: secure airway (intubate if needed, favoring rocuronium over succinylcholine to avoid hyperkalemia risk), support breathing, and stabilize circulation. -Remove the patient from the heat source, strip clothing, and launch aggressive cooling to target 38-39°C (102-102°F) before halting to prevent rebound hypothermia. -For exertional cases, ice-water immersion reigns supreme—it's the fastest method, with immersion in cold water resulting in near-100% survival if started within 30 minutes. -Nonexertional benefits from evaporative cooling: mist with tepid water (15-25°C) plus fans for convective airflow. -Adjuncts include ice packs to neck, axillae, and groin; -room-temperature IV fluids (avoid cold initially to prevent shivering); -refractory cases, invasive options like peritoneal lavage, endovascular cooling catheters, or even ECMO. -Fluid resuscitation with lactated Ringer's or normal saline (250-500 mL boluses) protects kidneys and counters rhabdomyolysis—aim for urine output of 2-3 mL/kg/hour. Arreaza: What about medications?Jacob: Benzodiazepines (e.g., lorazepam) control agitation, seizures, or shivering; propofol or fentanyl if intubated. Avoid antipyretics like acetaminophen. For intubation, etomidate or ketamine as induction agents. Hypotension often resolves with cooling and fluids; if not, use dopamine or dobutamine over norepinephrine to avoid vasoconstriction. Jacob: What IV fluid is recommended/best for patients with heat stroke?Both lactated Ringer's solution and normal saline are recommended as initial IV fluids for rehydration, but balanced crystalloids such as LR are increasingly favored due to their lower risk of hyperchloremic metabolic acidosis and AKI. However, direct evidence comparing the two specifically in the setting of heat stroke is limited. Arreaza: Are cold IV fluids better/preferred over room temperature fluids?Cold IV fluids are recommended as an adjunctive therapy to help lower core temperature in heat stroke, but they should not delay or replace primary cooling methods such as cold-water immersion. Cold IV fluids can decrease core temperature more rapidly than room temperature fluids. For example, 30mL/kg bolus of chilled isotonic fluids at 4 degrees Celsius over 30 minutes can decrease core temperature by about 1 degree Celsius, compared to 0.5 degree Celsius with room temperature fluids. Arreaza: Getting cold IV sounds uncomfortable but necessary for those patients. Our favorite topic.Screening and Prevention:-Heat stroke prevention focuses on public health and individual awareness rather than routine testing. -High-risk groups—elderly, children, athletes, laborers, or those on impairing meds—should acclimatize gradually (7-14 days), hydrate preemptively (electrolyte solutions over plain water), and monitor temperature in exertional settings. -Communities during heat waves need cooling centers and alerts. -For clinicians, educate patients with CVD or obesity about early signs like dizziness or nausea. -No formal "screening" exists, but vigilance in EDs during summer surges saves lives. -Arreaza: I think awareness is a key element in prevention, so education of the public through traditional media like TV, and even social media can contribute to the prevention of this catastrophic condition.Jacob: Ya so heat stroke is something that should be on every physician's radar in the central valley especially in the summer time given the hot temperatures. Rapid recognition is key. Arreaza: Thanks, Jacob for this topic, and until next time, this is Dr. Arreaza, signing off.Even without trying, every night you go to bed a little wiser. Thanks for listening to Rio Bravo qWeek Podcast. We want to hear from you, send us an email at RioBravoqWeek@clinicasierravista.org, or visit our website riobravofmrp.org/qweek. See you next week! References:Gaudio FG, Grissom CK. Cooling Methods in Heat Stroke. J Emerg Med. 2016 Apr;50(4):607-16. doi: 10.1016/j.jemermed.2015.09.014. Epub 2015 Oct 31. PMID: 26525947. https://pubmed.ncbi.nlm.nih.gov/26525947/.Platt, M. A., & LoVecchio, F. (n.d.). Nonexertional classic heat stroke in adults. In UpToDate. Retrieved September 7, 2025, from https://www.uptodate.com/contents/nonexertional-classic-heat-stroke-in-adults. (Key addition: Emphasizes insidious onset in at-risk populations and the role of urban heat islands in exacerbating classic cases.) Heat Stroke. WikEM. Retrieved December 3, 2025, from https://wikem.org/wiki/Heat_stroke. (Key additions: Details on cooling rates for immersion therapy, confirmation that anhidrosis is not diagnostic, and fluid titration to urine output for rhabdomyolysis prevention.)Theme song, Works All The Time by Dominik Schwarzer, YouTube ID: CUBDNERZU8HXUHBS, purchased from https://www.premiumbeat.com/.
Hey all, Happy new year! This is Alex, writing to you for the very fresh start of this year, it's 2026 already, can you believe it? There was no live stream today, I figured the cohosts deserve a break and honestly it was a very slow week. Even the chinese labs who don't really celebrate X-mas and new years didn't come out with a banger AFAIK. ThursdAI - AI moves fast, we're here to make sure you never miss a thing! Subscribe :) Tho I thought it was an incredible opportunity to finally post the Will Brow interview I recorded in November during the AI Engineer conference. Will is a researcher at Prime Intellect (big fans on WandB btw!) and is very known on X as a hot takes ML person, often going viral for tons of memes! Will is the creator and maintainer of the Verifiers library (Github) and his talk at AI Engineer was all about RL Environments (what they are, you can hear in the interview, I asked him!) TL;DR last week of 2025 in AIBesides this, my job here is to keep you up to date, and honestly this was very easy this week, as… almost nothing has happened, but here we go: Meta buys ManusThe year ended with 2 huge acquisitions / aquihires. First we got the news from Alex Wang that Meta has bought Manus.ai which is an agentic AI startup we covered back in March for an undisclosed amount (folks claim $2-3B) The most interesting thing here is that Manus is a Chinese company, and this deal requires very specific severance from Chinese operations.Jensen goes on a new years spending spree, Nvidia buys Groq (not GROK) for $20BGroq which we covered often here, and are great friends, is going to NVIDIA, in a… very interesting acqui-hire, which is a “non binding license” + most of Groq top employees apparently are going to NVIDIA. Jonathan Ross the CEO of Groq, was the co-creator of the TPU chips at Google before founding Groq, so this seems like a very strategic aquihire for NVIDIA! Congrats to our friends from Groq on this amazing news for the new year! Tencent open-sources HY-MT1.5 translation models with 1.8B edge-deployable and 7B cloud variants supporting 33 languages (X, HF, HF, GitHub)It seems that everyone's is trying to de-throne whisper and this latest attempt from Tencent is a interesting one. a 1.8B and 7B translation models with very interesting stats. Alibaba's Qwen-Image-2512 drops on New Year's Eve as strongest open-source text-to-image model, topping AI Arena with photorealistic humans and sharper textures (X, HF, Arxiv)Our friends in Tongyi decided to give is a new years present in the form of an updated Qwen-image, with much improved realismThat's it folks, this was a quick one, hopefully you all had an amazing new year celebration, and are gearing up to an eventful and crazy 2026. I wish you all happiness, excitement and energy to keep up with everything in the new year, and will make sure that we're here to keep you up to date as always! P.S - I got a little news of my own this yesterday, not related to AI. She said yes
00:00-15:00: Pete Alonso to Baltimore. ML breaks it down. Thanks to CH Insurance and Rosie's Corner. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
From building LMArena in a Berkeley basement to raising $100M and becoming the de facto leaderboard for frontier AI, Anastasios Angelopoulos returns to Latent Space to recap 2025 in one of the most influential platforms in AI—trusted by millions of users, every major lab, and the entire industry to answer one question: which model is actually best for real-world use cases? We caught up with Anastasios live at NeurIPS 2025 to dig into the origin story (spoiler: it started as an academic project incubated by Anjney Midha at a16z, who formed an entity and gave grants before they even committed to starting a company), why they decided to spin out instead of staying academic or nonprofit (the only way to scale was to build a company), how they're spending that $100M (inference costs, React migration off Gradio, and hiring world-class talent across ML, product, and go-to-market), the leaderboard delusion controversy and why their response demolished the paper's claims (factual errors, misrepresentation of open vs. closed source sampling, and ignoring the transparency of preview testing that the community loves), why platform integrity comes first (the public leaderboard is a charity, not a pay-to-play system—models can't pay to get on, can't pay to get off, and scores reflect millions of real votes), how they're expanding into occupational verticals (medicine, legal, finance, creative marketing) and multimodal arenas (video coming soon), why consumer retention is earned every single day (sign-in and persistent history were the unlock, but users are fickle and can leave at any moment), the Gemini Nano Banana moment that changed Google's market share overnight (and why multimodal models are becoming economically critical for marketing, design, and AI-for-science), how they're thinking about agents and harnesses (Code Arena evaluates models, but maybe it should evaluate full agents like Devin), and his vision for Arena as the central evaluation platform that provides the North Star for the industry—constantly fresh, immune to overfitting, and grounded in millions of real-world conversations from real users. We discuss: The $100M raise: use of funds is primarily inference costs (funding free usage for tens of millions of monthly conversations), React migration off Gradio (custom loading icons, better developer hiring, more flexibility), and hiring world-class talent The scale: 250M+ conversations on the platform, tens of millions per month, 25% of users do software for a living, and half of users are now logged in The leaderboard illusion controversy: Cohere researchers claimed undisclosed private testing created inequities, but Arena's response demolished the paper's factual errors (misrepresented open vs. closed source sampling, ignored transparency of preview testing that the community loves) Why preview testing is loved by the community: secret codenames (Gemini Nano Banana, named after PM Naina's nickname), early access to unreleased models, and the thrill of being first to vote on frontier capabilities The Nano Banana moment: changed Google's market share overnight, billions of dollars in stock movement, and validated that multimodal models (image generation, video) are economically critical for marketing, design, and AI-for-science New categories: occupational and expert arenas (medicine, legal, finance, creative marketing), Code Arena, and video arena coming soon Consumer retention: sign-in and persistent history were the unlock, but users are fickle and earned every single day—"every user is earned, they can leave at any moment" — Anastasios Angelopoulos Arena: https://lmarena.ai X: https://x.com/arena Chapters 00:00:00 Introduction: Anastasios from Arena and the LM Arena Journey 00:01:36 The Anjney Midha Incubation: From Berkeley Basement to Startup 00:02:47 The Decision to Start a Company: Scaling Beyond Academia 00:03:38 The $100M Raise: Use of Funds and Platform Economics 00:05:10 Arena's User Base: 5M+ Users and Diverse Demographics 00:06:02 The Competitive Landscape: Artificial Analysis, AI.xyz, and Arena's Differentiation 00:08:12 Educational Value and Learning from the Community 00:08:41 Technical Migration: From Gradio to React and Platform Evolution 00:10:18 Leaderboard Delusion Paper: Addressing Critiques and Maintaining Integrity 00:12:29 Nano Banana Moment: How Preview Models Create Market Impact 00:13:41 Multimodal AI and Image Generation: From Skepticism to Economic Value 00:15:37 Core Principles: Platform Integrity and the Public Leaderboard as Charity 00:18:29 Future Roadmap: Expert Categories, Multimodal, Video, and Occupational Verticals 00:19:10 API Strategy and Focus: Doing One Thing Well 00:19:51 Community Management and Retention: Sign-In, History, and Daily Value 00:22:21 Partnerships and Agent Evaluation: From Devon to Full-Featured Harnesses 00:21:49 Hiring and Building a High-Performance Team
From pre-training data curation to shipping GPT-4o, o1, o3, and now GPT-5 thinking and the shopping model, Josh McGrath has lived through the full arc of OpenAI's post-training evolution—from the PPO vs DPO debates of 2023 to today's RLVR era, where the real innovation isn't optimization methods but data quality, signal trust, and token efficiency. We sat down with Josh at NeurIPS 2025 to dig into the state of post-training heading into 2026: why RLHF and RLVR are both just policy gradient methods (the difference is the input data, not the math), how GRPO from DeepSeek Math was underappreciated as a shift toward more trustworthy reward signals (math answers you can verify vs. human preference you can't), why token efficiency matters more than wall-clock time (GPT-5 to 5.1 bumped evals and slashed tokens), how Codex has changed his workflow so much he feels "trapped" by 40-minute design sessions followed by 15-minute agent sprints, the infrastructure chaos of scaling RL ("way more moving parts than pre-training"), why long context will keep climbing but agents + graph walks might matter more than 10M-token windows, the shopping model as a test bed for interruptability and chain-of-thought transparency, why personality toggles (Anton vs Clippy) are a real differentiator users care about, and his thesis that the education system isn't producing enough people who can do both distributed systems and ML research—the exact skill set required to push the frontier when the bottleneck moves every few weeks. We discuss: Josh's path: pre-training data curation → post-training researcher at OpenAI, shipping GPT-4o, o1, o3, GPT-5 thinking, and the shopping model Why he switched from pre-training to post-training: "Do I want to make 3% compute efficiency wins, or change behavior by 40%?" The RL infrastructure challenge: way more moving parts than pre-training (tasks, grading setups, external partners), and why babysitting runs at 12:30am means jumping into unfamiliar code constantly How Codex has changed his workflow: 40-minute design sessions compressed into 15-minute agent sprints, and the strange "trapped" feeling of waiting for the agent to finish The RLHF vs RLVR debate: both are policy gradient methods, the real difference is data quality and signal trust (human preference vs. verifiable correctness) Why GRPO (from DeepSeek Math) was underappreciated: not just an optimization trick, but a shift toward reward signals you can actually trust (math answers over human vibes) The token efficiency revolution: GPT-5 to 5.1 bumped evals and slashed tokens, and why thinking in tokens (not wall-clock time) unlocks better tool-calling and agent workflows Personality toggles: Anton (tool, no warmth) vs Clippy (friendly, helpful), and why Josh uses custom instructions to make his model "just a tool" The router problem: having a router at the top (GPT-5 thinking vs non-thinking) and an implicit router (thinking effort slider) creates weird bumps, and why the abstractions will eventually merge Long context: climbing Graph Blocks evals, the dream of 10M+ token windows, and why agents + graph walks might matter more than raw context length Why the education system isn't producing enough people who can do both distributed systems and ML research, and why that's the bottleneck for frontier labs The 2026 vision: neither pre-training nor post-training is dead, we're in the fog of war, and the bottleneck will keep moving (so emotional stability helps) — Josh McGrath OpenAI: https://openai.com https://x.com/j_mcgraph Chapters 00:00:00 Introduction: Josh McGrath on Post-Training at OpenAI 00:04:37 The Shopping Model: Black Friday Launch and Interruptability 00:07:11 Model Personality and the Anton vs Clippy Divide 00:08:26 Beyond PPO vs DPO: The Data Quality Spectrum in RL 00:01:40 Infrastructure Challenges: Why Post-Training RL is Harder Than Pre-Training 00:13:12 Token Efficiency: The 2D Plot That Matters Most 00:03:45 Codex Max and the Flow Problem: 40 Minutes of Planning, 15 Minutes of Waiting 00:17:29 Long Context and Graph Blocks: Climbing Toward Perfect Context 00:21:23 The ML-Systems Hybrid: What's Hard to Hire For 00:24:50 Pre-Training Isn't Dead: Living Through Technological Revolution
In this episode of Data in Biotech, Ross Katz chats with Mitra Mosharraf, Chief Scientific Officer at HTD Biosystems, about how AI and machine learning are revolutionizing nanomedicine. They explore the use of AI in drug discovery, formulation, manufacturing, and clinical development, highlighting how data-driven strategies are improving safety, reducing costs, and enabling more personalized therapies in the biotech space. What you'll learn in this episode: >> How AI and ML reduce costs and increase success rates in nanomedicine development. >> Key challenges in nano drug delivery and how machine learning helps overcome them. >> How HTD Biosystems' iFormulate platform speeds up formulation with predictive modeling. >> How wearables and real-time data are reshaping clinical trial design. >> The future of personalized and automated drug delivery systems. Meet our guest Mitra Mosharraf is the Chief Scientific Officer at HTD Biosystems and co-founder of Engimata Inc. With 20+ years of experience, she leads innovation in biologics, nanomedicine, and lipid-based delivery systems. Mitra is a recognized thought leader in pharmaceutical sciences. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancyConnect with Mitra Mosharraf on LinkedIn Connect with Us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode!Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.
From Berkeley robotics and OpenAI's 2017 Dota-era internship to shipping RL breakthroughs on GPT-4o, o1, and o3, and now leading model development at Cursor, Ashvin Nair has done it all. We caught up with Ashvin at NeurIPS 2025 to dig into the inside story of OpenAI's reasoning team (spoiler: it went from a dozen people to 300+), why IOI Gold felt reachable in 2022 but somehow didn't change the world when o1 actually achieved it, how RL doesn't generalize beyond the training distribution (and why that means you need to bring economically useful tasks into distribution by co-designing products and models), the deeper lessons from the RL research era (2017–2022) and why most of it didn't pan out because the community overfitted to benchmarks, how Cursor is uniquely positioned to do continual learning at scale with policy updates every two hours and product-model co-design that keeps engineers in the loop instead of context-switching into ADHD hell, and his bet that the next paradigm shift is continual learning with infinite memory—where models experience something once (a bug, a mistake, a user pattern) and never forget it, storing millions of deployment tokens in weights without overloading capacity. We discuss: Ashvin's path: Berkeley robotics PhD → OpenAI 2017 intern (Dota era) → o1/o3 reasoning team → Cursor ML lead in three months Why robotics people are the most grounded at NeurIPS (they work with the real world) and simulation people are the most unhinged (Lex Fridman's take) The IOI Gold paradox: "If you told me we'd achieve IOI Gold in 2022, I'd assume we could all go on vacation—AI solved, no point working anymore. But life is still the same." The RL research era (2017–2022) and why most of it didn't pan out: overfitting to benchmarks, too many implicit knobs to tune, and the community rewarding complex ideas over simple ones that generalize Inside the o1 origin story: a dozen people, conviction from Ilya and Jakob Pachocki that RL would work, small-scale prototypes producing "surprisingly accurate reasoning traces" on math, and first-principles belief that scaled The reasoning team grew from ~12 to 300+ people as o1 became a product and safety, tooling, and deployment scaled up Why Cursor is uniquely positioned for continual learning: policy updates every two hours (online RL on tab), product and ML sitting next to each other, and the entire software engineering workflow (code, logs, debugging, DataDog) living in the product Composer as the start of product-model co-design: smart enough to use, fast enough to stay in the loop, and built by a 20–25 person ML team with high-taste co-founders who code daily The next paradigm shift: continual learning with infinite memory—models that experience something once (a bug, a user mistake) and store it in weights forever, learning from millions of deployment tokens without overloading capacity (trillions of pretraining tokens = plenty of room) Why off-policy RL is unstable (Ashvin's favorite interview question) and why Cursor does two-day work trials instead of whiteboard interviews The vision: automate software engineering as a process (not just answering prompts), co-design products so the entire workflow (write code, check logs, debug, iterate) is in-distribution for RL, and make models that never make the same mistake twice — Ashvin Nair Cursor: https://cursor.com X: https://x.com/ashvinnair_ Chapters 00:00:00 Introduction: From Robotics to Cursor via OpenAI 00:01:58 The Robotics to LLM Agent Transition: Why Code Won 00:09:11 RL Research Winter and Academic Overfitting 00:11:45 The Scaling Era and Moving Goalposts: IOI Gold Doesn't Mean AGI 00:21:30 OpenAI's Reasoning Journey: From Codex to O1 00:20:03 The Blip: Thanksgiving 2023 and OpenAI Governance 00:22:39 RL for Reasoning: The O-Series Conviction and Scaling 00:25:47 O1 to O3: Smooth Internal Progress vs External Hype Cycles 00:33:07 Why Cursor: Co-Designing Products and Models for Real Work 00:34:14 Composer and the Future: Online Learning Every Two Hours 00:35:15 Continual Learning: The Missing Paradigm Shift 00:44:00 Hiring at Cursor and Why Off-Policy RL is Unstable
CECI EST UNE REDIFFUSION ! Et une rediffusion de saison...Dans cet épisode, on parle de 650 ML de mucus, des innocents, et d'aller à la mer sous la contrainte, entre autres.SOURCES :https://www.youtube.com/watch?v=XrMYL6p9Jd8https://www.topito.com/top-signes-drama-queenhttps://www.youtube.com/watch?v=4bM4I1_B7E4http://ssaft.com/Blog/dotclear/?post/2015/11/28/Le-Mercredi-on-Converge-Pour-rester-cool-Cornets-Nasauxhttps://www.youtube.com/watch?v=t7rejCEWLh8https://www.youtube.com/watch?v=DpPE4Ks6V2oPour acheter des places pour Vulgaire à la Comédie de Paris : https://www.fnacspectacles.com/artist/marine-baousson/marine-baousson-vulgaire-comedie-de-paris-paris-3198003/---Retrouvez Vulgaire sur Instagram : @vulgaire_lepodcast---Un podcast de Marine Baousson---Écrit et produit par Marine Baousson / Studio BruneRéalisé par Antoine OlierMusique de Guillaume Bérat du collectif BranksIllustré par Juliette PoneyLa transcription de cet épisode est dispo ici : https://drive.google.com/drive/folders/12IDU2ly4oBrzBHWMPcRd9HNZJe_d7NJF---VULGAIREUn podcast de Marine Baousson et Marie Missetproduit par Marine Baousson / Studio BruneRéalisé par Antoine OlierGénérique : Romain BaoussonGraphisme et illustrations : Juliette PoneyCapsules Vidéo : Emma Estevezprogrammation : Louise TempéreauDécouvrez Pourquoi Pourquoi, le spectacle pour enfants adapté de Vulgaire : https://www.theatre-michel.fr/Spectacles/pourquoi-pourquoi/ Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.
00:00-15:00: ML breaks down Syracuse men's basketball. When does it get better? Thanks to CH Insurance. In your corner. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
As 2025 wraps up, it is normal to ask, "What's next for my fertility?" Maybe your cycles felt unpredictable, lab results felt confusing, or you have been living in constant action mode with supplements, protocols, and timelines. In this episode, we slow it down and get intentional. Instead of piling on more, we look at what your body has been signaling and how to enter 2026 with a clear, steady plan using a functional fertility lens. You'll learn: Why pausing at year-end can support ovarian signaling and reduce the stress loop that keeps you stuck How to review 2025 without spiraling, including what helped, what added pressure, and what your symptoms have been communicating Which labs to consider rechecking in 2026 and why they matter for fertility strategy, including TSH, vitamin D, ferritin, hsCRP, AMH, and FSH. The foundation for egg quality support through mitochondria basics, including sleep, protein, minerals, and CoQ10. How to build a realistic nervous system plan that fits a Type A life, so your next step is aligned, not rushed Sarah Clark is the founder of Fab Fertile Inc. and the host of Get Pregnant Naturally. Her team specializes in functional approaches for low AMH, high FSH, diminished ovarian reserve, premature ovarian insufficiency, recurrent miscarriage and helping couples prepare their bodies for pregnancy success naturally or with IVF. This episode is especially for you if: You have low AMH (ng/mL), high FSH, DOR, or POI and want to enter 2026 with a plan that supports your body without adding more overwhelm You have been pushing through and want to make decisions based on insight, not urgency You want a functional fertility approach that connects testing, nutrition, lifestyle, and emotional balance in a practical way Next Steps in Your Fertility Journey Subscribe to Get Pregnant Naturally for evidence-based guidance on functional fertility, and share this episode with anyone on their fertility journey. Not sure where to start? Download our most popular guide: Ultimate Guide to Getting Pregnant This Year If You Have Low AMH/High FSH it breaks everything down step by step to help you understand your options and take action For personalized support to improve pregnancy success, book a call here. --- Timestamps 00:00 – Reflecting on fertility as 2025 ends and why slowing down matters 01:05 – Why constant doing and hypervigilance disrupt ovarian signaling 02:10 – Nervous system dysregulation in low AMH, high FSH, DOR, and POI 03:15 – Why rushing into IVF at year-end can backfire 04:40 – Secondary infertility and when fertility issues appear unexpectedly 05:20 – Reviewing what actually helped your energy, sleep, digestion, and mood 06:15 – Supplements vs personalized testing and why guessing adds stress 07:30 – Gut health, thyroid, inflammation, and missed underlying imbalances 08:45 – Retesting labs and focusing on mitochondria and egg quality 10:05 – Choosing your next fertility step intentionally, not from fear --- Resources
Varant Zanoyan is the Co-founder & CEO at Zipline AI, working on building a next-generation AI/ML infrastructure platform that streamlines data pipelines, model deployment, observability, and governance to accelerate enterprise AI development. Nikhil Simha Raprolu is the Co-founder & CTO at Zipline AI, focused on architecting and scaling the company's AI data platform — extending the open-source Chronon engine into a developer-friendly system that simplifies building and operating production AI applications.Real-time features, AI search, Agentic similarities, Varant Zanoyan & Nikhil Simha Raprolu // MLOps Podcast #354Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps Swag/Merch: [https://shop.mlops.community/]And huge thanks to Chroma for hosting us in their recording studio// AbstractFeature stores might be the wrong abstraction. Varant Zanoyan and Nikhil Simha Raprolu explain why Cronon ditched “store-first” thinking and focused on compute, orchestration, and real-time correctness—born at Airbnb, battle-tested with Stripe. If embeddings, agents, and real-time ML feel painful, this episode explains why.// Related LinksWebsite: https://zipline.ai/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Varant on LinkedIn: /vzanoyan/Connect with Nikhil on LinkedIn: /nikhilsimha/Timestamps:[00:00] Feature Platform Insights[02:00] Zipline and Feature Stores[05:19] Cronon and Zipline Origins[10:49] Feast and Feather Comparison[13:27] Open source challenges[20:52] Zipline and Iceberg Integration [23:54] Airbnb Agent Systems[28:16] Features vs Embeddings[29:07] Wrap up
Emergency departments across the U.S. are under unprecedented strain, with overcrowding, staffing shortages, and inpatient bed constraints converging into a throughput crisis. The American Hospital Association reports that hospital capacity and workforce growth have lagged, intensifying delays from arrival to disposition. At the same time, advances in artificial intelligence are moving from experimental to operational—raising the stakes for how technology can meaningfully improve patient flow rather than add complexity.So, how can emergency departments reduce bottlenecks and move patients more efficiently through care without compromising clinical judgment or trust?Welcome to I Don't Care. In the latest episode, host Dr. Kevin Stevenson sits down with Mitch Quinn, Director of AI/ML at ChoreoED, to explore how AI-driven insights can help hospitals anticipate admissions and discharges earlier, coordinate downstream services, and ultimately improve ED throughput. Their conversation spans the real-world operational challenges ED leaders face, the practical application of machine learning in high-acuity settings, and what it takes to deploy AI tools that clinicians actually trust and use.What you'll learn…How AI models trained on a hospital's own historical data can accurately anticipate admissions up to hours earlier, enabling parallel workflows.Why focusing on “high-certainty” admissions and discharges—rather than rare edge cases—creates immediate operational value in the ED.How adaptive, continuously retrained models can support both experienced clinicians and newer providers in high-turnover environments.Mitch Quinn is a Director of AI and Machine Learning and a computer scientist with 20+ years of experience building production-grade AI systems across healthcare and cybersecurity. He specializes in deep learning, large-scale model architecture, and end-to-end ML pipelines, with leadership roles spanning applied research at Blue Cross NC, enterprise AI consulting, and real-time cyber threat detection. His career highlights include designing high-performance deep neural networks, anomaly detection systems operating at enterprise scale, and foundational software frameworks used by large engineering organizations.
0:00 - Vic, Mose, and Mat Smith talk Nuggets and hear from head coach David Adelman after their 135-112 win over the Jazz on Monday night. Up next they give their Keys to the Game as the team travels to Dallas to take on the Mavericks.15:35 - One of the most special-est guests in ML&K history joins the program: Denver Broncos legend, all-around Denver legend, NFL Hall of Famer John Elway hops on the show to talk his Netflix documentary out streaming today. He also talks Bo Nix, highlights from his career, and more! I'm serious Vic and Mose are like two kids in a candy store I've never seen em so happy.34:57 - The boys are still riding high from that John Elway interview, but there's still some leftovers to take care of from Broncos-Jags. They listen to some audio from Coach Payton's Zoom conference on Monday to finally debrief last Sunday's loss.
Alex Salazar is the CEO and Co-Founder of Arcade.dev, working on secure AI agents and real-world automation integrations.Chiara Caratelli is a Data Scientist at Prosus Group, working on AI agents, web automation, and evaluation of robust multimodal models.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractAgents sound smart until millions of users show up. A real talk on tools, UX, and why autonomy is overrated.// BioChiara CaratelliChiara is a Data Scientist at Prosus, where she develops AI-driven solutions with a focus on AI agents, multimodal models, and new user experiences. With a PhD in Computational Science and a background in machine learning engineering and data science, she has worked on deploying AI-powered applications at scale, collaborating with Prosus portfolio companies to drive real-world impact.Beyond her work at Prosus, she enjoys experimenting with generative AI and art. She is also an avid climber and book reader, always eager to explore new ideas and share knowledge with the AI and ML community.Alex SalazarAlex is the CEO and co-founder of Arcade.dev, the unified agent action platform that makes AI agents production-ready. Previously, Salazar co-founded Stormpath, the first authentication API for developers, which was acquired by Okta. At Okta, he led developer products, accounting for 25% of total bookings, and launched a new auth-centric proxy server product that reached $9M in revenue within a year. He also managed Okta's network of over 7,000 auth integrations. Alex holds a computer science degree from Georgia Tech and an MBA from Stanford University.// Related LinksWebsite: https://www.prosus.com/Website: https://www.arcade.dev/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Alex on LinkedIn: /alexsalazar/Connect with Chiara on LinkedIn: /chiara-caratelli/Timestamps:[00:00] Intro[00:15] Insights from iFood[06:22] API vs agent intention[09:45] Tool definition clarity[15:37] Preemptive context loading[27:50] Contextualizing agent data[33:27] Prompt bloat in payments[41:33] Agent building evolution[50:09] Agent program scalability[55:29] Why multi-agent is a dead end[56:17] Wrap up
Lois Houston and Nikita Abraham explore how Oracle APEX integrates with AI to build smarter low-code applications. They are joined by Chaitanya Koratamaddi, Director of Product Management at Oracle, who explains the basics of Oracle APEX, its global adoption, and the challenges it addresses for businesses managing and integrating data. They also explore real-world use cases of AI within the Oracle APEX ecosystem Oracle APEX: Empowering Low Code Apps with AI: https://mylearn.oracle.com/ou/course/oracle-apex-empowering-low-code-apps-with-ai/146047/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University. Nikita: Hi everyone! We hope you've been enjoying these last few weeks as we've been revisiting our most popular episodes of the year. Today's episode is the last one in this series and is a throwback to a conversation on APEX with Chaitanya Koratamaddi, Director of Product Management for Oracle APEX. 00:57 Lois: We began by asking Chaitanya what Oracle APEX is and why it's so widely used. So, let's jump right in! Chaitanya: Oracle APEX is the world's most popular enterprise low code application platform. APEX enables you to build secure and scalable enterprise-scale applications with world class features that can be deployed anywhere, cloud or on-premises. And with APEX, you can build applications 20 times faster with 100 times less code. APEX delivers the most productive way to develop and deploy mobile and web applications everywhere. 01:40 Lois: That's impressive. So, what's the adoption rate like for Oracle APEX? Chaitanya: As of today, there are 19 million plus APEX applications created globally. 5,000 plus APEX applications are created on a daily basis and there are 800,000 plus APEX developers worldwide. 60,000 plus customers in 150 countries across various industry verticals. And 75% of Fortune 500 companies use Oracle APEX. 02:19 Nikita: Wow, the numbers really speak for themselves, right? But Chaitanya, why are organizations adopting Oracle APEX at this scale? Or to put it differently, what's the core business challenge that Oracle APEX is addressing? Chaitanya: From databases to all data, you know that the world is more connected and automated than ever. To drive new business value, organizations need to explore and exploit new sources of data that are generated from this connected world. That can be sounds, feeds, sensors, videos, images, and more. Businesses need to be able to work with all types of data and also make sure that it is available to be used together. Typically, businesses need to work on all data at a massive scale. For example, supply chains are no longer dependent just on inventory, demand, and order management signals. A manufacturer should be able to understand data describing global weather patterns and how it impacts their supply chains. Businesses need to pull in data from as many social sources as possible to understand how customer sentiment impacts product sales and corporate brands. Our customers need a data platform that ensures all this data works together seamlessly and easily. 04:00 Lois: So, you're saying Oracle APEX is the platform that helps businesses manage and integrate data seamlessly. But data is just one part of the equation, right? Then there's AI. How are the two related? Chaitanya: Before we start talking about Oracle AI, let's first talk about what customers are looking for and where they are struggling within their AI innovation. It all starts with data. For decades, working with data has largely involved dealing with structured data, whether it is your customer records in your CRM application and orders from your ERP database. Data was organized into database and tables, and when you needed to find some insights in your data, all you need to do is just use stored procedures and SQL queries to deliver the answers. But today, the expectations are higher. You want to use AI to construct sophisticated predictions, find anomalies, make decisions, and even take actions autonomously. And the data is far more complicated. It is in an endless variety of formats scattered all over your business. You need tools to find this data, consume it, and easily make sense of it all. And now capabilities like natural language processing, computer vision, and anomaly detection are becoming very essential just like how SQL queries used to be. You need to use AI to analyze phone call transcripts, support tickets, or email complaints so you can understand what customers need and how they feel about your products, customer service, and brand. You may want to use a data source as noisy and unstructured as social media data to detect trends and identify issues in real time. Today, AI capabilities are very essential to accelerate innovation, assess what's happening in your business, and most importantly, exceed the expectations of your customers. So, connecting your application, data, and infrastructure allows everyone in your business to benefit from data. 06:54 Oracle University is proud to announce three brand new courses that will help your teams unlock the power of Redwood—the next generation design system. Redwood enhances the user experience, boosts efficiency, and ensures consistency across Oracle Fusion Cloud Applications. Whether you're a functional lead, configuration consultant, administrator, developer, or IT support analyst, these courses will introduce you to the Redwood philosophy and its business impact. They'll also teach you how to use Visual Builder Studio to personalize and extend your Fusion environment. Get started today by visiting mylearn.oracle.com. 07:35 Nikita: Welcome back! So, let's focus on AI across the Oracle Cloud ecosystem. How does Oracle bring AI into the mix to connect applications, data, and infrastructure for businesses? Chaitanya: By embedding AI throughout the entire technology stack from the infrastructure that businesses run on through the applications for every line of business, from finance to supply chain and HR, Oracle is helping organizations pragmatically use AI to improve performance while saving time, energy, and resources. Our core cloud infrastructure includes a unique AI infrastructure layer based on our supercluster technology, leveraging the latest and greatest hardware and uniquely able to get the maximum out of the AI infrastructure technology for scenarios such as large language processing. Then there is generative AI and ML for data platforms. On top of the AI infrastructure, our database layer embeds AI in our products such as autonomous database. With autonomous database, you can leverage large language models to use natural language queries rather than writing a SQL when interacting with the autonomous database. This enables you to achieve faster AI adoption in your application development. Businesses and their customers can use the Select AI natural language interface combined with Oracle Database AI Vector Search to obtain quicker, more intuitive insights into their own data. Then we have AI services. AI services are a collection of offerings, including generative AI with pre-built machine learning models that make it easier for developers to apply AI to applications and business operations. The models can be custom-trained for more accurate business results. 09:47 Nikita: And what specific AI services do we have at Oracle, Chaitanya? Chaitanya: We have Oracle Digital Assistant Speech, Language, Vision, and Document Understanding. Then we have Oracle AI for Applications. Oracle delivers AI built for business, helping you make better decisions faster and empowering your workforce to work more effectively. By embedding classic and generative AI into its applications, Fusion Apps customers can instantly access AI outcomes wherever they are needed without leaving the software environment they use every day to power their business. 10:32 Lois: Let's talk specifically about APEX. How does APEX use the Gen AI and machine learning models in the stack to empower developers. How does it help them boost productivity? Chaitanya: Starting APEX 24.1, you can choose your preferred large language models and leverage native generative AI capabilities of APEX for AI assistants, prompt-based application creation, and more. Using native OCI capabilities, you can leverage native platform capabilities from OCI, like AI infrastructure and object storage, etc. Oracle APEX running on autonomous infrastructure in Oracle Cloud leverages its unique native generative AI capabilities tuned specifically on your data. These language models are schema aware, data aware, and take into account the shape of information, enabling your applications to take advantage of large language models pre-trained on your unique data. You can give your users greater insights by leveraging native capabilities, including vector-based similarity search, content summary, and predictions. You can also incorporate powerful AI features to deliver personalized experiences and recommendations, process natural language prompts, and more by integrating directly with a suite of OCI AI services. 12:08 Nikita: Can you give us some examples of this? Chaitanya: You can leverage OCI Vision to interpret visual and text inputs, including image recognition and classification. Or you can use OCI Speech to transcribe and understand spoken language, making both image and audio content accessible and actionable. You can work with disparate data sources like JSON, spatial, graphs, vectors, and build AI capabilities around your own business data. So, low-code application development with APEX along with AI is a very powerful combination. 12:51 Nikita: What are some use cases of AI-powered Oracle APEX applications? Chaitanya: You can build APEX applications to include conversational chatbots. Your APEX applications can include image and object detection capability. Your APEX applications can include speech transcription capability. And in your applications, you can include code generation that is natural language to SQL conversion capability. Your applications can be powered by semantic search capability. Your APEX applications can include text generation capability. 13:30 Lois: So, there's really a lot we can do! Thank you, Chaitanya, for joining us today. With that, we're wrapping up this episode. We covered Oracle APEX, the key challenges businesses face when it comes to AI innovation, and how APEX and AI work together to give businesses an AI edge. Nikita: Yeah, and if you want to know more about Oracle APEX, visit mylearn.oracle.com and search for the Oracle APEX: Empowering Low Code Apps with AI course. Lois: We hope you've enjoyed revisiting some of our most popular episodes of the year. We always appreciate your feedback and suggestions so do write to us at ou-podcast_ww@oracle.com. That's ou-podcast_ww@oracle.com. We're taking a break next week and will be back with a brand-new season of the Oracle University Podcast in January. Happy holidays, everybody! Nikita: Happy holidays! Until next time, this is Nikita Abraham... Lois: And Lois Houston, signing off! 14:34 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
🧭 REBEL Rundown 🗝️ Key Points 💨 Peak vs. Plateau Pressures: PIP reflects total airway resistance and compliance, while Pplat isolates alveolar compliance—elevations in both suggest decreased lung compliance (e.g., ARDS, pulmonary edema, pneumothorax).🧱 PEEP Protects Alveoli: Maintains alveolar recruitment and prevents collapse; typical range 5–8 cmH₂O, but higher levels may benefit moderate–severe ARDS.️ Driving Pressure (ΔP = Pplat − PEEP): Lower ΔP reduces atelectrauma and improves outcomes; optimize by adjusting PEEP thoughtfully.💥 Prevent VILI: Keep Pplat < 30 cmH₂O, use low tidal volumes (6 mL/kg IBW), and monitor for barotrauma, volutrauma, atelectrauma, and biotrauma.📚 Evidence-Based Practice: ARDSNet and subsequent trials confirm that lung-protective ventilation—low Vt, limited pressures, and individualized PEEP—improves survival in ARDS. Click here for Direct Download of the Podcast. 📝 Introduction This episode reviews essential ventilator pressures and how to interpret them during ICU rounds. 🚀 Under Pressure Peak Inspiratory Pressure (PIP)Definition: Total pressure required to deliver a breath.Reflects: Airway resistance + lung/chest wall compliance.Common Causes of ↑ PIP:Mucus pluggingBiting the endotracheal tubeKinked tubing or bronchospasmPlateau Pressure (Pplat)Definition: Alveolar pressure measured after an inspiratory hold.Reflects: Lung compliance (stiffness of lung tissue).When Both PIP & Pplat Are Elevated:→ Indicates poor compliance (e.g., ARDS, pulmonary edema, pneumothorax).Positive End-Expiratory Pressure (PEEP)Definition: Pressure remaining in airways at end-expiration to prevent alveolar collapse.Typical Range: 5–8 cmH₂O but needs to titrated to meet patient requirements Notes:Provides physiologic “glottic” PEEP in intubated patients.Using high PEEP strategy shows mortality benefit only in moderate–severe ARDS in meta-analysis.Driving Pressure (ΔP)Definition: ΔP = Pplat − PEEP.Reflects: Pressure needed to keep alveoli open during the respiratory cycle.Goal: Lower ΔP → less atelectrauma & improved outcomes.Optimize: Increase PEEP to reduce ΔP and alveolar cycling. 📖 Interpreting High PIP/High Pplat ↑ PIP & ↑ PplatInterpretation: ↓ ComplianceCommon Causes: ARDS, pulmonary edema, pleural effusion, pneumothorax↑ PIP & Normal/Low PplatInterpretation: ↑ Airway ResistanceCommon Causes: Mucus plug, bronchospasm, tube obstruction or biting 🤕 Ventilator-Associated Lung Injury (VILI) Barotrauma:Mechanism: Excessive airway pressure damages alveoli.Prevention: Keep Pplat < 30 cmH₂O.Volutrauma:Mechanism: Overdistension from excessive tidal volumes.Prevention: Use low tidal volume ventilation (6 mL/kg ideal body weight).ARDSNet trial: 6 mL/kg → lower mortality compared to 12 mL/kg.Ideal Body Weight: Based on height and sex, not actual weight.Typical patient: Tidal Volume: 6–8 mL/kg IBWARDS: Tidal Volume: 4–6 mL/kg IBWAtelectrauma:Mechanism: Repeated opening/collapse of unstable alveoli.Prevention: Optimize PEEP to keep alveoli open and reduce driving pressure.Biotrauma:Mechanism: Inflammatory cascade (↑ IL-6, TNF-α) from mechanical injury.Effect: Can trigger systemic inflammation & multiorgan dysfunction.Prevention: Minimize all other forms of VILI. Post Peer Reviewed By: Marco Propersi, DO (Twitter/X: @Marco_propersi), and Mark Ramzy, DO (X: @MRamzyDO) 👤 Show Notes Joel Rios Rodriguez, MD PGY 3 Internal Medicine Resident Cape Fear Valley Internal Medicine Residency Program Fayetteville NC Aspiring Pulmonary Critical Care Fellow 🔎 Your Deep-Dive Starts Here REBEL Core Cast – Pediatric Respiratory Emergencies: Beyond Viral Season Welcome to the Rebel Core Content Blog, where we delve ... Pediatrics Read More REBEL Core Cast 143.0–Ventilators Part 3: Oxygenation & Ventilation — Mastering the Balance on the Ventilator When you take the airway, you take the wheel and ... Thoracic and Respiratory Read More REBEL Core Cast 142.0–Ventilators Part 2: Simplifying Mechanical Ventilation – Most Common Ventilator Modes Mechanical ventilation can feel overwhelming, especially when faced with a ... Thoracic and Respiratory Read More REBEL Core Cast 141.0–Ventilators Part 1: Simplifying Mechanical Ventilation — Types of Breathes For many medical residents, the ICU can feel like stepping ... Thoracic and Respiratory Read More REBEL Core Cast 140.0: The Power and Limitations of Intraosseous Lines in Emergency Medicine The sicker the patient, the more likely an IO line ... Procedures and Skills Read More REBEL Core Cast 139.0: Pneumothorax Decompression On this episode of the Rebel Core Cast, Swami takes ... Procedures and Skills Read More The post REBEL Core Cast 147.0–Ventilators Part 5: Key Mechanical Ventilator Pressures & Definitions Made Simple appeared first on REBEL EM - Emergency Medicine Blog.
00:00-15:00: Dallas Stars. Fun to watch. ML says they are well run and will be here the whole season. Thanks to Rosie's Corner and Ken's Auto Detailing. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
The search landscape has fundamentally shifted. 60% of Google searches now result in zero clicks. People are getting answers from AI without ever visiting websites. If you've noticed declining organic traffic or wondered why your content isn't getting clicks despite good rankings, this episode reveals what's happening and what to do about it. This compilation brings together five search and PR experts on how AI is changing buyer behavior and what actually works now. Jon Gillham from Originality.ai explains why citations, statistics, and quotes are essential for LLM visibility. Patty Parobek reframes the conversation with a surprising stat: Google grew by four ChatGPTs in 2024. Maurice White details the technical foundation that makes GEO work, while Chris Harihar explains why PR is now both top and bottom of the funnel. Gareth Cunningham ties it together with the reality that GEO only works when solid SEO fundamentals are in place. The shift isn't about producing more content. It's about strategic placement, quality sourcing, and building topical authority that AI engines trust. From structured data to brand mentions, from Google Business profiles to cited content, this episode provides a roadmap for 2026 and beyond. Featured Experts: Jon Gillham - Founder of Originality.ai shares proven strategies for LLM visibility and avoiding AI content detection. Listen to the full episode Patty Parobek - VP of AI and ML at Mod Op Transformation Breaks down the zero-click phenomenon and what it means for traffic strategies. Listen to the full episode Maurice White, Senior SEO Strategist at Mod Op, details the technical foundation required before GEO optimization works. Listen to the full episode Chris Harihar - EVP of PR at Mod Op explores how PR and SEO converge in the AI search era. Listen to the full episode Gareth Cunningham - Director of Search Experience at Mod Op explains why GEO only works on top of solid SEO fundamentals. Listen to the full episode This isn't about abandoning SEO. It's about evolving your strategy for how people actually research and buy today.
00:00-15:00: Bills-Patriots recap. ML breaks it down. These are simply your 2025 Buffalo Bills. Thanks to CH Insurance and Rosie's Corner. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
It's a little more ML, A LOT more unfiltered. In this episode, we talk about "Momterns" last day with the show, plus she brought us gifts we opened during the show! Find out why we had to end this episode abruptly!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Join Chief Technologist, John Janek, and Data Scientist, Mariana Ritchie, as they discuss various aspects of artificial intelligence from its influence on daily work environments, the use and advancements in technology, and its role in shaping future developments.Mariana discusses her journey at the company, touching on her early projects building ML pipelines to diving into the rapid progress and expansion of AI, following the release of ChatGPT. John and Mariana continue the conversation discussing the evolution of AI from natural language processing to multi-agent systems, highlighting the integration of large language models (LLMs) and the transition towards more specialized and efficient use cases.The conversation also shares insights into the future of AI, emphasizing the relevance of LLMs, the rise of smaller, specialized models, and the role of robust data practices. The episode concludes with Mariana sharing advice for aspiring technologists, urging them to build strong foundational knowledge and highlighting the criticality of being an active, engaged practitioner in the rapidly evolving AI landscape.
00:00-15:00: ML breaks down Quinn Hughes getting traded to the Wild. Thanks to Byrne Dairy and Ken's Auto Detailing. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
Datawizz is pioneering continuous reinforcement learning infrastructure for AI systems that need to evolve in production, not ossify after deployment. After building and exiting RapidAPI—which served 10 million developers and had at least one team at 75% of Fortune 500 companies using and paying for the platform—Founder and CEO Iddo Gino returned to building when he noticed a pattern: nearly every AI agent pitch he reviewed as an angel investor assumed models would simultaneously get orders of magnitude better and cheaper. In a recent episode of BUILDERS, we sat down with Iddo to explore why that dual assumption breaks most AI economics, how traditional ML training approaches fail in the LLM era, and why specialized models will capture 50-60% of AI inference by 2030. Topics Discussed Why running two distinct businesses under one roof—RapidAPI's developer marketplace and enterprise API hub—ultimately capped scale despite compelling synergy narratives The "Big Short moment" reviewing AI pitches: every business model assumed simultaneous 1-2 order of magnitude improvements in accuracy and cost Why companies spending 2-3 months on fine-tuning repeatedly saw frontier models (GPT-4, Claude 3) obsolete their custom work The continuous learning flywheel: online evaluation → suspect inference queuing → human validation → daily/weekly RL batches → deployment How human evaluation companies like Scale AI shift from offline batch labeling to real-time inference correction queues Early GTM through LinkedIn DMs to founders running serious agent production volume, working backward through less mature adopters ICP discovery: qualifying on whether 20% accuracy gains or 10x cost reductions would be transformational versus incremental The integration layer approach: orchestrating the continuous learning loop across observability, evaluation, training, and inference tools Why the first $10M is about selling to believers in continuous learning, not evangelizing the category GTM Lessons For B2B Founders Recognize when distribution narratives mask structural incompatibility: RapidAPI had 10 million developers and teams at 75% of Fortune 500 paying for the platform—massive distribution that theoretically fed enterprise sales. The problem: Iddo could always find anecdotes where POC teams had used RapidAPI, creating a compelling story about grassroots adoption. The critical question he should have asked earlier: "Is self-service really the driver for why we're winning deals, or is it a nice-to-have contributor?" When two businesses have fundamentally different product roadmaps, cultures, and buying journeys, distribution overlap doesn't create a sustainable single company. Stop asking if synergies exist—ask if they're causal. Qualify on whether improvements cross phase-transition thresholds: Datawizz disqualifies prospects who acknowledge value but lack acute pain. The diagnostic questions: "If we improved model accuracy by 20%, how impactful is that?" and "If we cut your costs 10x, what does that mean?" Companies already automating human labor often respond that inference costs are rounding errors compared to savings. The ideal customers hit differently: "We need accuracy at X% to fully automate this process and remove humans from the loop. Until then, it's just AI-assisted. Getting over that line is a step-function change in how we deploy this agent." Qualify on whether your improvement crosses a threshold that changes what's possible, not just what's better. Use discovery to map market structure, not just validate hypotheses: Iddo validated that the most mature companies run specialized, fine-tuned models in production. The surprise: "The chasm between them and everybody else was a lot wider than I thought." This insight reshaped their entire strategy—the tooling gap, approaches to model development, and timeline to maturity differed dramatically across segments. Most founders use discovery to confirm their assumptions. Better founders use it to understand where different cohorts sit on the maturity curve, what bridges or blocks their progression, and which segments can buy versus which need multi-year evangelism. Target spend thresholds that indicate real commitment: Datawizz focuses on companies spending "at a minimum five to six figures a month on AI and specifically on LLM inference, using the APIs directly"—meaning they're building on top of OpenAI/Anthropic/etc., not just using ChatGPT. This filters for companies with skin in the game. Below that threshold, AI is an experiment. Above it, unit economics and quality bars matter operationally. For infrastructure plays, find the spend level that indicates your problem is a daily operational reality, not a future consideration. Structure discovery to extract insight, not close deals: Iddo's framework: "If I could run [a call where] 29 of 30 minutes could be us just asking questions and learning, that would be the perfect call in my mind." He compared it to "the dentist with the probe trying to touch everything and see where it hurts." The most valuable calls weren't those that converted to POCs—they came from people who approached the problem differently or had conflicting considerations. In hot markets with abundant budgets, founders easily collect false positives by selling when they should be learning. The discipline: exhaust your question list before explaining what you build. If they don't eventually ask "What do you do?" you're not surfacing real pain. Avoid the false-positive trap in well-funded categories: Iddo identified a specific risk in AI: "You can very easily run these calls, you think you're doing discovery, really you're doing sales, you end up getting a bunch of POCs and maybe some paying customers. So you get really good initial signs but you've never done any actual discovery. You have all the wrong indications—you're getting a lot of false positive feedback while building the completely wrong thing." When capital is abundant and your space is hot, early revenue can mask product-market misalignment. Good initial signs aren't validation if you skipped the work to understand why people bought. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM
Richard Bonneau, Vice President of Machine Learning for Drug Discovery at Genentech and Roche, provides Pitt's HexAI podcast host, Jordan Gass-Pooré, with an insider view on how his team is fundamentally changing and accelerating how new drug candidate molecules are designed, predicted, and optimized.Geared for students in computational sciences and hybrid STEM fields, the episode introduces listeners to uses of AI and ML in molecular design, the biomolecular structure and structure-function relationships that underpin drug discovery, and how distinct teams at Genentech work together through an integrated computational system.Richard and Jordan use the opportunity to touch on how advances in the molecule design domain can inspire and inform advances in computational pathology and laboratory medicine. Richard also delves into the critical role of Explainable AI (XAI), interpretability, and error estimation in the drug design-prototype-test cycle, and provides advice on domain knowledge and skills needed today by students interested in joining teams like his at Genentech and Roche.
Together, Leon and Oscar share how applied EDA practices remain the backbone of trustworthy analytics pipelines in both academic and industry settings. Their discussion highlights the challenges and lessons learned from building the EDA Toolkit, and why reproducible workflows are more important than ever in the age of AI and ML.Key Highlights:Reproducible EDA: How to standardize exploratory data analysis workflows for consistent and trustworthy insights.Open-Source Innovation: The design and impact of the EDA Toolkit, bridging research, healthcare, and education.Best Practices for Analytics: Lessons learned from creating tools that make EDA more intuitive and scalable across projects.The Future of Data Science Workflows: Why reproducibility and standardization matter in modern AI/ML pipelines.
It's all about Data Pipelines. Join Pure Storage Field Solution Architect Chad Hendron and Solutions Director Andrew Silifant for a deep dive into the evolution of data management, focusing on the Data Lakehouse architecture and its role in the age of AI and ML. Our discussion looks at the Data Lakehouse as a powerful combination of a data lake and a data warehouse, solving problems like "data swamps” and proprietary formats of older systems. Viewers will learn about technological advancements, such as object storage and open table formats, that have made this new architecture possible, allowing for greater standardization and multiple tooling functions to access the same data. Our guests also explore current industry trends, including a look at Dremio's 2025 report showing the rapid adoption of Data Lakehouses, particularly as a replacement for older, inefficient systems like cloud data warehouses and traditional data lakes. Gain insight into the drivers behind this migration, including the exponential growth of unstructured data and the need to control cloud expenditure by being more prescriptive about what data is stored in the cloud versus on-premises. Andrew provides a detailed breakdown of processing architectures and the critical importance of meeting SLAs to avoid costly and frustrating pipeline breaks in regulated industries like banking. Finally, we provide practical takeaways and a real-world case study. Chad shares a customer success story about replacing a large, complex Hadoop cluster with a streamlined Dremio and Pure Storage solution, highlighting the massive reduction in physical space, power consumption, and management complexity. Both guests emphasize the need for better governance practices to manage cloud spend and risk. Andrew underscores the essential, full-circle role of databases—from the "alpha" of data creation to the "omega" of feature stores and vector databases for modern AI use cases like Retrieval-Augmented Generation (RAG). Tune in to understand how a holistic data strategy, including Pure's Enterprise Data Cloud, can simplify infrastructure and future-proof your organization for the next wave of data-intensive workloads. To learn more, visit https://www.purestorage.com/solutions/ai/data-warehouse-streaming-analytics.html Check out the new Pure Storage digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 03:15 Data Lakehouse Primer 08:31 Stat of the Episode on Lakehouse Usage 10:50 Challenges with Data Pipeline access 13:58 Assessing Organization Success with Data Cleaning 16:07 Use Cases for the Data Lakehouse 20:41 Case Study on Data Lakehouse Use Case 24:11 Hot Takes Segment
Онбординг часто воспринимают как набор экранов в начале продукта, хотя на самом деле это один из самых сильных продуктовых инструментов, который напрямую влияет на активацию, удержание и LTV. В этом выпуске разбираем, зачем нужен онбординг, как он работает в B2C и B2B, почему интерактив почти всегда выигрывает у статичных экранов, как персонализация и локализация меняют конверсию, и почему хороший онбординг не спасёт плохой продукт, но плохой способен испортить даже отличный. Говорим про реальные кейсы, метрики, A/B-тесты, friction, empty states и ошибки, которые команды продолжают повторять. Партнёр команды Podlodka — наши давние друзья @AvitoTech. Это команда с крутыми процессами, культурой здравого смысла и эксперимента. Узнать про их технологии, подходы и прокачку компетенций в командах можно по ссылкам: – Статья "Решаем задачи ML эффективнее: платформа, которая экономит ресурсы, время и нервы" clc.to/7EaWdQ – Статья "Всегда ли сегментация данных при анализе увеличивает эффективность экспериментов?" clc.to/clok5w – Статья "Как продакту выжить в мире ИИ-фичей" clc.to/gFffpQ Реклама. ООО "Авито Тех”, ИНН 9710089440, erid:2SDnjcAfMuH Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях! Telegram-чат: t.me/podlodka Telegram-канал: t.me/podlodkanews Страница в Facebook: www.facebook.com/podlodkacast/ Twitter-аккаунт: twitter.com/PodcastPodlodka Ведущие в выпуске: Андрей Смирнов, Аня Симонова Полезные ссылки: Вакансии Adapty adapty.io/careers/
Simba Khadder is the founder and CEO of Featureform, now at Redis, working on real-time feature orchestration and building a context engine for AI and agents.Context Engineering 2.0, Simba Khadder // MLOps Podcast #352Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractFeature stores aren't dead — they were just misunderstood. Simba Khadder argues the real bottleneck in agents isn't models, it's context, and why Redis is quietly turning into an AI data platform. Context engineering matters more than clever prompt hacks.// BioSimba Khadder leads Redis Context Engine and Redis Featureform, building both the feature and context layer for production AI agents and ML models. He joined Redis via the acquisition of Featureform, where he was Founder & CEO. At Redis, he continues to lead the feature store product as well as spearhead Context Engine to deliver a unified, navigable interface connecting documents, databases, events, and live APIs for real-time, reliable agent workflows. He also loves to surf, go sailing with his wife, and hang out with his dog Chupacabra.// Related LinksWebsite: featureform.comhttps://marketing.redis.io/blog/real-time-structured-data-for-ai-agents-featureform-is-joining-redis/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Simba on LinkedIn: /simba-k/Timestamps:[00:00] Context engineering explanation[00:25] MLOps and feature stores[03:36] Selling a company experience[06:34] Redis feature store evolution[12:42] Embedding hub[20:42] Human vs agent semantics[26:41] Enrich MCP data flow[29:55] Data understanding and embeddings[35:18] Search and context tools[39:45] MCP explained without hype[45:15] Wrap up
AI models feel smarter than their real-world impact. They ace benchmarks, yet still struggle with reliability, strange bugs, and shallow generalization. Why is there such a gap between what they can do on paper and in practiceIn this episode from The Dwarkesh Podcast, Dwarkesh talks with Ilya Sutskever, cofounder of SSI and former OpenAI chief scientist, about what is actually blocking progress toward AGI. They explore why RL and pretraining scale so differently, why models outperform on evals but underperform in real use, and why human style generalization remains far ahead.Ilya also discusses value functions, emotions as a built-in reward system, the limits of pretraining, continual learning, superintelligence, and what an AI driven economy could look like. Resources:Transcript: https://www.dwarkesh.com/p/ilya-sutsk...Apple Podcasts: https://podcasts.apple.com/us/podcast...Spotify: https://open.spotify.com/episode/7naO... Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures](http://a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
In episode 220, host Galit Friedlander and guest Chad Geiger (dance agent at The Movement Talent Agency) pull back the curtain on what representation really looks like from the agency side and what dancers often misunderstand about it. We talk about essential pieces of a sustainable dance career: communication, contracts, headshots and resumes that actually serve you, and how your choices off the floor impact your opportunities just as much as your training on it. Chad shares insight on navigating direct bookings, building trust with your team, and why “doing the basics well” is still one of the biggest differentiators in today's industry. 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 You can connect with Chad Geiger on https://www.instagram.com/chad_geiger Listen to DanceSpeak on Apple Podcasts and Spotify.
00:00-20:00: ML breaks down ND getting left out of the CFP. Thanks to Byrne Dairy 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.
Being told you have Primary Ovarian Insufficiency (POI) or premature menopause can feel like the door has closed on your fertility. But these terms don't mean the same thing and understanding the distinction is essential, especially if you're still hoping to conceive. In this episode, we break down what's actually happening hormonally in each condition, why they're often confused, and how a functional fertility approach can help you understand what may still be possible. You'll learn: The key differences between POI, premature menopause, and early menopause What your labs are really telling you about ovarian function Signs that your ovaries may still be active even if your cycle has stopped Which functional tests give deeper insight into thyroid, immune, gut, and adrenal factors that influence ovarian health How inflammation, autoimmune activity, stress physiology, and nutrient imbalances can drive ovarian shutdown Supportive nutritional, lifestyle, and mind-body strategies that may improve hormone communication and egg health When to combine functional and conventional care to optimize your chances of conception This episode is especially for you if: You've been told you have POI, premature menopause, or early menopause and want clarity about whether your ovaries have truly stopped functioning You're under 45 with irregular or missing cycles, hot flashes, or elevated FSH, and want to understand your next steps from a functional-fertility lens You've felt dismissed or told "it's over," yet you want to explore supportive strategies that may help your hormones and ovaries regain activity, naturally or alongside medical care Next Steps in Your Fertility Journey Subscribe to Get Pregnant Naturally for evidence-based guidance on functional fertility, and share this episode with anyone on their fertility journey. Not sure where to start? Download our most popular guide: Ultimate Guide to Getting Pregnant This Year If You Have Low AMH/High FSH it breaks everything down step by step to help you understand your options and take action For personalized support to improve pregnancy success, book a call here. --- Timestamps 00:00 Understanding POI and early menopause and why the distinction changes your fertility options when cycles are irregular or absent. 01:45 What POI before age 40 means and how irregular periods and fluctuating FSH can still indicate remaining ovarian activity. 03:00 Real examples of women with AMH at 0.04 ng/mL and 0.08 ng/mL who conceived by addressing inflammation, gut health, thyroid, and stress patterns. 04:00 How disrupted communication between the brain and ovaries drives POI and the role of autoimmunity, nutrient status, and the nervous system. 05:00 What premature menopause looks like on labs and why confirming ovarian shutdown matters when planning next steps. 06:10 How some women in their forties regain cycles and conceive naturally and what this reveals about hormonal resilience. 08:00 Factors that accelerate ovarian aging, including elevated hsCRP, gut infections, thyroid imbalance, environmental toxins, and nutrient gaps. 09:50 Why the gut and vaginal microbiome influence egg quality and implantation and how hidden infections affect fertility outcomes. 10:50 How functional thyroid ranges guide fertility decisions and why a TSH below 2 mIU/L supports better ovarian signaling and hormone balance. 14:40 Nutrition, mitochondrial support, mineral balance, and mind body work that help improve egg health and ovulation signaling. --- Resources
In this episode of Data in Biotech, host Ross Katz sits down with Eshani Galermo, Staff Scientist at SCIEX, to explore the next generation of mass spectrometry in pharma and biopharma. Eshani explains how innovations like the ZenoTOF 8600 are redefining sensitivity, selectivity, and workflow efficiency in bioanalytical chemistry. Discover how high-resolution accurate mass (HRAM) systems are unlocking new capabilities in drug discovery, clinical studies, and regulatory science. What you'll learn in this episode: >> Why traditional mass spectrometry falls short in modern bioanalysis >> How the ZenoTOF 8600 enhances sensitivity and reduces sample volume needs >> The role of high-resolution systems in detecting complex drug metabolites >> How automation tools are streamlining workflows for bioanalytical scientists >> The complementary role of AI and ML in mass spec data analysis Meet our guest Eshani Galermo is a Staff Scientist at SCIEX, where she leads global strategic marketing initiatives for pharma and biopharma quant applications. With deep expertise in bioanalytical chemistry and mass spectrometry, she has held multiple scientific roles across SCIEX, Emery Pharma, and Genentech. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancyConnect with Eshani Galermo on LinkedIn Connect with Us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode!Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.
What does it actually take to move machine learning from experiments into production reliably, responsibly, and at scale?In this episode of Alexa's Input (AI), Alexa talks with Maria Vechtomova, co-founder of Marvelous MLOps and an O'Reilly author-in-progress on MLOps with Databricks. Maria shares how her background in data science led her into MLOps, and why most teams struggle not because of tools, but because of missing processes, traceability, and shared understanding across teams.Alexa and Maria dive into what separates good MLOps from fragile deployments, why shipping notebooks as “production” creates long-term pain, and how traceability across code, data, and environment forms the foundation for reliable ML systems. They also explore how LLM applications are reshaping MLOps tooling, and where the biggest skill gaps still exist between platform, data, and AI engineers.A must-listen for anyone building, operating, or scaling machine learning systems and for teams trying to make MLOps less magical and more marvelous.Learn more about Marvelous MLOps and Maria's work below.LinksWatch: https://www.youtube.com/@alexa_griffithRead: https://alexasinput.substack.com/Listen: https://creators.spotify.com/pod/profile/alexagriffith/More: https://linktr.ee/alexagriffithWebsite: https://alexagriffith.com/LinkedIn: https://www.linkedin.com/in/alexa-griffith/Find out more about the guest at:LinkedIn: https://www.linkedin.com/in/maria-vechtomova/TakeawaysMaria started as a data analyst and transitioned into MLOps.She emphasizes the importance of tracking data, code, and environment in MLOps.MLOps is a practice to bring machine learning models to production reliably.Good deployment processes require modular code and proper tracking.MLOps differs from DevOps due to the complexities of data and model drift.Education is crucial for bridging gaps between teams in AI.Small steps can lead to better MLOps practices.Scaling MLOps requires understanding the unique data of different brands.The rise of LLMs is changing the MLOps landscape.Effective teaching methods involve step-by-step guidance.Chapters00:00 Introduction to MLOps and Maria's Journey02:11 Maria's Path to MLOps and Knowledge Sharing04:41 The Importance of MLOps in AI Deployments10:12 Defining MLOps and Its Challenges11:38 MLOps vs. DevOps: Key Differences13:00 Overcoming Stagnation in MLOps16:04 Small Steps Towards Better MLOps Practices19:29 Scaling MLOps in Large Organizations21:58 The Impact of LLMs on MLOps23:58 The Shift from Traditional ML to AI Applications26:51 Evolving Roles in AI Engineering28:33 Databricks: A Comprehensive AI Platform31:45 Future of AI Platforms and Regulations34:26 Bridging Skill Gaps in AI Teams38:42 The Importance of Context in AI Development40:40 Foundational Skills for MLOps Professionals45:43 Integrating Personal Passions with Professional Growth47:30 Building Impactful AI Communities
00:00-15:00: Colorado Avalanche. Unstoppable. ML breaks it down so far. Thanks to Rosie's Corner 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.
00:00-15:00: ML breaks down what's next for the Bengals now that the playoffs are out of the picture. How do they get back in contention for 2026? Thanks to CH Insurance and Byrne Dairy. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
00:00-20:00: Bills beat Bengals 39-34. Full recap from ML. Plus, can this jump start Buffalo the rest of the way? Thanks to Rosie's Corner and CH Insurance. 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 episode, I sit down with Alvitta Ottley and Paul Parsons to recap everything that happened at the 2025 IEEE VIS Conference in Vienna. We talk about our experiences co-organizing the VisCom workshop, the surprising attendance, and the standout keynote from Moritz Stefaner. Alvitta shares insights on accessibility research and the surge of LLM-focused visualization papers, while Paul walks us through his award-winning work on design cognition and how practitioners develop ideas. We also reflect on the evolving identity of the visualization field, from methodological rigor to the role of practitioners, interdisciplinarity, and ethical tensions. It's a wide-ranging, candid conversation about where visualization research is headed — and what we hope to see next year in Boston.Subscribe to the PolicyViz Podcast wherever you get your podcasts.Become a patron of the PolicyViz Podcast for as little as a buck a monthCheck out the VIS website.Follow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTubeEmail: jon@policyviz.com
A secret marriage in Detroit and a secretive engagement party in Lansing start this week's show, before ML and Marc […]
00:00-20:00: ML says the Rams have it all and are peaking at the right time. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
Send us a textDr. Shalini Ojha, Professor of Neonatal Medicine at University of Nottingham, presents the Feed One trial examining full enteral feeding (60 mL/kg/day) from day one versus gradual advancement in 30-32 week infants. While the primary outcome of hospital length of stay showed no difference (median 32 days), full feeding significantly reduced parenteral nutrition use, IV cannulations, and associated interventions without increasing necrotizing enterocolitis risk (4 versus 6 cases). This pragmatic trial challenges the unfounded fear that early full feeding causes NEC—demonstrating that moderate preterm infants can safely receive complete enteral nutrition from birth, simplifying care particularly in resource-limited settings while avoiding complications from IV access and parenteral nutrition. Support the showAs always, feel free to send us questions, comments, or suggestions to our email: nicupodcast@gmail.com. You can also contact the show through Instagram or Twitter, @nicupodcast. Or contact Ben and Daphna directly via their Twitter profiles: @drnicu and @doctordaphnamd. The papers discussed in today's episode are listed and timestamped on the webpage linked below. Enjoy!
Major health organizations, including the CDC and ACOG, recommend universal Hepatitis C Virus (HCV) screening for all pregnant women during each pregnancy and at time of delivery. Ideally, pregnant women should be screened for hepatitis C virus infection at the first prenatal visit of each pregnancy. If the antibody screen result is positive, hepatitis C virus RNA polymerase chain reaction testing is done to confirm the diagnosis. The risk of perinatal transmission of HCV is up to 9%, with at least one-third of transmissions occurring antenatally. While antiviral therapy is recommended for Hepatitis B in pregnancy with a viral load greater than 200,000 international units/mL to decrease the risk of vertical transmission, the same is not the case for Hep C. According to the ACOG CPG #6 from September 2023, there are no standard treatment protocols for Hep C in pregnancy but a new publication from the PINK journal (7 Dec 2025) is calling for a change. That new publication is, “Hepatitis C Treatment During Pregnancy: Time for a Practice Change”. Listen in for details. 1. ACOG CPG #6; Sept 20262. Bhattacharya D, Aronsohn A, Price J, Lo Re V. Hepatitis C Guidance 2023 Update: AASLD-IDSA Recommendations for Testing, Managing, and Treating Hepatitis C Virus Infection. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America. 2023;:ciad319. doi:10.1093/cid/ciad319.3. Chappell CA, Kiser JJ, Brooks KM, et al. Sofosbuvir/¬Velpatasvir Pharmacokinetics, Safety, and Efficacy in Pregnant People With Hepatitis C Virus. Clinical Infectious Diseases : An Official Publication of the Infectious Diseases Society of America. 2025;80(4):744-751. doi:10.1093/cid/ciae595.4. Reau N, Munoz SJ, Schiano T. Liver Disease During Pregnancy. The American Journal of Gastroenterology. 2022;117(10S):44-52. doi:10.14309/ajg.0000000000001960.5. Dutra, Karley et al. Hepatitis C Treatment During Pregnancy: Time for a Practice Change. American Journal of Obstetrics & Gynecology MFM, Volume 0, Issue 0, 1018656. Society for Maternal-Fetal Medicine Consult Series #56: Hepatitis C in Pregnancy-Updated Guidelines: Replaces Consult Number 43, November 2017. Society for Maternal-Fetal Medicine (SMFM). Electronic address: pubs@smfm.org, Dotters-Katz SK, Kuller JA, Hughes BL. American Journal of Obstetrics and Gynecology. 2021;225(3):B8-B18. doi:10.1016/j.ajog.2021.06.008
🧭 REBEL Rundown 🗝️ Key Points ❌ Don’t chase perfect numbers: Adequate and safe is often better than “perfect but harmful.”💨 Oxygenation levers: Start with FiO₂ and PEEP, but remember MAP is the true driver.🫁 Ventilation levers: Adjust RR and TV, tailored to underlying physiology.🚫 Watch your obstructive patients: Sometimes less RR is more. Click here for Direct Download of the Podcast. 📝 Introduction Ventilator management can feel overwhelming—there are so many knobs to turn, numbers to watch, and changes to make. But before adjusting any settings, it’s crucial to understand why the patient is in distress in the first place, because the right strategy depends on the underlying cause. In this episode, we’ll walk through three different cases to see how the approach changes depending on the problem at hand. ️ The 4 Main Ventilator Settings Tidal Volume (Vt) 🌬️ Amount of air delivered with each breath Typically set based on ideal body weight (6–8 mL/kg for lung protection) Respiratory Rate (RR) ⏱️ Number of breaths delivered per minute Adjusted to control minute ventilation and manage CO₂ FiO₂ (Fraction of Inspired Oxygen) ⛽ Percentage of oxygen delivered Adjusted to maintain adequate oxygenation (goal SpO₂ 92–96%, PaO₂ 55–80 mmHg). PEEP (Positive End-Expiratory Pressure) 🎈 Pressure maintained in the lungs at the end of exhalation to prevent alveolar collapse and improve oxygenation 🧮 Modes of Ventilation AC/VC (Assist Control – Volume Control)How it Works: Delivers a set tidal volume with each breath (whether patient- or machine-triggered).When It’s Used / Pros: Most common initial mode; guarantees minute ventilation; good for patients with variable effort.Limitations / Cons: May cause patient–ventilator dyssynchrony if set volumes don’t match patient’s demand.AC/PC (Assist Control – Pressure Control)How it Works: Delivers a set inspiratory pressure for each breath; tidal volume varies depending on lung compliance/resistance.When It’s Used / Pros: Useful in ARDS (lung-protective strategy), limits peak airway pressures.Limitations / Cons: Tidal volume not guaranteed; must closely monitor volumes and minute ventilation.PRVC (Pressure-Regulated Volume Control)How it Works: Hybrid: set target tidal volume, ventilator adjusts inspiratory pressure breath-to-breath to achieve it (within limits).When It’s Used / Pros: Common default mode on newer vents; combines benefits of VC (guaranteed volume) + PC (pressure limitation).Limitations / Cons: Can increase pressures if compliance worsens.SIMV (Synchronized Intermittent Mandatory Ventilation)How it Works: Delivers set breaths, but allows spontaneous patient breaths in between (without guaranteed volume).When It’s Used / Pros: Used for weaning; allows patient effort.Limitations / Cons: Risk of increased work of breathing if spontaneous breaths are inadequate.PSV (Pressure Support Ventilation)How it Works: Every breath is patient-initiated; ventilator provides preset pressure support to overcome airway resistance.When It’s Used / Pros: Weaning trials; patients with intact drive who just need assistance.Limitations / Cons: Not a full-support mode; not for unstable patients without spontaneous drive. ♟️ Ventilation Strategies Airway ProtectionLow GCS, seizure, strokeLoss of gag/cough reflexHigh aspiration risk (vomiting, GI bleed, poor mental status)Hypoxemic Respiratory FailureSevere pneumoniaARDSPulmonary edemaInhalation injuryVentilatory (Hypercapnic) Failure / Increased Ventilation DemandSevere metabolic acidosis (DKA, sepsis, renal failure) → need high minute ventilationCOPD, asthma (if decompensating)Neuromuscular weakness (myasthenia, Guillain–Barré, spinal cord injury)Airway Obstruction / Anticipated Loss of AirwayTumor, anaphylaxis, angioedemaFacial or airway traumaPre-op / anticipated deterioration Post Peer Reviewed By: Marco Propersi, DO (Twitter/X: @Marco_propersi), and Mark Ramzy, DO (X: @MRamzyDO) 👤 Show Notes Priyanka Ramesh, MD PGY 1 Internal Medicine Resident Cape Fear Valley Internal Medicine Residency Program Fayetteville NC Aspiring Pulmonary Critical Care Fellow 🔎 Your Deep-Dive Starts Here REBEL Core Cast – Pediatric Respiratory Emergencies: Beyond Viral Season Welcome to the Rebel Core Content Blog, where we delve ... Pediatrics Read More REBEL Core Cast 143.0–Ventilators Part 3: Oxygenation & Ventilation — Mastering the Balance on the Ventilator When you take the airway, you take the wheel and ... Thoracic and Respiratory Read More REBEL Core Cast 142.0–Ventilators Part 2: Simplifying Mechanical Ventilation – Most Common Ventilator Modes Mechanical ventilation can feel overwhelming, especially when faced with a ... Thoracic and Respiratory Read More REBEL Core Cast 141.0–Ventilators Part 1: Simplifying Mechanical Ventilation — Types of Breathes For many medical residents, the ICU can feel like stepping ... Thoracic and Respiratory Read More REBEL Core Cast 140.0: The Power and Limitations of Intraosseous Lines in Emergency Medicine The sicker the patient, the more likely an IO line ... Procedures and Skills Read More REBEL Core Cast 139.0: Pneumothorax Decompression On this episode of the Rebel Core Cast, Swami takes ... Procedures and Skills Read More The post REBEL Core Cast 146.0–Ventilators Part 4: Setting up the Ventilator appeared first on REBEL EM - Emergency Medicine Blog.
00:00-15:00: ML breaks down what's next for the Lions after topping the Cowboys on TNF. Playoff possibilities and more. Thanks to CH Insurance and Rosie's Corner. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
Send us a textDr. Ariel Salas, recent R01 recipient, discusses challenging traditional feeding volume targets in preterm infants. His multi-center trial compares 180-200 versus 140-160 mL/kg/day volumes, examining body composition changes rather than weight alone. Salas emphasizes targeting fat-free mass gains over simple weight gain, as this component associates with better long-term neurodevelopmental outcomes. Body composition analysis reveals compartmental changes invisible to daily weights—distinguishing extracellular versus intracellular water shifts. This outcome provides reasonable compromise between immediate intervention effects and long-term results. Salas advocates acknowledging practice variation as opportunity for equipoise and fair testing, challenging arbitrary standards that persist despite limited evidence supporting them. Support the showAs always, feel free to send us questions, comments, or suggestions to our email: nicupodcast@gmail.com. You can also contact the show through Instagram or Twitter, @nicupodcast. Or contact Ben and Daphna directly via their Twitter profiles: @drnicu and @doctordaphnamd. The papers discussed in today's episode are listed and timestamped on the webpage linked below. Enjoy!
This podcast is brought to you by Outcomes Rocket, your exclusive healthcare marketing agency. Learn how to accelerate your growth by going to outcomesrocket.com Thoughtful, problem-first innovation drives real clinical impact in healthcare. In this episode, Saul Marquez and Ed Gaudet from Censinet host Dr. Jason Hill, Innovation Officer at Ochsner Health, and David Leingang, Director of Innovation Data Science at Ochsner Health, to discuss how their team uses machine learning, workflow redesign, and data-driven insights to reduce physician message burden and improve patient routing. They share how analyzing 2.4 million inbox messages revealed that 4% were tied to weight-loss drugs, prompting the creation of a new weight-management digital medicine program instead of an AI tool. They explain how reorganizing message flows, adding e-visits, and using ML to uncover hidden system strain has improved efficiency, while predictive deterioration models saved lives but had to be retrained as outcomes changed. The conversation closes with an exploration of value-based care, problem-solving in AI, and the AHEAD Network's role in advancing healthcare innovation. Tune in and learn how practical AI, smarter workflows, and cross-industry collaboration are reshaping modern healthcare! Resources Connect with and follow Jason Hill on LinkedIn. Follow and connect with David Leingang on LinkedIn. Follow Ochsner Health on LinkedIn and explore their website!
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