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00:00-15:00: WGR 550 Buffalo Bills insider Sal Capaccio breaks down the Bills promoting Joe Brady to head coach. Then, ML shares who Brady could bring on as offensive/defensive coordinators. Thanks to Byrne Dairy 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.
WANTED: Developers and STEM experts! Get paid to create benchmarks and improve AI models. Sign up for Alignerr using our link: https://alignerr.com/?referral-source=briankeating One of the most powerful AI systems we've ever built is succeeding for reasons we still don't understand. And worse, they may succeed for reasons that might lock us into the wrong future for humanity. Today's guest is Anil Ananthaswamy, an award-winning science writer and one of the clearest thinkers on the mathematical foundations of machine learning. In this conversation, we're not just talking about new demos, incremental improvements, or updates on new models being released. We're asking even harder questions: Why does the mathematics of machine learning work at all? How do these models succeed when they suffer from problems like overparameterization and lack of training data? And are large language models revealing deep structure, or are they just producing very convincing illusions and causing us to face an increasingly AI-slop-driven future? KEY TAKEAWAYS 00:00 — Book explores why ML works through math 02:47 — Perceptron proof shows simple math guarantees learning 05:11 — Early AI failed due to single-layer limits 07:12 — Nonlinear limits caused the first AI winter 09:04 — Backpropagation revived neural networks 10:59 — GPUs + big data enabled deep learning 15:25 — AI success risks technological lock-in 17:30 — LLMs lack human-like learning and embodiment 22:57 — High-dimensional spaces power ML behavior 27:36 — Data saturation may slow future gains 31:11 — Continual learning is still missing in AI 33:46 — Neuromorphic chips promise energy efficiency 41:49 — Overparameterized models still generalize well 45:05 — SGD succeeds via randomness in complex landscapes 48:27 — Perceptrons remain the core of modern neural net - Additional resources: Anil's NEW Book "Why Machines Learn: The Elegant Math Behind Modern AI": https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749 Get My NEW Book: Focus Like a Nobel Prize Winner: https://www.amazon.com/dp/B0FN8DH6SX?ref_=pe_93986420_775043100 Please join my mailing list here
Paulo Vasconcellos is the Principal Data Scientist for Generative AI Products at Hotmart, working on AI-powered creator and learning experiences, including intelligent tutoring, content automation, and multilingual localization at scale.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// Abstract“Agent as a product” sounds like hype, until Hotmart turns creators' content into AI businesses that actually work.// BioPaulo Vasconcellos is the Principal Data Scientist for Generative AI Products at Hotmart, where he leads efforts in applied AI, machine learning, and generative technologies to power intelligent experiences for creators and learners. He holds an MSc in Computer Science with a focus on artificial intelligence and is also a co-founder of Data Hackers, a prominent data science and AI community in Brazil. Paulo regularly speaks and publishes on topics spanning data science, ML infrastructure, and AI innovation.// Related LinksWebsite: paulovasconcellos.com.brCoding Agent - Virtual Conference: https://home.mlops.community/home/events/coding-agents-virtual ~~~~~~~~ ✌️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/]MLOps GPU Guide: https://go.mlops.community/gpuguideConnect with Demetrios on LinkedIn: /dpbrinkmConnect with Paulo on LinkedIn: /paulovasconcellos/Timestamps:[00:00] Hotmart Data Science Challenges[02:38] LLMs vs spaCy[11:38] Use Cases in Production[19:04] Coding Agents Virtual Conference Announcement![29:27] ML to AI Product Shift[34:49] Tool-Augmented Agent Approach[38:28] MLOps GPU Guide[41:24] AI Use Cases at Hotmart[49:34] Agent Tool Access Explained[51:04] MLOps Community Gratitude[53:22] Wrap up
Host Philip Gervasi talks with Uber's Vishnu Acharya about how Uber applies machine learning and MLOps to network operations at hyperscale. Vishnu explains Uber's intentionally simple network design across on-prem and multi-cloud, then shares practical machine learning use cases like predictive capacity planning, hardware failure rate-tracking, and alert correlation to reduce noise and speed mitigation. They also discuss organizational issues, including building blended network/software teams, partnering with internal ML groups, and focusing on service-level outcomes over hype.
Today, we hear how to leverage the much-vaunted AI and ML technologies to make practical cybersecurity improvements for the federal government. The analysis includes comments about setting a base line, prioritizing alerts and a quick dive into the characteristics of Operational Technology (OT). BASELINE: Signature-based risk analysis has proven to be easy to deceive. Alex Maier from August Schell suggests that the solution is a move to a "behavior" based tool. In other words, see where a user's behavior varies from the norm. If that is the case, then you must know what "typical" is all about and begin by observing typical patterns to discern noticeable differences. AERTS: Some estimates suggest that a Security Operations Center (SOC) can receive 10,000 alerts a day. It is no wonder operators suffer from "alert fatigue" and miss problems. Rubrik has technology that can establish a risk-based alerting system to filter out low-level concerns. ELEMENTS OF OT: Mark Hadley of Pacific Northwest National Laboratory describes OT as deterministic. That is to say, given a signal, always produce the same output with a fixed set of rules. Given that understanding, a heighted importance must be given to the value of the specific commands given to OT devices. The discussion also covered the need for transparency and accountability, as well as the potential risks of AI-based attacks.
00:00-30:00: ML breaks down Sean McDermott getting fired and who the candidates for the HC job in Buffalo are. 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.
ML is impatient for peace in Ukraine, while Marc says he's fine if we snatch Greenland. Disagreement over college football […]
Our guest on this week's episode is Tony Bradley, president and CEO of the Arizona Trucking Association and the executive director of the Arizona Trucking Association Foundation. We have seen huge changes within the trucking industry during the past year based on two big issues – the licensing of non-domiciled drivers and the huge surges we see in freight fraud. Victoria Kickham finds out more about what is being done to address these issues in this week's guest interview. One of the technology topics that has gotten a lot of buzz lately has been humanoid robots, which of course are that family of robots that have heads and bodies and torsos, and either walk on two legs or roll on a moving base like an AMR. This technology is very new of course, and has been seen only in research labs until recently. Ben Ames reports on an example of how one of these critters might fit into a real world workflow.A recent survey of North American transportation, logistics, and supply chain executives reveals a disconnect between what those leaders see as the promise of advanced artificial intelligence (AI) solutions and their readiness to implement them. Victoria Kickham reports on a new survey that examines the effects of adopting AI and machine learning (ML) in logistics, and it revealed some interesting information about Agentic AI and its role in the industry.Supply Chain Xchange also offers a podcast series called Supply Chain in the Fast Lane. It is co-produced with the Council of Supply Chain Management Professionals. A new series is now available on Top Threats to our Supply Chains. It covers topics including Geopolitical Risks, Economic Instability, Cybersecurity Risks, Threats to energy and electric grids; Supplier Risks, and Transportation Disruptions Go to your favorite podcast platform to subscribe and to listen to past and future episodes. The podcast is also available at www.thescxchange.com.Articles and resources mentioned in this episode:Arizona Trucking Associationtruckingresurgence.comSiemens completes pilot test of humanoid robot42% of logistics leaders are holding back on Agentic AI, survey showsVisit Supply Chain XchangeListen to CSCMP and Supply Chain Xchange's Supply Chain in the Fast Lane podcastSend feedback about this podcast to podcast@agilebme.comThis podcast episode is sponsored by: WernerOther linksAbout DC VELOCITYSubscribe to DC VELOCITYSign up for our FREE newslettersAdvertise with DC VELOCITY
Corey Zumar is a Product Manager at Databricks, working on MLflow and LLM evaluation, tracing, and lifecycle tooling for generative AI.Jules Damji is a Lead Developer Advocate at Databricks, working on Spark, lakehouse technologies, and developer education across the data and AI community.Danny Chiao is an Engineering Leader at Databricks, working on data and AI observability, quality, and production-grade governance for ML and agent systems.MLflow Leading Open Source // MLOps Podcast #356 with Databricks' Corey Zumar, Jules Damji, and Danny ChiaoJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterShoutout to Databricks for powering this MLOps Podcast episode.// AbstractMLflow isn't just for data scientists anymore—and pretending it is is holding teams back. Corey Zumar, Jules Damji, and Danny Chiao break down how MLflow is being rebuilt for GenAI, agents, and real production systems where evals are messy, memory is risky, and governance actually matters. The takeaway: if your AI stack treats agents like fancy chatbots or splits ML and software tooling, you're already behind.// BioCorey ZumarCorey has been working as a Software Engineer at Databricks for the last 4 years and has been an active contributor to and maintainer of MLflow since its first release. Jules Damji Jules is a developer advocate at Databricks Inc., an MLflow and Apache Spark™ contributor, and Learning Spark, 2nd Edition coauthor. He is a hands-on developer with over 25 years of experience. He has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/LoudCloud, VeriSign, ProQuest, Hortonworks, Anyscale, and Databricks, building large-scale distributed systems. He holds a B.Sc. and M.Sc. in computer science (from Oregon State University and Cal State, Chico, respectively) and an MA in political advocacy and communication (from Johns Hopkins University)Danny ChiaoDanny is an engineering lead at Databricks, leading efforts around data observability (quality, data classification). Previously, Danny led efforts at Tecton (+ Feast, an open source feature store) and Google to build ML infrastructure and large-scale ML-powered features. Danny holds a Bachelor's Degree in Computer Science from MIT.// Related LinksWebsite: https://mlflow.org/https://www.databricks.com/~~~~~~~~ ✌️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 Corey on LinkedIn: /corey-zumar/Connect with Jules on LinkedIn: /dmatrix/Connect with Danny on LinkedIn: /danny-chiao/Timestamps:[00:00] MLflow Open Source Focus[00:49] MLflow Agents in Production[00:00] AI UX Design Patterns[12:19] Context Management in Chat[19:24] Human Feedback in MLflow[24:37] Prompt Entropy and Optimization[30:55] Evolving MLFlow Personas[36:27] Persona Expansion vs Separation[47:27] Product Ecosystem Design[54:03] PII vs Business Sensitivity[57:51] Wrap up
Are self-driving databases the Waymos of the future? In Episode 35 of Talking Postgres, Luigi Nardi—founder and CEO of DBtune and Stanford researcher—joins Claire Giordano to explore his journey from academic research to Level 5 autonomous database tuning. We dig into Luigi's early days with a Commodore 64, how he began his PhD in Paris before he had learned to speak French, and how "professor privilege" in Sweden helped him bootstrap his startup. You'll learn why the DBtune team chose database tuning and Postgres as their focus, what the Jevons paradox means for the future of developers, and how the “Level 5” vision fuels the DBtune team's work toward a truly self-driving system. Previously on Talking Postgres:Talking Postgres Ep30: AI for data engineers with Simon WillisonTalking Postgres Ep23: How I got started as a developer & in Postgres with Daniel GustafssonLinks mentioned in this episode:CFP: POSETTE: An Event for Postgres 2026's CFP closes on Sun Feb 1, 2026 @ 11:59pm PSTVideo of POSETTE 2024 talk: Autotuning PostgreSQL on Azure Flexible Server, by Luigi NardiVideo of PGConf India 2025 talk: ML for Systems and Systems for ML, by Luigi NardiPGConf India 2025: Round Table Discussion about AIOxide and Friends podcast: Engineering Rigor in the LLM AgeWikipedia: Jevons paradoxWikipedia: Neuro-symbolic AIConference: PGDay Lowlands (Boriss Mejías calls it the second-best Postgres conference in Europe)Calendar invite: LIVE recording of Ep36 of Talking Postgres to happen on Wed Feb 18, 2026
The biotech industry stands on the verge of a radical transformation thanks to artificial intelligence (AI) and machine learning (ML). But even the most sophisticated algorithms are only as smart as the data feeding them.David Brühlmann sits down with Troy Lionberger, Chief Business Officer at A-Alpha Bio, whose team has quietly shattered the data ceiling by measuring and curating more than 1.8 billion protein interactions. Troy Lionberger brings an insider's perspective from the frontlines of machine learning-powered drug discovery. From partnering with leading biotechs to redesigning classic antibodies for previously “impossible” targets, Troy's work pushes the edges of what's tractable in biologic therapeutics.What you'll hear in this episode:Limitations of public data sources like the Protein Data Bank and their impact on current protein engineering approaches (03:11)Why combining energetic (ΔG) and structural data matters for building predictive protein engineering models (05:43)A-Alpha Bio's approach to generating 1.8 billion protein interaction measurements for machine learning—what this enables today and what's possible next (06:30)Examples of how A-Alpha Bio's platform solves challenging therapeutic problems, such as optimizing molecules for 800+ HIV variants and engineering dual-specific antibodies (07:36)The ongoing debate: What capabilities should biotech companies keep in-house, and what works best outsourced to service providers? (09:59)The potential of synthetic epitopes as vital tools for training models beyond the Protein Data Bank—introducing the Synthetic Epitope Atlas (12:09)Key takeaways for scientists: the importance of diligence amidst rapidly evolving AI claims, and advice for accelerating R&D with the right data (14:57)Wondering how to move protein therapeutics from “interesting” to “impactful” without waiting for years of crystal structures? Listen in to learn how you can harness next-gen machine learning tools and custom datasets for your development projects.Connect with Troy Lionberger:LinkedIn: www.linkedin.com/in/troylionbergerA-Alpha Bio website: www.aalphabio.comNext step:Need fast CMC guidance? → Get rapid CMC decision support hereSupport the show
In episode 221, host Galit Friedlander and guest Kim Holmes (widely respected director, choreographer, dance educator) explore the roots of house and hip-hop culture through lived experience, mentorship, and time spent inside New York City's party and club scenes before these styles became widely visible. Kim shares her journey into dance, discovering house at a young age, and learning directly with pioneers like Marjory Smarth during a formative era that shaped how she moves, teaches, and thinks about longevity. Together, Galit and Kim reflect on what it meant to come up in spaces where culture was built in real time—long before social media or conventions—and how being “the it kids” back then came with both opportunity and responsibility. The conversation also moves into technique, recovery, listening to the body, trusting timing, and how mindset and intuition quietly guide long careers in dance. Originally recorded in 2019, this episode feels especially relevant today as dancers revisit foundations, lineage, and what it truly means to sustain a life in dance beyond trends. 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 Kim Holmes on Instagram https://www.instagram.com/kimd.holmes. Listen to DanceSpeak on Apple Podcasts and Spotify.
SaaStr 837: 10 Things To Do Right Now to Become AI Native with Filevine's CEO & Founder Ryan Anderson, Co-Founder and CEO of Filevine, shares the playbook for how his legal tech company successfully transitioned from a traditional SaaS business to an AI-native company, now generating more new revenue from AI products than their core SaaS platform. With 6,000 customers, 700 employees, and $200M+ ARR growing at nearly 60%, Filevine has cracked the code on AI transformation. Ryan breaks down the strategic, technical, and cultural changes required to make the shift. Key Takeaways: Nothing is Sacred – Be prepared to tear down working systems that don't serve your AI future. Use a simple framework: keep what's critical to your competitive moat and keeps you fast; eliminate what slows you down. Content → Context – Your SaaS data becomes the competitive advantage when it serves as context for AI agents. Think Cher's closet in Clueless—you need both the organized system AND the AI. Restructure Your Architecture – AI can't just be "sprinkled on top." Your ML team needs to own the AI data layer and iterate daily without bottlenecks. Hire AI Natives – They want access to rich data and distribution. Sell them on what you have that AI-only startups don't. Consider Acquisitions – Filevine acquired Parrot to jumpstart their ML capabilities. Speed matters. Rebrand with Intent – Signal the change internally and externally. It's symbolic but powerful. Obsess Over Usage – If you can't measure it, don't ship it. Track DAU/WAU/MAU religiously. Leverage Your Data – Control API access, monitor AI traffic for product ideas, and don't give away your advantage for free. Price to Dominate – Your high SaaS margins let you undercut AI-only competitors on blended gross margin. Build One Product – Stop selling to customers who won't buy AI. Assume AI is implicit in everything you build. About the Speaker: Ryan Anderson is the Co-Founder and CEO of Filevine, an AI-powered legal operating system. Under his leadership, Filevine has achieved 96% gross revenue retention and 124% net revenue retention while successfully pivoting to AI-native operations. --------------------- This episode is Sponsored in part by HappyFox: Imagine having AI agents for every support task — one that triages tickets, another that catches duplicates, one that spots churn risks. That'd be pretty amazing, right? HappyFox just made it real with Autopilot. These pre-built AI agents deploy in about 60 seconds and run for as low as 2 cents per successful action. All of it sits inside the HappyFox omnichannel, AI-first support stack — Chatbot, Copilot, and Autopilot working as one. Check them out at happyfox.com/saastr --------------------- Hey everybody, the biggest B2B + AI event of the year will be back - SaaStr AI in the SF Bay Area, aka the SaaStr Annual, will be back in May 2026. With 68% VP-level and above, 36% CEOs and founders and a growing 25% AI-first professional, this is the very best of the best S-tier attendees and decision makers that come to SaaStr each year. But here's the reality, folks: the longer you wait, the higher ticket prices can get. Early bird tickets are available now, but once they're gone, you'll pay hundreds more so don't wait. Lock in your spot today by going to podcast.saastrannual.com to get my exclusive discount SaaStr AI SF 2026. We'll see you there.
On this #TequilaTuesday edition, our thirsty gents belly up to the sports betting bar. First up, our drunkards fire off action while riding the PLUS BUS -- including early action in the NFL Divisional Round. From there, Brad counts down his college basketball themed Fade Five, featuring wagers on Virginia/Louisville, Oregon/Nebraska, West Virginia/Houston, Grand Canyon/New Mexico and more. Where did Lundy dare FADE him? Where did Lundy blindly FOLLOW? Rounding out today's episode, Brad stretches ML legs on his #TeamHuevos Parlay Play and Lundy grabs tickets in the NHL in BONUS TIME. Listen to the full show in just 30 minutes. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Luke Hedrick, Dave Furfaro, and recurrent RFJC guest Robert Wharton are joined again today by Nicole Ng to discuss the FIBRONEER-IPF trial investigating Nerandomilast in patients with IPF. This trial was published in NEJM in 2025 and looked at Neradomilast vs placebo for treating patients with IPF, on or off background anti-fibrotic therapy. This agents is now FDA approved for pulmonary fibrosis, and understanding the trial results is essential for any pulmonary physician treating patients with IPF or progressive pulmonary fibrosis. Article and Reference Today’s episode discusses the FIBRONEER-IPF trial published in NEJM in 2025. Richeldi L, Azuma A, Cottin V, Kreuter M, Maher TM, Martinez FJ, Oldham JM, Valenzuela C, Clerisme-Beaty E, Gordat M, Wachtlin D, Liu Y, Schlecker C, Stowasser S, Zoz DF, Wijsenbeek MS; FIBRONEER-IPF Trial Investigators. Nerandomilast in Patients with Idiopathic Pulmonary Fibrosis. N Engl J Med. 2025 Jun 12;392(22):2193-2202. doi: 10.1056/NEJMoa2414108. Epub 2025 May 18. PMID: 40387033. https://www.nejm.org/doi/abs/10.1056/NEJMoa2414108 Meet Our Guests Luke Hedrick is an Associate Editor at Pulm PEEPs and runs the Rapid Fire Journal Club Series. He is a senior PCCM fellow at Emory, and will be starting as a pulmonary attending at Duke University next year. Robert Wharton is a recurring guest on Pulm PEEPs as a part of our Rapid Fire Journal Club Series. He completed his internal medicine residency at Mt. Sinai in New York City, and is currently a pulmonary and critical care fellow at Johns Hopkins. Dr. Nicole Ng is an Assistant Profess of Medicine at Mount Sinai Hospital, and is the Associate Director of the Interstitial Lung Disease Program for the Mount Sinai National Jewish Health Respiratory Institute. Infographic Key Learning Points Why this trial mattered IPF therapies remain limited: nintedanib and pirfenidone slow (but do not stop) decline and often cause GI side effects. Nerandomilast is a newer agent (a preferential PDE4B inhibitor) with antifibrotic + immunomodulatory effects. Phase 2 data (NEJM 2022) looked very promising (suggesting near-“halt” of FVC decline), so this phase 3 trial was a big test of that signal. Trial design essentials Industry-sponsored, randomized, double-blind, placebo-controlled, large multinational study (332 sites, 36 countries). Population: IPF diagnosed via guideline-aligned criteria with central imaging review and multidisciplinary diagnostic confirmation. Intervention: nerandomilast 18 mg BID, 9 mg BID, or placebo; stratified by background antifibrotic use. Primary endpoint: change in FVC at 52 weeks, analyzed with a mixed model for repeated measures. Key secondary endpoint: time to first acute exacerbation, respiratory hospitalization, or death (composite). Who was enrolled Typical IPF trial demographics: ~80% male, mean age ~70, many former smokers. Many were already on background therapy (~45% nintedanib, ~30–33% pirfenidone). Notable exclusions included significant liver disease, advanced CKD, recent major cardiovascular events, and psychiatric risk (suicidality/severe depression), reflecting class concerns seen with other PDE4 inhibitors. Efficacy: what the primary endpoint showed Nerandomilast produced a statistically significant but modest reduction in annual FVC decline vs placebo (roughly 60–70 mL difference). Importantly, it did not halt FVC decline the way the phase 2 data suggested; patients still progressed. Important nuance: interaction with pirfenidone Patients on pirfenidone had ~50% lower nerandomilast trough levels. Clinically: 9 mg BID looked ineffective with pirfenidone, so 18 mg BID is needed if used together. In those not on background therapy or on nintedanib, 9 mg and 18 mg looked similar—suggesting the apparent “dose-response” might be partly driven by the pirfenidone drug interaction Secondary and patient-centered outcomes were neutral No demonstrated benefit in the composite outcome (exacerbation/resp hospitalization/death) or its components. Quality of life measures were neutral and declined in all groups, emphasizing that slowing FVC alone may not translate into felt improvement without a disease-reversing therapy. The discussants noted this may reflect limited power/duration for these outcomes and mentioned signals from other datasets/pooling that might suggest mortality benefit—but in this specific trial, the key secondary endpoint was not positive. Safety and tolerability Diarrhea was the main adverse event: Higher overall with the 18 mg dose, and highest when combined with nintedanib (up to ~62%). Mostly mild/manageable; discontinuation due to diarrhea was relatively uncommon (but higher in those on nintedanib). Reassuringly, there was no signal for increased depression/suicidality/vasculitis despite psychiatric exclusions and theoretical class risk. How to interpret “modest FVC benefit” clinically The group framed nerandomilast as another tool that adds incremental slowing of progression. They emphasized that comparing absolute FVC differences across trials (ASCEND/INPULSIS vs this trial) is tricky because populations and “natural history” in placebo arms have changed over time (earlier diagnosis, improved supportive care, etc.). They highlighted channeling bias: patients already on antifibrotics may be sicker (longer disease duration, lower PFTs, more oxygen), complicating subgroup comparisons. Practical takeaways for real-world use All three antifibrotics are “fair game”; choice should be shared decision-making based on goals, tolerability, dosing preferences, and logistics. Reasons they favored nerandomilast in practice: No routine lab monitoring (major convenience advantage vs traditional antifibrotics). Generally better GI tolerability than nintedanib. BID dosing (vs pirfenidone TID). Approach to combination therapy: They generally favor add-on rather than immediate combination to reduce confusion about side effects—while acknowledging it may slow reaching “maximal therapy.” Dosing guidance emphasized: Start 18 mg BID for IPF, especially if combined with pirfenidone (since dose reduction may make it ineffective). 9 mg BID may be considered if dose reduction is needed and the patient is not on pirfenidone (e.g., monotherapy or with nintedanib).
Euro Beinat is the Global Head of AI and Data Science at Prosus Group, working on scaling AI-driven tools and agent-based systems across Prosus's global portfolio, deploying internal assistants like Toqan and generative AI platforms such as PlusOne, and building initiatives like AI House Amsterdam and interdisciplinary AI residencies to explore intent-driven AI and strengthen Europe's AI ecosystem.Mert Öztekin is the Chief Technology Officer at Just Eat Takeaway.com, working on advancing the company's platform with AI-driven ordering and personalised user experiences, scaling cloud and generative AI tooling for engineering productivity, and exploring innovative delivery technologies like automation to make ordering and delivery more seamless. 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.// BioEuro Beinat Euro is a technology executive and entrepreneur specializing in data science, machine learning, and AI. He works with global corporations and startups to build data- and ML-driven products and businesses. His current focus is on Generative AI and the use of AI as a tool for invention and innovation.Mert ÖztekinMert is the current Chief Technology Officer at Just Eat Takeaway.com with previous experience as a CTO at Delivery Hero Germany GmbH, Director of Engineering at Delivery Hero, and IT Manager at yemeksepeti.com. They have a background in software engineering, system-business analysis, and project management, with a master's degree in Computer Engineering. Mert has also worked as an IT Project Team Lead and has experience in managing mobile teams and global expansions in the online food ordering industry.// Related LinksWebsite: https://www.prosus.com/Website: https://justeattakeaway.com/~~~~~~~~ ✌️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/]MLOps GPU Guide: https://go.mlops.community/gpuguideConnect with Demetrios on LinkedIn: /dpbrinkmConnect with Euro on LinkedIn: /eurobeinat/Connect with Mert on LinkedIn: /mertoztekin/Timestamps:[00:00] AI Transformation Challenges[00:29] AI Productivity[04:30] Developer Tool Freedom[09:40] AI Alignment Bottleneck[22:17] Exploring Agent Potential[25:59] Governance of AI Agents[33:24] Shadow AI Governance[40:57] AI Budgeting for Growth[46:27] MLOps GPU Guide announcement!
Prodcast: ПоиÑк работы в IT и переезд в СШÐ
Гость выпуска — Николай Фролов, ML-инженер с более чем семилетним опытом в Fintech, E-commerce и Legal Tech, работавший в Sberbank, Tinkoff и X5 Retail Group, а сегодня — инженер финансовой компании в Нью-Йорке.В этом эпизоде мы подробно разобрали путь переезда в США через гуманитарный пароль, долгий и непростой поиск работы на американском рынке и столкновение с новой реальностью найма. Мы обсудили, почему сильный технический бэкграунд не гарантирует офер, как софт-скиллы, английский язык и умение продавать свой опыт становятся ключевыми факторами успеха. Поговорили о статистике откликов и интервью, различиях между техническими и менеджерскими собеседованиями, роли LinkedIn и рефералов, важности STAR-метода и бизнес-метрик для инженеров, а также о типичных ошибках иммигрантов при поиске работы в США и о том, что реально помогло в итоге получить офер.Николай Фролов (Nikolai Frolov) — AI Engineer в DTCC, ex-Tinkoff, Sberbank, X5 Group. Более 7 лет опыта в ML в FinTech, e-Commerce и LegalTech. Живет в Нью-Йорке с 2023 года.LinkedIn: https://www.linkedin.com/in/nikolaifrolov/Записаться на карьерную консультацию (резюме, LinkedIn, карьерная стратегия, поиск работы в США):https://annanaumova.comКоучинг (синдром самозванца, прокрастинация, неуверенность в себе, страхи, лень):https://annanaumova.notion.site/3f6ea5ce89694c93afb1156df3c903abТелеграм: https://t.me/prodcastUSAИнстаграм: https://www.instagram.com/prodcast.usТикТок: https://www.tiktok.com/@us.job⏰ Timecodes ⏰ 00:00 Начало 08:02 Как ты работал в Мексике и почему не остался там? 13:58 Сколько времени ты искал работу? Сколько откликов сделал? 18:51 LinkedIn-профиль. Как ты его прокачивал? 24:37 Какая была конверсия в интервью? 31:00 Как проходили первые собеседования? 38:54 Как ты работал над улучшением английского? 48:46 Какие ошибки ты допускал на нетехнических интервью? 54:55 Какие метрики важны для ML разработчика? 1:00:00 Какие подкасты тебе помогли подготовится? 1:02:22 STAR-метод. Почему это критично для американских собеседований? 1:09:15 Собеседование в JetBlue. Что пошло не так? 1:13:14 Что помогло тебе получить оффер? 1:15:43 Какие 3 главных совета ты дашь ML-инженерам, которые ищут работу в США?
Also on YouTube Two AI experts who actually love the technology explain why chasing AGI might be the worst thing for AI's future—and why the current hype cycle could kill the field we're trying to save. Want to dive deeper? Head to datascienceathome.com for detailed show notes, code examples, and exclusive deep-dives into the papers we discuss. Subscribe to our newsletter for weekly breakdowns of cutting-edge research delivered straight to your inbox—no fluff, just science!
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.
The Pentagon is looking to launch a new Enterprise Command and Control Program Office in a move that would consolidate and refresh its long-standing efforts to provide common operating panes and user-specific AI tools to track and target enemies in real time. This envisioned hub would combine and expand the Chief Digital and Artificial Intelligence Office's Maven Smart System (MSS) and Edge Data Mesh capabilities into the “Enterprise C2 Suite” — a new platform and program of record for Combined Joint All-Domain Command and Control and Al-enabled warfighting options, according to sources familiar with the plan who requested anonymity to discuss it ahead of a forthcoming, official announcement. Internal guidance regarding a new EC2 Program Office suggests that its establishment would ensure that the Defense Department has the “authority, resources, and accountability to deliver capability at the speed of relevance.” DOD's undersecretariats for Intelligence and Security (I&S) and Research and Engineering (R&E) would be directed to deliver a plan for “the expedient transition of MSS authorities, infrastructure, support activities, and responsibilities” from the National Geospatial-Intelligence Agency to the EC2 Program Office. This new program office would essentially fuse multiple Pentagon elements that have come to fruition since the late 2010s, and are associated with digitizing command-and-control processes and deploying AI across the joint force. The Defense Department is soliciting ideas for how artificial intelligence and machine learning capabilities can assist in the zero-trust assessment process as the deadline to reach target-level compliance approaches. According to a request for information posted Tuesday, the DOD's Zero Trust Portfolio Management Office is interested in leveraging “automation, AI and ML to accelerate and scale [zero trust] assessments” across the entire department — specifically for “purple team assessments.” The technologies will help the Pentagon mitigate its limited capacity to validate initial compliance and conduct continuous assessments, the RFI noted. Zero trust is a cybersecurity concept that assumes IT networks and systems are constantly under attack by adversaries, requiring the Pentagon to continuously monitor and authenticate users and their devices as they move through the network. The department's Zero Trust Strategy mandates all DOD components to achieve “target levels” of zero trust by the end of fiscal 2027. Validating compliance requires a combination of internal and third-party assessments. A key part of the Pentagon's independent evaluation process is a method called purple teaming, which analyzes and tests both how “red team” adversaries and “blue force” cyber defenders move and interact in an IT network. However, officials have previously noted that conducting comprehensive purple teaming can be a time-consuming process that can take warfighters away from other important missions. The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast on Apple Podcasts, Soundcloud, Spotify and YouTube.
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/.
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
🧭 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.
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.
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.
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
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
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.
A secret marriage in Detroit and a secretive engagement party in Lansing start this week's show, before ML and Marc […]
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