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Robbie Deckard is a professional middle-distance triathlete and triathlon coach, and the founder of Freaky Fast Aero Accessories. In this episode, Robbie and Mikael dig into the real differences between American and European training methods, using lactate and VLaMax to optimise training and performance, and how age-group triathletes can apply lab-grade thinking to swim, bike and run training without actually going into a lab. HIGHLIGHTS AND KEY TOPICS: The real difference between FTP-centric American coaching and the German/Benelux style built around VLaMax, VO2max and physiological profiling. The evolution of VLaMax based training: from Alois Mader's 1970s model through Jan Olbrecht to current day coaches like Dan Lorang. Robbie's case for still doing dedicated VO2max work, including a case study that took an amateur cyclist's VO2max from 74 to 87 mL/kg/min over 58 weeks of block periodisation. Why building maximal aerobic power early is more valuable long-term than chasing threshold gains, and how that shapes the "durability" that separates junior and U23 cyclists from senior professional cyclists. How Robbie profiles athletes using LT1, LT2 and 5-minute power (or 500 yd swim time, or mile run time), and what typical percentage utilisation looks like for age-groupers versus elites. How he prescribes endurance, threshold and VO2max sessions in practice, including cues (power, pace, RPE), lactate targets, and why exact wattage precision on threshold work matters less than people think. Practical advice for age-groupers: open-water swim skills and ankle-band drills, running road races for pacing and durability, and hill running for strength and economy. Improving aerodynamics on a budget: reduce frontal area first, then refine airflow, and how to measure progress without a wind tunnel using Golden Cheetah or a simple speed sensor. SHOWNOTES, LINKS, RESOURCES AND RELATED EPISODES: Follow Robbie Deckard and Freaky Fast Aero Accessories on his website, blog, Instagram, and Freaky Fast Aero Accessories' Instagram. The shownotes for today's episode can be found here. The full podcast episode archives (including category filters) can be found here. A Scientific Approach to Improve Physiological Capacity of an Elite Cyclist - Rønnestad & Hansen 2018 FTP, VO2max and VLaMax with Sebastian Weber | EP#169 VLaMax, Polarised training, Fatigue and Complexity with Mark Burnley, PhD | EP#331 Training talk with Sebastian Zeller | EP#259 Training structure, periodisation and the science of winning with Jan Olbrecht, PhD | EP#198 Training, testing, metabolism and physiology with Björn Kafka | EP#286 Kolie Moore – Overrated and Underrated Factors for Cycling Performance The Science of Winning: Planning, Periodizing and Optimizing Swim Training - book by Jan Olbrecht The Triathlete's Training Bible - book by Joe Friel Software mentioned: Aerotune, Golden Cheetah, INSCYD, Sentiero SPONSORS: Precision Fuel & Hydration produce our favourite gels, sports drinks, and electrolyte and carbohydrate products here at That Triathlon Show and Scientific Triathlon. Use the free Fuel & Hydration Planner to get a personalised plan for your carbohydrate, sodium and fluid intake in your next event, and get 15% off your first 2026 order by using the code TTS2026 at checkout. Rouvy is hands down the most complete indoor cycling platform for triathletes. Among their thousands of beautiful bike courses from all around the world, all filmed in stunning quality, they have over 75 IRONMAN and IRONMAN 70.3 race courses plus 20+ Challenge Family courses, so you can pre-ride your race from home. Real gradients, real visuals, and real feel! Head to rouvy.com and use the code TTS to get your first month free on top of a 7-day free trial. Effortless Swimming produce the best swim goggles for triathletes and open water swimmers. Their NanoClear anti-fog lenses give you clear, fog-free vision that lasts and doesn't wear off. Don't let foggy or leaky goggles ruin another swim. Go to shop.effortlessswimming.com and use the code TTS15 to get 15% off your goggles, and get a free two-month Effortless Swimming course membership. ZenAI is the next frontier of podcasting. It lets you turn your ideas into a podcast without any knowledge of audio editing, video, or production. You do the talking, and the AI handles the production, editing, clipping, and the rest, and you can direct your AI producer in plain English. You can even record from your phone, so no expensive studio gear is needed to sound great. ZenAI is a new product from Zencastr, a platform I've been a paying customer of for over nine years here at That Triathlon Show. ZenAI is currently in invite-only beta. Join the waitlist today here.LEARN MORE ABOUT SCIENTIFIC TRIATHLON: The Scientific Triathlon website is the home of That Triathlon Show and everything else that we doContact us through our contact form or email me directly (note - email/contact form messages get responded to much more quickly than Instagram DMs)Subscribe to our NewsletterFollow us on InstagramLearn more about our coaching, training plans, and training camps. We have something to offer for everybody from beginners to professionals.HOW CAN I SUPPORT THAT TRIATHLON SHOW (FOR FREE)? I really appreciate you reading this and considering helping the show! If you love the show and want to support it to help ensure it sticks around, there are a few very simple things you can do, at no cost other than a minute of your time. Subscribe to the podcast in your podcast app to automatically get all new episodes as they are released.Tell your friends, internet and social media friends, acquaintances and triathlon frenemies about the podcast. Word of mouth is the best way to grow the podcast by far!Rate and review the podcast (ideally five stars of course!) in your podcast app of choice (Spotify and Apple Podcasts are the biggest and most important ones).Share episodes online and on social media. Share your favourite episodes in your Instagram stories, start a discussion about interesting episodes on forums, reference them in your blog or Substack. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
00:00-25:00: Pinstripe People Crossover. Yanks' 2nd Half Preview. Sal and ML break it down. Thanks to CH Insurance and Marz Motors. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
In this talk, Maryam Ramezani-Bartsch, Data and AI Leader with over 20 years of experience at companies like adidas and Zalando, shares her extensive career journey from building foundational ML systems at adidas to coaching data experts through the modern AI landscape. We explore the critical intersection of technical strategy and the essential human skills needed to thrive in the AI era.LINKS:- https://maryamramezani.com/designyourdatacareerYou will learn about:- The surprising similarities and critical differences between the current Generative AI boom and the previous Big Data era.- Why the traditional boundaries between data roles are disappearing and the specific T shaped profile companies are actually hiring for today.- The hidden danger of perfectionism in corporate tech, and what a healthy margin of failure actually looks like in practice.- How to stop leaving your career trajectory to chance by applying product Design Thinking to your own life.- A practical framework for navigating industry uncertainty and tech career anxiety without burning out.- Battle tested strategies for regaining your footing and standing out in a highly competitive job market after a layoff.- The specific non technical human skills that will become your ultimate career moat against automation.TIMECODES:00:00 Human Skills in the AI Era07:07 Building ML Systems at Adidas12:49 Generative AI vs Big Data Era18:39 T-Shaped Data Engineering Roles24:04 Overcoming Perfectionism in Tech30:34 Design Thinking for Data Careers38:59 Managing Tech Career Anxiety45:07 Aligning Passion with Tech Skills50:18 Job Search Strategies After Layoffs56:03 Essential Soft Skills for JuniorsThis talk is essential for data professionals, software engineers, and tech leaders looking to future proof their careers in an increasingly automated world. Whether you are a junior developer navigating a tough job market, an engineer bouncing back from layoffs, or a senior professional looking to strategically design your next pivot, this session provides the tools to build a highly resilient career.Connect with Maryam- Linkedin - https://www.linkedin.com/in/maryam-ramezani-bartsch/- Website - https://maryamramezani.com/- Substack - https://maryamramezani.substack.com/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/
Owner of one of the effortlessly sexy voices among male seiyuu, Takuya Sato is no stranger to singing in 2D music projects and even had his own solo career (and it was genuinely solid). However, something was missing in his singing that acappella music - and namely, the Aoppella franchise - unlocked for him in 2019.Since then, he has been a show stealer in all 2D music projects he is a part of.Music suggestions:- FYA'M "Think about U"- TRIGGER "Triple Down"- TRIGGER "SOL"Thanks to M L for inspiring this series of episodes!
Lieve Freundinnen (m/v/x), niet schrikken maar we hebben een MAN te gast in de nieuwste aflevering van DAMN, HONEY de podcast *gasp* Het gebeurt niet vaak, maar soms komt er eentje op je pad die Echt Wat Te Zeggen heeft. En tja, dan moet je! Dan ga je! Het is historicus en schrijver Alex Bakker, gespecialiseerd in transgendergeschiedenis. Zijn nieuwste boek heet Transgender pioniers en speelt zich af in het roerige Berlijn, in de jaren tussen de 1e en 2e Wereldoorlog aka het Interbellum. De levensreddende medische zorg voor trans mensen begon toen en daar. Alex deed voor zijn boek uitvoerig onderzoek naar een revolutionair seksuologisch instituut waar oprichter en arts Magnus Hirschfeld probeerde te begrijpen wat trans mensen nodig hebben. Twee van zijn cliënten waren Dora Richter (een trans vrouw van in de 30, opgegroeid op het platteland en diepgelovig katholiek) en Gerd Katter (een jonge trans man van 18 uit de stad, die in de leer was voor timmerman). Dankzij bewaard gebleven bronnen wist Alex hun levens (grotendeels) in kaart te brengen. We hameren altijd erg op historisch besef en als het gaat om transgendergeschiedenis weten we lang niet genoeg. Wat kunnen we leren van de verhalen van Dora en Gerd? Waarom is het zo belangrijk dit soort verhalen te kennen? En hoe staat de transgenderzorg er nu voor? Verder: Wat zien we daar bewegen daar op de bovenlip van ML? Is het een... snor? Ga voor de shownotes en het transcript naar damnhoney.nl/aflevering-291DAMN, HONEY wordt gemaakt door Marie Lotte Hagen en Nydia van VoorthuizenIn deze aflevering hoor je een advertentie voor ONSZELF! Steun ons maandelijks vanaf 1 euro en krijg onze eeuwige dank én toegang tot onze tweewekelijkse bonuspodcast ‘Je doet het er maar mee’ die je gewoon kunt luisteren via je favoriete podcastapp. Bonusbal word je via petjeaf.com/damnhoney.editwerk: Daniël van de Poppejingles: Lucas de Gier website: Liesbeth Smit DAMN, HONEY is onderdeel van Dag & Nacht Media. Heb je interesse om te adverteren in deze podcast? Neem dan contact op met Dag en Nacht Media via adverteren@dagennacht.nlSee omnystudio.com/listener for privacy information.
Imagine a dark warehouse. Racks and racks of devices with wires, tubes, and electronics sticking out. The next AI data center? No. This is Lila Sciences‘ dream for the future of science. A dark warehouse full of AI-guided robotics and lab equipment, cranking out new experiments 24/7, building toward a scientific superintelligence.Their automated lab is almost hypnotizing to watch. They have floating plates zipping around on Wall-E-esque tracks, used vision-language models to control Windows 95 boxes, and created the world's largest collection of voided warranties. In the process they've built a massive library of scientific reasoning tokens. Over 10 trillion of them, all experimentally validated.No warranties were voided in the making of this videoTo say Lila is ambitious is an understatement. Their goal is a scientific superintelligence wired directly into the wet lab. They are all in on the bitter lesson, and the thesis follows from it: a lab is an infinite token generator. Produce data at scale, and the synergies give you a general reasoner that can tackle any scientific problem. They are committing hard. Biology, chemistry, drug discovery, and materials science, all at the same time. Time will tell if it works, but it is an exciting hypothesis.In our latest episode we sat down with Lila's very own Andy Beam (CTO) and Rafa Gómez-Bombarelli (CSO, physical sciences) and went on a journey through the possibilities of AI-run science, almost as wide-ranging as Lila's goals.Did we mention they do both materials science and biology? In the same AI science factory? Same time, same lab, same AI. Finally a guest who can settle a long-running debate we've had amongst ourselves: is biology or materials science harder?Watch to find out!We discuss:* The internet is spent, science is next. Why Lila thinks the scientific method is the last untapped internet-scale dataset, and why they treat RL as a data generation mechanism with nature as the verifier.* The lab as a data center. Instruments as nodes on a graph, a magnetically levitating “PCI bus” transport layer between them, orchestration as a slurm queue. Andy is not short on analogies.* Why Lila insists it is not an automation company. They optimize for flexibility and generalizability over raw throughput, which means humans stay below the API line wherever automating does not pay.* Your experiment has a runtime. We put Escalante Bio's question to Andy: if science is the token generator, what is the runtime of your data collection? His answer, in short, is that you cannot make the ribosome go faster. Why Lila bets on fast round-over-round iteration rather than big noisy multiplexed screens, and how Rafa's team rebuilt a gas sorption measurement to run roughly 2,500x faster.* What is actually in 10 trillion scientific tokens. Not sequences. Experimentally verified reasoning traces, a kind of data that Andy argues exists on the internet in quantities that round to zero.* Breadth as a path to depth. Small molecule chemistry priors transferring to metal organic frameworks for carbon capture, and the claim that the general model beats domain-specific models sample for sample.* If you have the data, what do you need the model for? Sri Kosuri's koan about the ML-for-drug-discovery business model, and Andy's answer: the coding model got better because it also read Shakespeare and carnitas recipes.* The serendipity they want to automate. Emily Whitehead survived the first pediatric CAR-T cure only because the doctor treating her happened to know, from pediatric arthritis, which antibody would blunt her IL-6 response. Roll that dice again and you probably lose her. Breadth is how you stop depending on luck.* Move 37 for catalysts. Model suggestions for platinum-group-free electrocatalysts that went from boring, to what a 40-paper expert called stupid, to the best performers they have made.* Six months to in vivo CAR-T data in non-human primates, and the zero-FTE virtual startup commercial model that fell out of it. For context on why that number is startling, AbbVie paid $2.1B for Capstan on the strength of preclinical in vivo CAR-T data.* You cannot have scientific superintelligence if you are just a good test taker. Ken Stanley, who wrote Why Greatness Cannot Be Planned, runs open-endedness at Lila. RL at scale gives you a ruthlessly Vulcan problem solver. Machine creativity is a different thing, and it is the part nobody has solved.* The chain of thought is an unreliable narrator. The model reasons in latent space and only emits tokens. Sometimes it skips the experiment entirely and is still right. So how much do you trust the reasoning versus the verifier?* Reward hacking when the rollout is physical. Chains of thought that collapse into repetition, and a model that got annoyed and swore at the scientist who kept asking it to redo a plate map. What happens when a pathological loop has a wet lab inside it?* The bittersweet lesson. Rafa's inversion of the bitter lesson: in AI, scaling is a roadmap. In materials, scaling is a filter, because only the things that scale end up mattering.* Not your typical Flagship company. Why a famously single-asset biotech incubator spun out a platform bet, and Andy's line that if Lila called itself a biopharma it would have a top-three GPU cluster.* Bottlenecks they would remove by fiat. Sim-to-real for physics-based simulation, and the fact that RL training runs at roughly 5% mean FLOP utilization.Watch on YouTube: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe
In this episode of the Prolonged Field Care Podcast, Dennis sits down with Alex to break down a hot-off-the-press retrospective study from the Journal of the American College of Surgeons titled “Challenging Legacy Burn Resuscitation Paradigms with Fluid Restriction and Early Plasma.”They dismantle the decades-old “swell to get well” mentality and the classic Parkland formula that has led to dangerous fluid overload, massive edema, and compartment syndromes in burn patients. Instead, they explore a more physiologic approach using lower crystalloid volumes (starting at 2 mL/kg adjusted body weight) plus early fresh frozen plasma (FFP) for patients with larger burns.Key Takeaways:The Parkland formula (4 mL/kg/%TBSA) frequently causes massive over-resuscitation; the new restrictive approach delivered significantly less fluid while maintaining (and often improving) urine output.Capillary leak from glycocalyx damage is the real enemy in burn shock — plasma helps restore oncotic pressure and may reduce third-spacing.Titrate everything to urine output (target 0.3–0.5 mL/kg/hr). Formulas are only a starting point.Use adjusted body weight (ideal body weight + 0.4 × [actual – ideal]) instead of actual body weight for fluid calculations.Early plasma (1–2 units for >30% TBSA) showed a strong signal toward lower mortality, less ventilator days, and reduced renal failure in this study.The Joint Trauma System (JTS) Burn Care CPG still emphasizes early consultation with a burn center — phone a friend early.This approach has direct application for prolonged field care and austere environments, though the study is retrospective and should be implemented thoughtfully.Whether you're a special operations medic, flight paramedic, or managing burns in a resource-limited setting, this conversation will fundamentally change how you think about burn shock resuscitation.Resources:prolongedfieldcare.org (free downloads, worksheets & more)Follow @prolonged_field_care on InstagramJTS Burn Care CPG (CPG #12) – includes the excellent burn resuscitation worksheetChapters: 00:00 – Introduction: Why Burn Care Still Terrifies Experienced Medics03:09 – The Horrifying Reality of Over-Resuscitation (Edema Photos & Leaky Pipe Analogy)05:30 – Understanding the Glycocalyx and Why Crystalloid Leaks So Fast09:05 – The One-Third Rule Myth & Why Fluids Disappear in Sick Burn Patients11:14 – Parkland Formula Breakdown: History, Math & Its Biggest Flaw13:00 – The New Study: PICO, Methods & the Shift to 2 mL/kg + Early Plasma16:54 – Elevator Pitch: What This Paper Actually Found20:06 – Primary Results: Dramatically Less Fluid with the Restrictive Protocol21:24 – Urine Output Reality Check: Why the “Less Fluid” Group Still Hit Targets24:23 – Practical Protocol Breakdown: Who Gets 2 mL vs 3 mL + When to Give Plasma25:30 – Adjusted Body Weight Calculation Explained (and Why It Matters)27:26 – Titration to Urine Output is King – Stop Chasing Vitals29:55 – Dennis Rates the Evidence on the PFC Gestalt Scale30:38 – Why Plasma Makes Physiologic Sense (and Whole Blood May Be Next)35:30 – Study Limitations & Provider Bias Discussion37:30 – Can We Implement This in Prolonged Field Care Right Now?38:38 – JTS Burn Care CPG: The Burn Center Contact You Need to Save42:53 – Final Advice: Titrate Aggressively, Phone a Friend Early, Close the GapFor more content, go to www.prolongedfieldcare.orgConsider supporting us: patreon.com/ProlongedFieldCareCollective or www.lobocoffeeco.com/product-page/prolonged-field-care
00:00-30:00: Sal and ML break down the Yanks' sweep of the Nats, chat Jazz gonna Jazz, ASG and Derby with break and more. Plus, Ben Rice rocks. Pinstripe People crossover. Thanks to CH Insurance and Marz Motors. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
Small teams with smart AI can solve problems no one imagined before. In this episode, I spoke with Zhen Lu, CEO of Runpod, about the rapidly evolving AI landscape and the future of software development. Zhen Lu shared how Runpod is empowering engineers to train AI models tailored to specific business needs, improve efficiency, and reduce waste. We also discussed his founder journey, the power of co-founder alignment, global innovation outside traditional tech hubs, and the importance of human accountability in AI-driven organizations. Here are the highlights: ● Runpod builds AI developer infrastructure. The platform supports tailored AI workloads, enabling businesses to efficiently train models specific to their needs. ● AI is changing how software is built. Engineers are challenged to rethink software design, not just accelerate existing processes, creating new opportunities for innovation. ● Efficiency and sustainability matter. Fine-tuning smaller, focused models reduces resource waste, energy consumption, and operational costs compared to massive off-the-shelf models. ● Co-founder alignment drives success. Implicit trust, complementary skills, and low ego between Zhen and his co-founder have accelerated decision-making and execution. ● Global networks and bootstrapping foster innovation. Scarcity encourages creativity, and building strong relationships outside Silicon Valley has enabled Runpod to grow and support AI developers worldwide. About the guest: Zhen Lu and co-founder Pardeep Singh started by running crypto mining rigs out of their New Jersey basements. When Ethereum's "The Merge" threatened to make that obsolete, they pivoted, converting the rigs into AI servers. As corporate developers at Comcast building ML projects, they saw firsthand that the GPU developer experience was, in Zhen's words, "just hot garbage." That insight became Runpod. Launched in early 2022, Runpod offers fast, developer-friendly GPU infrastructure: clean APIs, CLI tools, serverless options, and easy configuration. Rather than the traditional VC route, they debuted on Reddit with "Hey Reddit, give us your worst" — and got "please take my money" in return. They went on to earn validation from Hugging Face co-founder Julien Chaumond and a seed round led by Dell Technologies Capital, hitting $120M ARR, 10 billion serverless requests, and serves ~900,000 developers across 31 global regions. Connect with Zhen Lu: LinkedIn: https://www.linkedin.com/in/zeen/ Website: https://www.runpod.io/ Connect with Allison: Feedspot has named Disruptive CEO Nation as one of the Top 25 CEO Podcasts on the web. LinkedIn: https://www.linkedin.com/in/allisonsummerschicago/ Website: https://www.disruptiveceonation.com/ #CEO #leadership #startup #founder #business #businesspodcast Learn more about your ad choices. Visit megaphone.fm/adchoices
Idaho National Laboratory (INL) was born in 1949 as the National Reactor Testing Station after the Atomic Energy Commission decided they needed a place to test controlled nuclear fission power systems. Before being taken over by the AEC, the land was controlled and used by the U.S. Navy to test refurbished weapons, up to and including the 16″ guns carried by battleships. After a five decade long hiatus in building new reactors while also implementing a significant level of diversification into other research areas, the INL is regaining strength in its original purpose as a place to test and demonstrate complete nuclear reactor power plants. (The term “plant” is not really applicable to micro-reactors, but it will serve as a general term for now.) This time through, the design, approval, construction and testing programs are not being led by a federal monopoly called the Atomic Energy Commission. It is not focused on developing reactors that can be used to test the ideas of scientists who don’t really care if anyone wants to buy the system they are developing. Instead, the reactor development efforts underway and in planning for the future are more cooperative, distributed and commercially driven. Josh Gillespie is the Chief Operating Officer of the National Reactor Innovation Center (NRIC). He visited the Atomic Show to talk about NRIC and its role in helping the Nuclear Renaissance gain traction and success. NRIC was created in 2019 as a result of directives and authorizations contained in the Nuclear Energy Innovation Capabilities Act (NEICA). Its purpose is to build bridges that enable private sector organizations to work with national laboratory scientists and physical resources to cross the “valley of death” between good ideas and commercially viable products. It is tasked with preparing facilities to serve as test beds for new reactor development and testing and to develop sites where new facilities can be built. Though it can be a challenge for any government organization – like a national lab – NRIC has been tasked to be able to operate at the speed of a start-up. It is taking strides in that direction, though it is still limited in speed by the federal government budget cycle. Josh described how NRIC is working closely with the Department of Energy Idaho Operations Office which is directly responsible for the DOE 1271 authorization process for both reactors and supporting facilities – like those that are in the fuel supply chain or involved in radioactive materials testing and evaluation. NRIC helps private sector companies develop their plans, find suitable facilities, prepare required submittals and engage in readiness reviews. NRIC’s reach extends beyond the boundaries of the Idaho National Laboratory; has been contracted to support several of the Reactor Pilot Program developers that have built or are building their reactors in Texas or Utah. Josh described DOME as the crowning jewel of NRICs facilities. It once served as the containment dome for the highly successful but prematurely retired Experimental Breeder Reactor II, the remains of which are encased in concrete and grout in the basement and foundations of the existing facility. DOME is designed to be able to host a test reactor that might be exercised to its limits while still preventing any release of radioactive materials. Radiant Nuclear was selected as the first tenant of the DOME. It is scheduled to complete its operational testing and to remove its equipment from the facility in a year to make room for the next tenant. Top shield covering Antares Mark-0 in RACE facility NRIC played an important role in the success of the Reactor Pilot Program. It helped to find and repurpose facilities for Antares (RACE – Reactor and Criticality Experiment), Deployable Energy (NRAD – Neutron Radiography Reactor) Aside: Bit of INL Trivia – The building that is now RACE housed the Army’s ML-1 reactor development program from the late 1950s until the program was ended in 1964. End Aside. Josh described NRIC’s role in the Nuclear Energy Launch Pad as similar to that of a subdivision developer. A 2,000 acre plot has been allocated. NRIC is responsible for developing basic infrastructure, including roads and common utility systems. It will arrange for site characterization studies in preparation for environmental assessments. Private sector developers will lease their sites and contract for any additional services desired from an available menu. These include additional security and fire services. There will be similar services offered to developers who are building on sites that are not INL; that part of the program is called Launch Pad – USA. Josh is an Idaho native who is happy to be involved in creating a winning partnership between private sector companies and government/national lab organizations. He believes that the new developments will help Idaho National Lab continue to thrive and lead in nuclear energy development. He is excited about helping to deploy abundant sources of clean electricity and heat that will contribute to a bright future for Idaho, the U.S. and the rest of the world. You’ll enjoy this show. Listen carefully and comment via X if desired.
This article is for educational purposes only and is not a substitute for individualized medical advice. Always talk to your own healthcare provider before changing your diet, supplements, or medications.Unlocking the Secrets of Ferritin: What Your Iron Levels Are Telling YouYour “normal” bloodwork might be hiding the real reason you're exhausted, foggy, and losing hairTL;DR: * Ferritin is your iron savings account — and most labs only flag it as “abnormal” once it's nearly empty. * A level of 14 or 22 ng/mL might get a “you're fine” from your doctor, but optimal energy, mood, cognition, and hair growth usually need ferritin closer to 70–100 ng/mL. * Low ferritin can come from menstrual blood loss, poor absorption (celiac disease, low stomach acid, H. pylori), or inflammation-driven hepcidin blocking iron uptake.* If you're fatigued, foggy, cold, or shedding hair, ask for a full iron panel — not just a ferritin number — and talk through the results with your doctor.There's an old Japanese proverb: “When the body speaks, the wise person listens. When the body whispers, the fool waits for it to scream.” In health diagnostics, one of the quietest whispers is your ferritin level. It's often overlooked, yet it can be the missing link behind exhaustion, hair loss, brain fog, or the frustrating experience of bloodwork that comes back “normal” while you still feel terrible.What Is Ferritin?Ferritin is your body's iron storage protein. Think of your iron levels like a financial setup: hemoglobin is your checking account, drawn on daily. Ferritin is your savings account, tapped only when things get tight. Under stress, your body will drain the savings account long before it lets the checking account — hemoglobin — run low. That's why you can have “normal” hemoglobin and still be iron-depleted. A low ferritin level means your reserves are running out, and that shows up as fatigue, brain fog, mood changes, and thinning hair.Normal vs. OptimalMost labs flag ferritin as “normal” above roughly 10–20 ng/mL. That threshold mostly means you're not in immediate danger — not that you're thriving. Levels associated with feeling genuinely well tend to run from 70 to 100 ng/mL. So if you've been told your ferritin of 14 or 22 is fine, but you still feel wiped out, you're not imagining it — you're just being measured against a bar set for avoiding crisis, not for feeling good.Why Your Ferritin Might Be Low* Menstrual blood loss. For many women, the cumulative loss over months and years outpaces dietary iron intake, slowly draining reserves.* Absorption issues. Even a solid iron intake doesn't help if it isn't absorbed. Silent celiac disease, low stomach acid, or an H. pylori infection can quietly block uptake for years.* Inhibitors and hepcidin. Coffee, tea, and dairy consumed close to meals can inhibit iron absorption. Separately, inflammation can push your liver to produce hepcidin, a hormone that shuts down iron uptake even when you're eating enough.Symptoms to WatchPersistent fatigue, thinning hair, feeling cold more easily than others, and brain fog are the classic signs. If two or more of these sound familiar, it's worth getting your ferritin checked specifically — not just assumed to be fine because your CBC looked normal.The Bigger PictureIron does far more than carry oxygen. It's involved in thyroid hormone conversion, dopamine production, mitochondrial energy synthesis, and hair follicle health. That means low ferritin can produce symptoms that look a lot like depression or hypothyroidism — even when your thyroid panel and mood screening come back clean.Taking Action* Review your bloodwork. Look specifically at ferritin. Anything under 70 ng/mL is worth a conversation with your doctor.* Ask for a full iron panel. Ferritin alone isn't the whole story — request serum iron, total iron binding capacity (TIBC), transferrin saturation, and CRP (to rule out inflammation skewing the picture).* Adjust absorption habits. Space coffee and tea away from meals, lean into iron-rich foods, and avoid taking calcium and iron supplements together.* Choose the right supplement, if needed. Ferrous sulfate is harsh on the gut for many people. Iron bisglycinate is gentler and pairs well with vitamin C for better absorption — but check with your provider before starting, especially if high ferritin is a concern.* Loop in a professional. This is especially important before making changes if you suspect elevated ferritin, since iron overload carries its own risks.Listening to Your Body's WhisperYour body is constantly sending signals. Ignored long enough, whispers become screams. Taking ferritin seriously — not just as a checkbox on a lab report, but as a meaningful marker — is one concrete way to catch a problem while it's still easy to fix.Final ThoughtsMonitoring and optimizing ferritin can meaningfully change how you feel day to day. It starts with a simple ask: get the right test, read the number in context, and act on what it's telling you. If you want a more personalized look at your own levels and symptoms, consider scheduling a comprehensive health session.Stay informed, stay healthy, and listen closely to what your body is telling you. Until next time, take care.References* Camaschella, C. (2015). Iron-deficiency anemia. New England Journal of Medicine.* WHO guidance on serum ferritin concentrations for the assessment of iron status.* Clinical literature on ferritin thresholds and symptomatic iron deficiency without anemia.* Hepcidin and inflammation's role in iron regulation — recent reviews in Blood and Haematologica. This is a public episode. 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July 14, 2026 ~ Chris Renwick and Lloyd Jackson check in with Detroit Free Press 'On Guard' Reporter, M.L. Elrick. ML watched every debate by candidate in Michigan, what did he take away from each of them? Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
Marion Azoulai est Staff Data Scientist chez Astronomer, l'éditeur d'Astro, la plateforme managée d'Airflow qui est aussi l'un des principaux contributeurs de la solution open source.Créé en 2018, Astronomer a levé plus de 370 millions de dollars et accompagne des centaines d'organisations dans le monde, parmi lesquelles la Société Générale, Booking.com, Autodesk ou encore WeWork.Marion nous raconte comment ils ont lancé l'équipe Data & IA et comment ils ont construit leur stack autour d'Airflow.On aborde :
Y!mobileの「moto g66y 5G」がMNPで9800円に【スマホお得情報】。 ソフトバンクは、Y!mobileオンラインストアで販売している「moto g66y 5G」を安価に販売中。通常2万6640円のところ、MNPで「シンプル3 M／L」を契約すると1万6840円割引され9800円となる。
In this episode, Mark Russinovich, CTO of Microsoft Azure revealed Brain, the AI-powered AIOps system that continuously monitors Azure's health, detects incidents, identifies root causes, and increasingly automates responses such as pausing problematic deployments and notifying affected customers. Built on Azure Resource Graph, Brain creates a real-time digital twin of Azure, mapping dependencies across hundreds of services, data centers, and regions. Although Brain predates the generative AI boom, years of data engineering, standardized service-level indicators (SLIs), and machine learning laid the foundation for today's capabilities. Brain combines standardized SLIs, service-specific monitoring, and third-party signals to detect anomalies, while ML models dynamically establish service baselines and correlate outages with software rollouts. Microsoft says automated notifications have reduced customer support tickets by four to six times, with 80–90% of Brain-covered services receiving notifications within 15 minutes, often in under five. The company is also layering LLM-powered agents, called Triangle, on top of Brain to streamline incident routing and eventually enable AI agents to autonomously troubleshoot and remediate outages. Learn more from The New Stack around the latest in Microsoft Azure: Meet Brain, the AI that decides when Azure is officially down Microsoft's pitch to enterprises: Ditch Azure Repos for GitHub, despite its rocky reliability record Join our community of newsletter subscribers to stay on top of the news and at the top of your game.
00:00-15:00: ML recaps another blown chance for Team USA men's soccer. Thanks to CH Insurance and Marz Motors. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
VOV1 - Nửa đầu năm 2026, HĐND TP.HCM đã ban hành 2 nghị quyết quan trọng liên quan đến các dự án treo hàng chục năm ngay trên các khu “đất vàng” trung tâm Thành phố. Cuộc đời những người dân tại đây sẽ sang trang, mang theo nhiều phấn khởi lẫn kỳ vọng.Người dân đồng tình, phấn khởi Khu Mả Lạng (phường Cầu Ông Lãnh, thuộc Quận 1 cũ) tồn tại từ trước năm 1968, nhiều gia đình sinh sống qua 2-3 thế hệ. Điều kiện sống tại khu vực này rất nhếch nhác nên TP.HCM có chủ trương giải tỏa để chỉnh trang đô thị vào năm 2000.Sau hàng chục năm không triển khai, đến năm 2023, UBND TP.HCM chấm dứt chủ trương đầu tư khu Mả Lạng. Tháng 6/2026, HĐND TP.HCM thông qua chủ trương chi hơn 16.300 tỷ đồng chỉnh trang khu Mả Lạng và khu chợ Gà – chợ Gạo (phường Bến Thành, thuộc Quận 1 cũ).Người dân Mả Lạng sắp thoát cảnh ở chật chội, nhếch nhác (Ảnh: Duy Phương)
What is AI, really? And how much of what you think you know is actually true? In this episode, we break down how AI actually works (ML vs. AI), debunk common misconceptions, and dig into why AI hallucinates and why it doesn't actually "think." Joined by Alex Oldemeier, AI enthusiast and CAIO of Formove, a Physical AI startup.Follow us on our LinkedIn page here: LinkedIn and on Instagram here: InstagramLearn more about what we do at Processand here: Processand
Welcome to the Personal Development Trailblazers Podcast! In today's episode, we're talking about how to restore your joy after loss by embracing healing, finding hope, and discovering that joy is possible again. Monique Lynelle Gray was born in Hartford, CT, and grew up in Philadelphia, PA. Monique Lynelle is a Transformational Joy Restoration Coach and Speaker/Entrepreneur, with experience in various backgrounds and has the spirit that just doesn't quit at the first sign of adversity. Having certifications as a Joy Restoration Coach/Grief Support Specialist, Stress Relief Coach, and Christian Counseling. A Doctoral student at Capella University, Master's Degree in Entertainment Business from Full Sail University graduating with a 3.67 GPA, and a Bachelor's in Strategic Communications from Temple University, she knows about finishing a few things. Continuing education after leaving the US Army, and completing her Cosmetology Instructor's simultaneously while completing her Bachelor's degree, then completing her 1st book in 2012, "My Journey at 30," and "A Teen's Guide to Succeed in High School," and in 2015 completing and publishing, "Blessed in the Midst of the Storm." Monique Lynelle went on to complete her 1st studio album entitled "Love of the Music," in 2013. And in 2016 and 2017 released the EP's "Get Lifted Up," and "I'm Still Blessed." And in 2021, "The Love of the Music-Compilation Album"ML produced shows for the Public Access Television Station DCTV in 2016 through 2018, "The Monique Lynelle Show," and "Monique Lynelle's Journey - The Documentary," She also wrote, produced and directed, and starred in her first film, "The PK's."As a Joy Restoration Coach, Coach ML is here to assist others in navigating through life issues to become the best versions of themselves, reaching towards their highest potential. She is also here to help people process their grief and manage their stress to achieve their goals with joy on their journey.Connect with Monique Here: https://instagram.com/MoniqueLyn_ellehttps://YouTube.com/MoniqueLyn_ellehttps://MoniqueLyn-elle.com===================================If you enjoyed this episode, remember to hit the like button and subscribe. Then share this episode with your friends.Thanks for watching the Personal Development Trailblazers Podcast. This podcast is part of the Digital Trailblazer family of podcasts. To learn more about Digital Trailblazer and what we do to help entrepreneurs, go to DigitalTrailblazer.com.Are you a coach, consultant, expert, or online course creator? Then we'd love to invite you to our FREE Facebook Group where you can learn the best strategies to land more high-ticket clients and customers. QUICK LINKS: APPLY TO BE FEATURED: https://app.digitaltrailblazer.com/podcast-guest-applicationDIGITAL TRAILBLAZER: https://digitaltrailblazer.com/
Makoto Furukawa has impressed since the start with his powerful vibrato and unique, fancy brand of jazz-pop. However, since his solo debut, he has been perfecting his control and bringing new tools to his arsenal that have elevated his singing from "good" to "stellar", making Makoto Furukawa one of the best singers among male seiyuu.Music suggestions:- BAD SKUNK "Violet Love"- Makoto Furukawa "Uso to Gekko"- RUBIA Leopard "Prisoner"Thanks to M L for inspiring this series of episodes!
Het was me het weekje wel. Een vrouw kan niet eens meer normaal een lekker banaantje eten / zichzelf uit het water hijsen of ze wordt alweer belaagd. Pierre van Hooijdonk zei dingen waar we hartelijk om gelachen hebben. Maar meiden, het is toch godgeklaagd dat zo'n man zendtijd krijgt. Verder: de overheid maakte excuses aan de afstandsmoeders, helaas bestonden die geheel uit gebakken lucht. ML pluist even helemaal uit waarom dat zo is. En Nydia heeft goed nieuws over een medische doorbraak waarbij niemand doodging. Ga voor de shownotes en het transcript naar damnhoney.nl/aflevering-290DAMN, HONEY wordt gemaakt door Marie Lotte Hagen en Nydia van VoorthuizenIn deze aflevering hoor je advertenties voor NordVPN en onszelf:- Profiteer NU van de exclusieve NordVPN-deal op nordvpn.com/damnhoney. Probeer zonder risico met de 30 dagen geld-terug-garantie! - Steun ons via PetjeAf.com/damnhoney. Vanaf 1 euro per maand help je ons blijven bestaan én krijg je toegang tot onze bonuspodcast 'Je doet het er maar mee' editwerk: Daniël van de Poppe jingles: Lucas de Gier website: Liesbeth Smit DAMN, HONEY is onderdeel van Dag & Nacht Media. Heb je interesse om te adverteren in deze podcast? Neem dan contact op met Dag en Nacht Media via adverteren@dagennacht.nlSee omnystudio.com/listener for privacy information.
In this episode, Professor Paola Cinnella - Professor of Fluid Mechanics at Sorbonne University and Director of the Sorbonne Cluster for Artificial Intelligence (SCAI) - joins Neil to discuss her path from classical fluid mechanics and high-order numerical methods into uncertainty quantification, Bayesian methods, data-driven turbulence modeling and AI for Science.Paola has built a career at the intersection of CFD, compressible and turbulent flows, dense gas dynamics, uncertainty quantification, robust optimization and machine learning. We discuss academic careers, dense gases, RANS uncertainty, AirfRANS, surrogate modeling, scientific publishing, education in the age of AI, and the idea of the "centaur scientist".Key topicsFluid mechanics, CFD and high-order schemesDense gases, real-gas effects and expansion shockwavesUncertainty quantification and Bayesian methodsRANS turbulence-model uncertaintyAirfRANS and CFD datasets for machine learningTurbulence modeling vs surrogate modelingScientific publishing and ML-for-CFD standardsSCAI and AI for ScienceEducation, ChatGPT and centaur scientistsPapersQuantification of model uncertainty in RANS simulations: A review - Heng Xiao, Paola Cinnellahttps://doi.org/10.1016/j.paerosci.2018.10.001Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression - Martin Schmelzer, Richard P. Dwight, Paola Cinnellahttps://doi.org/10.1007/s10494-019-00089-xBayesian estimates of parameter variability in the k-epsilon turbulence model - W.N. Edeling, P. Cinnella, R.P. Dwight, H. Bijlhttps://doi.org/10.1016/j.jcp.2013.10.027AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutionshttps://arxiv.org/abs/2212.07564Data-driven turbulence modeling - Paola Cinnellahttps://arxiv.org/abs/2404.09074Direct numerical simulations of supersonic turbulent channel flows of dense gases - Luca Sciacovelli, Paola Cinnella, Xavier Gloerfelthttps://doi.org/10.1017/jfm.2017.237LinksPaola Cinnella named Director of SCAIhttps://scai.sorbonne-universite.fr/news/paola-cinnella-new-directorSCAIhttps://scai.sorbonne-universite.fr/Paola Cinnella - HAL publicationshttps://cv.hal.science/paola-cinnellaPaola Cinnella - Google Scholarhttps://scholar.google.com/citations?hl=fr&user=wBRA0JAAAAAJERCOFTAC SIG 54 - Machine Learning for Fluid Dynamicshttps://www.ercoftac.org/special_interest_groups/54-machine-learning-for-fluid-dynamics/master-of-science-internships/Chapters00:00 Podcast intro00:39 Introducing Prof. Paola Cinnella03:28 Conversation begins03:56 How Paola found fluid mechanics07:09 Moving from Italy to France08:37 High-order schemes and compressible flows09:30 Building an academic career12:06 Dense gases and uncertainty quantification15:16 Expansion shockwaves and real-gas effects19:17 Returning to Paris and academic mobility24:52 Academia, passion and persistence27:51 Bayesian methods and turbulence uncertainty30:47 Learning statistics across disciplines33:07 LearnFluidS, AirfRANS and CFD datasets36:33 Skepticism and physics in ML turbulence modeling40:41 Could ML lead to a universal turbulence model?42:59 Turbulence models, surrogate models and RANS45:03 Why LES alone cannot solve optimization47:15 Multi-fidelity modeling49:08 What Computers & Fluids looks for in ML-for-CFD papers54:05 CFD metrics vs machine-learning metrics57:13 Overselling, publication pressure and quality62:22 SCAI and AI for Science66:07 Cross-disciplinary AI for Science69:26 Education in the AI era72:44 Critical thinking and AI outputs78:15 AI as a companion, not a replacement81:42 AlphaFold and the future of discovery83:43 Training centaur scientists85:11 Closing thoughts
We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li
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One of Detroit's most notorious crimes inspires a murder mystery set Up North. Author Bryan Gruley joins ML and Marc […]
00:00-30:00: Sal and ML break down a critical series against the Rays, another bad one against the Twins, just how bad this lineup is and more. The Twins, really? Thanks to CH Insurance and Batavia Downs Gaming. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
You were told your AMH is low, and maybe that IVF was your only option. Maybe you went through it, and it did not work. Maybe you already have one child and have been told this time is different. And some part of you has been wondering whether that number is really the whole story. Here is what most women are never told. AMH reflects egg quantity, not egg quality, and not your ability to conceive. It predicts how your ovaries respond to medication. It does not explain why a cycle failed, why embryos stopped developing, or why outcomes have not changed despite every protocol adjustment. In this episode, I walk through a real case. Her AMH was 0.27 ng/mL. She had a failed IVF cycle behind her and was navigating secondary infertility. One pattern that stood out was inflammation. Her hs-CRP was 1.3 mg/L, read as normal by conventional ranges but not optimal, and something was driving it. She conceived naturally, not because the number changed, but because the systems shaping egg development were finally looked at. This is not about avoiding IVF or chasing a better lab value. It is about reading what your body has already shown you, so your next decision is an informed one. CHAPTERS 00:00 What your AMH actually tells you, and what it does not 01:00 Why a failed IVF cycle can be more informative than the number 02:00 This was not unexplained, age, or bad luck 03:00 The patterns we see, inflammation, nutrient absorption, brain, and hormone signaling 04:00 Addressing systems in sequence, not a random checklist 05:00 What it looked like when the body started responding 06:00 Natural conception at AMH 0.27, and why the number was not the point 07:00 Pausing before the next cycle, readiness over urgency 07:30 The Functional Fertility Second Opinion WHAT YOUR CLINIC MISSED The companion guide walks through the patterns a standard fertility workup tends to miss, with the markers behind each one, so you can take it to your next appointment and ask the questions. Email hello@fabfertile.ca, subject line MISSED, and we will send you the guide. FUNCTIONAL FERTILITY SECOND OPINION A free 45-minute call where I review your bloodwork, your history, and your partner's results with you. You leave knowing what your biology has been telling you and what your next decision could be. Email hello@fabfertile.ca, subject line FERTILE, or book here: https://fabfertile.com/pages/book ABOUT THE HOST I'm Sarah Clark, founder of Fab Fertile and host of Get Pregnant Naturally, a podcast with over one million downloads. My functional fertility team works with couples navigating low AMH and failed IVF, reviewing functional lab results, gut microbiome, food sensitivity, vaginal microbiome, nutrigenomics, HTMA, DUTCH, toxin testing, and bloodwork alongside nervous system work, to help identify patterns that may not have been considered. We work alongside your medical team, not instead of them. Sarah Clark, founder of Fab Fertile, host of Get Pregnant Naturally (1M+ downloads), and author of Fabulously Fertile. If this episode helped, leave a review on Apple Podcasts. It is how other women find this work.
Jim McDonald takes the Identity at the Center podcast on the road to Rome, Italy, for a special two-part episode. The first segment is an IdentiBeer roundup where Jim gathers quick-fire takes from practitioners in the Italian IAM community, including Andrea Rossi and Alessandro Piscopo of IAMONES and Marco Venuti of Thales on the biggest trends shaping identity today. The second segment is a three-course meal where Jim sits down with Alessandro Piscopo, Head of AI and Co-founder at IAMONES, to discuss AI and identity over food and wine.Across a seafood starter, scialatielli alla pescatora, and tiramisu, the conversation covers the history of AI in identity, why LLMs represent a revolution rather than an evolution, the AI-first product philosophy versus retrofitting AI onto legacy systems, compute and architecture constraints facing large language models, and what life looks like for the IAM practitioner in 2030. Alessandro envisions an identity equivalent of Claude Code, a specialized AI tool that democratizes identity expertise the way coding assistants have transformed software development.0:00 Intro and IdentiBeer Rome roundup7:01 Alessandro on AI for IAM vs. IAM for AI12:00 Three-course dinner begins - Course 1: Seafood starter14:09 History of AI in identity, from ML models to LLMs17:51 Course 2: Scialatielli alla pescatora and Falanghina wine19:56 AI-first products vs. AI layered onto legacy systems22:00 Transition period and the new world of identity24:04 The ChatGPT moment vs. the iPhone moment27:05 Compute constraints, energy costs, and architecture breakthroughs30:46 Smaller models and cost-efficiency tradeoffs32:35 Course 3: Tiramisu, baba, and espresso33:00 Life as an IAM practitioner in 203035:19 Claude Code for IAM and democratizing identity tools37:24 App store ecosystem analogy for AI platforms43:07 Closing thoughtsKeywords: IAM, identity and access management, AI for IAM, IAM for AI, agentic AI, non-human identity, IGA, LLMs, large language models, AI-first, machine learning, Alessandro Piscopo, IAMONES, Jim McDonald, Jeff Steadman, Identity at the Center, IDAC, IdentiBeer, Rome, Italy, Marco Venuti, Thales, Andrea Rossi, agentic identity, transformer architecture, compute efficiency, identity practitioner 2030, Claude Code for IAM, identity democratization, Identiverse, European Identity Conference
Hosts: Ed Jones (Owner – Nutrition World) & Clint Powell A variety of topics all about a healthy life Presented by: Nutrition World www.nutritionw.com Broadcasting from the Nooga Dentistry Studio www.noogadentistry.com Production of: Whitfield Media Group www.vitalhealthradio.com High baseline of general anxiety in pets; fireworks can escalate it dramatically Rough estimates from Dr. Smith: ~30% with noticeable anxiety ~10% with severe reactions: Destruction of property Defecating/urinating in house Extreme escape behaviors (e.g., dog through plate glass window) Natural vs Pharmaceutical Support & Timing Severe cases: Trazodone often used; Dr. Smith avoids drugs that completely knock pets out Mentions older drugs (e.g., acepromazine) that overly sedate animals She prefers starting treatment the day before fireworks: Anxiety and pain wind up; if you get behind, it's hard to control Natural options she likes: Melatonin, tryptophan, theanine, GABA Pet products that combine several of these Start 1–2 days before fireworks because neighbors often start early [0:18:58] Melatonin Dosing & CBD for Pets + Environmental Concerns Melatonin for dogs: Start around 3 mg, can go up, even up to ~10 mg in some cases Must be given at bedtime to preserve serotonin/melatonin rhythm Human reference: some serious disease protocols use up to 50 mg CBD: She likes CBD: generally very safe, large margin before toxicity Important: oil directly in mouth, not hidden in food (stomach acid breaks it down) Treats are OK, but observe individual response Ed notes TN hemp rule change (July 1) hurting many businesses; pet CBD appears less restricted for now Environmental side of fireworks: Harm to birds and nocturnal wildlife Startled flocks flying at night, running into obstacles [0:21:57] About CHAI: Services & Ozone Therapy Chai = Chattanooga Holistic Animal Institute On Main Street, Southside Open ~13–14 years Services: Conventional: surgery, X-rays Integrative: herbal medicine, nutrition-first approach, acupuncture, chiropractic Heavy focus on nutrition as foundation Ozone therapy: Used for cancer (mixing with blood + UVB), ear infections (ear cups), GI issues (ozone enema + fecal transplant) Antiviral, antifungal, antibacterial; research supports it but not mainstream due to lack of patentability Ed shares parallel human ozone experiences and enthusiasm [0:24:27] Regulatory Limits on CBD Advice & Practical Dosing Forms California example: Vets cannot legally discuss CBD with clients, while retail hemp shops can freely advise For cats: Liquids in vegetable glycerin are best Alcohol-based tinctures: cats won't like them (foaming, spitting) Liquids can go in food or directly in mouth For dogs: Easier to hide products, but Dr. Smith dislikes many chews: Often have rice flour, tapioca starch, molasses, smoke flavor (potential carcinogen) Prefers powders and liquids [0:26:16] Why Kibble Is Harmful & Heat Safety for Pets Core problems with kibble: Ultraprocessing damages proteins and fats Produces advanced glycation end products (AGEs)—carcinogenic High carbohydrate; not species-appropriate for carnivorous animals Expensive kibble = “expensive Skittles” – processing is still the issue Better options: Raw, dehydrated, freeze-dried Balanced homemade diets Light cooking under ~200°F for seniors to aid digestibility without denaturing nutrients Heat and Summer Safety for Pets Hot ground + hot air → limit daytime walks, adjust exercise Brachycephalic (“smushy-faced”) dogs at special risk: Pugs, Boston Terriers, bulldogs, etc. Shortened face doesn't reduce internal soft tissue; narrow airways = breathing through a straw Heat + humidity = much higher risk of heat exhaustion; many just lie on A/C vents Cats handle heat better (tend to stay inside), but should still be kept cool and supervised Pool safety: pets often don't know how to get out, so human supervision is essential [0:30:41] Independent Practice vs Corporate Vet Medicine CHAI is one of the last independent practices in town Independence allows: Thinking outside the box and the standard “cookbook” Corporate practices: Strict protocols, less flexibility Vets can't always practice as they'd like [0:36:09] Chemical Aging, Peakspan, and Electrolytes Ed quotes Dr. Keith Scott-Mumby (81-year-old MD) on “chemical aging”: Modern environment “poisons” us: Plastics, can linings, pesticides (glyphosate), microplastics Addictive refined carbs, seed oils Many of these mimic hormones and drive accelerated aging “Chemical aging” shows up as: Hair thinning, dry/crepey skin, age spots Persistent belly fat, “man boobs,” fragile bones, poorly fitting clothes Ed's own book: “Are You Sick and Tired of Being Sick and Tired?” He believes it lays out an A–Z game plan for aging better / “peakspan” Available as an ebook on TheHolisticNavigator.com Electrolytes vs Gatorade; Critique of Mainstream Sports Drinks Context: intense summer heat and need for electrolytes Our bodies run on electrical currents (heart, brain, nervous system) regulated by electrolytes Daily potassium need ≈ 3,400 mg Comparison: 20 oz Gatorade: ~75 mg potassium (very low) 0 mg magnesium ~270 mg sodium ~34–36 g carbohydrates/sugar Gatorade Zero: no sugar but uses sucralose, which Ed says can disrupt the gut microbiome Ed's personal take: Would rather “spend” that sugar on a cheesecake dessert than on Gatorade Example True Grace electrolyte formula (carried at Nutrition World): ~750 mg sodium ~250 mg potassium ~100 mg magnesium ~100 mg cordyceps (supportive for lungs/endurance) Great Naturally has ~700 mg potassium per serving Conclusion: many store-brand sports drinks are nutritionally weak and sugar-heavy compared to targeted electrolyte blends [0:44:27] Pepcid (Famotidine), Serotonin, and Essential Oils for Sore Throat Ed introduces Pepcid (famotidine): H2 blocker commonly used OTC for heartburn Prefers it over long-term proton pump inhibitors (e.g., Nexium) Key claim from research Ed cites: Famotidine uniquely blocks certain serotonin activity Can sometimes help with: Chronic pain Inflammation Fatigue Case example: life-threatening serotonin syndrome reversed in 15 minutes with famotidine Elevated serotonin may: Impair mitochondrial energy production Promote chronic inflammation and, paradoxically, some depression and pain Ed has bought a box himself; recommends it as the safer short-term choice for bad heartburn Essential Oils vs Antibiotics for Sore Throats Study on sore throat treatment: 97 adults with clinically diagnosed sore throat Group 1: penicillin twice daily Group 2: oral essential oils capsule 3× daily Group 3: both Outcomes: 100% improvement in antibiotic group 88% improvement in essential oil–only group 100% improvement in combined group Essential oil blend ingredients: Oregano, eucalyptus, lemon, cinnamon, pine oils Clint raises important question: absent a no-treatment control, some percentage may have improved naturally) Quick guide to buying quality beef: Prefer “100% grass-fed” over just “grass-fed” Look for or confirm grass-finished (often not on labels due to cost; best to know your farmer) Beware empty buzzwords: “natural,” “farm raised,” “pasture inspired” Real grass-fed/finished usually costs more due to land/time inputs Fat color: Slight yellow hue suggests carotenoids from real forage [0:53:35] Strength, Independence, and Vitamin D Ed references recent high-production video interview in Atlanta Draws inspiration from Jack LaLanne: Early television fitness and vitamin pioneer Nutrition World once helped bring him to Chattanooga; he lived to ~95 Paraphrased LaLanne theme: Strength gives you options: Carry your own groceries Climb stairs confidently Travel, explore, stay active Play with grandchildren, work in the yard, maintain independence Without strength: Tasks become difficult Confidence drops Independence shrinks; world gets smalle Ed reiterates: muscle is the organ of longevity and needs: Regular weight training Adequate protein Targeted supplementation Discussion of vitamin D: Ed's recent lab: ~54 ng/mL despite summer tan Wants to remain above 50 ng/mL year-round; may increase winter dosing Clint mentions his last check (~3 years ago) was ~70 ng/mL, even before supplementation The post Radio Show / Podcast – July 5, 2026 first appeared on Vital Health Radio.
Yuichiro Umehara has had one of the most impressive transformations as a singer in the past decade. Originally lacking confidence, he's evolved into a singer who steals the show if needed, going toe to toe with some of the best singers without disappearing in the mix. This was something we couldn't say a couple of years back but now, Umehara has managed to consistently impress with Sir Vanity, SolidS, Beit and BAD SKUNK.Music suggestions:- SolidS "Cross x Notes"- Sir Vanity "Home"- BAD SKUNK "Cheeky Girl"Thanks to M L for inspiring this series of episodes!
Seth Woolcock and Andrew Erickson break down their favorite early NFL Week 1 bets, including spreads, totals, moneyline underdogs, and where they believe sportsbooks have already made mistakes. The duo discusses why the Cowboys could continue their dominance over the Giants, whether the Steelers are being undervalued against Atlanta, why several Week 1 unders stand out, and which underdogs offer intriguing value before kickoff. If you're betting NFL futures, building your Week 1 card, or simply looking for the sharpest early betting angles, this episode has you covered. Timestamps: (May be off due to ads) Intro - 0:00:00 Dallas Cowboys -1.5 vs. New York Giants - 0:04:04 Pittsburgh Steelers -2.5 vs. Atlanta Falcons - 0:06:43 Tennessee Titans -2.5 vs. New York Jets - 0:10:43 BettingPros App - 0:12:38 New York Jets vs. Tennessee Titans Under 39.5 - 0:13:12 San Francisco 49ers vs. Los Angeles Rams Under 49.5 -0:15:44 Baltimore Ravens vs. Indianapolis Colts Under 49.5 - 0:18:58 Hard Rock Bet - 0:22:02 Houston Texans (-104) ML vs. Buffalo Bills - 0:24:15 Miami Dolphins ML (+176) vs. Las Vegas Raiders - 0:28:03 Minnesota Vikings ML (+100) vs. Green Bay Packers - 0:30:54 Helpful Links: Hard Rock Bet - Sign up for Hard Rock Bet and make a $5 bet and you'll get $150 in bonus bets if you win. Head over to Hard Rock Bet, sign up and make your first deposit today. Payable in bonus bet(s). Not a cash offer. Offered by the Seminole Tribe of Florida in FL. Offered by Seminole Hard Rock Digital, LLC, in all other states. Must be 21+ and physically present in AZ, CO, FL, IL, IN, NJ, OH, TN or VA to play. Terms and conditions apply. Concerned about gambling? In FL, call 1-888-ADMIT-IT. In IN, if you or someone you know has a gambling problem and wants help, call 1-800-9-WITH-IT. GAMBLING PROBLEM? CALL 1-800-GAMBLER (AZ, CO, IL, NJ, OH, TN, VA) BettingPros App - Make winning bets with advice and picks from top sports betting experts. The BettingPros app puts consensus and expert-driven sports betting advice at your fingertips to help you pinpoint the best odds and make winning bets. Download it today on the App Store or Google Play. BettingPros Discord - Looking to up your game in sports betting? Join our exclusive sports betting Discord community at bettingpros.com/chat! Not only can you connect with expert handicappers who provide free picks for NBA, NFL, MLB, NHL, player props, live betting, and more, but now you can also participate in our weekly community picks. Cast your vote, see how your picks stack up against the experts, and track your success! BettingPros Pick Tracker – Want to track all of your wagers in one place? Check out the BettingPros Pick Tracker. It syncs up with your sportsbooks to tally which picks hit, and which miss AND gives you a live look at what the public is doing so you can use real-time tracking to determine which plays to make, and which to fade: bettingpros.com/pick-trackingSee omnystudio.com/listener for privacy information.
Fluent Fiction - Mandarin Chinese: Finding Home: Jiā Hào's Journey Through Miao Traditions Find the full episode transcript, vocabulary words, and more:fluentfiction.com/zh/episode/2026-07-02-07-38-20-zh Story Transcript:Zh: 在贵州的夏天，山间的雾气轻柔地笼罩着苗族小村。En: In the summer of Guizhou, the mist gently envelops the Miao village nestled in the mountains.Zh: 村子里，五彩斑斓的传统苗族建筑在绿色的群山背景下显得格外明亮。En: In the village, the colorful traditional Miao buildings stand out brightly against the lush green mountainous backdrop.Zh: 空气中弥漫着准备节日的声音，许多人忙碌着装饰，欢声笑语不断。En: The air is filled with the sounds of festival preparations, with many people busy decorating, and laughter constantly echoing.Zh: 贾豪，自小在城市长大，对于自己的故乡记忆模糊。En: Jiā Hào, who grew up in the city, has a blurry memory of his hometown.Zh: 他站在村子的入口，感到陌生，却充满期待。En: He stands at the entrance of the village, feeling unfamiliar yet full of anticipation.Zh: 他的目标是了解自己的文化根源，找到归属感。En: His goal is to understand his cultural roots and find a sense of belonging.Zh: 村里的人都友善地欢迎贾豪，但他心里仍有些不安。En: The villagers warmly welcome Jiā Hào, but he still feels a bit uneasy inside.Zh: 他望向繁忙的村落，看到他的堂兄强正在大树下协助其他人准备节日用的长桌。En: He gazes at the bustling village and sees his cousin Qiáng assisting others in preparing the long tables for the festival under a large tree.Zh: 强从未离开过村子，他熟悉这片土地的每一个角落。En: Qiáng has never left the village; he knows every corner of this land.Zh: 贾豪上前，微笑着向他打招呼。En: Jiā Hào approaches with a smile to greet him.Zh: “贾豪，你终于来了！En: "Jiā Hào, you finally came!"Zh: ”强热情地说道，“准备帮忙吗？En: Qiáng says enthusiastically, "Ready to help?"Zh: ”贾豪点头，心下决定参与到节日的准备中去，也许这样能让他更快融入这个他几乎已遗忘的世界。En: Jiā Hào nods, deciding inwardly to participate in the festival preparations.Zh: 米琳是一位充满智慧的长者，也是村里的文化传承者。En: Perhaps this will help him integrate into this world he has almost forgotten.Zh: 她看着贾豪参与进来，满意地点了点头。En: Mǐ Lín is a wise elder and the cultural inheritor of the village.Zh: 米琳耐心地教贾豪如何制作特殊的节日糕点。En: She watches Jiā Hào joining in and nods with satisfaction.Zh: 贾豪学习得很快，尽管刚开始有些生疏，但他努力模仿村民们的每一个动作。En: Mǐ Lín patiently teaches Jiā Hào how to make special festival pastries.Zh: 节日的高潮来到了，村子中央搭起舞台，锣鼓声敲响，村民们开始聚集。En: Jiā Hào learns quickly; although he feels a bit clumsy at first, he diligently imitates every move of the villagers.Zh: 突然，米琳走到贾豪面前，微笑着示意他加入跳舞。En: The climax of the festival arrives, with a stage set up in the village center, drums sounding, and villagers gathering.Zh: 贾豪犹豫片刻，但在众人的鼓励下，他走上了舞台。En: Suddenly, Mǐ Lín approaches Jiā Hào, smiling and signaling for him to join the dance.Zh: 强上前与他并肩共舞，让贾豪感受到无比温暖。En: Jiā Hào hesitates for a moment, but with everyone's encouragement, he steps onto the stage.Zh: 随着音乐的节奏，贾豪找到了舞步的节拍，他心中的不安渐渐消退。En: Qiáng comes up to dance alongside him, making Jiā Hào feel incredibly warm.Zh: 村民们的欢呼声盖过了他的紧张，他终于感受到了一种从未有过的归属感。En: As the music's rhythm guides him, Jiā Hào finds the beat of the dance steps, and his inner unease gradually fades away.Zh: 当夜幕降临，灯火通明的舞台上，跳舞的人们渐渐散去。En: The cheers of the villagers overshadow his nervousness, and he finally experiences a sense of belonging he never felt before.Zh: 贾豪坐在长桌旁，心中萌发了一个新的决心。En: As night falls, and the dancers gradually disperse from the brightly lit stage, Jiā Hào sits by the long table, a new determination taking root in his heart.Zh: 他要更深刻地了解自己的文化根源，把这些美好分享给更多的人。En: He wants to understand his cultural roots more deeply and share these beautiful experiences with more people.Zh: 在这个夏夜，星空下，贾豪明白了自己的选择。En: On this summer night, under the starry sky, Jiā Hào understood his choice.Zh: 在这片苗族故土，他第一次真正找到了自己的位置，亦如绿意盎然的山间溪流，融入了他生命的背景中。En: In this Miao homeland, he truly found his place for the first time, like the flowing stream in the emerald mountains, blending into the backdrop of his life. Vocabulary Words:mist: 雾气envelops: 笼罩nestled: 坐落backdrop: 背景anticipation: 期待belonging: 归属感uneasy: 不安bustling: 繁忙integrate: 融入clumsy: 生疏diligently: 努力imitates: 模仿climax: 高潮rhythm: 节奏overshadow: 盖过nervousness: 紧张disperse: 散去determination: 决心inheritor: 传承者pastries: 糕点echoing: 回响villagers: 村民preparations: 准备hometown: 故乡gazes: 望向signal: 示意hesitate: 犹豫encouragement: 鼓励feasible: 可行的stream: 溪流
Seth Woolcock and Andrew Erickson break down their favorite early NFL Week 1 bets, including spreads, totals, moneyline underdogs, and where they believe sportsbooks have already made mistakes. The duo discusses why the Cowboys could continue their dominance over the Giants, whether the Steelers are being undervalued against Atlanta, why several Week 1 unders stand out, and which underdogs offer intriguing value before kickoff. If you're betting NFL futures, building your Week 1 card, or simply looking for the sharpest early betting angles, this episode has you covered. Timestamps: (May be off due to ads) Intro - 0:00:00 Dallas Cowboys -1.5 vs. New York Giants - 0:04:04 Pittsburgh Steelers -2.5 vs. Atlanta Falcons - 0:06:43 Tennessee Titans -2.5 vs. New York Jets - 0:10:43 BettingPros App - 0:12:38 New York Jets vs. Tennessee Titans Under 39.5 - 0:13:12 San Francisco 49ers vs. Los Angeles Rams Under 49.5 -0:15:44 Baltimore Ravens vs. Indianapolis Colts Under 49.5 - 0:18:58 Hard Rock Bet - 0:22:02 Houston Texans (-104) ML vs. Buffalo Bills - 0:24:15 Miami Dolphins ML (+176) vs. Las Vegas Raiders - 0:28:03 Minnesota Vikings ML (+100) vs. Green Bay Packers - 0:30:54 Helpful Links: Hard Rock Bet - Sign up for Hard Rock Bet and make a $5 bet and you'll get $150 in bonus bets if you win. Head over to Hard Rock Bet, sign up and make your first deposit today. Payable in bonus bet(s). Not a cash offer. Offered by the Seminole Tribe of Florida in FL. Offered by Seminole Hard Rock Digital, LLC, in all other states. Must be 21+ and physically present in AZ, CO, FL, IL, IN, NJ, OH, TN or VA to play. Terms and conditions apply. Concerned about gambling? In FL, call 1-888-ADMIT-IT. In IN, if you or someone you know has a gambling problem and wants help, call 1-800-9-WITH-IT. GAMBLING PROBLEM? CALL 1-800-GAMBLER (AZ, CO, IL, NJ, OH, TN, VA) BettingPros App - Make winning bets with advice and picks from top sports betting experts. The BettingPros app puts consensus and expert-driven sports betting advice at your fingertips to help you pinpoint the best odds and make winning bets. Download it today on the App Store or Google Play. BettingPros Discord - Looking to up your game in sports betting? Join our exclusive sports betting Discord community at bettingpros.com/chat! Not only can you connect with expert handicappers who provide free picks for NBA, NFL, MLB, NHL, player props, live betting, and more, but now you can also participate in our weekly community picks. Cast your vote, see how your picks stack up against the experts, and track your success! BettingPros Pick Tracker – Want to track all of your wagers in one place? Check out the BettingPros Pick Tracker. It syncs up with your sportsbooks to tally which picks hit, and which miss AND gives you a live look at what the public is doing so you can use real-time tracking to determine which plays to make, and which to fade: bettingpros.com/pick-trackingSee omnystudio.com/listener for privacy information.
This episode has a fun personal twist: There's a counterfactual world where I was employee #1 at Genesis Molecular AI, the company behind today's episode. A certain introduction happened a few weeks too late and I had already happily signed at Atomwise, another ML-for-drug-discovery startup. Same problem, different company. I was certain ML was going to transform small molecule drug discovery. Early results were underwhelming. Useful at times, but nowhere near revolutionary. In the last year I've seen signs that ML is finally ready to deliver on my convictions from a decade ago. Genesis is one of the places that might have finally cracked this problem. I was super excited to come full circle and catch up with co-founder Evan Feinberg and CTO Sergey Edunov.If you are at all interested in small molecule drug discovery, we think you will find this fascinating!In our nearly two hour chat we cover:* What is small molecule drug discovery, and why is it hard* Structure prediction as a hotbed of innovation in AI algorithms* How advances in AI elsewhere have enabled stepwise improvements in predictive power* How the community benchmarks are essentially calling AI slop good enough* The Genesis flagship model (PEARL) can routinely hit a threshold that is necessary for real-world applications* New agentic workflows enabled by these highly accurate modelsRead on for more, and also some personal thoughts on the future at the end.The coolest diffusion research is happening at GenesisSergey Edunov came to Genesis from Meta where he led Llama 2 training and Llama 3 pretraining. Sergey was a former physicist who thought he was done with physics after many years of training LLMs. Then, he discovered Genesis, and was blown away with all the novel architecture work they've been developing.It probably surprises no one that modern LLM research has not resulted in fundamentally novel or exciting updates in architectures since almost the advent of the transformer — the entire field is using variants on the same idea that came out in the original “Attention is all you need” paper. Sure, some were quite useful (mixture-of-experts in particular allowed for the massive model paradigm we're at today), but there was very little conceptually exciting.“We sort of had to wait for the right primitive to get created, and that turned out to be diffusion… Actually, some of the most innovative diffusion research that's happening in our field is happening in 3D structure prediction right now.” — Evan FeinbergThe field of 3D structure prediction on the other hand has been a hotbed of research. Genesis' recent model PEARL (Place Every Atom at the Right Location) is able to understand protein flexibility, and model not just where the ligand goes, but also make small adjustments of the protein so that the two fit better than either alone. The field knew this was missing for a long time, but it was really hard to model until now.Agentic DiscoveryWhat makes this problem so hard? As Sergey points out, there are 10^60 possible drug-like small molecules. You'll never be able to search them all, and trying to find the good ones is something like finding a needle in a haystack — except everything except your needle is dangerous.“There are 10 to the 60 drug-like small molecules in the universe… it's like finding a needle in a haystack, where everything except your needle is very, very dangerous.” — Sergey Edunov“Or finding hay in a needle stack might be a more apt analogy.” — Evan FeinbergTrying to solve the multi-parameter optimization problem is even worse. What makes a strong binder and a molecule with good “ADMET Properties” are oftentimes at tension with each other. For example, a good binder is likely greasy, but a greasy molecule is likely insoluble so it won't enter the bloodstream and get to where it needs to go!Genesis' advances in generative AI have now pushed them beyond the threshold where they believe agentic drug discovery loops are finally possible. We all remember the early days of LLMs. They were great chatbots but terrible agents, as small errors compounded rapidly into uselessness. As LLMs got better, the usefulness of agents rapidly improved. Evan and Sergey argue that their models at Genesis recently passed a similar threshold. Their internal agentic drug-discovery system (code named SAPPHIRE) can now iterate like a chemist: look at and reason about poses, form hypotheses, read literature, use internal tools, create candidates for the next iteration. Combining this with automated lab partnerships like the one Genesis has with Incyte, we're rapidly approaching a time of drug discovery agents running 24/7 making/testing new molecules. Exciting times!Benchmark crisis: Everyone's favorite benchmark is slopOne surprising point that isn't talked enough about: the academic field of “co-folding” has settled on a benchmark value of “2 Angstrom RMSD” as a metric for a “good pose”. Evan does not mince words: this threshold is just bad. Perhaps even deceptively bad. For many strong binders, there's a very clear pose, one that you can even directly resolve in the PDB electron density! And yet, with a 2Å RMSD threshold, you can get the pose quite wrong in ways that might even mislead a medicinal chemist. For example, flip around an aromatic ring, and everything looks reasonable, but you're no longer modeling the right interactions.Evan makes the strong claim that 1Å RMSD is really the threshold necessary to ensure the core of the molecule is sitting where it needs to be, and models all interactions.“If your model is sitting at 1.8, 1.9 Angstrom RMSD, that's slop, most likely.” — Evan FeinbergAs a simple example, he points out hydrogen bonds which are responsible for many of the most important interactions in protein-ligand systems. Hydrogen bonds only have a 0.6Å range to be valid! Clearly if you're accurately resolving all H-bonds, you generally have to be doing much better than the 2Å threshold.This is clearly a hard-fought lesson for Evan and Genesis. In their opinion, the community is stuck on these benchmarks because academics developing methods were not users. Evan does see signs of life, with the use of new metrics such as lDDT for co-folding. Hopefully soon the community can agree that “1.8Å RMSD is slop”, and start hill climbing on this much harder task.For a more thorough exploration of the weaknesses in conventional benchmarks, see the PEARL technical report.PEARL tops OpenBindWhich makes what happened next all the more striking. Near the end of the podcast, we talked about a recent “proof-is-in-the-pudding” moment for Genesis — evaluating their PEARL model on a recently released OpenBind benchmark. This benchmark featured 802 never before seen co-complexes on a target protein EV-A71. This target seems almost custom-chosen to give most classical docking methods a problem. When a ligand binds to the main binding site, the protein moves around to close off the path the ligand used to enter the binding pocket. This process, known as “induced fit” is notoriously hard for traditional methods to model. The tradeoff is easy to understand: treating the protein as a static structure, it becomes difficult to place a ligand in a binding pocket. Treat the protein as dynamic, and now you have to simulate complicated processes that take a long time to resolve.PEARL was able to model the induced fit of the ligand without running long MD simulations. Across the different evaluation metrics, PEARL came out not just ahead, but oftentimes well ahead of any public model. A truly impressive result.“Where PEARL was exceptionally good is figuring out how to move this loop. We are basically correct for every single pose.” — Sergey EdunovEven more exciting, this was done without any fine-tuning, or using any data on the target or homologous targets — the template PDB was released after PEARL's training cutoff.Where does co-folding go now?As someone who has followed or participated in ML techniques for protein-ligand interactions for almost a decade, I was genuinely impressed with the results that Genesis has released recently. This has been many years in development, and I'm sure Evan and the team had many sleepless nights trying to get to this point. I also think other teams are making similar progress — both Isomorphic and Deep Origin have released results that seem spiritually similar and combine computation, wetlab data, ML, to achieve genuine predictive power that seemed impossible a decade ago. Sadly, all of the above are closed source so there's no way to honestly compare them. Looking at the results I think there might be a time in the not so distant future where we can consider protein-ligand binding “solved”.I sincerely hope that the academic community can take inspiration from these developments. Once you know something can be done, it's much easier to execute. Still, I believe that the key enabler in all of the above was the tight integration of ML, large-scale computation, and real-world drug discovery applications. Sadly academia is just not structured in a way that makes such a development easy.With those parting thoughts, we hope you give the podcast a listen! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe
In episode 226, host Galit Friedlander and guest Link (dancer, educator, and one of the most respected voices on hip-hop and house culture) discuss what it means to truly understand hip-hop and house, not just as dance styles, but as cultures shaped by people, places, music, and community. Link shares stories from the clubs that influenced generations of dancers, the mentors who shaped his perspective, and why he never thought of himself as someone who "trained." He simply loved to dance. Together, we explore how understanding who you're learning from can deepen your understanding of the movement itself, why dancing and teaching require different skill sets, and how the social spaces that gave birth to these dances continue to shape the way we move today. Whether you're a street dancer, commercial dancer, educator, or simply someone who wants to deepen your understanding of hip-hop and house, this conversation offers a thoughtful perspective that may change the way you approach both the dance and the culture. Follow Galit: Instagram - https://www.instagram.com/galitfriedlander Website - https://www.gogalit.com/ Fit From Home - https://galit-s-school-0397.thinkific.com/courses/fit-from-home You can connect with Link on Instagram https://www.instagram.com/link.efc Herbert Holler LPR Party https://www.instagram.com/herbertholler
In this episode, we cover Shoya Chiba, popular seiyuu who has been a sought-after talent in 2D music, particularly since 2020. He is the perfect example of why vocal direction in 2D groups can make a seiyuu either shine or not.Music suggestions:- Shoya Chiba "Kanjouron"- Shoya Chiba (as part of SparQlew) "Criminal"- Shoya Chiba "You/Me"Thanks to M L for inspiring this series of episodes!
Digital transformation in biotech is no longer just about adopting new tools, it's about building a foundation where automation, data standardization, and AI integration actually lead to real value and long-term success.For today's episode, David Brühlmann is joined by David Hardy, a leader at Thermo Fisher Scientific. With years spent guiding automation and digital lab transformation projects around the globe, David's perspective is equal parts pragmatic and visionary. He's watched automation go from pilot to scale, advised on the messy realities of lab data, and seen firsthand what separates science fiction from science fact in fully connected labs.In this episode:Bottlenecks in lab automation, especially the challenge of scaling data volume and adapting processes (02:26)Differences between machine learning (ML) and generative AI in lab contexts, and why ML remains central to value extraction (04:17)The key requirements for successful AI adoption: quality data, robust data checking processes, and a cyclical approach to model training (05:25)The vision for an AI-enabled, fully connected lab and the role of predictive maintenance and data quality checks (07:24)Data governance strategies: balancing access and security, and the case for data democratization within organizations (09:32)How data standardization paves the way for better AI and smoother connectivity (11:25)The necessity of treating digital transformation as an ongoing journey, not a one-time project (12:15)Smart insight: digital transformation is not a one-off project but a long-term journey. The most important takeaway for any scientist or leader? Prioritize good quality, standardized data; invest in the foundational work; and foster a culture of collaboration and learning.The connectivity problem doesn't stop at the data layer. These episodes tackle the automation failures, digital infrastructure decisions, and AI readiness questions that determine whether your lab's data ever becomes an asset.Episodes 215 - 216: From Data Silos to Autonomous Biomanufacturing: Digital Twins and AI-Driven Scale-Up with Ilya BurkovEpisodes 233 - 234: Why Most Bioprocess Automation Projects Fail Before the Robot Is Even Ordered with Anthony CatacchioEpisodes 153 - 154: The Future of Bioprocessing: Industry 4.0, Digital Twins, and Continuous Manufacturing Strategies with Tiago MatosEpisodes 17 - 18: How Extracting Gold From Your Data Accelerates Process Development with Ioscani Jiménez del Val - Part 1Connect with David Hardy: LinkedIn: www.linkedin.com/in/david-hardy-46331823 Thermo Fisher Scientific website: www.thermofisher.comNext: If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you. Stay tuned for more inspiring biotech insights in our next episode.Support the show
In this episode, Professor Ricardo Vinuesa - Associate Chair for Research and Associate Professor of Aerospace Engineering at the University of Michigan - explores with Neil one of the biggest questions in modern fluid mechanics: can AI help us move beyond faster CFD and toward genuine autonomous scientific discovery? Drawing on his work at the intersection of turbulence, machine learning, explainable AI, reduced-order modeling, and flow control, Neil and Prof. Vineusa discusses the promise and limits of foundation models for fluids, why the right latent representations may matter more than simply scaling data, and how agentic AI systems could uncover physical mechanisms that humans might otherwise miss.Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations — Abhijeet Vishwasrao et al.https://arxiv.org/abs/2604.09584The episode's most direct follow-up: multi-agent LLMs and latent foundation models autonomously explore flow physics in a tandem-cylinder setup.Enhancing computational fluid dynamics with machine learning — Ricardo Vinuesa, Steven L. Bruntonhttps://doi.org/10.1038/s43588-022-00264-7A concise roadmap for useful ML in CFD, from faster simulations and turbulence modelling to reduced-order models.Identifying regions of importance in wall-bounded turbulence through explainable deep learning — Andrés Cremades et al.https://doi.org/10.1038/s41467-024-47954-6Uses explainable AI to identify flow structures that matter for prediction and control, not just visually striking turbulence features.β-Variational autoencoders and transformers for reduced-order modelling of fluid flows — Alberto Solera-Rico et al.https://doi.org/10.1038/s41467-024-45578-4Shows how disentangled latent spaces, autoencoders, and transformers can support interpretable reduced-order models of nonlinear flows.Improving turbulence control through explainable deep learning — Miguel Beneitez et al.https://arxiv.org/abs/2504.02354Links explainable AI with deep reinforcement learning to target turbulence-sustaining mechanisms, with relevance for flow control, drag reduction, and energy efficiency.LinksVinuesaLabhttps://www.vinuesalab.com/Ricardo Vinuesa — University of Michigan Aerospace Engineeringhttps://aero.engin.umich.edu/people/ricardo-vinuesa/AI and ML for Fluid Dynamics course — Ricardo Vinuesa & Sergio Hoyashttps://www.flowthermolab.com/courses/ai-ml-for-fluids/VinuesaLab YouTube channelhttps://www.youtube.com/@VinuesaLabAI for Fluid Mechanics, Sustainability & XAI — Ricardo Vinuesahttps://www.youtube.com/watch?v=TOfwf4ffPnURicardo Vinuesa — Modelling and controlling turbulent flows through deep learninghttps://www.youtube.com/watch?v=0AOY_agZ8WMChapters00:00 Podcast Intro03:20 The Evolution of Foundation Models in Fluid Dynamics10:22 Understanding Explainable AI in Fluid Mechanics15:34 Challenges in Data Fidelity for Foundation Models20:29 Machine Learning vs. Reduced Order Modeling24:22 The Shift in Focus: Turbulence Modeling to Surrogate Models29:48 Exploring Agentic Systems for Scientific Discovery37:21 Exploring Latent Representations in Fluid Dynamics40:40 The Role of AI in Autonomous Discovery41:57 Bridging Fluid Mechanics and Computer Science45:28 Data-Driven vs Physics-Driven Models51:34 The Role of Academia in AI and Fluid Mechanics56:27 Optimization and Control in Machine Learning01:00:28 Future of AI in Fluid Dynamics: Beyond ChatGPT
İkili Görüş'te İlkan Dalkuç ve Aydın Selcen 7-8 Temmuz Ankara'da düzenlenecek 36. NATO Zirvesi'ni, AK Parti'nin milletvekili ve belediye başkanı transferlerini ve muhalefetin kalan hareket olanaklarını tartışıyor.00:00 Giriş01:10 İç gündem konularımız03:30 Aydın Selcen gecikme için özür diliyor04:10 CHP ne zaman ayağa kalkıp koşmaya başlasa bir çelme takılıyor05:40 İktidar artık geri dönüşü olmayan bir yola girdi, istese de dönemez06:55 Gün geçmiyor bir CHP'li belediye budanmasın08:30 CHP, engelin öte yanına nasıl geçer?09:20 Özgür Özel yeni parti kurmada neden ağır davranıyor?14:00 Dokunulmazların kaldırılmasına dair16:40 Transferler, tutuklamalar, çilingirler, Kemal Bey, Erdoğan ne için uğraşıyor?19:40 Erdoğan, transferleri partisinin tabanını genişletmek için yapmıyor21:05 Dış konjonktürün (ABD, Rusya, Ukrayna, İran) Erdoğan'a sağladığı imkânlar23:10 Buyur buradan yak: Güney Kıbrıs'ta Barış Kurulu toplantısına katıl24:00 NATO Zirvesi'nde pürüz yaşamamak için sadece muhalif değil yandaş gazetecileri bile akredite etmediler25:00 Nedir bu başlıktaki Potemkin Köyü ifadesi25:40 Ankara'nın yollarını kapatıyorum, Macron jest görsün27:10 Beştepe'nin butik havalimanları27:45 NATO Zirvesi için Patriot takviyeleri33:20 NATO Zirvesi konusunda magazini bırakalım35:10 Rusya'nın/Lavrov'un Hakan Fidanı Kazan'a davet etmesine dair43:30 NATO Zirvesi yapılır da Zelenski olmadan yapılır mı?46:50 Venezuela'daki depremlere dair47:55 Bize uzak diye çok bakmıyoruz ama Latin Amerika ülkelerinden öğreneceklerimiz var (aşırı sağ-radikal sol)01:04:40 Zelenski'ye güven yüzde 60'ın üzerine01:05:45 Kamuoyu yoklamalarına neden güvenilmez?01:06:45 Avrupa'da ekstrem sıcaklıklar01:13:30 Türkiye'nin Dünya Kupası macerasına dair⌨️━━━━━━━DAKTİLO1984 AİLESİNİN BİR PARÇASI OLUN!━━━━━━━⌨️
00:00-10:00: ML breaks down how offense impacts interest or lack of in soccer in America and Internationally. Thanks to Marz Motors and Batavia Downs Gaming. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
We're excited to have Databricks join us at AIEWF, among hundreds of the top companies in the AI Engineer ecosystem. LS subscribers can use their discount to get past the late bird pricing and access over $50k in sponsor offers! Everyone is still talking about Satya's Frontier Ecosystems post, but few have actually built a (now $175 billion) frontier ecosystem and cloud like our guests today.From open-sourcing the layer above coding agents to rethinking databases for the agent era, Databricks cofounders Matei Zaharia and Reynold Xin are pushing the company beyond the lakehouse into a full data-and-AI operating system. In this episode, Matei and Reynold join swyx at the 2026 Data + AI Summit to unpack Omnigent, LTAP, Lakebase, agent security, open formats, Mosaic, and why databases may matter more than ever once AI agents start doing real work.We go deep on Omnigent: Databricks' open-source meta-harness for combining, controlling, and sharing agents across Claude Code, Codex, Cursor, Pi, custom agents, and internal tools. Matei explains why coding agents and enterprise agents run into the same problems: portability, collaboration, session history, security, spend controls, and the need for a common API above every harness.Then Reynold walks through Databricks' database dream: why CDC is brittle enough to joke that it means “continuous data corruption,” why HTAP has been the holy grail of database engineering, and why Databricks thinks LTAP gets most of the benefits by unifying the storage layer instead of collapsing every query engine. We also cover Databricks' infrastructure scale, the culture behind rapid prototyping, the difference between tech and enterprise customers, Databricks vs Snowflake, whether vector databases should have ever existed, the Mosaic model strategy, Genie, AI Runtime, RL fine-tuning, and the thesis that traditional software gets rewritten once the data is in the right place and agents sit on top.Databricks began as a company for the big data era. The origination of Spark from the Berkeley AMPLab which eventually turned into the product Lakehouse convinced enterprises that they didn't need a separate data lake, warehouse, ML platform, and governance layer. They just needed one open foundation where all of their data could live and be reasoned over.Since then a lot has changed, but data has only become more important. Data is no longer something you keep track of and analyze ad hoc, it's the necessary context agents need in order to act. So the framing has shifted from “where do we put all of our data?” to “how do we expose the right slice of state, history, permissions, and business logic to an AI system at the exact moment it's doing work?”If frontier model performance becomes commoditized, the durable advantage then becomes the company-specific context around them: proprietary data, governed access, operational state, transaction logs, workflows, and feedback loops. Which makes Databricks positioned perfectly.Now coming fresh off the Data + AI Summit 2026, the company is moving just as fast to keep up, announcing Genie One, Omnigent, LTAP, and many more, indicating a central mission in its newer work: Databricks is trying to become the operating system for enterprise agents.Models are getting good enough, but agents are only useful if they have the right context, permissions, memory, state, cost controls, and access to live business data. Fundamentally it appears that significantly better model performance in production is a systems problem, one that data guys like us are remarkably well prepared to solve!We discuss:* Why Databricks built Omnigent as a meta-harness above existing AI agents* Why coding agents and custom enterprise agents need the same infrastructure* The common API for agent sessions, files, streams, tool calls, and cancellation* Why persistent sessions, cloud sandboxes, sharing, search, and collaboration matter* Why Databricks open-sourced Omnigent instead of keeping it proprietary* Databricks' internal agent usage, cloud sandboxes, and coding workflows* The scale of Databricks: 50–60 million virtual machines a day and exabytes before breakfast* Why agent security needs contextual and stateful policies* How an agent could read confidential docs, install a compromised npm package, and leak data* Why spend control matters when an agent can burn $500 reading logs* Startup opportunities around coding-agent analytics, quality, skills, and spend* LTAP, Lakebase, and why Databricks wants to rethink the database stack* OLTP vs OLAP, CDC, and why data pipelines break at 3 a.m.* Why HTAP has historically been the holy grail of database engineering* Why Databricks thinks LTAP is “HTAP done right”* How writing transactional data into column-oriented formats changes analytics* Why agents need live operational context from databases, not just telemetry* How Databricks prototypes strategic systems without endless process* Enterprise vs tech customers, governance, procurement, and DIY culture* The “second system syndrome” risk of rewriting a database engine* Building a database engine from a decade of traces and quadrillions of data points* Why vector databases should never have been a separate category* Why open formats and AI changed the race with Snowflake* The Mosaic story, DBRX, Genie, document parsing models, and specialized model training* Why model customization and RL fine-tuning may become mainstream* Why “get the data there, slap some agent on top” may rewrite traditional softwareMatei Zaharia* LinkedIn: https://www.linkedin.com/in/mateizaharia* X: https://x.com/matei_zahariaReynold Xin* LinkedIn: https://www.linkedin.com/in/rxin* X: https://x.com/rxinDatabricks* Website: https://www.databricks.com* X: https://x.com/databricksTimestamps00:00:00 Introduction00:02:22 Omnigent and the Agent Infrastructure Layer00:08:39 Agent Clouds, Common APIs, and Open Source00:16:52 Databricks Scale and Internal AI Workflows00:18:03 Agent Security, Governance, and Spend Controls00:27:34 LTAP and the Database Dream00:30:30 CDC, HTAP, and Why Data Pipelines Break00:34:05 Lakebase, Parquet, and Live Data for Agents00:36:47 Databricks' Culture of Fast Prototyping00:43:40 The Dream Engine and Rewriting the Database Stack00:51:02 Vector Databases, Query Engines, and LTAP00:52:36 Databricks vs Snowflake00:57:48 Mosaic, DBRX, Genie, and Specialized Models01:03:11 Context, AI Runtime, and RL Fine-Tuning01:06:15 Why Data + Agents May Rewrite Software01:07:09 Closing ThoughtsTranscriptIntroduction: Databricks, Data + AI Summit, and Founder DynamicsSwyx [00:00:00]: Matei and Reynold from Databricks, welcome to Latent Space.Reynold Xin [00:00:06]: Hey, thanks for having us.Swyx [00:00:07]: Yeah.Matei Zaharia [00:00:08]: Yeah, thanks so much.Swyx [00:00:09]: thanks for taking time out. You have your Databricks, Data AI Summit going on. You were just telling me how the first summit that you guys ran was just 50 peopleReynold Xin [00:00:17]: Yeah, it wasSwyx [00:00:17]: in BerkeleyReynold Xin [00:00:18]: little meetup at Berkeley, I thinkMatei Zaharia [00:00:19]: YeahReynold Xin [00:00:19]: put togetherMatei Zaharia [00:00:20]: We were doing these tutorials and, yeah, just teach people Spark.Swyx [00:00:23]: Yeah. obviously now it's like, I think like the headline number's like 100,000 people around the world, 30,000 in person.Swyx [00:00:30]: it's a crazyMatei Zaharia [00:00:31]: AmazingSwyx [00:00:31]: community. Well, I just saw the keynote.Swyx [00:00:35]: Ali's just. Did was it obvious or that back when that Ali would be, like, such a great, like, CEO? LikeReynold Xin [00:00:42]: OhSwyx [00:00:42]: such a great presenter?Reynold Xin [00:00:43]: What do you think?Matei Zaharia [00:00:44]: I think among our group of founders it was clear that, I think he'd be the best at this.Swyx [00:00:50]: Yeah.Matei Zaharia [00:00:50]: And yeah, it turned out great. And he's, he's ramped up on so many topics growing a company. He would just go in and, like, study it and, be talk to all the experts. Like, even if he can't hire the person, learn enough about, like, finance and sales and whatever it was, and, and go from there. Yeah.Swyx [00:01:09]: Yeah.Reynold Xin [00:01:10]: he's obviously very high IQ and a very high EQ, but it wasn't. Like, Ali today is quite different from Ali from, like 10 years ago. I think there's a lot of work that he put in to, get to this point.Swyx [00:01:20]: Yeah. no, to me the most appealing thing about him is that he's funny. And like, it, it's, it'Matei Zaharia [00:01:26]: It's true, yeahSwyx [00:01:26]: it's hard to make jokes about, data warehousesReynold Xin [00:01:30]: About serious topicsSwyx [00:01:31]: securityMatei Zaharia [00:01:32]: YeahSwyx [00:01:32]: what have you.Matei Zaharia [00:01:33]: Oh, yeah. That's for sure.Swyx [00:01:34]: Yeah. So you guys launched a whole bunch of things. I'll, I'll just name check briefly, the stuff because we're not gonna cover everything. Omnigentt, your baby. LTAP, your baby, your dream engine.Swyx [00:01:47]: we're also gonna cover Genie, cover CustomerLake, you acquired PantherMatei Zaharia [00:01:52]: YeahSwyx [00:01:52]: Open Sharing, and there's Unity AI Gateway. A lot of these, I think, like, are things that you would expect a Databricks to do. It's, it's like part of the roadmap. Everyone in your category has similar things. But I think, probably the two of you are leading the two most unique and differentiated initiativesOmnigent and the Agent Infrastructure LayerSwyx [00:02:09]: on, in the landscape. Maybe we'll start with, Omnigentt we'll, we'll, we'll, we'll go into it. I do think that a lot of people are exploring this meta harness concept.Matei Zaharia [00:02:21]: Yeah, totally.Swyx [00:02:21]: What led you to it?Matei Zaharia [00:02:22]: Yeah. There were a couple of, like, converging lines, which I think is a good sign that you need something new. So on the one hand, there's all the coding agent info internally. We have really great, dev infra team. they built something called Isaac, that's like a wrapper on Claude Code and Codex, and, lets you use them either on the web in, like, sandboxes or, just on your dev machine or on your laptop or whatever. And then, they were adding all kinds of stuff there. And we saw all the more advanced engineers like, were building their own workflows with tons of agents, and they were building their own UIs and stuff on top or even on top of that. And then the other one was, like, us building agents. We ship this, like, data science agent called Genie on the research team, which I lead. We also build a lot of internal ones for various things, and then we have all the customer ones. And all of them running into this thing of like, “Oh, I need to switch model and harness and so on,” every few months. Plus the agent is, like, completely useless if you can't share sessions with someone and have history and have search and all this, like, layer on top of it for collaboration. I thought a bit about it from both contexts and, at first people thought it was weird. They're like, “Why are you doing coding agents and custom agents in the same thing?” But I said it's, it's the same problems and, you just wanna build the stuff that lets you deliver the agent, maybe control it if you care about security, and, make it portable across things. And then we prototyped some things as experiments. We saw, yeah, we can make it work, and then we built that for real.Swyx [00:04:06]: I'm wondering if this let's call it architectureMatei Zaharia [00:04:11]: YeahSwyx [00:04:11]: maps to anything in your careers in the past. like I always think about how a lot of things just tie back to operating systems.Swyx [00:04:18]: A lot of operatingMatei Zaharia [00:04:19]: YeahSwyx [00:04:20]: systems tie back to databases,Matei Zaharia [00:04:21]: SoSwyx [00:04:21]: or the other way aroundMatei Zaharia [00:04:22]: so the thing, I do think it ties a lot to, like, network protocols, internet protocol. we alsoSwyx [00:04:29]: Communication between entities.Matei Zaharia [00:04:30]: Yeah. We did stuff with, like, data sharing also, which is probably, most viewers probably won't know unless they'Swyx [00:04:36]: Yeah, open protocol is the term.Matei Zaharia [00:04:37]: Yeah.Swyx [00:04:38]: Open sharing. Open sharing.Matei Zaharia [00:04:38]: Open sharing.Swyx [00:04:39]: Yes.Matei Zaharia [00:04:39]: Yeah. So it's like you have a company, you maintain some table, like let's say like a Walmart or something. They have like the, inventory and what's been sold in each store. And then you also have suppliers, and they would love to produce more things and ship them, like, exactly the moment you need them. So they would love, like, real-time access to your table. So instead of like sending emails around or Excel sheets or phone calls, why can't you share like a view of that table in real time with them? Then they query, they, join it with their data, and they decide what to send. So it's one of these things where you, like you might ask like today since we can vibe code anything so fast, why do we even need to design like protocols or APIs or software? Why can't you just vibe code things on demand? But for this type of interoperability where multiple parties that are moving at different speeds are building stuff and you still want some layer on top to coordinate, you do wanna design it and build it. So it reminds me of that, like agents talking to each other and, users talking to agents and tools.Agent Clouds, Cloud Sandboxes, and Keeping Sessions AliveSwyx [00:05:42]: Reynold, any other comments alternative viewpoints?Reynold Xin [00:05:46]: I think, by the way, we had a debate on exactly which set of benefits would, matter a lot, and I think around the time we decided to do this thing I was telling Matei, “Hey,” it just happened to be there's a particular week that I was coding nonstopSwyx [00:06:00]: from the moment I woke up to, like, the moment I went to bed, I was, like, looking at my Claude sessions, my Codex sessions. And one of the things that was particularly annoying was having to keep my laptop open.Swyx [00:06:12]: I was driving to a doctor's appointment, and I remember because I wanted to make sure the whole thing continues working.Matei Zaharia [00:06:18]: But by the way, it's so comforting to hear you say that because I'm like, “I don't know if I'm a clown and I'm doing this or like.”Swyx [00:06:25]: Yeah. Like honestly, I was driving and I was tethering my laptop to my phone.Matei Zaharia [00:06:29]: huh.Swyx [00:06:29]: Keeping it on the side. Whenever I hit a red light, I started looking at what's going on my laptop.Matei Zaharia [00:06:35]: Yeah.Swyx [00:06:35]: And I just felt that was ridiculous.Matei Zaharia [00:06:37]: Yeah.Swyx [00:06:37]: It felt like we went back to the dark agesMatei Zaharia [00:06:39]: YeahSwyx [00:06:40]: programming. the productivity you gain from all this coding age is amazing, but, yeah.Matei Zaharia [00:06:45]: Have you heard of cloud?Swyx [00:06:47]: Yeah.Swyx [00:06:48]: It was crazy to me.Matei Zaharia [00:06:49]: Oh, the thing you were working on was the sandboxes or was this before that?Swyx [00:06:52]: It was a sandbox.Matei Zaharia [00:06:53]: Okay.Swyx [00:06:54]: I was workMatei Zaharia [00:06:54]: So you were inSwyx [00:06:55]: So I was approaching from a very different angle. I wanted to, “Hey, we're gonna have cloud sandboxes that doesn't shut down. You can get one very quickly,” but not just for running agentic sessions.Matei Zaharia [00:07:06]: Yeah.Swyx [00:07:06]: It's also for running development. So I was personally building that week, and through building that, I ran into all these issues, and then I wroteMatei Zaharia [00:07:15]: YeahSwyx [00:07:15]: a document for Matei, it's like, “Here's my wish list of what the actual environment should do.” And I think he ended up almost implementingMatei Zaharia [00:07:22]: YeahSwyx [00:07:22]: every single one of them.Matei Zaharia [00:07:23]: Yeah, I remember Reynolds saying, ‘cause my first prototype of this had just chats with your agent and he said, “I have to be able to open a shell, like my own shell and like list files and like tail them and stuff.” SoSwyx [00:07:36]: So SSH into a mainframe.Matei Zaharia [00:07:37]: Yeah. it has that now.Swyx [00:07:39]: Tailing my log.Matei Zaharia [00:07:40]: Yeah.Matei Zaharia [00:07:41]: Yeah.Swyx [00:07:41]: And also another thing I think I asked was, I had. I still use cursor for the sole purpose of rendering markdown files.Matei Zaharia [00:07:48]: huh. Yes.Swyx [00:07:49]: So I said, “If you just give me a way to see my markdown files and renderMatei Zaharia [00:07:53]: YeahSwyx [00:07:53]: them properly, I don't need a separate tool anymore.”Matei Zaharia [00:07:55]: Yeah.Swyx [00:07:56]: And I think you also built that in.Matei Zaharia [00:07:57]: Yeah, we, yeah, we did that, yeah. Yeah, we had a lot of engineers building, their own vibe coding setup. But then the other thing they all said is like, “Hey, I built something that's amazing for me, but, like, no one else on the team can use it ‘cause I don't have a server to collaborate.” And this is why we tried to set up, Omnigent, so you can have a server and have the security, set up in there. So, like log in with Google or whatever and, like securely share stuff. which. And that's where we've seen a lot of other agents like hit things. Like people think they prototyped an awesome agent, but it's not allowed to connect to like some really important data or whatever because of the security team.Omnigent Architecture, Open Source, and Common APIsSwyx [00:08:38]: Yeah.Matei Zaharia [00:08:38]: So yeah.Swyx [00:08:39]: Yeah. At this point, so for those watching along on YouTube, we're gonna putting up a image of the structure here, and we can talk a little bit of the architecture. I think I just want to have people understand, ‘cause like when we're talking about software, it can be very abstract and like here is what we're talking about. You've worked out in open source this entire platform and there's a runner component and server component with a uniform API that you've, you've figured out. any other element and obviously you can plug in all this, persistence layers and compute layers. This is a whole cloud. It's an agent cloud.Matei Zaharia [00:09:12]: Yeah. It's, it's got these components to work with it. The, a lot of the action happens like on the machine where you deploy your agent too. So whatever you've got on there, you can run. But yeah, it's, I think it's the minimal thing you want to have hosted, like collaborative agents and to have that server. And one of the reasons we open sourced it is, anyone building agents, this gives them an app they can start with and customize, which we were seeing in Databricks too. Like someone would make a nice, agent app and then other teams would ask, “Oh, can I just use yours for my agent?”Swyx [00:09:45]: Yeah, I think we had like five or six different agentic frameworksMatei Zaharia [00:09:48]: YeahSwyx [00:09:48]: built by every different team. They do all do more or less the same thing. Yeah, you need to. people wanna take something that works in Forkit, and you might as well have something open source. Yeah, which also was another question, which is interesting for Databricks. Like what do you choose to open source? What do you choose to make it proprietary? It's in. this goes back to Spark, right?Matei Zaharia [00:10:05]: Yeah.Matei Zaharia [00:10:06]: One, so one of the reasons to open source something is if you think it's a layer that will there'll be some network effect, it'll benefit from many, people collaborating, on it. So, for example, with Spark, I don't know if when Spark came out, we also focused a lot on letting you have libraries on top. So like there used to be differentSwyx [00:10:28]: EcosystemMatei Zaharia [00:10:28]: distributed computing engines for like machine learning and graph computation. We said they should all be libraries that you can compose. And we made it super easy to add connectors to data sources too. And then we benefit because, we don't have the time to write like connectors to like, 1,000 like different databases and file formats, but we can just use the ones people make, and of course they benefit from joining, this thing. So that's like one of these as it. Another way to think about it is like imagine, we our thing wasn't open. We had some agent hosting thing, but it's not open and then there is an open one. if you're. Which one's gonna win in the long run? So like here, because there is this benefit from like people writing integrations, it'll be, it'll be that. And then there are other things that like you just can't, even deliver as open source that are things the company does. Like for example, how do you make sure you're like streaming, jobs or your Lakebase database doesn't like, lose all your data at night? Well, that requires an operational team that's gonna sit there. There's no way it has to be a service. So like we wanna make sure as a company we're really good at those infra services and then we're as open as we can in terms of like what you build on top.Swyx [00:11:42]: speaking from a benefits, I think we are already seeing pull requestsMatei Zaharia [00:11:45]: YeahSwyx [00:11:45]: of all kinds of ecosystem integration, even though it was only released on Saturday.Matei Zaharia [00:11:50]: Yeah, Saturday. Yeah. So someoneSwyx [00:11:51]: Let's see, let's see what's going on. Yeah, you can look at the merge ones. I asked Sam Nigon this morning aboutMatei Zaharia [00:11:59]: 400 merge already?Matei Zaharia [00:12:00]: Yeah. I think Recent quite, I would guess around half are not from our team. but for example, someone added support for running it on Kubernetesrnetes. people added, many cloud sandboxes, so this can launch a cloud sandbox and run your agent in there, which is great for sharing too, ‘cause it's not, like, on your laptop and someone's, like, running scary code on there. so yeah, many startups have put those in, and, we expect to see more of them. We also have more agent harnesses already. Cursor, CLI, and Antigravity also.The Modern Data Stack and the Emerging AI StackMatei Zaharia [00:12:34]: Yeah. That's all, beautiful. And I, I feel like the last time this happens, there was the rise of the modern data stack.Matei Zaharia [00:12:42]: I don't know if it's that useful. I'm, I'm curious in your postmortem.Matei Zaharia [00:12:46]: I think most peopleSwyx [00:12:47]: AgreeMatei Zaharia [00:12:47]: will agree that it is finally dead. but maybe this arises to a new modern AI stack that, like, does the same thing.Matei Zaharia [00:12:52]: I don't know.Reynold Xin [00:12:54]: I think the modern data stack was a pretty useful thing, probably even up until this day. I think what, maybe for the audience who don't understand the history, I think the modern data stack is effectively decomposed into you need a layer to ingest the data in, you need a layer to transform your data, and then all of this are run, and then you need a layer to maybe visualize your data. And all of this runs on some data warehouse, or later on, as we're doing data warehouse or lakehouse.Reynold Xin [00:13:21]: I think that concepts are all very powerful and very useful. They enable a lot of workloads. What people eventually run into is a question of unification and consolidation is, hey, do you really need to chop all this into different pieces and work with so many different vendors and platforms in order to get, like, a very simple visualization done, right? So I think, like, over time, everybody started realizing that customers are pushing us. We started, we can realize that, so we started building more and more capabilities and trying to consolidate. And at the end of the day now, customers don't have to worry about having me hook up five different systems in orderMatei Zaharia [00:13:55]: YeahReynold Xin [00:13:55]: produce a chart. But the. I think, honestly, something like this is probably happening, in how many different frameworks do you want to hook up together in order to produce, like do a very simple agent.Matei Zaharia [00:14:06]: Just to be clear, I would say the core of this is this common API on top of all the harnesses. So the API is like, you've got an agent session, and you can send in a message or, like, a file. That's what you can send in, and then you get out, these streams as it's streaming text or as it's doing tool calls. And, or the other thing you can send in is you can, like, tell it to cancel a turn. So that's the API. Now, the thing we did is we could get you that on top of, like, cloud code running in a terminal, Codex, Py, OpenAI SDK, all that stuff. We map them all to that same interface. So that is something that you'd have to maintain yourself if you built your own, like, agent orchestrator, and then whenever cloud changes its API, you gotta, tweak your thing or it's gonna lose some messages. So that's the thing that's valuable to maintain. Then on top of that, like, we built a few apps. I think we built a pretty cool UI and stuff, but that's, And we built a security and control piece, which I'm excited about. But it's that common interface, so we don't. We. That doesn't try to be a stack. And in fact, you could plug in your own UI on top of this, server. That, and that's one of the use cases we care a lot about, ‘cause we want to use this in our own products.Compute, Sandboxes, and Databricks ScaleSwyx [00:15:20]: Yeah. It should be everywhere.Matei Zaharia [00:15:22]: Yeah.Swyx [00:15:22]: I think one of those things that is really interesting to me is, like, well, first of all, I'll, I'll endeavor to do everything and not call it the modern AI stack because like it needs a different name.Matei Zaharia [00:15:32]: Yeah.Swyx [00:15:32]: But like, yes, like, so one of the first people that told me about compute, sandboxing was Nikita from Neon.Swyx [00:15:39]: Because a lot of people think about Neon as like, well, it's serverless Postgres with, like, the separation of compute and storage and, instant branching and all those things. But every database company is also a compute company.Matei Zaharia [00:15:51]: Yeah. Yeah.Swyx [00:15:52]: And so he was showing to me his whole, his sandboxing solution. I don't think he have ever launched it.Matei Zaharia [00:15:57]: So our sandbox solution, the reason we could build it so quickly was because we realized if you just take the actual Lakebase architectureSwyx [00:16:05]: YeahMatei Zaharia [00:16:05]: and remove the database from it, by the coming from NeonSwyx [00:16:08]: Exactly, rightMatei Zaharia [00:16:09]: you have this sandboxSwyx [00:16:09]: Every database company has it already, yeah.Matei Zaharia [00:16:11]: Now, there are some differences. For example, in the one to support this particular workflow, it's important to have local persistence,Swyx [00:16:19]: YeahMatei Zaharia [00:16:19]: because you want your state to persist. Your libraries, you don't have to install your library every time, right?Matei Zaharia [00:16:24]: whereas the Neon architecture, because of the separation of storage from compute, you don't need persistent local disk.Swyx [00:16:30]: Yeah.Matei Zaharia [00:16:30]: So there's some differences.Swyx [00:16:32]: Yeah.Matei Zaharia [00:16:32]: But the, at the end of the day, yeah, it's, Yeah, so this is when you run, like, a coding sandbox. Like, if I use it, yeah, we have the dev env internally at Databricks. There's, like, many, like, tens of gigabytes of data just for, like, all the source code and, like, artifacts and stuff that I built, and I want that to come back next time, so.Matei Zaharia [00:16:51]: Yeah.Matei Zaharia [00:16:51]: But yeah.Matei Zaharia [00:16:52]: Before the show, we was talking about some statistics that might be surprising at the adoption.Matei Zaharia [00:16:56]: It could be internal, it could be external, whatever comes to mind, just to impress people the scale this is happening.Swyx [00:17:02]: So we, on the analytics side, I think we launchedReynold Xin [00:17:06]: Maybe 50 or 60 million virtual machines a day across all three clouds, so we're one of the biggest compute orchestrators out there.Reynold Xin [00:17:13]: Stuff for sure for CPU compute.Swyx [00:17:14]: Yeah.Matei Zaharia [00:17:14]: Yeah.Reynold Xin [00:17:15]: the. And all of this process, I think exabytes of data, I joked about depending on which time zone you are, typically before you have breakfast, Databricks would have processed exabytes of data already on that day. and on Neon, it's pretty interesting, too. It's launching, I think, 13 million databasesSwyx [00:17:34]: YeahReynold Xin [00:17:34]: a day now.Swyx [00:17:35]: Yeah, to me that was, like, aReynold Xin [00:17:36]: And that's just likeSwyx [00:17:37]: Like, what do you mean?Matei Zaharia [00:17:38]: Yeah. And that's the point.Reynold Xin [00:17:40]: And a lot of those were thanks to agent- agents and branching experimentationSwyx [00:17:44]: YeahReynold Xin [00:17:44]: because we made it so easy and so quickly, and thanks a lot to Nikita's team, to launch databases. It's, the. So it's changing the way people use databases.Swyx [00:17:54]: Yeah. Okay, we're gonna go into more database talk in a bit, but I wanna make sure we close up anything on Omnigentt. you mentioned, you were excited about the securityOmnigent Security, Contextual Policies, and Spend ControlsSwyx [00:18:03]: control side.Matei Zaharia [00:18:04]: Yeah.Swyx [00:18:04]: a lot of companies are figuring that out right now, as well as the spend side.Matei Zaharia [00:18:08]: Yep.Swyx [00:18:09]: what have you found there?Matei Zaharia [00:18:11]: Yeah, so I spent quite a bit of time talking to internal users, developers, security team, managers, and also lots of customers, and there's a few things. Like, first of all, one thing, that immediately was. became obvious is for security, there's this tension between, like, usability and security. And, the way people do. Like, a lot of coding agents today have very basic things like you can tell me which tool patterns I'll allow or disallow or whatever. It's like yes or no. But that puts you in a very tough spot. So just as an example, like, should my agent be able to read, some confidential documents, or let's say, should it be able to install new packages from npm, which, maybe it's compromised. Yes or no? Like, maybe I wanna allow it. Should my agent be able to publish stuff to the company website? Well, if I'm using it to code on the website, yes. But should it be able to do both, so it can, like grab a confidential document and be prompt injected and leak it? Probably not. So the thing we decided we need is stateful or what we call contextual policies where you keep track of the state of that session. It's not like is it allowed to push to the marketing site or not, but, like, hey, if it did a risky thing, like it installed, a old package from npm, or it read, like, 1,000 confidential docs, then no. Then don't, don't do it. Otherwise, maybe it's okay. That's one example of, like, moving that trade-off so it's both more secure and more useful by having a more powerful engine, essentially. This requires tracking sessions. The other piece that was interesting there is, like, there are these very level events it's doing, and you want some libraries on top that parse them. Like, for example, we have a, MCP server on Google Drive internally. It's got 60 API calls. like, how do I know which of those, like, will share a document with stuff on the internet and which ones won't? It's, it's annoying. So we designed in Omnigentt the policy layer so that it's functions and you can have libraries. Like, someone can make something that maps the level events to high-level ones, and then you write a policy about the high-level things that came out. so and thatSwyx [00:20:25]: This is related to the Panther,Matei Zaharia [00:20:27]: Yeah, Panther is. will help with that. PantherSwyx [00:20:30]: YeahMatei Zaharia [00:20:30]: a similar idea on the event processing side, and it's Python-based versus a weird custom language. this is more, as in realSwyx [00:20:39]: I didn't even know we were good yeah.Matei Zaharia [00:20:41]: Those things are happening, yeah.Swyx [00:20:42]: Yeah.Matei Zaharia [00:20:42]: So yeah, but these are the cool things. I think the contextual or stateful part, and then the way it can be libraries, and that was another reason to make it open source because others will write libraries and, like, we and our customers can use them. And the final thing, because it's stateful, one of the states we track is how much you spent in that session. So I can. I've had, like, I ask an agent to debug something, and it spent $500 because it decided to read a lot of log files and burn a lot of tokens. but I can literally say, “Okay, launch a agent to do this and cap it to spending $5.” Like, ask me for permission if it needs more. And because we're counting that within that session, it'll pop up and tell me, “Okay, you spent five, $5. Do you wanna go on?”Reynold Xin [00:21:27]: So important context here. Matei spent the last five years, a lot of his time was architecting Unity Catalog at DatabricksMatei Zaharia [00:21:34]: YeahReynold Xin [00:21:34]: which is the governance layer for data.Matei Zaharia [00:21:35]: That's right, yeah.Reynold Xin [00:21:36]: And he's combining expertise at that layer together with all the AI governance he knows.Matei Zaharia [00:21:41]: Yeah.Swyx [00:21:41]: DoMatei Zaharia [00:21:41]: But I also spent a lot of time being annoyed by coding agents and getting prompts.Matei Zaharia [00:21:46]: And also as theReynold Xin [00:21:48]: All the aboveMatei Zaharia [00:21:48]: I don't want to end up on the front page as, like, I installed some weird npm package and leakedSwyx [00:21:53]: YeahMatei Zaharia [00:21:53]: all the code, so I'm especially paranoid. But also I have very little time, so I don't want to sit there approving, like, do you want to run a 20-line, bash script, yes or no? so that's why I spend a lot of time figuring out, like, how can I make it as safe as possible and not annoying?Swyx [00:22:10]: Yeah. Is safety and mmm, let's call it security a bigger concern than token maxing or token budgets? which one is, likeMatei Zaharia [00:22:19]: Oh, yeah, they're both there. I don't know. I guess it depends on the type of company you are. So I think, some companies, like, the budget is, limited and, they really care about thatSwyx [00:22:34]: you can be Uber and still be concerned?Matei Zaharia [00:22:36]: Yeah. Oh, yeah, totally. Yeah. If you haveReynold Xin [00:22:38]: for us, securityMatei Zaharia [00:22:39]: YeahReynold Xin [00:22:40]: super paramount.Matei Zaharia [00:22:40]: For us, security is absolutely critical as a, cloud provider. It's, it's the most important thing, and, token maxing, we're not so worried about it yet, but I've seen the Like, for example, I talked to some consulting companies. They have, like, 100,000 employees who are all coding for customers. If those each spend, like, an extra $1,000 a month, that's, that's not fun.Swyx [00:23:04]: YeahMatei Zaharia [00:23:04]: we have, like, only a few thousand engineers.Swyx [00:23:06]: What's the policy in Databricks? Is it just unlimited or what'Matei Zaharia [00:23:08]: It's, it's unlimited, but we do. we use our own product to, like, analyze the traces and stuff, and we have a team that'looking to optimize and to see if anyone's doing something weird. And, we had some really cool insights just from analyzing current traces, like whichSwyx [00:23:24]: YeahMatei Zaharia [00:23:25]: models are better at, say, Rust versus like TypeScript or whatever. So yeah, at least in our code base.Swyx [00:23:31]: Yeah. Amazing. Obviously, I have to ask the token question, obviously.Matei Zaharia [00:23:34]: Yeah.Swyx [00:23:34]: I think it'sReynold Xin [00:23:34]: YeahSwyx [00:23:34]: it's a key thing. But yes, security and control above that, and figuring out a sane layer there you can have some autonomy, but, not too much.Matei Zaharia [00:23:43]: Yeah. Yeah, and we wanna make it super easy. As a engineer, you should set a thing. So in Omnigentt, you can ask your agent, “Set a policy on yourself to do this.” So it can likeSwyx [00:23:52]: But if there's something I should be showingMatei Zaharia [00:23:53]: YeahSwyx [00:23:53]: I don't, I don't see it on the GitHub, but,Matei Zaharia [00:23:55]: Oh, yeahSwyx [00:23:56]: there's justMatei Zaharia [00:23:56]: Well, in the docs there's something.Swyx [00:23:57]: Yeah, this is it.Matei Zaharia [00:23:58]: You can look at it later.Swyx [00:23:59]: Okay. Yeah.Matei Zaharia [00:23:59]: Just look in the docsSwyx [00:24:00]: YeahMatei Zaharia [00:24:00]: contextual policies if you wanna see.Swyx [00:24:04]: I just like to point peopleMatei Zaharia [00:24:05]: look at the built-in policies.Swyx [00:24:06]: Yeah.Reynold Xin [00:24:06]: Yeah.Swyx [00:24:06]: If you want to, follow up on this is exactly where to look, right?Reynold Xin [00:24:10]: Yeah.Matei Zaharia [00:24:10]: Yeah. yeah, and the story of these is, like, I just wrote, like, I wrote a doc with like 10 ideas for things before as you were working on them. Well, that was, like, my wish list of things people asked, and I told the team, like, “Hey, can you do like at least five of these for the launch?” And then they just got back with all of them, so.Swyx [00:24:29]: Oh, wow.Matei Zaharia [00:24:29]: so you can come up with more, but them- some of them are just meant to be examples. really you can intercept, like, any event the agent is making, and you can then either block or force it to ask the user or, like, allow, and you can update state to keepSwyx [00:24:45]: YeahMatei Zaharia [00:24:45]: track stuff.Swyx [00:24:46]: Yeah, ‘cause ultimately you're, I think of you as, like, a systems designer.Swyx [00:24:50]: You let people plug in, right? That's the wholeMatei Zaharia [00:24:51]: YeahSwyx [00:24:52]: modus operandi of what you do.Matei Zaharia [00:24:53]: Yeah.Swyx [00:24:54]: It's likeMatei Zaharia [00:24:54]: And we care a lot about also composab- like, can someone else write a library that others use, whichSwyx [00:24:59]: YeahMatei Zaharia [00:24:59]: this is meant to.Reynold Xin [00:25:00]: There's also a batteries included philosophy hereMatei Zaharia [00:25:03]: YesReynold Xin [00:25:03]: probably very similar to how you did Spark, which is you could just start using.Swyx [00:25:06]: Yeah.Matei Zaharia [00:25:06]: Yeah, that's right. It has to be good out of the box at certain things, and then you can build your own things on top that, like, we don't wanna do. But in Spark, if you just wanna like, I don't know, like read a table or do, like, a aggregation, it should be awesome at that out of the box.Building on Omnigent: Contributions, Startups, and AnalyticsSwyx [00:25:23]: Yeah. People wanna catch up on Omnigentt, they should watch your keynote.Swyx [00:25:26]: they should go through the GitHub and the docs. If they wanted to contribute, or they want to build on this ecosystem what would you call out as the most high-leverage places get involved?Matei Zaharia [00:25:36]: Yeah, do get involved in the Discord and in GitHub. Our team is there, is monitoring, and, some of the things people ask for we just built ourselves. Some of them, we're, we're collaborating with them to build it. and also tell us, likeSwyx [00:25:49]: Yeah, they're gonna be veryMatei Zaharia [00:25:49]: how you would like to use it because I think especially for developers, like, everyone wants it to work their own way, and a really good developer tool, like you have to hear the feedback on all the ways and figure out the abstractions and how to let people customize. So we'd love to hear, like, if you think, “Hey, I, I don't want it to work this way,” tell us. We really just wanna get that compatibility layer across agents and then let you do stuff on top.Swyx [00:26:14]: Yeah. is there any, in terms of like the startup side, I'm, I'm a founder.Swyx [00:26:18]: I wantMatei Zaharia [00:26:18]: YeahSwyx [00:26:18]: I see an opportunity, I wanna get in front of you. What's your request for, like, a startup that, like, I wish someoneMatei Zaharia [00:26:23]: Oh, like you wanna integrate with us?Swyx [00:26:24]: someone was working on this.Matei Zaharia [00:26:26]: Oh, for a startup?Swyx [00:26:27]: Yeah.Swyx [00:26:28]: Like, your, you got your own startup. It's doing well.Matei Zaharia [00:26:30]: Yeah.Swyx [00:26:30]: But like, if you weren't working on your own startup, what is, like, obvious that you should You advise many startups too, obviously.Matei Zaharia [00:26:37]: I do think, just as a company with a lot of engineers, like anything that helps me make sense of how people are usingSwyx [00:26:46]: SpendMatei Zaharia [00:26:46]: coding agents and,Swyx [00:26:48]: Yeah. AnalyticsMatei Zaharia [00:26:48]: spend, but also quality or like you should write, you should add this skill, or you should write this thing, or your agents are really horrible at tasks involving this service, so I go spend time. That would be nice. yeah.Swyx [00:27:00]: Yeah. The closest I've found is, this team, GitAI.Matei Zaharia [00:27:03]: Oh, cool. Yeah.Swyx [00:27:04]: They started with, like, we will just do, code and human attribution, but they're building the analytics layer on top of that.Matei Zaharia [00:27:12]: Yeah.Swyx [00:27:12]: I do think, like, there are a bunch of, like, artificial analysis is obviously,Matei Zaharia [00:27:18]: Yeah, they have their benchmarksSwyx [00:27:18]: doing super wellMatei Zaharia [00:27:19]: YeahSwyx [00:27:19]: with their stuff. so there's, there will be people. I think this is like the domain of consultants first, but then peopleMatei Zaharia [00:27:26]: YeahSwyx [00:27:26]: will build software that, let's say, it's kinda like the management planeMatei Zaharia [00:27:29]: YeahSwyx [00:27:30]: for coding agents.Matei Zaharia [00:27:30]: Yeah, I think there'll be a lot of insights there. You have it in other areas.Swyx [00:27:34]: Okay. Well, and then the other, big thing is your dream engine.LTAP: Lake Transactional/Analytical ProcessingSwyx [00:27:39]: maybe you wanna tell the story of, LTAP.Reynold Xin [00:27:45]: So, and background with. I'm, I'm gonna make people listen to our Ankur Goyal episode where we talked about SingleStore, HTAPMatei Zaharia [00:27:52]: YeahReynold Xin [00:27:52]: and all that history.Matei Zaharia [00:27:52]: Yeah. The LTAP idea is pretty simple. so if people have heard of the, Ankur's, talk about HTAP, it's effectively the world of databases. Sorry, there's like maybe a lot of context needs to be injected here. The world of databasesSwyx [00:28:06]: I am happy to be the database podcast that I'm forcing people to, like, learn your databases, guys.Swyx [00:28:11]: You cannot vibe code with just markdown files.Reynold Xin [00:28:13]: Yeah.Swyx [00:28:13]: Like,Reynold Xin [00:28:14]: It's one of the most important fundamental systems technologies out there. But the world of database effectively split into roughly two halves. There's what we call OLTP databases, which are transactional, and think of your Postgres, your MySQL, your Oracle databases, and the other side is what we call analytics, and sometime might refer to term OLAP. And the difference is on OLTP, you typically have maybe run some transaction on some event that looks up at one specific row. We update that row, right? It's a very oriented data structure. And on analytics, you're trying to reason on the data. You're trying to compute, “Hey, what's my revenue per store? What's my. How's my website doing every day?” And then you, eventually want to probably end up running anal- machine learning on it to predict, “Hey, how will my maybe sales be going in the future?” they are so very different architecture, and everybody start with OLTP databases. Every app, when you become serious enough, that needs more than markdown files, you need to have a database. You want to lose your data, you want to have some transactional consistency. But once you want to reason on the data, if you only have like- A hundred rows, it's probably okay to run it on your Postgres or your own, your MySQL database. But once you have more data and want to run more complicated analysis, the very analysis might crush your Postgres database. So you start doing, getting data out of the OLTP databaseSwyx [00:29:35]: Replication.Reynold Xin [00:29:36]: Replicate them into the analytic systems and just startSwyx [00:29:39]: Yeah, which for people, Elasticsearch is, like, aReynold Xin [00:29:42]: Yeah. So some of them get into Elasticsearch for, like, blocked analysis. A lot of our customers obviously get into Databricks to run more sophisticated things.Swyx [00:29:51]: Yeah.Reynold Xin [00:29:51]: And there's this term called CDC, whichMatei Zaharia [00:29:54]: Change data captureReynold Xin [00:29:55]: change data capture. and what it does, it reads the binlog of the database, and if you don't understand what binlog is, it's fine. The, but it's a little delta of the data, and it reconstructs based on the delta, the state of the database, on the analytics side. But CDC is, like, a very painful thing. It's how standard in the industry, everybody uses it, but, it ends up being. I think many data engineers ends up being waken up at, like, 3:00 a.m, because there's some pipeline thing.Swyx [00:30:22]: my explanation is, like, Airbyte is like a, became a $5 billion company just doing CDC.Reynold Xin [00:30:27]: Yeah, exactly.Reynold Xin [00:30:28]: CDC is, like, a veryMatei Zaharia [00:30:30]: It's hard.Reynold Xin [00:30:30]: It's one of the most boring but one of the most fundamental operations, like, powering modern society.Matei Zaharia [00:30:37]: huh.Reynold Xin [00:30:37]: But it's so brittle that, we joke that it's, should be called continuous data corruption, because you might change your schema on your OLTP database, and then the CDC pipeline fails to handleSwyx [00:30:48]: YeahReynold Xin [00:30:48]: the schema change.Swyx [00:30:49]: Yeah.Reynold Xin [00:30:49]: And then everything goes out.Swyx [00:30:51]: And there's all sorts of tricks that you can do, like, you add in, like, some versioning or whatever, but yeah.Reynold Xin [00:30:55]: Yeah, but it's a very, in general, very complicated. Like, I think at my keynote, I asked the audience put up their hand if they love their CDC pipeline. Only, like, maybe two people put it up. So if single store, like, about maybe a decade ago, I think the industry had this idea, hey, what if I built a single database that can handle both workloads? Now I don't.Swyx [00:31:12]: Which, like, by the way, every database person ever has ever always dreamed about this.Reynold Xin [00:31:15]: Yes. Yes.Reynold Xin [00:31:16]: This is the holy grail of database engineering is why not build a single system that can do both of this? But it ends up just being a lot of compromises. one, I think one of the first issue is that, hey, each. they say Postgres has a massive ecosystem, right? You want to be using the tools that's built for Postgres. And Spark, for example, had a massive ecosystem. There's a lot of libraries you want to use. If you were to create now a new thing, you don't have a ecosystem. You tend to create a new, smaller proprietary API, and you're lacking both, and it's also very difficult to make it performance-wise to be, comparable on either side. So it ends up being sucking on both. And our whole idea of LTAP, it's obviously a wordplay on the term HTAP, is that we think this is HTAP done right. HTAP wants to build a single engine for both. We think you can get 99% of what you need by unifying the storage, and just have a single storage layer. And once you have the single storage layer, if your Postgres databases are writing data in a column-oriented format, everything analytics can just go read that data directly without any delay, right? There's no pipeline in between, so all the data will immediately be available for reasoning analytics. I think I was telling some customers earlier, hey, when we talked about this is gonna be super useful for agents, I at first didn't really believe in it myself, even though we wrote that positioning.Lakebase, Agents, and Live Operational DataMatei Zaharia [00:32:39]: Yeah.Reynold Xin [00:32:40]: But then last night I was having dinner with a Australian customer, and they told me, “Oh, hey, one of the big issue we have is we have all these logs from our services, and we see SLA dips and want to investigate. But then there's no way for those agents to even understand what's going on in the actual databases themselves. All we see is just, like, product telemetry of the database and the services.” It would make those agents 10 times more powerful if understand, for example, who's placing those orders, what is happening, what exactly are they doing. So now I'm sold on our own message.Swyx [00:33:13]: Yeah.Reynold Xin [00:33:14]: I think it's really. It gets you the almost all of the benefits of the HTAP holy grail, which is, hey, make the data available immediately for reasoning analyticsSwyx [00:33:26]: Yeah, I think,Reynold Xin [00:33:27]: without compromiseSwyx [00:33:28]: in the way that humans are generally intelligent and want to have the ability and access to query anythingReynold Xin [00:33:34]: YeahSwyx [00:33:35]: while they do the work, they also need history and need context.Swyx [00:33:38]: And, like, where else does they get context? That's it's an analytical workload.Reynold Xin [00:33:41]: Exactly.Matei Zaharia [00:33:42]: Yeah. Yeah. And I remember when we had incidents with our databases and engineers said, “Well, I can't just run a giant query on it to see what's going on because that's gonna bring down the database and hoard it even more.” Like, that's the stuff that this gets rid of, because you spin up a whole separate fleet of machines that's doing the analytics. You're not overloading, like, the main databaseReynold Xin [00:34:02]: RightMatei Zaharia [00:34:02]: that's still trying to serve stuff.Reynold Xin [00:34:04]: Yeah.Matei Zaharia [00:34:04]: Yeah.Why LTAP Works Now: Parquet, Postgres, and LakebaseSwyx [00:34:05]: So this has been a dream for a while. what had to get done in order to get to today? Like,Reynold Xin [00:34:11]: Yeah.Swyx [00:34:11]: I feel like, you have announced variants of this several times, but it wasn't as clear as LTAP.Reynold Xin [00:34:18]: Yeah.Swyx [00:34:18]: I think LTAP is like Like, okay, we've got it, guys.Matei Zaharia [00:34:21]: This thing, yeah.Reynold Xin [00:34:21]: I was talking to somebody at Meta, and then he was asking me, “Hey, what's the catch? Why is it possible now?” And I think the reality is we took a lot of time to work on the Lakebase architecture. obviously a lot of it came from the Neon team, which is a separation of storage from compute. And it turned out it was just a tiny little step away going from that to this LTAP idea, which is, hey, we just. in the Neon architecture and in Lakebase architecture, we're writing data in oriented format to the open data lake, but in there we're writing in Postgres pages. Ali and I were spending a lot of time debating, hey, can we just change that to write in column-oriented format? And we're just debating, and one day, one of our engineers who's, like, super smart came in, he's like, “Hey, I just prototyped it. It works.”Swyx [00:35:07]: Wait, it's, prototype what?Reynold Xin [00:35:09]: Prototype, instead of storing the data in the data lake in the oriented formatSwyx [00:35:15]: ColumnReynold Xin [00:35:15]: like Postgres pagesSwyx [00:35:15]: YeahReynold Xin [00:35:16]: write them in Parquet.Swyx [00:35:17]: Yeah.Reynold Xin [00:35:18]: and he just made the observation that, hey, our storage fleet has a lot of extra idle CPUs And we could use those CPUs to do the transcoding from row to column, where row is good for OLTP, but column is good for analytics. so let's do that transcoding at that time. And as a matter of fact, once you transcode the data compresses better. So from those services writing to, for example, S3 or other data lake, like object stores, you can write them faster ‘cause now they are now smaller.Matei Zaharia [00:35:49]: Yeah.Reynold Xin [00:35:49]: So there's no overhead, it's no compromise in performanceMatei Zaharia [00:35:52]: Some CPU overhead.Swyx [00:35:54]: Yeah, because,Matei Zaharia [00:35:55]: YeahSwyx [00:35:55]: we had extra CPUs anyway.Matei Zaharia [00:35:56]: We had that fleet anyway, yeah.Swyx [00:35:57]: so the debate ended. it's one of the classics of, tech, issue of a lot of debate, but then somebody went ahead and just tried to prototype it and it worked.Matei Zaharia [00:36:06]: But, like, something this strategicSwyx [00:36:07]: That's rightMatei Zaharia [00:36:07]: and important to the company, I expect there to be, like, a kickoff thing, like a design doc. Nothing like that.Swyx [00:36:13]: Nothing like that.Swyx [00:36:14]: He just. We were debating in many meetingsMatei Zaharia [00:36:17]: Yeah.Swyx [00:36:17]: and then we're just debating whether it's possible or not from first principle.Matei Zaharia [00:36:20]: YeahSwyx [00:36:20]: and then, somebody just did it.Matei Zaharia [00:36:23]: Yeah, if you set yourself up so people do that'll be great. And that happened a bit with Omnigentt too. I think if I just had a doc on, like, we can make these together, everyone would, would think, “Oh, what about this? What about this?” But then you. if you try it out, it helps. And then if you have real users and they bash it and, like, it's still working, or in this case, if you have the workload, what the workload looks like, you can just test the same pattern then.Databricks' Culture of Fast PrototypingSwyx [00:36:47]: Yeah.Matei Zaharia [00:36:47]: Yeah.Swyx [00:36:47]: Tech aside, which is very cool, this is, like, the most important thing, the culture of innovation, and you don't have to ask my permission, you don't have like, do a whole form- formal process, just do it?Matei Zaharia [00:36:59]: Well, especially these days, I think withSwyx [00:37:01]: YeahMatei Zaharia [00:37:01]: AI, it's easier to buildSwyx [00:37:02]: But so, likeMatei Zaharia [00:37:03]: a prototypeSwyx [00:37:03]: I think you are very I made a lot of suite of, like, large companies and, like, I think that at scale, things slow down, and I'm sure you felt it already, but somehow you have this core of people that, like, are exempt. How? I think we hire and we work with really good people, and that's a very important part of it, and empowering them, but also spending a lot of time, maybe us in the trenches matter a lot also.Matei Zaharia [00:37:28]: Yeah, I think, I think first, people can adapt to being in the larger company, so that helps. And we wanna make sure they know that they can try stuff and settle debates and have a lot of examples of how it was done before, or launch a thing in beta or whatever. and then the other thing I do think as a company, like despite the size, we don't launch that many, like, products. We try to keep it pretty coherent. That's, that was the whole, like, theory of the company, was like instead of having, like, 20 Amazon services you need to set up, like a analytics and machine learning stack, you just have one, and it's, like, the same API, the same semantics across all of them, the same copy of the data. So that requires, like, unification. And then we added one more thing at a time. Like, we added storage with Delta Lake. We didn't used to do any storage. Then we added SQL, we added, machine learning platform stuff. So, but yeah, don't, don't do too many, but do those things well and, that also helps, it helps keep it manageable.Reynold Xin [00:38:33]: Yeah. The other thing we encourage a lot is instead of building, boil the ocean for everything, let's figure out how do we do it incrementally, how do we do it very quickly. Like, many of our productsMatei Zaharia [00:38:43]: YeahReynold Xin [00:38:43]: they're built in the span of weeks, and then we go to, hey. Like, usually my first question to whoever team is building is who's the target customer? Who are you working with? Are you on a first-name basis with them? Are you texting with them? I think having that very tight loop,Matei Zaharia [00:38:59]: Can you bring up another launch that comes to mind when, in this thing? I just want to give examples.Reynold Xin [00:39:04]: Omnigentt itself happened that way.Reynold Xin [00:39:05]: Yeah.Matei Zaharia [00:39:06]: Who's the customer? That's a good oneReynold Xin [00:39:34]: storage layer we did. we had, our largest customer at the time said like, “Okay, I need some. I want something in the cloud ‘cause, I. if the rest of our network is compromised, like this thing needs to be separate to store and query the events.” And then, talked to us, he said, “Okay, this is the rate of events per second. This is, like, the freshness I want. Can you do it?” So that was, like, way larger than any workload we had, and we had our, engineer, working on that, Michael Armbrust, and he worked just to make this work. And once it worked for them, it worked for everyone else. Yeah. This was early in the company, probably like four years in or something.Matei Zaharia [00:40:24]: 20- 2018?Swyx [00:40:26]: Yeah, ‘17, ‘18.Matei Zaharia [00:40:28]: Few companiesSwyx [00:40:28]: Do you have other examples?Matei Zaharia [00:40:30]: there'Swyx [00:40:31]: Maybe you have othersMatei Zaharia [00:40:31]: yeah, Clean Room, which is how you share data in a way without sharingSwyx [00:40:35]: YeahMatei Zaharia [00:40:35]: underlying data, but you allow specific operations. Those were done effectively initially just for two customers. I think the industry has a sense of, hey, maybe if you overfit to, like, one or two customers, it's gonna be really bad for you. But I think the, downside of overfitting is much smaller than the upside itself. And if you try to be too ambitious and boil the ocean, it's a much bigger problem.Swyx [00:40:58]: Yeah. Yeah.Matei Zaharia [00:40:58]: ‘Cause you might end up having no customer.Swyx [00:41:00]: Yeah, that's more, that's the more likely outcome.Matei Zaharia [00:41:02]: Yeah.Tech Companies vs. EnterprisesSwyx [00:41:03]: than you can pivot from there. I do think there is such a thing as a bad customer that sometimes you should fire. Yeah.Matei Zaharia [00:41:08]: They could exist sometimes if you drive. well, one of the challenge I think we probably see, and maybe many AI, so newer generation companies are seeing is, so tech companies are very different from tech companies or traditional enterprises.Swyx [00:41:22]: Yeah.Matei Zaharia [00:41:22]: And, if you optimize everything just for tech companies, you might have various challengesSwyx [00:41:27]: OhMatei Zaharia [00:41:27]: scaling them outside of tech companies.Swyx [00:41:28]: Okay, what likeMatei Zaharia [00:41:30]: YeahSwyx [00:41:30]: what like top three differences that you always think about?Reynold Xin [00:41:33]: Governance is a big oneMatei Zaharia [00:41:34]: I think, yeah, a big one is like, yeah, security, data privacy, governance, all that stuff. So usually if you're building some kinda like B2B or developer tool, like your biggest market is gonna be enterprises, but it's just very different. A company that's existed for like, it's had some form of IT for like 30 years, they have so many legacy systems or they operate in a regulated space. whereas a startup or, even like a, like sorta more recent tech company, all the. everything is new and pristine. So yeah, it's just different, and if you've never worked with enterprises or been in one, you just won't know about it.Reynold Xin [00:42:13]: Yeah.Matei Zaharia [00:42:13]: Yeah.Reynold Xin [00:42:13]: And the procurement process is probably quite different. There's far more stakeholders.Matei Zaharia [00:42:17]: Yeah, that is one. Yeah.Matei Zaharia [00:42:18]: Another piece that's interesting is I think some tech companies, people, will say, “Oh, I can build that myself,” right? I'll just build that myself.Matei Zaharia [00:42:27]: So then you go,Reynold Xin [00:42:28]: I don't think people say that about Databricks, butMatei Zaharia [00:42:31]: yeah, it dependsReynold Xin [00:42:32]: They do.Matei Zaharia [00:42:32]: They do?Matei Zaharia [00:42:32]: Yeah, the. Yeah, and it depends on the teams and things. So, but, on the other hand, like many of the enterprises say, “I don't, I never wanna be in the business of building that.” Like, I don't want my, whatever, I'm a retailer or something, I never wannaReynold Xin [00:42:45]: Yeah, sell clothes,Matei Zaharia [00:42:46]: be down because like some weird like nerd like couldn't get streaming pipelines working.Matei Zaharia [00:42:51]: That is not what I'm doing.Reynold Xin [00:42:53]: Yeah.Reynold Xin [00:42:53]: Yeah. This makes them great customers, to be honest, right?Matei Zaharia [00:42:55]: Yeah. But you have to understand that it's hard without having worked there and stuff, like you may not appreciate.Reynold Xin [00:43:01]: Look, I think they're all great. don't get me wrong, they have different challenges. But the, many of the tech companies, for sure there's a lot, far more DIY.Matei Zaharia [00:43:10]: On the flip side, you have people who are. they're very much experts in their domain, like they're building airplanes, they're, designing medicines, whatever, and they just want to bridge the technology, where like they don't wanna learn, databases or whatever. As cool as we think it is, even as interesting as the average software engineer might think it is to read a little bit, like they just never wanna know. They just say, “I have a, giant like, matrix or whatever with my, clinical data, like how do I, how do I like cluster it or whatever?” So yeah.The Dream Engine and Rewriting the Database StackReynold Xin [00:43:40]: Yeah. That's true. Okay, so and then I wanted to build out the dream engine, vision. where does this all lead? So one of the thing we, realized maybe a couple years back is that every single database engine out there, especially on the analytics side, are a decade old. pretty much everything that have reasonable traction are about a decade old. And they all started targeting some very specific narrow use cases, and then over time it's become more and more successful. They have grown in their ambition, and then they try to support more and more use cases. But the fastest way to support those use cases tend to be hacked around the abstractions that were initially created, that were not for those use cases.Matei Zaharia [00:44:23]: Yeah.Reynold Xin [00:44:23]: And then, but you can support them more or less okay. And before it, after 10 years of organic evolution that way, it becomes a gigantic pile of s**t.Reynold Xin [00:44:31]: the. And, but that includes Databricks. And very few company or very few systems, I think, have the gut to say, let's go start from scratch. Let's go back to the drawing board and design, knowing everything we know today after a decade of workloads and probably billions in revenue, let's attempt to rewrite it from scratch and make sure it will work and it can support all of these use cases. So we started doing that, but it's a very ambitious project. by the way, you can search on Wikipedia, there's this thing called second system syndrome.Matei Zaharia [00:45:08]: Yeah, I know that. Yes.Reynold Xin [00:45:09]: Or second system effect.Matei Zaharia [00:45:11]: Every developer must know what a second syndrome is.Reynold Xin [00:45:12]: It's you built your first thing and it works out great, and the second one's bound to fail because you become too ambitious.Reynold Xin [00:45:19]: And then you ask so many requirements.Matei Zaharia [00:45:20]: Or like you think everythingReynold Xin [00:45:21]: YeahMatei Zaharia [00:45:21]: and then you're likeReynold Xin [00:45:22]: You justMatei Zaharia [00:45:22]: you're, “I'm gonna design the perfect system this time.”Reynold Xin [00:45:24]: Yeah. And it turned out it's not perfect, and then it start failing and you're too ambitious, never launch, and you get killed. The, and the engineering team that started this, they were brilliant. I think we hired some of the best database engineers, on the planet into Databricks, and they were brilliant. Thank God it's not their second system. Many of them have built more than two in the past.Matei Zaharia [00:45:44]: Ah, nice.Reynold Xin [00:45:45]: But they were still worried about this, hey, building a database engine from scratch, I think the conventional wisdom is gonna take like five years to mature. This would be a very long-term project. It could fail. I think one of the engineers jokingly said, “Hey, maybe we just call it Reynolds Stream Engine.” If we name after a founder, maybe we then may get canceled or killed. But I think they built something pretty remarkable. they went back to. They changed the way the database engines were built from a paradigm point of view. Usually when y
00:00-15:00: ML explains why Aaron Judge never winning a World Series ring is very possible. Thanks to CH Insurance and Batavia Downs Gaming. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
In this episode of the Prolonged Field Care Podcast, Dennis sits down with Dr. Mike Falk — pediatric ICU physician with multiple deployments to Iraq, Gaza, and Ukraine — for a raw, practical, deep dive into pediatric care when you're the only asset and evacuation is denied.Most combat medics carry 99% adult gear. Kids still show up. Dr. Falk breaks down the absolute minimalist kit that actually works in austere and combat environments: canine tourniquets for toddlers, the single blue IO you really need, simplified airway choices, push-pull resuscitation with a syringe and stopcock, and a field-expedient needle cric setup.Then he walks through three real cases that expose the brutal decision-making required in prolonged field care:A 4-year-old pulled from rubble with a head injury who decompensates from rising ICPAn 8-year-old with a penetrating chest wound and tension pneumothorax at the thoracoabdominal junctionA 4-year-old with an infected blast wound fracture who develops septic shock days later in a denied environmentYou'll learn weight-based dosing that actually works in the field, why kids decompensate differently, how to mix and run an epinephrine drip with limited supplies, the realities of black-tagging children in mass casualty events, and why these cases stay with providers long after the mission.Key Takeaways:The truly minimalist pediatric kit that won't break your weight limitPractical field management of rising ICP when you have no CT or neurosurgeryPush-pull volume resuscitation and epinephrine drip mixing for pediatric shockWhy penetrating trauma at the 6th–7th rib level is often thoracoabdominalThe emotional and ethical weight of black-tagging kids — and why you must train itMalnutrition's hidden impact on wound healing and sepsis in prolonged scenariosChapters00:00 - Welcome & Why Most Medics Are Unprepared for Pediatric Patients00:57 - The Bare Essential Pediatric Combat Medic Bag02:25 - Canine Tourniquet for Under-2s & Minimalist Hemorrhage Control02:25 - Vascular Access: Why the Blue IO is Usually All You Need03:22 - Simplified Airway: OPAs, NPAs & i-gel Sizes That Actually Matter03:22 - ET Tubes: Why Only 4.0, 5.0 & 6.0 Cuffed Are Necessary04:24 - Push-Pull Resuscitation Technique (Syringe + Stopcock)04:56 - Needle Cricothyrotomy Setup & Critical I:E Ratio Warning07:09 - Case 1 Begins: 4-Year-Old Blast Victim Pulled from Rubble08:47 - Initial Assessment, C-Spine Considerations in Kids & Access12:16 - GCS 11, Pain Control & Why Fluids Make Sense Early14:17 - Hours Later: Decompensation & Rising ICP18:17 - Positioning, Hypertonic Saline Dosing (5 mL/kg) & Decision to Intubate23:13 - Ketamine-Only Intubation, Permissive Hyperventilation & Realities27:51 - The Emotional Toll: Black Tagging Kids in MCI29:44 - Case 2: 8-Year-Old with Right Chest GSW & Tension Pneumothorax31:36 - Chest Seal + Needle Decompression (Anterior Approach Preference)34:23 - Blood Resuscitation (10 mL/kg) & Why Location Matters (Diaphragm Level)40:20 - Case 3: 4-Year-Old with Infected Blast Wound Fracture – Septic Shock42:51 - Broad-Spectrum Antibiotics & Source Control in Denied Environments45:26 - Push-Pull Boluses, Epinephrine Drip Mixing & Permissive Hypotension51:09 - Malnutrition's Impact on Healing & Infection in Prolonged Care56:49 - Final Lessons: Training Black Tags, Calling for Help & Provider PTSD57:32 - Outro & Where to Find More PFC ContentFor more content, go to www.prolongedfieldcare.orgConsider supporting us: patreon.com/ProlongedFieldCareCollective or www.lobocoffeeco.com/product-page/prolonged-field-care
Q: What are you doing right now to stay ahead of AI — not get replaced by it? As a business coach, I'm talking with clients every day about how to use AI to drive growth, save time, and stay competitive. On this episode of THINK Business, I am talking with Dr. Michael Housman, Founder of AI Accelerator and author of Future Proof: Transform Your Business with AI or Get Left Behind. We dig into where AI is heading — and how to make it your edge, not your threat. Here are my Top 3 Takeaways: 1️⃣ The power isn't in automation — it's in collaboration. Treat AI like a teammate, not a tool. 2️⃣ Every industry built on knowledge work is being reshaped. 3️⃣ The gap is widening between those who lean in and those who wait. --- Dr. Michael Housman is the founder and CEO of AI-ccelerator where he helps organizations decipher and leverage advances in artificial intelligence. He's spent his career leading technology teams at early-stage companies, having architected platforms to hire job applicants, communicate more effectively with customers, catch fraudsters, and transact real estate. His research has been published in a variety of peer-reviewed journals, presented at dozens of academic and practitioner-oriented conferences, and profiled by such media outlets as The New York Times, Wall Street Journal, The Economist, and The Atlantic. Dr. Housman received his A.M. and Ph.D. in Applied Economics and Managerial Science from The Wharton School of the University of Pennsylvania and his A.B. from Harvard University Founder / CEO of AI-ccelerator. 15 years of experience building and deploying ML platforms. 6 years of experience building HR technology. Has led 100+ data scientists / engineers in his career. Connect with Jon Dwoskin: Twitter: @jdwoskin Facebook: https://www.facebook.com/jonathan.dwoskin Instagram: https://www.instagram.com/thejondwoskinexperience/ Website: https://jondwoskin.com/LinkedIn: https://www.linkedin.com/in/jondwoskin/ Email: jon@jondwoskin.com Get Jon's Book: The Think Big Movement: Grow your business big. Very Big! Connect with Dr. Michael Housman:Websites: https://ai-ccelerator.com https://michaelhousman.com LinkedIn: https://www.linkedin.com/in/michaelhousman *E - explicit language may be used in this podcast.
00:00-15:00: Hurricanes win Stanley Cup. ML breaks down how it happened. Thanks to Marz Motors and Batavia Downs Gaming. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
On the Science pod, we've been covering a lot of the ground on how AI is revolutionizing STEM, but one of our favorite off the record topics since our launch is which field is harder to accelerate: math, bio, or physics? Today we're back in Materials Science land with Radical — Unlike biological molecules that can be represented (and predicted!) by token strings, the success of materials involve many more macro complex variables like supply chains, microstructures, and manufacturing processes. If you recall the LK99 drama of 2023, while the basic ingredients were known, part of the confusion came from the lack of disclosure around manufacturing, and therefore defeated reproducibility. There is probably no "one-shot" model capable of designing a material that works perfectly at scale.How Radical is accelerating materials discovery >10x the pace of DARPA/GE MACHJoseph Krause is a materials scientist through and through. And after spending his career watching industries stall out waiting for better materials, he founded Radical AI to do something about it.We recently sat down with Joseph to talk about Radical AI, materials discovery, self-driving labs, and the future of AI science. Joseph did not sugar coat anything: accelerating the materials discovery pipeline is a hard problem. But it's one that he strongly believes we need to invest in, for the future of consumer products, aerospace, computing, and defense, and get them into every day use:“We count it as a discovery when you pick up your phone and there's a new material sitting inside of it.”How does Joseph plan on accelerating the rate of discovery? To understand this, it's important to understand why this is such a hard problem in the first place. The first thing to keep in mind is that the material that is manufactured is far more than a chemical formula going into it. The process of mixing, annealing, growing, or generating the final material can result in wildly different outcomes. The entire materials discovery process, both from early discovery to large scale manufacturing, needs to be understood and characterized.The Self-Driving LabThis philosophy has grown into a key insight at Radical AI: The construction of the self-driving lab. This lab is one that is not just automated, but in fact uses an “AI scientist” that combines scientific knowledge, computational techniques, and human intuition to generate and test hypotheses in an automated lab. Creating an AI scientist was key to making Radical's self-driving labs work, since Joseph argues that no single AI model can one-shot materials.“In materials, the ground truth is the material itself. You have to be able to test it and characterize it.”Joseph talked at length about the self-driving labs at Radical. Joseph argues that experimental data is the true “moat” in this industry. An SDL functions as a closed-loop system where an AI scientist generates hypotheses, and automated robotics synthesize and characterize materials, running research campaigns in parallel rather than serially. The successes here were both on the automation side and on the science side. Radical has managed to scale their alloy discovery pipeline up to producing and characterizing 1200 alloys in six months — this nearly 10x speedup over the DARPA/GE MACH program that aimed to create 500 new alloys in a year. Joseph claims they can scale this up even more and estimates they can produce a hundred new alloys tested and characterized in a day. A truly new paradigm in high-throughput alloy experimentation.On the science side, their AI scientist proposed and tested 300 new materials, ten of which were found to have novel state-of-the-art properties that are already being further developed for commercial applications. The robustness of this first materials campaign reinforces Joseph's claim that the moat is the lab and data.“It's moved into elemental families or alloy families no one has ever published on before.”Interestingly, Radical's AI scientist has made some novel discoveries, expanding into elements that just were not explored prior. This is fascinating from a scientific perspective, but it's also important for helping reduce supply chain bottlenecks for vital industries!Joseph spent a lot of time in D.C. before founding Radical, and he's clear-eyed about the competitive threat. China's centralized model lets it stand up manufacturing hubs and immediately scale new materials from lab to production. We can't replicate that, and Joseph is very clear we shouldn't try. But we do need an answer. For Joseph, that means transforming the scientific workforce, investing in self-driving lab infrastructure at the national lab level, and leaning hard into public-private partnerships.“Now imagine every scientist in the United States doing 10 times the research output. That's fundamental. That just changes the trajectory of discovery.”Before we close, we'd like to give a shout out to Joseph and Radical for publishing and open sourcing much of their internal tooling pipeline. This includes:* TorchSim (preprint, blog): an open-source PyTorch-based MD simulation framework, which has been spun off into its own non-profit.* MATRIX/MATRIX-PT (preprint, blog): An open-source dataset for benchmarking autonomous self-driving labs (MATRIX), along with with an open source model based upon this dataset (MATRIX-PT). We could talk about this extensively, but a fun data point is that improving reasoning in the area of materials also improved reasoning for biological systems! This is a truly unexpected result.Big shout-out to the Radical team for sharing their work!Materials discovery has been stuck on a 20–30 year timeline for generations. Joseph thinks that's about to change, and Radical AI is putting that thesis to the test in the lab, one sample at a time.We had a great time talking with Joseph. We hope you give it a listen!Timestamps* 0:00 Introduction to the challenges of AI in material science* 0:52 Welcome and introduction to Joseph Krause and Radical AI* 1:38 Why Radical AI is different: The focus on experimental data and Self-Driving Labs (SDLs)* 6:19 The process: Candidate generation, synthesis, and characterization* 11:05 The application of exotic alloys in extreme environments (aerospace and defense)* 13:20 Barriers to entry: The slow process of qualification and manufacturing* 16:06 Supply chain constraints in material science* 19:24 Human-in-the-loop: Training the AI using scientific intuition* 20:35 The engineering challenges of automating a laboratory* 23:17 Defining the “Self-Driving Lab”: Research campaigns vs. just automation* 24:39 Mechanical challenges: Handling high-temperature samples* 27:41 Future scaling plans and the “Vertical Integration” strategy* 30:08 Validation timelines for high-tech industries (semiconductors, aerospace)* 31:47 The active learning loop and handling “negative results”* 35:32 AI exploring elemental families beyond human bias* 39:13 Throughput targets and the difference between AI and human exploration* 43:52 Why the dataset size is less critical than the quality of experimental feedback* 46:20 Addressing the lack of an “AlphaFold” for materials* 53:49 War stories from the lab: Building the infrastructure* 58:12 The shift in industry sentiment toward SDLs and tool interfaces* 1:01:14 Geopolitical considerations and the race in material science innovation* 1:06:12 Calls to action for ML and AI engineers: Rethinking the scientific stack* 1:09:53 The Matrix model and using VLM for scientific knowledge extraction* 1:13:10 Why Radical AI is open-sourcing their work This is a public episode. 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Erika Erickson makes a big splash with a story about a pool party gone wild before ML discusses the disastrous […]
Aisha Francis has built a career as a performer, choreographer, teacher, and one of the dance industry's most respected heels educators. In this conversation, she shares the unexpected story of how she ended up helping Beyoncé learn to dance in heels, along with the lessons she's learned from decades of working in the industry. We discuss confidence as a trainable skill, the physical and psychological foundations of performance, what dancers often misunderstand about building a career, and why training with intention matters. Aisha also opens up about burnout, losing her love for dance, finding it again through teaching, and the realities of navigating a constantly changing industry. From unforgettable stories on stage to practical insights on artistry, professionalism, and longevity, this episode offers a candid look at what it takes to grow not only as a dancer, but as a performer and person. 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 Aisha on Instagram https://www.instagram.com/iamaishafrancis and through her website https://aishafrancis.com/ Listen to DanceSpeak on Apple Podcasts and Spotify.
Welcome back to our weekend Cabral HouseCall shows! This is where we answer our community's wellness, weight loss, and anti-aging questions to help people get back on track! Check out today's questions: Kay: Hi Dr. Cabral, Thanks for your very informative and interesting podcasts. How would you advise a post-menopausal 60 y.o family member if they tested low in ferritin (39.4 ng/mL)? I've read that this biomarker shows how much energy your body's cells have and low levels would result in symptoms like fatigue, low energy/easily tired and excessive hair shedding. This family member suffers from these symptoms. Other biomarkers revealed low AM cortisol and low LDL-C/ApoB ratio (1.1) and low basal metabolic rate of 1143 kcals/day. Although her TSH tested normal (1.3 uIU/mL), she's been on levothyroxine 75 mcg to manage hypothyroid. Her high-sensitivity CRP was not optimal at 1.49 mg/L and she has a family history of heart disease. What would you recommend for this family member? Thanks Earl: I am currently on 20 mg of lisinopril daily. Also, my GFR is 62. Would either of these be a concern when considering creatine? Alesi: Dr.Cabral, can you please explain Alpha-gal syndrom? Why does it happen, how to confirm it by testing and how would you approach it? Is it treatable? Thank you Peter: Hello, Dr.Cabral. I am an integrative health practitioner and would like to thank you for helping me understand the underlying causes of human imbalances. There is one thing that makes no sense to me though…regarding IgG testing, why would you recommend to test every year? Why doesn't suffice to test once and simply stay away from intolerant food items? Why would these intolerances change? Also, in my country there are IgG4 vs IgG1-3 testing options, what are the differences? Thank you very much for your time and knowledge you share with us. Dipali: Hi I want to start 7 days detox plan, I already did your minerals and heavy metal test, I got my results back. My question is I am taking berberine, oregano oil and magnesium citrate,( I am prediabetic my Hba1c is 6.2)do I need to stop before starting detox method. Thanks Thank you for tuning into today's Cabral HouseCall and be sure to check back tomorrow where we answer more of our community's questions! - - - Show Notes and Resources: StephenCabral.com/3774 - - - Get a FREE Copy of Dr. Cabral's Book: The Rain Barrel Effect - - - Join the Community & Get Your Questions Answered: CabralSupportGroup.com - - - Dr. Cabral's Most Popular At-Home Lab Tests: > Complete Minerals & Metals Test (Test for mineral imbalances & heavy metal toxicity) - - - > Complete Candida, Metabolic & Vitamins Test (Test for 75 biomarkers including yeast & bacterial gut overgrowth, as well as vitamin levels) - - - > Complete Stress, Mood & Metabolism Test (Discover your complete thyroid, adrenal, hormone, vitamin D & insulin levels) - - - > Complete Food Sensitivity Test (Find out your hidden food sensitivities) - - - > Complete Omega-3 & Inflammation Test (Discover your levels of inflammation related to your omega-6 to omega-3 levels) - - - Get Your Question Answered On An Upcoming HouseCall: StephenCabral.com/askcabral - - - Would You Take 30 Seconds To Rate & Review The Cabral Concept? The best way to help me spread our mission of true natural health is to pass on the good word, and I read and appreciate every review!
60 Minutes staff and new boss clash, Meghan Markle expert Mark Dolan joins us, Kristin Cavallari victimized by nude move, Pride Month, Erika Kirk's dating life, Black Crowes v. USA chants, J-Lo insists her new movie won't suck, and The Orgy Dome launches a GoFundMe. MMA Fighter Sean Strickland vs Dylan Mulvaney. This again? Erika Kirk was allegedly drunk and all over some dude at a bar, but she denies the report and says she'll never date again. Bret Michaels can't win as he's now catching crap for canceling the Freedom 250 Concert. MARK DOLAN IS HERE! We go deep on Meghan Markle, her narcissism, her failures, and how she's tainted the British Royal Family. Jill Biden has been ALL OVER the place trying to do damage control about her new book. Everything J Lo touches turns to crap...Including this new movie with Brett Goldstein. Some people are saying they're dating, but don't dare ask her about it. The Black Crowes are making news again. Some people in Tampa are no longer Black Crowes fans. Chet Hanks is a douche, but he's oddly likeable. Brad Pitt was recently in France doing some cool things. But back home, Maddox is still hating his dad. Karmelo Anthony, the alleged murderer, not the basketball player, is still getting donations in his Give Send Go. Marc's favorite new GoFundMe is for the Orgy Dome at Burning Man. Stuttering John is back online! 60 Minutes is under fire as Scott Pelley dresses down the new boss in a private meeting. Brad Galli was on ML's Soul of Detroit today. Give it a listen. Jason Carr is still out there getting free food. Kristin Cavallari was a victim of the nude move. Who was it? Marlon Jackson took a huge spill on stage. Kelsey Grammer was not around for comment. We might have some merch left. Click here to check what's available. If you'd like to help support the show… consider subscribing to our YouTube Channel, Facebook, Instagram and Twitter (Drew Lane, Marc Fellhauer, Trudi Daniels, Jim Bentley, BranDon, and Roberto).
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