Podcasts about Chai

Join FPL Chai (@FPLChai) and Josh Cuthbert as they discuss Chai's GW3 team reveal! They'll also picking out the best players to target as they build a GW3 wildcard. Join them for FPL tips and advice following a lively opening to the 26/27 season! RATE YOUR TEAM FREE

Mohnish Pabrai's Interview with Brandon van der Kolk at New Money on July 21, 2026. (00:00:00) - Introduction (00:00:35) - Investing in Berkshire Hathaway Class B shares (00:03:02) - Berkshire Hathaway: Handling a trillion-dollar portfolio (00:06:19) - Berkshire's investment in Google (00:09:36) - The gold rush: Memory businesses; Micron (00:12:36) - Kaspi (00:16:19) - Your deepest desire is your destiny; Finding stocks on Value Investors Club (00:22:15) - Invest in your highest conviction bets (00:25:50) - How to decide about investing in a stock; Adobe vs. Kaspi (00:28:44) - SpaceX; Elon Musk (00:32:54) - The too hard pile (00:35:59) - Evaluating the management; NVR (00:39:55) - Focus: The most important mental model (00:41:20) - When to exit from a position; Impact of business moats The contents of this website are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation or needs and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. Views expressed on Chai with Pabrai are exclusively those of Mohnish Pabrai and not of any affiliated firm or organization.

Join FPL Chai (@FPLChai) and Josh Cuthbert as they discuss Chai's GW2 team reveal! He's tripled up on Liverpool ahead of their game against Forest and currently has the armband on Haaland. Will that stay the same? Join them for FPL tips and advice following a lively opening to the 26/27 season! RATE YOUR TEAM FREE

A special cross-over episode with new PodFriend and fellow Pakistani Pop Culture, Psychology, Reality TV Girlies, Shabnam and Huma from Chai After Dark, to discuss the gaslighting adjacent experience of being a viewing of The Valley.

What is the crystalline body? In today's episode, Katie explores the parallels between the ancient wisdom of Tantra, modern neuroscience and the crystalline body. She shares how we can expand past our limitations, clear blockages in our energy body and ultimately move from confusion to clarity. If you want to learn more about the crystalline body and how to regulate your nervous system through Ayurvedic practices, join Katie LIVE for our free Somatic Ayurveda Workshop. Click here to save your spot! This beautiful talk was originally a Chai with CAAM event hosted by our dear friends at The California Association for Ayurvedic Medicine (CAAM). Click here to learn more about CAAM!   In this episode about the crystalline body, you'll hear: ~ Common misconceptions about Tantra ~ The intersection of Tantra, Ayurveda and the crystalline body ~ The root definition of the word Tantra ~ Expanding past our limitations ~ Moving from confusion to clarity ~ What is the crystalline body? ~ Parallels between Tantra and modern Neuroscience ~ How unprocessed past experiences shape our reality ~ A Tantric formula for digesting the past ~ Radical self-honesty ~ Clearing blockages from the crystalline body ~ Cultivating the ability to stay with yourself ~ The effects of the "Information Age" on our minds ~ The Validation Breakthrough by Naomi Feil and Vicki de Klerk-Rubin Read the full show notes here: https://theshaktischool.com/ep-251-the-crystalline-body-tantra/   Connect with Katie and The Shakti School: ~ Learn more about Ayurveda School! ~ Sign up for our free mini-course about Women's Wisdom and Ayurveda! ~ Follow The Shakti School on Instagram and Facebook ~ Read Katie's latest book, Glow-Worthy!

Happy feast of St. Bernard of Clairvaux! On today’s show, Matt Swaim and Anna Mitchell welcome Kris McGregor to share wisdom from St. Bernard that shows up in the Office of Readings. Other guests include Gary Michuta with more Messianic prophecies, pastoral counselor Kevin Prendergast, and Rita Heikenfeld with Bible Foods. Plus news, weather, sports, and more… ***** The Memorare of St. Bernard of Clairvaux Remember, O most gracious Virgin Mary,that never was it knownthat any one who fled to thy protection,implored thy help,and sought thy intercession,was left unaided. Inspired with this confidence,I fly unto thee,O Virgin of virgins, my Mother,to thee I come,before thee I stand sinful and sorrowful. O Mother of the Word Incarnate!despise not my petitions,but, in thy mercy, hear and answer me. Amen. ***** RECIPES FROM RITA: ZUCCHINI BREAD Double chocolate zucchini bread/cake Ingredients:1 -1/2 cups shredded zucchini1 cup all purpose flour – I use King Arthur unbleached1/2 cup unsweetened cocoa powder, sifted1 teaspoon baking soda1/4 teaspoon baking powder1/4 teaspoon salt1/2 to 3/4 teaspoon cinnamon1/4 teaspoon allspice1/2 cup canola oil1/2 cup sugar1/2 cup light brown sugar2 large eggs1 teaspoon vanilla3/4 cup semi- sweet chocolate chips Instructions:Preheat oven to 350. Spray 9 x 5 loaf pan. Set aside shredded zucchini. Whisk together flour, cocoa, baking soda, baking powder, salt, cinnamon, and allspice. Set aside. Beat oil, sugars, eggs, and vanilla until well blended and fold in zucchini. Add flour mixture, mixing just until combined. Fold in chips. Bake until toothpick inserted in center comes out clean, about 55 to 65 minutes. Place on wire rack to cool 10 minutes, then remove and finish cooling. Large Batch moist and marvelous zucchini bread with Chai essence Ingredients:3 cups grated unpeeled zucchini (squeeze moisture out before measuring and pack zucchini firmly into measuring cups)3 cups sugar1-1/2 cups vegetable oil – I used canola4 large eggs2 teaspoons vanilla3-1/4 cups all purpose flour1 teaspoon cinnamon2-3 teaspoons dry Chai tea blend – 2 will give a mild taste; 3 a more robust taste2 teaspoons baking powder1 teaspoon baking soda1/2 teaspoon salt1-1/4 cups chopped nuts – optional – I used toasted pecans InstructionsPreheat oven to 350.Spray 2 large loaf or 3 regular loaf pans.Mix zucchini, sugar, oil, eggs and vanilla. Beat on medium speed 2 minutes.Combine flour, cinnamon, Chai blend, baking powder, baking soda, salt and nuts together. Add to zucchini mixture and blend well.Pour into prepared pans. Bake on middle rack 45 minutes to one hour or until done. A toothpick inserted deep into center will come out clean. My 2 large loaf pans took 55 minutes to bake. Tips:Ball bat sized zucchini works great If you grow zucchini, you know it can go from slender to ball bat size seemingly overnight. Overgrown zucchini has good flavor and shreds up just fine. Can you peel zucchini? I don’t, but Cheryl Bullis, a professional baker from Clermont County, does peel her zucchini before measuring. Squeeze hard to get moisture out. Zucchini is notorious for a high moisture content which can make batter too runny. Add nuts to dry ingredients; This insures that the nuts stay suspended throughout the bread and prevents them from sinking to the bottom of the loaf. ***** Full list of guestsSupport the show: https://www.sacredheartradio.com/donate-now/See omnystudio.com/listener for privacy information.

FPL Chai and Josh Cuthbert talk through their latest drafts ahead of FPL Gameweek 1! RATE YOUR TEAM FREE

Pat Tharp (accompanied by his daughter and head brewer Noelle) stop by Craft Cannery to record an episode with Pauly and in the process, drop some ELITE entrepreneur knowledge. Pat's story of becoming The Chai Guy is inspiring, full of lucky moments, but also full of hard work, determination, and some of the strongest work ethic you'll ever hear.Mentioned in this episode:Joe Bean RoastersVisit joebeanroasters.com to get fresh roasted specialty coffee either by the bag or with a Perpetual Joy subscription!Food About TownFood About Town hosted by Chris Lindstrom, focusing on restaurants, food and drink of all kinds, and whatever topics I want to cover! https://foodabouttown.captivate.fm/Behind the GlassPodcast and gallery focusing on underrepresented artists utilize the space to amplify their work. Curated by @Richardbcolon @qua.jay. Check out the podcast or join them in person first Fridays at 240 E Main St, Rochester, NY! https://behind-the-glass-gallery.captivate.fm

Měla to být rutinní zkouška hackerských dovedností velkých AI modelů, pokus britského AI Security Institutu ale musel být na konci července předčasně ukončen. Jeden z testovanýchAI agentů se místo úprav softwaru pustil do přesvědčování lidí, lhaní, manipulací a následně i zametání stop. Co nám to říká o současných schopnostech generativních jazykových modelů? S komentářem přišel do studia Josef Šlerka. Ptá se Petr Gojda.

The Scoutcast boys are back to present their latest FPL drafts and advice ahead of the 26/27 season. With Andy away on international duty, FPL Chai is standing in and will be talking us through his no-Fernandes draft

Raise Your Words Host Amani sits down with Author M.J. Soni to discuss her book, "Masala Chai Mystery Club." Manju (MJ) Soni grew up in South Africa and wrote, DEFYING APARTHEID, a part-memoir about being a doctor/ activist under apartheid. Since then, drawing on her American, African, and Indian background, she's written mystery short fiction and her debut cozy mystery, THE MASALA CHAI MYSTERY CLUB, which are full of heart and humor, with a saree-wearing librarian protagonist will be coming out on 21 July with Crooked Lane Books. https://mjsoniauthor.com/  

This January, four big AI × Pharma tools deals were announced at the huge JPM Pharma conference that takes over San Francisco every year. OpenAI-backed Chai Discovery (now worth $4B) was somehow at the heart despite being all of 2 years old. The Science team is proud to bring you the first podcast with cofounder Matt McPartlon and product lead Neil Patil to tell the full story! Editor's note: not to be confused with Chai AI, which was another top pod of ours.Pharma suddenly doing big AI tools dealsFor the non-pharma people, JPM is JP Morgan's annual conference for pharma deal-making that takes over San Francisco for a week in January with hundreds of side events, etc. It's a big thing. Tools deals for pharma are also a big (new) thing: companies that start as AI for Pharma usually end up building their own drug pipelines instead, and the reason is something like this: convincing pharma to use your tool requires proof that your tool works. Proof means good targets, maybe with good clinical validation. If you have that, then it's easier to raise money (with a known, if long path to commercialization) or sell (e.g payment in biobucks) for a specific target than it is to sell to lots of companies on a promise that it will work across their portfolios. The “we'll just partner / build our own drug” optionality proved to be the only good path up until January. What changed? In short, the tools got good enough for drug design teams to trust.Good-enough-to-trust unlocks the ability to scale discovery: get more, better candidates into the lab and animal trials faster. More screening for toxicity, better delivery, etc. This means that what you push to the clinic is more likely to succeed.Tools also unlock new capabilities: mechanisms that are very hard or impossible to develop using lab-based discovery. Designing an antibody that precisely triggers a very specific molecular cascade takes many years of trial and error. Designing bi-specific antibodies (that bind to two different proteins) is similarly difficult. Good design tools can unlock this.RJ: The fact that the quality of the model has jumped means you're enabling things you just plain couldn't do. So it's a step change. It's not an efficiency argument at all, or not so much.Matt: Yeah, exactly. It's kind of interesting, even for us — it took me a while to believe in the thesis, actually. I talked to Josh for months before Chai started... It's like, can I beat a mouse, and then can I do what mice can't do? And then how many levels of interaction can you just keep building on top of that?Everyone playing in the structural / binding space has an angle here, and some will be better than others, but Chai is pointing to a different unlock: getting good molecules right out of the gate (meaning they don't then need as much lab work) means that the iteration time is faster. This turns science into engineering: you can design your systems to reduce friction and hill climb towards one-shotting molecules all the way to the clinic.This, per-se, is not a new thesis: a16z articulated a version of this in 2020. What has changed is that structural models became binding models (how well doesn't this molecule bind to this molecule, aka “binding affinity). Binding models unlock design, which has been steadily improving. Chai's observation is that for engineering problems the best product tends to win, and good technology is a necessary but not sufficient condition. Photoshop for moleculesWith that in mind Chai has invested heavily in partnerships that allow them to learn from their Pharma counterparts. What is kind of cool about working so closely and supporting so many of these partners is we get to really learn about what is the stuff that would be helpful in research. So rather than doing research in a vacuum, based on what would hypothetically be cool, we're able to do informed research based on what our partners have just been organically asking us for help with.— Neil Patil, (Chai product lead)This means better UX, such as a molecule editor that is more like a CAD or graphics design program than a chatbot.Their approach has paid off: since June, Chai has announced three more major deals: Lilly, Novartis, argenx, plus an expansion of their Eli Lily program. This episode is too full of quotable moments for a short blog, so tune in to learn about * Why protein tokens have the highest downstream value of any token * Climbing levels of abstraction as models improve * How Pharma, VC, and research are all just portfolio optimization * How better tech changes the whole portfolio * How relentless focus on simplicity leads to scalePlus much more! 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

FPL Chai is joined by his new co-host Josh Cuthbert! They'll be discussing Chai & Josh's latest drafts and sharing tips ahead of the FPL 26/27 season. RATE YOUR TEAM FREE

Paul Chai joins Alan Dunne to discuss what it takes to manage a perpetual investment portfolio designed to support future generations. As CIO of the Kansas State University Foundation, Paul explains how disciplined asset allocation, thoughtful manager selection and strong governance create resilient long-term results. The conversation explores endowment investing, private markets, hedge funds, portfolio construction and the importance of building a decision-making culture where diverse perspectives are encouraged. It is a wide-ranging discussion about investing with humility, managing uncertainty and creating an investment process that can endure through changing market environments.-----50 YEARS OF TREND FOLLOWING BOOK AND BEHIND-THE-SCENES VIDEO FOR ACCREDITED INVESTORS - CLICK HERE-----Follow Niels on Twitter, LinkedIn, YouTube or via the TTU website.IT's TRUE ? – most CIO's read 50+ books each year – get your FREE copy of the Ultimate Guide to the Best Investment Books ever written here.And you can get a free copy of my latest book “Ten Reasons to Add Trend Following to Your Portfolio” here.Learn more about the Trend Barometer here.Send your questions to info@toptradersunplugged.comAnd please share this episode with a like-minded friend and leave an honest Rating & Review on iTunes or Spotify so more people can discover the podcast.Follow Alan on Twitter.Follow Paul on LinkedIn.Episode TimeStamps: 00:00 - Why better investment decisions start with diverse thinking01:02 - Paul Chai's journey from engineering to institutional investing04:50 - Managing a perpetual endowment for future generations08:48 - Building resilient strategic asset allocations11:54 - The advantages of managing a $1.2 billion endowment15:29 - Portfolio construction beyond the traditional 60/40 model20:57 - Strategic asset allocation versus total portfolio investing24:05 - Lessons from the Yale Endowment model26:26 - Finding unconventional investment opportunities31:21 - Building a diversifying hedge fund portfolio38:02 - Why CTA strategies no longer fit the portfolio43:01 - Manager selection, due diligence and finding alpha45:39 - The importance of grit when selecting investment managers51:25 - Building better investment teams and decision-making cultures57:20 - Advice for the next generation of long-term investorsCopyright © 2025 – CMC AG – All Rights Reserved----PLUS: Whenever you're ready... here are 3 ways I can help you in your investment Journey:1. eBooks that cover key topics that you need to know about In my eBooks, I put together some key discoveries and things I have learnt during the more than 3 decades I have worked in the Trend Following industry, which I hope you will find useful. Click Here2. Daily Trend Barometer and Market Score One of the things I'm really proud of, is the fact that I have managed to published the Trend Barometer and Market Score each day for more than a decade...as these tools are really good at describing the environment for trend following managers as well as giving insights into the general positioning of a trend following strategy! Click Here3. Other Resources that can help youAnd if you are hungry for more useful resources from the trend following world...check out some precious resources that I have found over the years to be really valuable. Click HerePrivacy PolicyDisclaimer

Most people treat biology as a bespoke, messy science. Josh Meier and Matt McPartlon, co-founders of Chai Discovery, treat it as an engineering problem. They make the case that drug design obeys the bitter lesson: scale data, models, and compute, and the model can learn what a hand-built pipeline simply couldn't capture. The results are concrete: Chai-2 pushed de novo antibody design from a sub 0.1% hit rate to 16%, turning a needle-in-a-haystack search into something more like designing a key to fit a lock. Josh argues, counterintuitively, that biology is more verifiable than code, and explains why the goal should be more lab experiments, not fewer. Their bet: a design suite that collapses drug discovery from nine months to nine days, and arms the pharma industry rather than competing with it. Hosted by Pat Grady and Sonali Singh, Sequoia Capital 00:00 Introduction 01:52 From Discovery to Design 03:25 Protein AI Breakthroughs Timeline 06:04 Why Start in 2024 10:13 Diffusion Models Intuition 11:41 Building the Avengers Team 15:22 Hit Rates and Scaling Laws 25:01 Molecular CAD Vision 25:24 Faster Design Loops 26:32 Future Drug Discovery 28:37 Platform Business Model 31:14 Partnering Reality Check 33:44 Data Flywheel Explained 37:16 Staying Ahead at Scale 39:44 Culture and What's Next

90 Day Fiance Season 12 Episode 12 For more THE OTHER WAY join Patreon! Patreon.com/TrashTalkPodcast Youtube: www.youtube.com/c/TrashTalkPodcasts Tiktok: @trashtalkpodcasts Instagram and Twitter @90daypodcast Traceycarnazzo.com Tracey Carnazzo @trixietuzzini Noelle Winters Herzog @noeygirl_ Bonus content at Patreon.com/TrashTalkPodcast quince.com/fiance

Join FPL Chai ( @FPLChai  ) as he discusses his latest FPL draft with Sam ( @FPLFamily ). He's sticking with no Fernandes in the team! RATE YOUR TEAM FREE

This week Duncan MacKenzie and Brian Andrews sit down with artist Sophia Chai at the Door County Contemporary Art Fair to discuss photography that insists on becoming something else. Working between painting, sculpture, installation, and large-format analog photography, Chai creates images that challenge our assumptions about perspective, representation, and the mechanics of seeing itself.  The conversation moves from camera obscuras and four-by-five view cameras to Plato's Allegory of the Cave, cave painting, Korean language, immigration, and the colonial language embedded within photographic practice. Chai reflects on transforming her graduate studio into a walk-in camera obscura after September 11th, collaborating with architectural space rather than simply depicting it, and asks whether photography might become a practice of witnessing rather than capturing. Name Drops & Links Sophia Chai – https://www.sophiachai.com Hair + Nails – https://hairandnailsart.com/ University of Illinois Chicago School of Art & Art History – https://artandarthistory.uic.edu/ Kerry James Marshall – https://www.davidzwirner.com/artists/kerry-james-marshall Judith Kirshner – https://stories.mcachicago.org/speaker/judith-kirshner/ Plato – https://plato.stanford.edu/entries/plato/ Camera Obscura – https://en.wikipedia.org/wiki/Camera_obscura Plato's Allegory of the Cave – https://en.wikipedia.org/wiki/Allegory_of_the_cave Jeff Wall – https://www.guggenheim.org/artwork/artist/jeff-wall Barbara Kasten – https://barbarakasten.net/ Johannes Vermeer – https://www.rijksmuseum.nl/en/rijksstudio/artists/johannes-vermeer Peter Jackson – https://en.wikipedia.org/wiki/Peter_Jackson The Lord of the Rings – https://en.wikipedia.org/wiki/The_Lord_of_the_Rings_(film_series)  

In this latest episode of Chai Can't Even, host Robin Linkhart sits down with David Wilson, a lifelong member of Community of Christ, who shares his journey from growing up in a church-centric family in Detroit to his current active involvement. David recounts his early church experiences, including youth activities and the impact of changes like the call of women to the priesthood, open communion, acceptance of LGBTQ+ members, and the name change to Community of Christ. David's story highlights the evolving nature of the church and the importance of its mission in fostering inclusivity and social justice.  Listen to more episodes in the Chai Can't Even series. Download the transcript. Thanks for listening to Faith Unfiltered!Follow us on Facebook and Instagram!Intro and Outro music used with permission: “For Everyone Born,” Community of Christ Sings #285. Music © 2006 Brian Mann, admin. General Board of Global Ministries t/a GBGMusik, 458 Ponce de Leon Avenue, Atlanta, GA 30308. copyright@umcmission.org “The Trees of the Field,” Community of Christ Sings # 645, Music © 1975 Stuart Dauerman, Lillenas Publishing Company (admin. Music Services). All music for this episode was performed by Dr. Jan Kraybill, and produced by Chad Godfrey.   NOTE: The series that make up Faith Unfiltered explore the unique spiritual and theological gifts Community of Christ offers for today's world. Although Faith Unfiltered is a Ministry of Community of Christ. The views and opinions expressed in this episode are those speaking and do not necessarily reflect the official policy or position of Community of Christ. 

FPL Chai (2 x Top 5K) joins Tom to reveal his first draft for FPL 26/27. RATE YOUR TEAM FREE

Today on AirTalk: Big Bear bald eagles (0:30) Cheap date ideas (17:25) How to drink chai right (37:27) FilmWeek (51:34) A new Watts documentary (1:21:44) Visit www.preppi.com/LAist to receive a FREE Preppi Emergency Kit (with any purchase over $100) and be prepared for the next wildfire, earthquake or emergency.

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Tetyana Shippee, PhD, Associate Director of Research, and Marti DeLiema, PhD, Associate Director of Education at the Center for Healthy Aging and Innovation at University of Minnesota join host Ken Haglind on “Living Healthy and Aging Well” to discuss their work and research. CHAI's mission is to advance interdisciplinary aging science; create meaningful and immersive…

The true scale of the warfare against our democratic system is finally coming to light!

The countdown to save our elections has officially begun!

#Bàigiảng của linh mục #GiuseHoàngNgọcDũng trong #thánhlễ Thứ Ba tuần XV Thường niên, cử hành lúc 17:30 ngày 14-7-2026 tại Nhà nguyện Trung tâm Mục vụ TGP Sài Gòn

Hi everyone, welcome back to another episode of The Chai on Life Podcast! It's summer and we all just want to feel our best this time of year and TBH, all year long. This episode is a roundup of five things that I have been doing to feel really good this summer (of course all a work in progress!) I've been thinking about this topic for awhile now and am so excited to get this solo episode out there — more of a rare occasion for me!Really hope you like it and get even one thing out of it.Here, some the other podcasts I reference in the episode:-Chai on Life Podcast with Elisheva Liss-The Liz Moody Podcast episode with Laura VanderkamIf there is someone you want to see on The Chai on Life Podcast, email me at alex@chaionlifemag.com or send me a DM @chaionlifemag. Thanks again, see you next week!

I started with sharing this beautiful wedding invitation from our friends. And I said that the quality and beauty of an invitation tells you a lot about who is inviting you, what you are being invited to, and how much that person values you. Jesus being sent from heaven to earth is the greatest invitation I can think of. All people are invited by the God of the universe to be in a marriage relationship with Him, and Jesus showed how much God values us by paying the eternal cost for sin with His body on the cross. The invitation of the gospel is not just to go to heaven one day in the future, but it is also to enjoy a personal relationship with God now. If it sounds too good to be true, you're starting to get it. And if the Christian life is about relationship and not our performance, then we can freely cling to Him with everything we are and everything we have. We can trust that the same hands that once held nails in them are the same hands that are strong enough to hold us forever.

Chương trình đổi vỏ chai lấy tiền của Victoria không chỉ giúp giảm rác thải và khuyến khích tái chế, mà với chị Nguyễn Lê Khánh Hạ ở Coburg, đó còn là cầu nối đưa chị đến gần hơn với một người hàng xóm đang sống cùng bệnh tâm thần phân liệt và cộng đồng, bắt đầu từ những cuộc trò chuyện rất nhỏ trên đường...

This week we're bringing you new bangers from Aaron Cole, Jon Keith & DaniLeigh, KB, Mission, Mike Teezy & Erica Campbell and Eris Ford! DJ Smallssss gives us mash ups of Josh P & Jermaine Dupri, indietribe & Junior Mafia and Miles Minnick/Key'ijah & YG! Our Back In The Day segment brings you classics from Sean Slaughter, 2Five and CMC's & Gospel Gangstas! We get Submissions from Jon Bolds, DANIEL DeGREE & Mitoga and DANIEL DeGREE & Bhreyion! Plus, Brandon Roots brings us new International Love from Anandelight, Horim & CHAI and KINGDOM FRONT-LINERS! Elevate Your Music & Elevate Your Mind!

[This episode originally aired on July 29, 2024] Hi everyone, welcome to another episode of The Chai on Life Podcast. I'm Alex Segal and today, we are speaking with Dr. Hilla Aboody, an amazing teacher, mother, wife and mentor currently living in Eretz Yisrael who I invited to come on to speak about this heavy time on the Jewish calendar.Hilla has such a beautiful way of explaining things — it's so deep and profound yet easy to comprehend at the same time.A little background on her:She is a wife and mother of five, living in Eretz Yisrael after making Aliyah 7 years ago from Brooklyn, NY. She is a teacher and Em Bayit (house mother) at Midreshet Eshel, a Sephardic seminary for post High school students from around the world. Her educational background includes studying at Michlalah and receiving her bachelor's degree from Bar-Ilan University and a Master's and PhD in Jewish History from New York University. She has published her study on Eliyahu the Prophet in Talmudic literature and midrashim in her book, “Through the Prism of Wisdom”. As an educator and kallah teacher, Hilla dedicates her time in guiding students to creating a relationship with Hashem and tips and tools to thrive in their marriages and in life.In our conversation, we speak about:-What the Jewish perspective is on sadness and pain-How we can connect to Hashem during this time and in this unique way, especially if it feels daunting for you right now-Why this particular time period brings about more heaviness and actually the reason why we take more precautions-The rich energy that lies in the months of Tammuz and Av — what the letters associated with each month mean, why the sense of each month is important and what we can individually and collectively take from all of that-Why crying is actually a sign of bravery-Practical things you can do now to get through this time with more faith, in a more connected way to yourself, Hashem and those around you-How to deal with difficult emotions with your kids…and SO MUCH MOREThis podcast with Hilla is basically an hour long shiur. I got chills like 17 times as I was listening to her and it's definitely one of the most important ones I've done so far in terms of our spirituality.If there is someone you want to see on The Chai on Life Podcast, email me at ⁠alex@chaionlifemag.com⁠ or send me a DM @chaionlifemag. Thanks again, see you next week!

Only a few Westerns contain explicitly Jewish stories or themes, and very rarely do Old West tales involve identifiably Jewish characters. Yet Jewish contributors have shaped the Western—once Hollywood's most popular genre—ever since the silent era, both onscreen and offscreen, and some filmmakers have sought to infuse the genre with a distinctly Jewish sensibility. In Chai Noon: Jews and the Cinematic Wild West (University of Wisconsin Press, 2025), Friedmann engages with larger themes of Jewish identity in popular film, including depictions of race, ethnicity, and foreignness. He also identifies similar concerns within the invention and creation of the imaginary West writ large in American culture. The juxtapositions prove to be both unexpected and intuitively understandable. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Only a few Westerns contain explicitly Jewish stories or themes, and very rarely do Old West tales involve identifiably Jewish characters. Yet Jewish contributors have shaped the Western—once Hollywood's most popular genre—ever since the silent era, both onscreen and offscreen, and some filmmakers have sought to infuse the genre with a distinctly Jewish sensibility. In Chai Noon: Jews and the Cinematic Wild West (University of Wisconsin Press, 2025), Friedmann engages with larger themes of Jewish identity in popular film, including depictions of race, ethnicity, and foreignness. He also identifies similar concerns within the invention and creation of the imaginary West writ large in American culture. The juxtapositions prove to be both unexpected and intuitively understandable. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/jewish-studies

Only a few Westerns contain explicitly Jewish stories or themes, and very rarely do Old West tales involve identifiably Jewish characters. Yet Jewish contributors have shaped the Western—once Hollywood's most popular genre—ever since the silent era, both onscreen and offscreen, and some filmmakers have sought to infuse the genre with a distinctly Jewish sensibility. In Chai Noon: Jews and the Cinematic Wild West (University of Wisconsin Press, 2025), Friedmann engages with larger themes of Jewish identity in popular film, including depictions of race, ethnicity, and foreignness. He also identifies similar concerns within the invention and creation of the imaginary West writ large in American culture. The juxtapositions prove to be both unexpected and intuitively understandable. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/film

Only a few Westerns contain explicitly Jewish stories or themes, and very rarely do Old West tales involve identifiably Jewish characters. Yet Jewish contributors have shaped the Western—once Hollywood's most popular genre—ever since the silent era, both onscreen and offscreen, and some filmmakers have sought to infuse the genre with a distinctly Jewish sensibility. In Chai Noon: Jews and the Cinematic Wild West (University of Wisconsin Press, 2025), Friedmann engages with larger themes of Jewish identity in popular film, including depictions of race, ethnicity, and foreignness. He also identifies similar concerns within the invention and creation of the imaginary West writ large in American culture. The juxtapositions prove to be both unexpected and intuitively understandable. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/american-studies

Only a few Westerns contain explicitly Jewish stories or themes, and very rarely do Old West tales involve identifiably Jewish characters. Yet Jewish contributors have shaped the Western—once Hollywood's most popular genre—ever since the silent era, both onscreen and offscreen, and some filmmakers have sought to infuse the genre with a distinctly Jewish sensibility. In Chai Noon: Jews and the Cinematic Wild West (University of Wisconsin Press, 2025), Friedmann engages with larger themes of Jewish identity in popular film, including depictions of race, ethnicity, and foreignness. He also identifies similar concerns within the invention and creation of the imaginary West writ large in American culture. The juxtapositions prove to be both unexpected and intuitively understandable. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/american-west

Mohnish Pabrai's Interview with Kim Kiho at Knowledge Inside podcast on June 8, 2026. (00:00:00) - Introduction (00:01:44) - Lunch with Warren Buffett vs. Eric Schmidt; Introduction to Charlie Munger (00:11:47) - Impact of declining population of South Korea; SK Hynix, Samsung & Micron (00:17:19) - KOSPI  (00:17:57) - My Investment checklist - an inspiration from the FAA; Buffett's Dexter Shoes investment (00:25:27) - Three most important items in a checklist; IKEA & Amorepacific (00:31:19) - Active vs. Passive investing; Look for risk-free investments (00:35:10) - Investing in Turkey; Reysas (00:39:24) - The Dhandho investing: Heads I win, Tails I do not lose much (00:41:20) - Investing in AI; Do not buy shiny items (00:43:39) - How to build wealth; The Rule of 72 & The Manhattan island deal in 1623 (00:50:24) - Giving back; The Dakshana Foundation (00:53:52) - Advice to listeners The contents of this website are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation or needs and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. Views expressed on Chai with Pabrai are exclusively those of Mohnish Pabrai and not of any affiliated firm or organization.

In this EpisodeWatch the full episode hereWe had one of our favorite return guests back on the show this week: Chetna Makan, the Great British Bake Off fan favorite turned YouTube institution (a million-plus devotees across her platforms, a decade of Food with Chetna), whose new book, Chetna's 5-Ingredient Indian, landed on American shelves the day before we recorded—which makes this, as far as we can tell, her first U.S. interview for it. Enjoy!Highlights & “Must-Listen” Moments* [00:00]—Welcome, Chetna: We open with Chetna's bio—Bake Off in 2014, a YouTube channel that's quietly become one of the most trusted resources for home Indian cooking, and book number nine (or ten, depending how you count)—before she walks us through the actual premise of Chetna's 5-Ingredient Indian: not a gimmick, but a direct answer to home cooks who assume Indian cooking requires a full spice cabinet and a free afternoon.* [02:00]—Why five ingredients, really: Chetna explains the constraint wasn't arbitrary—it came out of watching people get intimidated by long ingredient lists, and a cookbook market she felt had nothing genuinely new to say. The test case: her red kidney bean curry, stripped of the tomatoes, extra coriander, and green chili most versions lean on, while keeping onion, ginger, and garlic as the flavor spine.* [09:43]—The spice blends are the real trick: This is the section to read twice. Garam masala, chaat masala, and tandoori masala all got reverse-engineered down to five ingredients apiece—“hard work,” Chetna says, because “every spice adds a different note.” Her own invention, sabji masala (turmeric, hing, cumin, coriander, plus one more), doesn't exist anywhere in traditional Indian cooking; she built it from scratch to solve her own five-ingredient math problem. David draws the parallel to his own Portuguese red pepper paste—built for the same reason, to save people from re-assembling the same six ingredients every time they cook.* [17:36]—Atom masala and the mango pickle: A listener question about Priya Krishna's New York Times piece on recreating her family's lost “Atom Masala” leads Chetna into the one recipe she's never gotten back: her grandmother's raw mango pickle, peeled rather than skin-on, with a spice blend nobody wrote down before she died.* [19:17]—The lost-recipe roundtable: What starts as a question to Amy turns into three generations of food going extinct—Amy's grandmother's mocha and marble cakes, a neighbor's famously unshared ricotta fritters, and David's own grandmother's pink Portuguese chicken soup (the inspiration, he explains, for both his entire career in food writing and the pink shirt he happened to be wearing). Chetna's take on people who refuse to share recipes: “Just tell me when you want to eat it” is not a substitute for a recipe card.* [24:39]—Between the Slices and the new fans: Chetna explains the unexpected second life of her sandwich series—millions of views, a flood of younger viewers in India who now stop her on the street for sandwiches instead of curry. “It's slightly annoying,” she says, “that they don't stop me for my Indian food.”* [28:49]—The one onion mistake everyone makes: If you only watch one clip from this episode, make it this one. Chetna's diagnosis of what home cooks get wrong: onions that never actually get cooked. “They don't give it time to get to deep golden—it needs to be a caramel color.” David's hack for speeding that up without babysitting the pan: a splash of water and a lid, early on, to soften the onions before they caramelize.* [32:52]—Cheddar cheese in chicken tikka: Chetna didn't invent this—it's a real, if under-discussed, move in Indian home kitchens—but she's the reason a lot of us now know about it. “It's not like the ones you get in restaurants,” she says. “It adds a layer of flavor and more depth.”* [34:21]—No filters, ever: On a book built around restraint, and an Instagram presence built on #nofilter: Chetna explains why she's never retouched a filter on a food photo in her life, AI imagery be damned, and why a “proper messy plate” beats anything styled for the grid.* [36:36]—The baking digression: Five ingredients don't stretch to dessert, so we made Chetna talk about her other books—The Cardamom Trail and Chetna's Healthy Indian among them—and her habit of slipping an Indian accent into classic bakes: a cardamom, coconut, and mango cake; a black sesame and lime cake; clove, cinnamon, and chocolate cookies; and a cardamom upside-down pear cake that's apparently a fixture in her kitchen every autumn.* [40:25]—The table salt defense: Chetna's case for plain table salt over sea salt, kosher, or Himalayan, in five ingredients or fewer: consistency. She's cooked with it her whole life, trusts it enough to season by feel for four people or forty, and doesn't love the uneven crunch sea salt can leave behind in a finished dish.* [42:23]—No process, on purpose: Asked how she keeps up a decade-long YouTube channel, a stack of cookbooks, and a constant stream of social content, Chetna's answer is refreshingly anti-productivity-hack: don't overthink it. Ten years, never missed an upload, and the one time she did agonize over a post—a single steak photo—she just didn't post it.* [44:14]—The closer: what would convert a skeptic: Chicken tikka gets an honorable mention, but Chetna's real answer for the person who claims Indian food “isn't their thing” is the chana dal—split yellow peas, no spice blend required, just five ingredients and patience.Recipes Mentioned* Red Kidney Bean Curry—Chetna's five-ingredient rework of a family staple* Garam Masala, Chaat Masala, Tandoori Masala, Podi Masala, and Sabji Masala—five spice blends from 5-Ingredient Indian, each capped at five ingredients* Cheddar Cheese Chicken Tikka* Chana Dal (Split Yellow Peas)—Chetna's pick for converting Indian-food skeptics* Cardamom, Coconut, and Mango Cake* Black Sesame and Lime Cake* Clove, Cinnamon, and Chocolate Cookies* Cardamom Upside-Down Pear CakeBooks and Publications* Chetna's 5-Ingredient Indian by Chetna Makan (Hamlyn)—her newest, just released in the U.S.* The Cardamom Trail by Chetna Makan—baking with Indian flavors* Chai, Chaat & Chutney by Chetna Makan—a street-food tour of India* Chetna's Healthy Indian by Chetna MakanWhere to Find Us* Amy Traverso* Instagram | Yankee Magazine* David Leite* Instagram | Pinterest | Facebook | YouTube* Chetna Makan* Website | YouTube—Food with Chetna | Instagram This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit davidleite.substack.com

« Dans La Boîte à Gants », c'est + de 200 épisodes, avec des légendes de l'automobile et de la moto !✚ Abonne-toi, ça nous aide à convaincre des invités incroyables pour les prochains épisodes.⬇️ N'hésite pas à nous dire qui tu souhaites voir dans l'émission dans les commentaires !▬▬▬▬▬▬▬▬ PARTENARIATS ▬▬▬▬▬▬▬▬

Un avant-goût de l'épisode sur l'épopée de la Peugeot 908 & 9X8 aux 24h du Mans.▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

This episode recorded live at the Becker's Rural Health Leadership Summit features Brian Anderson, President and Chief Executive Officer, CHAI. He discusses the growing need for AI governance, evidence-based adoption of emerging technologies, and how healthcare leaders can balance innovation with trust, workforce readiness, and responsible deployment of agentic AI.

Mohnish Pabrai's Interview with Shaan Puri at My First Million on May 5, 2026. (00:00:00) - Introduction (00:00:30) - Value investing in the US; Importance of patience in investing (00:02:15) - Mental models: The mistress is always hotter than the wife (00:04:58) - Introduce randomness in your life; Peter Lynch's One Up on Wall Street (00:08:12) - Elon Musk (00:09:42) - From admiring to executing; Sam Walton & Cloning (00:13:24) - Tesla; Blue Origin vs. SpaceX (00:14:10) - Randomness & Cloning; Farm Con & Kevin Van Trump to Milk road (00:16:40) - McDonald's vs. Burger King (00:17:05) - The Bedrock model: Take a simple idea and take it seriously; Turkey vs. Indian markets (00:20:39) - Mental model conflicts; Circle of competence (00:23:13) - The salad oil crisis; Buffett's stake in AmEx and Disney (00:26:12) - Traits of great investors: Keep investing simple; Warren's Too Hard Pile (00:30:27) - Aksarben racetrack and Buffett's tickets adventure; Moody's Manual (00:33:02) - Japanese Company Handbook; Look for needles in haystacks  (00:34:40) - Stock market: Church with a Casino (00:38:42) - Lunch with Warren Buffett; Leverage lesson from Rick Guerin (00:41:39) - Inner scorecard vs. Outer scorecard (00:43:25) - Cash and capital allocation at Berkshire Hathaway (00:45:14) - My best investments; Investing in Turkey - Reysas & TAV Airports (00:54:57) - Active vs. Passive investing (00:57:22) - Business Moats; McDonald's & FICO (00:59:25) - Investing with AI (01:02:58) - Constellation Software Services; Mark Leonard (01:09:45) - GLP-1 (01:10:48) - Bitcoin vs. Gold (01:11:32) - Do not die at 25 and get buried at 75; Get your music out (01:15:37) - Studying great investors: Ed Thorp (01:20:45) - Ken Griffin: Citadel (01:23:01) - Advice to listeners: Lead an aligned life - My owner's manual by Jack Skeen (01:28:08) - Guy Spier's letter to me The contents of this website are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation or needs and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. Views expressed on Chai with Pabrai are exclusively those of Mohnish Pabrai and not of any affiliated firm or organization.

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

Hi everyone, welcome back to another episode of The Chai on Life Podcast. I'm Alex Segal and today I'm speaking with Katia Bolotin, the author of Making it Relevant: Timeless Torah Wisdom for an Ever-Changing World which came out at the end of last year.Katia is an author and speaker as well as a pianist, songwriter and composer of contemporary classical music. She is known for inspiring audiences with teachings that are both deeply rooted in Torah as well as being contemporary. Her writing is warm, clear, and emotionally resonant — grounded in real experience rather than theory.As I moved through her book, I really felt that. Each chapter focuses on a different parsha and is relatable and easy to read. It makes the parsha approachable and offers at least one practical takeaway or exercise you can do to really bring the Torah's teachings immediately into your life. It could be fun to learn with a friend or chavruta for that purpose as well and really motivate each other.In the episode, we speak about:-Why she decided to write a book on parsha specifically-How we can use the Torah as the greatest self-help book-Why life is more like a ladder and less like a bridge and what that ladder can teach us -How Katia grounds herself through Torah-The dangers of comparing oneself to others and how to work on that in both a physical and spiritual way-What the duality present in the Torah can teach us about the world-What masculine and feminine energy look like through a Torah lens-The best way to transmit Torah to our children...and so much more!Get Katia's book here.Follow Katia on Instagram here and through her website here.If there is someone you want to see on The Chai on Life Podcast, email me at alex@chaionlifemag.com or send me a DM @chaionlifemag. Thanks again, see you next week!

Welcome back to The Chai on Life Podcast! Today, I'm speaking with Guila Sandroussy, the creator of Tasty and Hasty which is an Instagram account and website filled with kosher food recipes that as the name suggests, are both fast and delicious.Since summer is finally here, I'm sure we all want to spend less time in the kitchen and more time in the sunshine and Guila is helping us with just that. She is spilling all her tips to cooking more efficiently whether it's for Shabbat or weeknight dinners.In our conversation, we speak about:-How she got started as a food blogger-How her Moroccan background influences her cooking today, and also how it doesn't-Where she gets the inspiration for her recipes and how you can do the same-How to plan out your weekly meals in a way that is not overwhelming and makes it feel so easy-Her Shabbat planning and cooking process broken down-Why challah has become a mitzvah she feels really connected to and how she makes time for it-She takes us behind the scenes of her content creation process from how long things take to film to the editing and even answering messages-How she brings cooking into her motherhood journey with her kids — from helping them become less picky eaters to bringing them into the kitchen with her when they're interested in learning…and SO MUCH MORESome of the things we discussed in the episode:Recipes from Guila with 9x13, sheet-pan ideas and freezer tipsQuick-meal links:Easy 9x13 Chicken and Rice9x13 Kids PastaFollow on Instagram:@TastyandhastyIf there is someone you want to see on The Chai on Life Podcast, email alex@chaionlifemag.com or send a DM @chaionlifemag.

Mohnish Pabrai's Interview with Stig Brodersen at The Investor's Podcast on March 23, 2026. (00:00:00) - Introduction (00:00:41) - Berkshire Hathaway: Warren Buffett vs. Greg Abel (00:04:39) - Greg Abel vs. Ajit Jain; Compensation at Berkshire (00:08:15) - Investing horizon of 50-100 years; Berkshire Hathaway vs. S&P 500 index (00:09:48) - Running my own company and team; Delegation and structuring (00:12:34) - Pabrai Wagons ETF (00:13:24) - Inner scorecard vs. Outer scorecard (00:16:16) - Investing in Turkey; Micro trumps the macro (00:18:44) - Diversification of portfolio; Walmart (00:21:25) - Constellation Software Services; Mark Leonard (00:25:14) - Frontline; Micheal Burry (00:29:58) - Met coal vs. IPSCO; CONSOL Energy & AMR (00:35:48) - Selling a stock; Walmart and Nifty 50 in 1970's (00:39:49) - Portfolio concentration (00:41:24) - What I Learned About Investing from Darwin by Pulak Prasad; Microsoft & Walmart (00:44:22) - Guy Spier The contents of this website are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation or needs and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. Views expressed on Chai with Pabrai are exclusively those of Mohnish Pabrai and not of any affiliated firm or organization.  The interview host is an investor in Pabrai Funds and therefore has a financial interest in the funds' performance, which creates a potential conflict of interest. The host was not compensated for this interview. The views expressed are those of the host and Mohnish Pabrai and do not constitute investment advice or a recommendation to invest. 

Mental Models for Exceptional Capital Allocation by Mohnish Pabrai at Heilbrunn Center for Graham and Dodd Investing on April 21, 2026. (00:00:00) - Introduction (00:02:03) - Charlie Munger's mental models (00:03:54) - Model 1: The Bedrock model: Take a simple idea and take it seriously (00:04:51) - Model 2: Ben Graham's three ideas on markets (00:05:28) - Model 3: Do not overdose on Ben Graham; Poor Charlie's Almanack, Philip Fisher, and Pulak Prasad (00:06:27) - Model 4: Buffett's lifetime 20-punch card (00:07:15) - Model 5: Stay in the epicentre of your circle of competence; John Arrillaga (00:09:09) - Model 6: A high error rate is guaranteed in investing (00:09:26) - Model 7: Circle the wagons: the 4% rule (00:10:36) - Berkshire's 12 best decisions in 60 years (00:12:02) - Mistakes in investing: Ferrari, Progressive Insurance & Goldman Sachs (00:12:55) - Model 8: Do not cut flowers and water weeds; The Nifty 50 crash in the 1970s & Walmart (00:15:34) - Model 9: Be a shameless cloner; VIC & Dataroma; Gimat Gross (00:16:43) - Model 10: History does not repeat itself; Investing in Turkey & Reysas (00:19:50) - Model 11: Explain your investment thesis in 3-4 sentences to a 10-year old (00:19:58) - Model 12: You always need a rope to get out of the deepest well (00:23:14) - Model 13: Nick Sleep; Zen and the Art of Motorcycle Maintenance (00:26:52) - Model 14: Thou shall not use Excel (00:27:17) - Model 15: Use a pre-investment checklist (00:28:06) - Model 16: Be singularly focused like Arjuna (00:29:27) - Read the footnotes; Turn every page: Robert Caro (00:31:16) - Model 17: Enjoy hunting for needles in haystacks; Buffett's childhood entrepreneurial adventures (00:33:40) - Japanese Company Handbook; My introduction to Charlie Munger & Debbie Bozanek (00:37:27) - Model 18: Your deepest desire is your destiny (00:38:53) - Model 19: You should always have someone to discuss your investment ideas with; Li Lu (00:40:45) - Model 20: The mistress is always hotter than the wife!  (00:41:12) - Model 21: Neither a short-term borrower nor a long-term lender be (00:41:33) - Model 22: Introduce randomness into your life; Peter Lynch's One up on Wall Street (00:43:11) - Model 23: Be a Swiss Army knife (00:43:24) - Model 24-26: Focus on spin-offs, uber cannibals & spawners; Alpha-Metallurgical Resources (00:44:02) - Model 27: Arbitrage is wonderful; Transocean vs. Valaris (00:44:17) - Model 28: Heads I win, Tails I don't lose much!; IPSCO and CONSOL Energy (00:46:10) - Model 29: Focus on low-risk; high uncertainty bets (00:46:45) - Model 30: Do not skim off the top (00:47:23) - Book recommendations: Poor Charlie's Almanack, Influence & Excellent advice for living (00:47:41) - Investing in Turkish vs. Indian markets (00:50:17) - Follow your passion  The contents of this website are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation or needs and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. Views expressed on Chai with Pabrai are exclusively those of Mohnish Pabrai and not of any affiliated firm or organization.