Podcasts about Chai

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/american-studies

CHILLED Magazine National Brand Ambassador, Chai Lee joins Dane Neal on WGN Radio. Heading into the 4th of July weekend our friends at CHILLED have suggestions, tips and trends for the cocktail scene, with classics and new takes on Tequila and low ABV alternatives too. Dane and Chai’ talk about some great brands, including Chicago’s […]

CHILLED Magazine National Brand Ambassador, Chai Lee joins Dane Neal on WGN Radio. Heading into the 4th of July weekend our friends at CHILLED have suggestions, tips and trends for the cocktail scene, with classics and new takes on Tequila and low ABV alternatives too. Dane and Chai’ talk about some great brands, including Chicago’s […]

Theo báo Khmer Times, Bộ Kinh tế và Tài chính Campuchia ngày 1-7 đã yêu cầu các nhà sản xuất, nhà nhập khẩu và nhà phân phối chấm dứt toàn bộ hoạt động khuyến mãi trúng thưởng đối với đồ uống có cồn và đồ uống có đường trước ngày 30-9.

Another Cowgirl Yap Session with Cass and Shelby. You know the drill. 

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

There are some weeks when the news cycle feels like a carefully organized sequence of important events. Then there are weeks like this one, where you find yourself discussing earthquake prophets, cricket, influencer narcissism, Elon Musk, and an apparent uprising of Wyoming deer all in the same conversation.Naturally, that became an episode of What The Frock?We started with one of the internet's favorite hobbies: predicting the end of the world. Depending on which social media account you follow, California is either perfectly fine or about fifteen minutes away from sliding into the Pacific Ocean. Every few months somebody discovers a prophecy, a prediction, a chart, or a mysterious warning that promises catastrophe is just around the corner. Human beings have been predicting disasters since the beginning of recorded history. The internet simply lets them do it faster.From there we wandered into a subject that seems to explain a surprising amount of modern behavior: Main Character Syndrome.You know the type. The person filming themselves while blocking traffic. The influencer convinced everyone around them exists as supporting cast. The activist who somehow turns every issue, every event, and every headline into a story about themselves. Somewhere along the line social media convinced a lot of people that life is not something you live. It is something you perform.The conversation eventually found its way to politics because, frankly, everything eventually does. We looked at the strange spectacle of anti-Musk protests, political celebrations, and the increasingly common habit of defining yourself entirely by who you oppose. There is a difference between having convictions and turning politics into your entire personality. America seems to be having trouble remembering that distinction.Sports provided a welcome break from all of that.We spent some time talking soccer, international competition, and the continuing effort to explain cricket to Americans. The more cricket I watch, the more I understand why the rest of the world is obsessed with it. The more I try to explain it, the less certain I am that I understand it myself.Then came one of my favorite stories of the week.Apparently there are reports out of Wyoming suggesting that deer are becoming a little more aggressive toward hunters. Whether this represents an actual wildlife counteroffensive or simply another strange internet headline remains unclear. Either way, it raised an important philosophical question: at what point does the hunted decide it has had enough?We wrapped up with a discussion that somehow became more serious than expected. What exactly is the difference between tea and chai? As it turns out, the answer says quite a bit about language, culture, and the strange ways words travel around the world.In other words, it was a perfectly normal episode of What The Frock?Well, normal for us anyway.Join Rabbi Dave and Friar Rod for another hour of headlines, humor, observations, arguments, and the occasional reminder that reality remains far more creative than anything Hollywood could write.#WhatTheFrock #RabbiDave #FriarRod #PodcastLife #CurrentEvents #CultureCommentary #MainCharacterSyndrome #ElonMusk #CricketUSA #SoccerTalk #InternetCulture #EarthquakeWarning #TeaVsChai #PoliticalHumor #Satire #SocialMediaCulture #CommonSense #HumorPodcast #RealityIsWeirderThanFiction #NewsAndViews

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!

Être restaurateur.ice aujourd'hui, c'est souvent être à la fois chef.fe, gestionnaire, recruteur.euse, communicant.e et entrepreneur.euse. Mais comment s'y préparer ? Peut-on vraiment apprendre ce métier sur le tas, ou faut-il repenser la formation pour mieux accompagner celles et ceux qui se lancent ?Et si formation obligatoire il y a, que contiendrait-elle, au juste ?Entre parcours autodidactes, écoles hôtelières et reconversions, ce TOAST interroge les chemins qui mènent à l'ouverture d'un restaurant, et les compétences (visibles ou invisibles) que cela exige. Une discussion pour partager expériences, doutes et pistes d'évolution pour la profession.Aux micros: • Etienne Tiberghien - Consultant ESS et formateur (la Cadenelle - Bonneveine)• Axelle Poittevin - Cheffe et propriétaire de Razzia• Louise Perrone - Cheffe propriétaire du restaurant Rouge• Edgar Baudin - Président de l'Abri, restaurant et Chai urbain• Juliette Laguionie - Responsable de formations (la Cadenelle) et ancienne restauratriceUn échange animé par Laurène Petit (journaliste et co-secrétaire générale de La Communauté Ecotable).Un format cuisiné par les associations Festin et La Communauté Ecotable, membres pilotes du mouvement Restaure, qui œuvre pour une restauration plus responsable, solidaire et durable !Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Être restaurateur.ice aujourd'hui, c'est souvent être à la fois chef.fe, gestionnaire, recruteur.euse, communicant.e et entrepreneur.euse. Mais comment s'y préparer ? Peut-on vraiment apprendre ce métier sur le tas, ou faut-il repenser la formation pour mieux accompagner celles et ceux qui se lancent ?Et si formation obligatoire il y a, que contiendrait-elle, au juste ?Entre parcours autodidactes, écoles hôtelières et reconversions, ce TOAST interroge les chemins qui mènent à l'ouverture d'un restaurant, et les compétences (visibles ou invisibles) que cela exige. Une discussion pour partager expériences, doutes et pistes d'évolution pour la profession.Autour de la table ronde :• Louise Perrone - Cheffe propriétaire du restaurant Rouge (Marseille)• Edgar Baudin - Cofondateur de l'Abri, restaurant et Chai urbain (Marseille)• Juliette Laguionie - Responsable de formations (la Cadenelle) et ancienne restauratriceUn échange animé par Laurène Petit (journaliste et co-secrétaire générale de La Communauté Ecotable).Un format cuisiné par les associations Festin et La Communauté Ecotable, membres pilotes du mouvement Restaure, qui œuvre pour une restauration plus responsable, solidaire et durable !Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

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.

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into a series of significant advancements shaping the landscape of our industry. As technology continues to redefine traditional paradigms, the collaboration between Pfizer and Chai Discovery exemplifies this trend. By harnessing artificial intelligence, particularly through custom models like Chai-3, this partnership aims to revolutionize drug discovery. The integration of AI promises not only to accelerate the identification of biologics and antibodies but also to optimize resource allocation in research and development. Such technological integration could pave the way for an enhanced pipeline of innovative treatments, marking a transformative shift in how therapeutic candidates are developed. In the realm of regulatory developments, Lupin's Ranluspec has recently received FDA approval as an interchangeable biosimilar targeting VEGF-A for various retinal conditions. This move underscores the importance of biosimilars in providing cost-effective alternatives to expensive biologics, thereby expanding patient access to essential treatments for conditions like macular degeneration. Additionally, the MHRA's marketing authorization for Aujemflu, an adjuvanted trivalent influenza vaccine for adults aged 50 and over, reflects ongoing efforts to bolster protection against infectious diseases among vulnerable populations. Clinical trial advancements continue to highlight significant progress in therapeutic development. Otsuka Pharmaceuticals' Phase 3 data on Voyxact has shown promising stabilization of kidney function in patients with Immunoglobulin A nephropathy. This protein therapy targets autoimmune pathways, offering new hope for managing this chronic kidney condition. Similarly, Autobahn Therapeutics' Elunetirom has advanced to a pivotal trial following Phase 2 success in treating bipolar depression. This showcases the potential of small molecule therapies targeting thyroid hormone receptors. Meanwhile, Hikma Pharmaceuticals' victory in a landmark patent case regarding skinny labels marks an important development in pharmaceutical intellectual property rights. The unanimous Supreme Court ruling against Amarin supports the legitimacy of using skinny labels to market generic versions of drugs for non-patented indications. This decision could enhance market competition and drive down healthcare costs, setting a precedent for future intellectual property disputes. On the business front, strategic partnerships and mergers continue to shape industry dynamics. Gilead Sciences' acquisition of Ouro Medicines for $1.675 billion strengthens its autoimmune inflammation pipeline. This transaction exemplifies how major deals are reshaping therapeutic portfolios in response to growing demand for treatments targeting rare diseases. Financially, Solix Pharmaceuticals' success in raising $71 million to advance its siRNA pipeline across multiple therapeutic areas demonstrates investor confidence in RNA-based therapeutics as a promising frontier for innovative treatments. Conversely, challenges persist as evidenced by Takeda's $2.5 billion legal provision over an antitrust case related to Amitiza, underscoring ongoing financial risks associated with litigation in the pharmaceutical sector. Corporate restructuring also signals shifts within the industry landscape. Fulcrum Therapeutics' decision to lay off 85% of its workforce following the discontinuation of its sickle cell disease candidate highlights the volatility and high stakes inherent in drug development. Overall, these developments illustrate a dynamic landscape where scientific innovation is propelled by AI-driven approaches and strategic collaborations while regulatory victories and financial maneuvers shape market dynamics. These trends have profound implications for patient care by potentially accelerating the availability of novel therapies and fostering a competitive environment that drives down costs. As we look ahead, stakeholders must navigate these complexities effectively to harness opportunities and address challenges within this rapidly evolving industry landscape. The ability to adapt and capitalize on emerging trends will be crucial as these sectors continue to evolve, ultimately enhancing patient care and advancing therapeutic frontiers globally. Thank you for joining us today on Pharma Daily; stay tuned for more insights into the ever-changing world of pharmaceuticals and biotech.Support the show

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. 

Hii leo jaridani tunakuletea mada kwa kina inatupeleka nchini nchini Rwanda. Kufuatia changamoto zinazoongezeka kutokana na mabadiliko ya tabianchi, kupanda kwa gharama za mbolea na ushindani wa masoko ya kikanda, Shirika la Umoja wa Mataifa la Chakula na Kilimo FAO linashirikiana na nchi ya Rwanda kutekeleza mkakati mpya wa kuimarisha sekta ya chai lengo likiwa ni  kuboresha zao hilo na kuinua pato la mkulima.Mlinda amani wa Umoja wa Mataifa anayehudumu nchini Lebanon amefariki dunia na wengine wawili wamejeruhiwa mapema leo Alhamisi baada ya makombora ya mizinga kulenga eneo lao la kazi karibu na Marjayoun kusini mashariki mwa nchi hiyo, imesema taarifa ya Kikosi cha Mpito cha Umoja wa Mataifa nchini Lebanon UNIFIL. Katibu Mkuu wa Umoja wa Mataifa anatarajiwa kutoa taarifa kuhusu tukio hilo muda wowote kuanzia sasa.Shirika la Umja wa Mataifa la afya Duniani, WHO, limesema chakula kisicho salama kinaendelea kuwa tishio kubwa kwa afya ya umma duniani, kikisababisha takribani wagonjwa milioni 866 na vifo milioni 1.5 kila mwaka, huku watoto wenye umri wa chini ya miaka mitano wakiwa katika hatari kubwa zaidi. Kwa mujibu wa ripoti mpya watoto hao wadogo, ambao ni asilimia 9 tu ya idadi ya watu duniani, wanachangia karibu theluthi moja ya magonjwa yote yanayosababishwa na chakula.Leo ni siku ya watoto wasio na hatia waathirika wa ukatili duniani, maadhimisho haya yanakuja wakati ambapo ripoti ya Mwaka ya Katibu Mkuu wa Umoja wa Mataifa kuhusu Watoto na Migogoro ya Silaha inaonesha kwamba ukatili dhidi ya watoto katika migogoro ya silaha ulifikia viwango visivyo vya kawaida mwaka 2024, huku kukiwa na ongezeko la asilimia 25 la ukiukwaji mkubwa wa haki za binadamu ikilinganishwa na mwaka uliopita.Na katika kujifunza lugha ya Kiswahili, leo mchambuzi wetu ni Dkt. Josephat Gitonga, ambaye ni Mhadhiri katika Chuo Kikuu cha Nairobi nchini Kenya, kwenye kitivo cha Tafsiri na Ukalimani anafafanua maana ya methali “La kiliwacho kisicholiwa ni sumu”."Mwenyeji wako ni Flora Nducha, karibu!

Perfectionism has a sneaky way of turning a perfectly good life into a project you can never finish. I sit down with author, fiber artist, and motivational speaker Lorie Kleiner Eckhert to talk about reinvention, resilience, and the moment you decide that “good enough” is not failure, it's freedom. Lorie's newest book, Chai On Life (a playful nod to “chai,” the Hebrew word for life), becomes our jumping-off point for how to move through big transitions with more self-trust and less self-criticism.We get practical and personal: why many of us wait decades to give ourselves permission to do what makes us happy, what divorce and midlife singlehood taught Lorie about independence, and how a complicated “perfect” chicken soup recipe captures the problem with overthinking everything. Lorie also shares how quilts became her visual storytelling tool on stages from PTAs to Procter & Gamble, and why returning to simpler, more durable creativity helped her come back to herself.If you're feeling stuck, Lorie offers a clear two-step reinvention plan: keep your healthy daily routine even when you're hurting, then take one tiny step a day toward your new life and write it down in an accountability log. We also normalize therapy and counseling as ongoing emotional support, not a last resort, and we end with a favorite idea: being “flawsome” and giving yourself credit for the brave next step.Subscribe for more conversations on personal growth, mental fitness, creativity, and navigating life transitions, then share this with a friend who needs a little lift and leave a review so more people can find us. What's one tiny step you'll take today?Connect with Lorie HEREHer newest book, Chai on Life, is available at Amazon and anywhere quality books are sold. Let me know what you'd like to hear more about on the showSupport the showI'm Carol Clegg, your host, an accountability coach and curious conversationalist inviting guests from a wide range of backgrounds to share insights on how they live, think, and navigate change.If you enjoy reflection, fresh perspectives, and honest dialogue, this space is for you.If you'd like to experience this work in community, I host a complimentary monthly Accountability Circle  a supportive space to pause, gain clarity, and choose a gentle next step forward. More info at https://carolclegg.com/accountabilitycircleFor those ready for deeper, more consistent support, I also offer a 90-day Accountability Package, designed to help you move from scattered ideas to steady, sustainable momentum.You can learn more at carolclegg.comLet's connect on LinkedIn and Instagram, or join my LinkedIn Group Flourish: A Community for Women Business Owners

Welcome back to The Chai on Life Podcast! Today, 'm speaking with Rebbetzin Sara Yoheved Rigler. For those who do not know Rebbetzin Rigler, she is a best-selling author, speaker and teacher and this is actually her second time on The Chai on Life Podcast. She is our first second-time guest and I am so grateful for her for returning.The reason she is back is to discuss her brand new book, 8 Seconds to Connect with Hashem: Mitzvah Mindfulness for Women. The whole idea of the book is about turning everyday actions into moments of connection. With clear guidance and short intention statements, even routine tasks―like drinking water or doing laundry―can become meaningful mitzvos.I was blessed to hear about this concept from Rebbetzin Rigler on one of her classes through Jewish Workshops and after reading an early copy of the book, tried practicing it on my own. As you'll hear more about in our interview, it's truly a transformational idea. In a really small way, you can change your whole relationship with Hashem for the better.In our conversation, we speak about:-Why she wanted to write this book-Why this action creates so much more love for and with Hashem-How to do this when things feel tough and you're going through challenges in life-The importance of doing a mitzvah with simcha-The two things that might get in the way of doing this practice effectively — and how to both watch out for and overcome those-Why you can apply this concept to literally anything you're doing throughout the day — even serving Hashem with your yetzer hara…and so much more!For the most up to date information, you can go to the book's website, 8secconnect.com.To listen to the first episode with Rebbetzin Rigler, click here.More relevant links:Order the book on AmazonSararigler.comHello Habit app

Clapping

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.

E chegou a vez de Jordana Morais e Gabi Saporito no PodDelas! As ex-participantes do BBB26 abriram o jogo sobre tudo o que viveram dentro e fora da casa mais vigiada do Brasil.No episódio, elas falam sobre rivalidades, estratégias, amizades, enfim, tudo o que viveram e que ficou marcado! Jordana relembra os embates com Ana Paula, a repercussão das brigas e como foi ser vista como uma das participantes mais intensas do BBB26. Já Gabi conta bastidores do Quarto Branco, a amizade com Chai e os momentos mais difíceis emocionalmente dentro da casa.Um papo sincero sobre BBB26, reality show, internet, julgamentos, fama e tudo o que aconteceu depois da eliminação.#PodDelas #BBB26 #JordanaMorais #GabiSaporito______________________________________________________ LISTERINE®️ Cuidado Total — o enxaguante mais completo da marca. Ele tem 5x mais poder de limpeza*, alcançando áreas que muitas vezes a gente não consegue chegar com apenas escovação e fio dental. Saiba mais: https://bit.ly/4eRxVYQ5x mais poder de limpeza* do que apenas o uso da escovação e fio dental. Redução de placa combatendo germes causadores; não substitui escovação e fio dental.______________________________________________________Ouça agora na Audible mais de 850 mil audiolivros! Acesse: https://www.audible.com.br/pd/Nem-te-conto-Audiolivro/B0GVL7VF1X?source_code=BAWP30DTRIAL7140504264272

This episode originally aired on June 3, 2024 but is just as relevant today. Enjoy and wishing everyone a beautiful Shavuos! Today we have a really exciting episode to gear up for Shavuot with Esteemed Educator, Speaker and Kallah Teacher Nalini Ibragimov.If you know Nalini, you already know how special she is. I had the privilege of meeting her nearly 12 years ago now when she co-founded a group called Souled, which was a weekly class for young professional women who had been on Israel trips or other programs and wanted to continue their learning while working in New York City. She is a role model to maybe thousands of women at this point through her work on college campuses, as a kallah teacher, someone who prepares brides for their upcoming wedding, Souled and other Jewish organizations.Here's some more formal info for those who aren't familiar with Nailni and her work:Nalini Ibragimov attended Barnard College and Moreshet Institute and studied Jewish history in Touro's master's program. She lived in Israel for four years where she taught in various institutions. For the following five years, she and her husband worked at Brooklyn College for JLIC, a joint program of the OU and Hillel, where she taught and ran various programs for Jewish students on campus. Nalini taught in Ateres Naava seminary in Brooklyn for 15 years until moving to Long Island a few years ago. Additionally, Nalini has taught hundreds of kallot and is a speaker for My Gift of Mikvah. She is also part of the educational staff of Core. Today, Nalini is the director of the ⁠Olami Women's House⁠, which provides a living space for young professional women who seek to live in a nurturing, Torah immersive environment. If you want to apply to live there, click ⁠here⁠!If you're a young professional woman, you can also hear Nalini speak Wednesday nights at Safra Synagogue on the Upper East Side at 7:30 p.m.Nalini resides in Woodmere, New York with her husband and takes great pride in her six children and the families they are building.Now, let's talk about our episode. In our conversation we speak about:-Nalini's Jewish journey and how she got to where she is today-How she manages and balances all of the work she does with being a wife and a mother of six children-How she decides what professional responsibilities to take on-What Shavuot really means and the perspective we can have going into it-How we can manage the heaviness all around us right now and channel that pain we're still feeling into the holiday and use it to create even more connection with Hashem-What a miracle actually looks like and how we can see more of them in our daily lives, right now-How we can use the time we have left before Shavuot to prepare for the holiday — and why the process and the preparation are key-How to connect on Shavuot when you're home with little kids and not going to learn all night-How the holiday of Shavuot is like a wedding and a beautiful lesson that we can all infuse into our marriages and our relationship to all of klal Yisrael…and SO MUCH MOREIf you have any questions for Nalini, you can reach out to her at Nalini419@gmail.com.If there's someone you want to see on The Chai on Life Podcast or a topic you want featured, send me an email at alex@chaionlifemag.com or a DM on Instagram at ⁠@chaionlifemag⁠.Thanks so much, see you next week!

Join FPL Chai as he reviews his GW37 team so far and reveals his early GW38 team selection. For more of Chai, you can subscribe to his YouTube Channel: @FPLChai

We will need each other to discover our gifts. We can't just pray in our room alone and expect God to send a delivery pigeon with our gift. Part of finding our gifts is serving the church body and learning along the way. Things like spiritual gifts tests are great, but those tests don't actually know you like other people can. Please do not wait to be certain about your gifting before you try serving the church. We all learn along the way as we are affirmed by fellow believers and pointed in the right direction by the Holy Spirit. I am just humbly asking that you prayerfully consider how the Spirit may be inviting you to serve your fellow Christians, whether at this church or in the various spaces you occupy outside the church. Either way, please don't remain a spectator - get in the game. Because all Christians need the church, and the church needs all Christians. We need you, and you need us. But it's important to remember - before we give Jesus our hands in service, He first wants our hearts in surrender. So I'm going to pray us out, and we're going to sing, “Lord, I give you my heart. I give you my soul. I live for you alone.” And I hope that as we sing this, we can make this our collective prayer as a church. As we surrender our hearts to God, I trust the Holy Spirit will open our hands in service to Him.

Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b

Turning 40 isn't the beginning of decline. It's the beginning of remembering who you really are.In this episode of The Brave Table, I sit down with Sarah Jane Ho, Netflix host of Mind Your Manners, entrepreneur, and educator in Traditional Chinese Medicine & Feng Shui, for a conversation that goes far beyond wellness trends.We talk about aging, identity, healing, tiger parenting, spiritual awakening, and the ancient Chinese practices that women have used for generations to protect their energy, hormones, and emotional well-being.From hot water rituals and emotional healing… to perimenopause, nervous system regulation, and why so many women feel disconnected from their bodies today, this episode is packed with wisdom that feels both ancient and deeply relevant.

Join FPL Chai as he reviews his GW36 team so far and reveals his early GW37 team selection. For more of Chai, you can subscribe to his YouTube Channel: @FPLChai

Your hosts return to Tel-Bastet, the city of cats, in Lynn Strong's Middle-Eastern-inspired cozy fantasy novel Chai and Charmcraft. They talk about romance focused character exploration, cat-based worlds, and cozy conflict.Find us on Discord / Support us on PatreonThanks to the following musicians for the use of their songs:- Amarià for the use of “Sérénade à Notre Dame de Paris”- Josh Woodward for the use of “Electric Sunrise”Licensed under Creative Commons: By Attribution 4.0 License

Today, I'm speaking with Rebbetzin Miriam Katz. Rebbetzin Katz is a mother of seven children, bli yin hara and lives in Jerusalem with her husband, Rabbi Doniel Katz who is a well known teacher on meditation and psychology through the lens of Kabbalah and Chassidus.She has a degree in elementary education, but explains she has learned the most about children by raising them. She has become a treasure trove of wisdom and has turned her teachings into a Substack newsletter called Mother 2 Mother where she shares insights from her two decades of motherhood. It's both practical and spiritual, relatable yet inspiring and it has been such a pleasure to be a part of her community and learn from her writings.We are approaching Mother's Day — at least in the States — and while it may be a holiday you gloss over, it's also a good opportunity to simply reflect on all you do as a mother. As we speak about in our interview, there's so much invisible work when you're a mother, keeping things running on a day-to-day basis — and it's important to make the invisible visible whether it's with your spouse, with your kids or without anyone else involved — just to yourself.In our conversation we speak about:-How Rebbetzin Katz found and connected to Orthodox Judaism-The crazy story of how she met her husband-How to figure out what we really need when it comes to our unique mothering journeys-How we can place more value on our role as a mother, if it's not something that comes easily to you-What a deep connection to Hashem can look like through mothering-How to feel better about the mental load motherhood comes with and ways to reframe it-Something you can do at home to cultivate more gratitude…and so much more!To reach out to Miriam, you can email her at Miriamkatz613@gmail.com.Please leave a rating and review, share the episode with a friend, family member or coworker who you think might enjoy it as well. 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!

Join FPL Chai as he reviews his GW35 team so far and reveals his early GW36 team selection. It's a double Gameweek and Chai already has triple City, will he be transferring in Palace players? For more of Chai, you can subscribe to his YouTube Channel: @FPLChai

Gol Bezan returns with the latest information on Team Melli's preparation for FIFA World Cup 2026, Asian Champions League, and Persian Gulf Pro League with an exclusive interview with Leyla Shams of Learn Persian with Chai and Conversation (https://www.chaiandconversation.com/). Chai and Conversation YouTube channel: https://www.youtube.com/c/LearnPersianwithChaiandConversation Host Samson Tamijani ramps up interviews with unifying voices in North American Iranian communities as we prep for #TeamMelli in the North American World Cup. Our hearts and prayers are with all Iranians traversing this difficult time, as we have faith that the Iranian people will always survive and thrive. Our graphics specialist, Mahdi, is unable to produce our usual great artwork as the Internet outage in the country continues. Chapters: 00:08 Welcome 01:58 Team Melli latest 02:35 Tickets for Iran residents 03:46 Players out for WC 04:34 World Cup prep schedule 08:00 March friendlies recap 08:25 World Cup roster draft 11:10 Interview with Leyla Shams 25:17 PGPL, ACL update with Erfan Hoseiny Follow us on social media @GolBezan, leave a like/review & subscribe on the platform you listen on - YouTube, Spotify, Apple Podcasts, Google Podcasts, SoundCloud, Amazon, Castbox. Outro Music: K!DMO / kidmo.foreal Sina - / iranfooty Arya - / arya_allahverdi Aryan - / aryan.ghasemi Samson - / gbpsamson Mahdi - / mativsh / @ball4allmedia Kian - / kianb575 Imann - / amuimann / golbezan / golbezanfarsi / golbezan / golbezanpodcast / golbezan

Today, I'm speaking with Esther Wein, Torah educator extraordinaire.For more than 35 years, Esther has guided students of all backgrounds in Torah learning. Along the way, she became increasingly aware that even well-educated learners were often working with over-simplified explanations or incorrect ideas about the core ideas of Judaism and the unfolding story of Am Yisrael.  Critical tools for navigating today's complex world were therefore missing.Because of this, she created an entirely new program called Torah Unlocked, and its upcoming flagship course, Reishis, where students will take a fresh look at the story the Torah is telling. Esther's approach allows the many Torah ideas we have all learned to fall into place within their true context, and many longstanding questions begin to resolve on their own. The result is renewed pride, enthusiasm, clarity  and devotion to the role one can play right now in Am Yisrael's story.  While Reishis is not out yet, it is launching in the next couple of months, so if you visit estherwein.com, you can sign up for updates and get notified once it's officially out.In this episode, we speak a lot more about:-The course Esther created-Some of the core conflicts of the Torah and how they relate to what we're going through both as a people and individually today.-The religious world — both as a way to focus more on its positive attributes and recognize what we can all work on to create growth and improvement. -What we can take from the Pesach story as she reframes the it in a way that will change how you view the entire Jewish people and our place in this world. -The omer period — what is really going on at this time that we are in and how practically, we can maximize its potential. -The one thing that brings more bracha to Am Yisrael than anything else....and SO much more.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!

Join FPL Chai as he reviews his GW34 Free Hit so far and reveals his early GW35 team selection. For more of Chai, you can subscribe to his YouTube Channel: @FPLChai