Podcasts about genie

Ang pagtulong sa kapwa ay kusang loob at hindi dapat gawing listahan para magi nilang utang na loob. Pakinggan ang kwento ni Genie sa Barangay Love Stories.

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Happy September comedy fans -- we have a treat for you this week. You may not know the name John Paragon but you sure know his work. Paragon was part of the Groundlings Improv Troupe that spawned such huge stars as Paul Reubens and Cassandra Peterson. He helped Paul turn into Pee Wee Herman and Cassandra into Elvira, Mistress of the Night. For Pee Wee he also played Jambi the Genie, a character carried over to the television version of the Pee Wee Herman Show. For Elvira, Paragon wrote many of her most famous lines and on her Movie Macabre show he appeared as a phone-in fan called "The Breather." John also had his own comedy special and worked steadily as an actor, writer and director. No wonder his special called him "The Paragon of Comedy." As always, find extra clips below and thanks for sharing our shows! Want more John Paragon? For Pee Wee Herman John played Jambi the Genie who would lead everyone in a very special chant -- all together now! https://www.reddit.com/r/Xennials/s/45EKQIxFKB John scored his own special in 1983 called The Paragon of Comedy. We've marked the clip to start with the full Ramon Azteca bit, but the whole thing is worth watching. https://youtu.be/lpL_Svu7o6M?si=76aZwVd2K8N7cmE4&t=2826   John was a close friend of Cassandra Peterson, aka Elvira, and wrote many great lines as well as playing The Breather on Elvira's Movie Macabre. https://youtu.be/QuLKSXe8Ah0?si=PX0MmEN8-GdYRipm

Genie has been leaving his body since he was 16 — and somewhere along the way, that took him places most people never get near. In this episode, he walks through what he says he encountered inside Area 51: a technology he later learned was real, an alien body he wasn't supposed to describe, and a childhood memory that reframes the entire story. We also go deep into out-of-body mechanics, why the world feels like it's falling apart right now (and why that might be the point), where humans actually come from, and what happens the moment you stop fearing death.This isn't guided-meditation content. This is someone who says he's mapped the other side and is still trying to figure out how much of it he's allowed to say.In this episode:His first out-of-body experience at 16, before he even knew what it wasWhy the current chaos in the world is part of an ascension cycle, not a collapseWhat he says he found inside Area 51 — and why he wasn't supposed to talk about itThe alien body, the technology, and the childhood memory that ties it togetherWhere humans actually originated, according to what he's been shownThe Egyptian deities, the 12 gates, and what "passing a test" on the other side really meansHow he lost his fear of death completely

Nous sommes le 10 décembre 1907, à Stockholm. Cérémonie de remise des prix Nobel. La première du genre a eu lieu six ans plus tôt. Rudyard Kipling est le premier lauréat anglophone, dans la catégorie des écrivains. Le secrétaire permanent de l'Académie suédoise, Carl David af Wirsen, se fend d'un discours qui associe l'auteur du « Livre de la jungle » à pas moins de trois siècles de littérature de son pays. Il dit : « l'Académie de Suède, en attribuant cette année le prix Nobel de littérature à Rudyard Kipling, souhaite rendre hommage à la littérature anglaise si riche de gloire diverses, ainsi qu'au plus grand génie que ce pays ait jamais produit dans le domaine de la narration. »nUn peu moins de trente ans plus tard, le 18 janvier 1936, Kipling tire sa révérence. Il aura eu le temps de se faire autant d'amis que d'ennemis. Il reste, en effet, l'un des écrivains les plus controversés au monde. Avec Myriam Campinaire, traductrice et professeur de littérature. sujets traités : Rudyard Kipling, génie, contesté, prix Nobel, littérature Merci pour votre écoute Un Jour dans l'Histoire, c'est également en direct tous les jours de la semaine de 13h15 à 14h30 sur www.rtbf.be/lapremiere Retrouvez tous les épisodes d'Un Jour dans l'Histoire sur notre plateforme Auvio.be :https://auvio.rtbf.be/emission/5936 Intéressés par l'histoire ? Vous pourriez également aimer nos autres podcasts : L'Histoire Continue: https://audmns.com/kSbpELwL'heure H : https://audmns.com/YagLLiKEt sa version à écouter en famille : La Mini Heure H https://audmns.com/YagLLiKAinsi que nos séries historiques :Chili, le Pays de mes Histoires : https://audmns.com/XHbnevhD-Day : https://audmns.com/JWRdPYIJoséphine Baker : https://audmns.com/wCfhoEwLa folle histoire de l'aviation : https://audmns.com/xAWjyWCLes Jeux Olympiques, l'étonnant miroir de notre Histoire : https://audmns.com/ZEIihzZMarguerite, la Voix d'une Résistante : https://audmns.com/zFDehnENapoléon, le crépuscule de l'Aigle : https://audmns.com/DcdnIUnUn Jour dans le Sport : https://audmns.com/xXlkHMHSous le sable des Pyramides : https://audmns.com/rXfVppvN'oubliez pas de vous y abonner pour ne rien manquer.Et si vous avez apprécié ce podcast, n'hésitez pas à nous donner des étoiles ou des commentaires, cela nous aide à le faire connaître plus largement. Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Are the extras at Walt Disney World really worth the money? In this episode, I'm breaking down some of the most popular Disney World extras and upgrades, including MagicBands, After Hours parties, table-service dining, Deluxe Resorts, and Genie+. I'll share the pros and cons of each, what you actually get for your money, and which extras may be worth adding to your Disney World vacation. If you're trying to build a Disney World budget and decide where to spend (and where to save,) this episode will help you figure out which Disney experiences are worth the extra cost.I hope you enjoyed today's show. I'd love to connect with you over on IG @mountains_of_magic or Facebook at Fantastical Vacations by Daniele. If you would like help in planning an upcoming Disney or Universal vacation, email me at danielerobbins@fantasticalvacations.com or fill out a quote form to get started planning the magic Get A QuoteMusic from Uppbeat (free for Creators!):https://uppbeat.io/t/andrey-rossi/bring-the-funLicense code: E9BZCTS1O3JRPERX

Genie und Wahnsinn liegen nah beieinander, so auch beim hochbegabten Anatoly, der auf der einen Seite ein breit gefächertes Wissen über Friedhöfe, Rituale und Okkultismus besitzt, auf der anderen Seite jedoch Menschen meidet und die Einsamkeit bevorzugt. Als die Polizei eines Tages im Haus des Wissenschaftlers seltsame Puppen entdeckt, kommen sie einem Geheimnis auf die Spur, das seinesgleichen sucht.

Der Sommer 2026 ist heiß. Die einen freuen sich über das schöne Wetter - die anderen sorgen sich wegen Waldbränden, Ernteausfällen und Niedrigwasser. Der Philosoph Christoph Quarch ist zwiegespalten. Was ihn im Gespräch mit SWR Aktuell-Moderator Stefan Eich jedoch am meisten umtreibt, ist die Ignoranz mancher Menschen.

On today's show we discuss Ara's garage door plan for integrating it into HomeKit, we help a listener decide on which receiver to buy, and we ask whether Atmos is worth it for a family room. We also read your emails and take a look at the week's news. News: Get YouTube TV For Just $67.99 a Month Down From $82.99 For The Rest of 2026 How many subscribers do major live TV streamers have? Other: Bingebuster  THIRDREALITY Smart Garage Door Opener Last week I discussed my garage door situation where MyQ does not integrate with Apple's HomeKit. I explained that I will be using a solution called ratgdo (Rage Against The Garage Door Opener). A few of the listeners emailed and suggested that I look at the ThirdReality Smart Garage Door Opener). Available at Amazon for $50.  Issues for me: When you install it in Apple home it installs as a lightbulb.  I added it to Home Assistant first because I knew Apple HomeKit was going to treat it like a light instead of a garage door. In Home Assistant, it shows up as a light entity, with the on off state following the tilt sensor. To make it behave like a real garage door, I created a Template Cover helper and set the device class to Garage. For the state, I used the template to report open when the light is on and closed when it's off. Then I set the Open and Close actions to call the light.turn_on service on the light entity, so pressing Open or Close in Home Assistant is basically the same as pressing the garage remote button. After that, it shows up and behaves like a garage door inside Home Assistant, with the right open/closed status based on the tilt sensor. It's mechanical. Batteries After Jorge's email,I am now looking past these issues and considering this solution. And yet another listener provided even more fuel for this fire! David writes in and says: Most door operators have a provision for a wall mounted pushbutton ( the same one used for years).  It's a momentary pushbutton closure. There is a cold contacts switch which operates in the Matter smart home standard.  As a result it works in all the various smart home systems.  It's a Shelly 1 Mini Gen 4 device…about $25.   I have installed mine into my Apple HomeKit system to operate my Genie garage door opener.  There is no sensing of the position of the door in the Shelly 1 Mini Gen 4 - it is only for operating the door. I have used the IKEA "MYGGBETT" door/window sensor ($10) to sense the position of the door - it is also Matter compliant.  It is the usual magnet/reed switch pair.   I have used this sensor to also trigger a light inside my house to signal an open garage door. The Apple Home automation could be written to close and open door after 10 PM.  Or close a door open for more than 1 hour. Marantz, Denon, or Onkyo • Marantz Cinema 70s • Denon AVR-X2800H • Onkyo TX-RZ30 Onkyo TX-RZ30 Pros 9 amplified channels (supports 5.2.4 or 7.2.2 Atmos) Full-bandwidth Dirac Live included (major value) THX Select + IMAX Enhanced certified Dual independent subwoofer outputs + full pre-outs Strong power (~100W/ch) and generally excellent reviews for sound and value Cons Dirac setup can be finicky Onkyo's past reliability reputation still in the back of my mind Denon AVR-X2800H Pros Reliable, polished, user-friendly experience Solid Audyssey MultEQ XT room correction Good, balanced sound and strong HDMI features Much better reliability and support Cons Only 7 channels (max 5.2.2 Atmos without external amps) No Dirac (Audyssey is good but not as advanced) Marantz Cinema 70s Pros Slim, stylish design that fits media cabinets Marantz sound  Pre-outs for future expansion Cons Lower power (~50W/ch) — best for smaller/efficient speakers Only 7 channels More expensive relative to performance and channel count Recommendation: Onkyo TX-RZ30 It delivers the most meaningful advantages for the money — 9 channels, including full Dirac Live, THX certification, and independent dual subs. The Denon is the safer "set-it-and-forget-it" choice if you prioritize polish and reliability, while the Marantz is best if you specifically need a slim form factor. For performance and value in a modern Atmos setup, the RZ30 is the clear winner right now. Is Atmos Worth it for a Family Room? Yes, Atmos is usually worth it in a typical family room — but with some practical caveats. Why it's worth it The overhead effects (rain, helicopters, debris, ambiance) add noticeable immersion even in a normal living/family room. A lot of modern movies, streaming shows, and games take advantage of it. Realistic expectations for a family room The "wow" factor is more subtle than in a dedicated theater. You're not going to get the same dramatic height effects you hear in a big cinema. Placement matters a lot. Up-firing Atmos speakers work okay if your ceiling isn't too high or absorbent, but actual in-ceiling or on-ceiling speakers sound better. Aesthetics Committee — extra speakers on or near the ceiling are more noticeable in a shared family space. Bottom line recommendation If you are starting from scratch and have access to wire ceiling speakers do it. If you don't have access you won't miss too much. Skip if the budget is tight, ceiling placement is difficult, or you mostly watch TV/sports rather than movies. A solid 5.1.2 Atmos setup is the sweet spot for most family rooms. You'll get most of the benefit without needing a full 7.1.4 layout.  

Nach Sonnenfinsternis: Viele Menschen mit Augenschmerzen in Kliniken! Damit es „Baywatch Berlin“ Hörern und Hörerinnen nicht ähnlich ergeht und sie mit blutigen Ohren irgendwann die aktuelle Folge stoppen, hier ein paar Worte der Warnung. Denn nicht erst seit Ikarus ist bekannt, wer zu nah an die Sonne fliegt oder eben in die blickt, der verbrennt sich oder mindestens die Netzhaut und das soll doch hier wenigstens den Ohren erspart bleiben. „Wir gehen rein“ (Jule Lobo Voice) Es gibt ein neues Spiel mit Suchtfaktor: „Welcher Promi ist reicher?“ Shaggy oder Roland Kaiser? Genießer dieses Textes können hier schonmal miträtseln. Freunde der Gastronomie erhalten wertvollen Rat! Was tun, wenn der Kellner eine riesige Holztafel mit allerlei Speisen in epischer Breite vorstellt? Tipps von Primetime Moderator Klaas Heufer-Umlauf sollen Abhilfe schaffen (auch wenn Clueso die unhöflich findet. (Na und!)). Wem bis hier hin nicht das Ohr blutet, der verwendet eine zertifizierte Höhrfolie aus dem Supermarkt oder ist genauso verblödet wie die Podcaster selber. Denn die bunte Intelligenz-Verweigerung läuft so dann ihrem Höhepunkt entgegen: Thomas Schmitt warnt in der Ratgeber-Rubrik „Lassen sie das“ eindringlich dem sogenannten Rappel NICHT nachzugeben, denn die Folgen können schlimm sein. Warum Klaas seit vielen Jahren ein künstliches Bein hat und deshalb nicht Fußball spielen kann, dass lässt endgültig das Trommelfell platzen, also sagen Sie nicht, wir hätten sie nicht vor dieser „Total Ecplise of the Ear“ gewarnt. Cover: Christoph Schlozer https://www.instagram.com/christophschlozer www.christoph-schlozer.ch Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte: https://linktr.ee/BaywatchBerlin Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

Der Tag in NRW: Wer bestimmt über Abschiebungen?; Immer mehr Fälle von Kindeswohlgefährdung; Wer hält das Land am Laufen? Abschleppdienst; Erster netzneutraler Großbatteriespeicher eröffnet; Kaum Industrie mit eigenem Anschluss an Windräder; Niedrigwasserfolgen: Zu Gast im Neusser Hafen; Genießer und Profiteure des heißen Sommers. Es moderiert: Wolfgang Meyer. Von WDR 5.

Craig Coady and Emma O Shea's reaction to the rejection of funding for the Skyclarys Drug. Cork's Heritage Week kicks off this Saturday, we'll hear about what to expect from the weekend. Nearly 900 carers in Cork are over the age of 80, and 5400 carers in Cork are over 60. Garda File. Dr Genie, the veterinary osteopath. Hosted on Acast. See acast.com/privacy for more information.

Was, wenn du genau die gleichen 24 Stunden hast wie Einstein, Da Vinci oder Mozart? Die meisten Menschen konsumieren von außen nach innen. Aber was, wenn der eigentliche Weg genau umgekehrt läuft — von innen nach außen?   Dieses Gespräch existiert bereits in seiner ursprünglichen Form. Diese Episode hebt noch einmal die wichtigsten Kernaussagen hervor und bringt sie in neu aufbereiteter Form auf den Punkt.   In dieser Folge spricht Curse mit Maxim Mankevich — Experte für Erfolgswissen und Host des Podcasts "Die Köpfe der Genies" — über Essenz, das Unterbewusstsein als Programmierbarkeit und warum die größten Genies der Geschichte alle das Gleiche taten.   Du erfährst: - Wie du bewusst deine letzten Gedanken vor dem Einschlafen wählst und warum genau diese 15 Minuten dein Unterbewusstsein über Nacht prägen - Warum Leonardo da Vinci trotz widrigster Startbedingungen zu einem der größten Genies aller Zeiten wurde und was das für deinen eigenen Weg bedeutet - Warum sehr erfolgreiche Menschen zu fast allem Nein sagen und wie ein Ja zu allem anderen immer ein Nein zu dir selbst ist   Eine inspirierende Folge über Fokus, das eigene innere Genie und die Frage, ob du dein Leben von außen nach innen konsumierst oder von innen nach außen erschaffst.   Viel Freude damit. Maxim's Webseite: ⁠https://maximmankevich.com⁠ Das neue Buch „Bad Meditators Club“ jetzt vorbestellen: ⁠https://wonderl.ink/@bmcbuch⁠     Wir haben einen neuen YouTube Kanal!!!! Gerne folgen, liken, teilen, Liebe da lassen:   ⁠https://www.youtube.com/@MeditationCoachingLife⁠     ⁠www.curse.de⁠ Curse auf Instagram: ⁠www.instagram.com/cursezeit⁠ Curse auf Facebook: ⁠www.facebook.com/curseofficial⁠ Curse auf TikTok: ⁠https://www.tiktok.com/@curseofficial⁠ Curse auf YouTube: ⁠https://www.youtube.com/@Curseofficial⁠

Adriana Popescu, PhD, is Chief Clinical Officer at Avery Lane recovery center in Novato, California, and the author of EFT for Addiction. As well as working directly with clients, she provides coaching and mentorship for the Avery Lane clinicians and leadership team. In her practice as a psychologist, she has treated a wide range of issues including anxiety, depression, trauma, chronic pain, and grief. Research into Avery Lane's results has shown that Adriana's energy psychology methods produce much larger reductions in symptoms and relapse than traditional treatment programs.   Here she and Dawson discuss: Adriana's background Discovering EFT (Emotional Freedom Techniques) Using The Prochaska Stages of Change Model to determine readiness to change The importance of numerical assessment of symptoms Partnering with Vista Research to collect data using standardized instruments Assessments of patients in addiction treatment programs The fight flight freeze response The limitations of traditional talk therapy How tapping calms the nervous system The way EFT can reach the brain through the connective tissue and calms the amygdala How to apply tapping with someone in the throes of an addiction craving Tapping on being willing and open to change Tapping on specific cravings with addiction treatment   Find Adriana at: https://www.adrianapopescu.org/ And to purchase EFT for Addiction: https://www.amazon.com/EFT-Addiction-Adriana-Popescu-PhD/dp/1604153164/   Connect with Dawson at: http://dawsongift.com/   To purchase Dawson the 20th Anniversary Edition of The Genie in Your Genes:  https://www.amazon.com/Genie-Your-Genes-Epigenetic-Consciousness/dp/1604153202/   #mindtomatter #blissbrain #spiritualintelligence #eft #meditation #highenergyhealth #emotionalhealing #genieinyourgenes #trauma #addiction #eftforaddiction #energypsychology 

July was a month about proof, and the providers who can document clean trips are pulling ahead. This month's Genie Journal Recap covers the regulatory, financial, and operational shifts shaping medical transportation, plus what each one means for providers.In this episode:Ohio SB 315 makes GPS-confirmed pickup, transport, and drop-off a condition of payment for NEMT, phasing in over 18 monthsIowa stands up a Medicaid Fraud Elimination Task Force with EVV in focus and a strategy due in 120 daysFederal officials defer more than $1 billion in Medicaid payments to California and Minnesota while documentation is verified (deferrals, not cuts)California raises its managed care tax to backfill Medi-Cal money, and Oregon weighs a broad spending adjustment while NEMT stays a mandatory benefitIllinois and Texas Blue Cross Medicaid plans move from Modivcare to MTM Health on October 1, plus leadership shuffles at both brokersThe FTA opens roughly $12 million in coordination funding at up to an 80% federal share, due September 9A DC robotaxi bill never mentions wheelchairs, and why that gap is a moat for accessible providersDeep Read: a Michigan State University study finds unreliable transportation a stronger predictor of missed care than illness itselfRouteGenie product news: passenger language support across 15 languages, and smarter per-position multi-load pricingChapters0:00 Intro0:22 July's theme: proof0:29 Ohio SB 315 and GPS-confirmed payment0:59 Iowa's Medicaid Fraud Elimination Task Force1:28 $1B+ in Medicaid deferrals (California, Minnesota)1:55 California's managed care tax and HR One2:19 Oregon's Medicaid gap2:49 Blue Cross drops Modivcare for MTM (IL and TX)3:23 MTM and Modivcare leadership moves3:49 FTA opens $12M in coordination funding4:31 The DC robotaxi bill and the accessibility gap5:06 Deep Read: transportation insecurity study5:50 Blog: 10 NEMT conferences worth attending in 20266:15 RouteGenie at NEMTAC Transform, Booth 2286:32 Product news: 15-language passenger support6:54 Product news: smarter multi-load pricing7:20 Wrap-upFull breakdown and source links: The RouteGenie Journal → https://go.routegenie.com/newsletterBlog mentioned: https://routegenie.com/blog/nemt-conferencesSee us in person: RouteGenie at Booth 228, NEMTAC Transform, August 16–19, JW Marriott Orlando Grande LakesSources this month: Ohio and Iowa governors' offices, Axios, the Associated Press, Mercury News, Oregon Public Broadcasting, Blue Cross Blue Shield of Illinois, Newswire, Business Wire, Tech Times, Electrek, and a Michigan State University study in the journal Sustainability.

For a long time, I thought prayer was about asking God to change my circumstances. But I've come to realise that God isn't a genie who grants wishes—He's more like the greatest coach you'll ever have. Instead of simply giving us what we want, He guides us to become the person who can receive it. In this episode, I explore why some prayers feel unanswered, how discomfort is often the answer we've been asking for, and why changing the way we pray can completely transform the way we grow. KEY TAKEAWAYS God transforms you before He transforms your circumstances. Instead of removing every obstacle, He often uses challenges to strengthen your character and prepare you for what you've been praying for. The answer to your prayers may look like discomfort. Confidence, patience, resilience and love are developed through experiences that require you to practise them—not by avoiding them. Pray for who you're becoming, not just what you want. Rather than asking only for outcomes, ask for your blind spots to be revealed, your courage to grow, and the wisdom to recognise the lessons that are shaping you.   HOST BIO I'm Summer, Since the age of eleven, I have been captivated by the question of what it truly means to live well. That curiosity led me deep into the worlds of self-development, spirituality, psychology, philosophy, and human potential, always searching for a deeper understanding of life and the human experience. Like everyone else, I am still learning. Still evolving. Still discovering. But along the way, I have encountered ideas, perspectives, and principles that have profoundly shaped the way I see the world and navigate my own journey. Inner Wealth was born from that exploration. It is a space where I share the most meaningful insights, lessons, and truths I have uncovered, with the intention of helping others think more deeply, live more consciously, and create a life of greater fulfilment, purpose, and prosperity. At the core of everything I do is a simple intention: to contribute something meaningful and leave people a little wiser. Ultimately, my purpose is to be of service to humanity.   CONTACT ME Follow me on Instagram for more insights and to stay up to date with the pod @thesummerkang This podcast was brought to you by Frankly Podcasting.  

Epcot or Magic Kingdom? Dole Whip or Mickey pretzel? Castaway Cay or Lighthouse Point? This week Lisa puts Steve and Debbie in the hot seat for a rapid-fire (okay… not-so-rapid-fire) round of "This or That." Two options, gut-instinct answers, and a whole lot of friendly disagreement — with plenty of insider reasoning packed in along the way. Some are total no-brainers, a few get controversial, and Lisa tries (and mostly fails) to predict everyone's answers. It's a fun, easy listen packed with real opinions from travel agents who live and breathe Disney. Play along at home and see how many you match! In This Episode Intro — (0:00) A smaller crew this week — Caitlyn's on baby watch and Sharpie's out — so Lisa runs the game with Steve and Debbie. Parks & Planning — (1:05) Magic Kingdom or Epcot? • Rope drop or stay late? (plus everyone's favorite ride to look at lit up at night) • Lightning Lane vs. standby • Disney World or Disneyland? (and yes, Steve really does drive from Atlanta) • Table service or quick service? Rides & Coasters — (7:56) Space Mountain or Big Thunder? • Rise of the Resistance — Disneyland vs. Disney World • Slinky Dog Dash or Seven Dwarfs Mine Train? • Pirates of the Caribbean East or West? • Matterhorn or Expedition Everest? Cruise & Castaway — (8:30) Castaway Cay or a Caribbean port? • Castaway Cay or Lighthouse Point? • Cruise dinner or brunch? • Newest ship or the classics? • Concierge or excursions? • Alaska or Caribbean? • Poolside day or excursion day? Resorts, Snacks & Sweets — (11:54) On-property or offsite savings (Debbie would pick All-Star Sports over the Ritz!) • Dole Whip or Mickey pretzel? • Beignets at Port Orleans or churros on Main Street? More Park Favorites — (14:26) Park hopper or one park? • Fireworks or one more ride? • Animal Kingdom or Hollywood Studios? • Genie+ or the old free FastPass+ days? • Haunted Mansion — Florida or the Disneyland Nightmare Before Christmas overlay? • Disney Springs or Downtown Disney? • Villains or princesses? • Monorail or Skyliner? • Adults-only trip or bring the whole family? • Very Merry Christmas Party or Not-So-Scary Halloween? ⚡ Lightning Round — Both Answer at Once! — (27:30) Splash Mountain or Tiana's Bayou Adventure? • Wishes or Happily Ever After? • IllumiNations or Luminous? • The dearly missed Magical Express • Extra Magic Hours or today's early entry + evening hours? • The Great Movie Ride or Runaway Railway? • Maelstrom or Frozen Ever After? • Ellen's Energy Adventure or Guardians Cosmic Rewind? • Paper FastPass or booking in the app? • Soarin' Over California or Around the World? • MaxPass nostalgia • Tower of Terror or Guardians Mission Breakout? • Old or new Test Track? • Main Street Electrical Parade or Paint the Night? Wrap-up — (44:10) Thanks for playing along!

Bach wollte kündigen. Sein Chef sperrte ihn dafür ins Gefängnis.

OpenAI 'Rogue Agent' Fallout, Minnesota Water Systems Hit, Exchange OWA Zero-Click Mailbox Takeover   David Shipley covers multiple security stories: the OpenAI "rogue agent" incident expands as Modal Labs says a customer's exposed endpoint was used as a launchpad in attacks on Hugging Face, while critics cite missing zero trust/defense-in-depth and disabled safeguards; Bruce Schneier and Bargath Raghaven label this the "genie effect" and propose a "genie coefficient" to measure instruction-to-outcome gaps.   Minnesota IT Services reports more than 30 community water systems hit in a coordinated OT attack July 26–27, with some running manually, as agencies assist and warnings persist about Iranian-linked PLC targeting; Canada also reports a NoName intrusion claim.   Proofpoint details Laundry Bear exploiting an Exchange OWA XSS (CVE-2026-42897) to maintain mailbox access even after password resets. MCBS reports a 2025 breach affecting 1.261M people. Lava finds ~25,000 internet-exposed IPMI/BMCs leaking crackable hashes.   00:00 Headlines and intro 00:29 OpenAI rogue agent fallout 02:18 Genie effect and benchmarks 03:29 Minnesota water systems hit 05:02 Iran-linked PLC warnings 06:23 Exchange OWA mailbox backdoor 08:24 Medical billing breach tally 09:43 IPMI BMCs exposed online 11:00 Wrap-up and next episodes

Seit über 20 Jahren wird in Oslo der Abelpreis verliehen. Nicht immer läuft bei der Verleihung auch alles glatt. Wir schauen auf amüsante Anekdoten der letzten Jahre. (00:04:32) Wer ist Robert Langlands? (00:12:26) Warum bat Langlands um Bedenkzeit? (00:18:59) Luis Caffarelli: Der Messi der Mathematik (00:24:52) Singularitäten und ihre Bedeutung (00:31:06) Michail Gromov: Revolution in der Geometrie (00:37:47) Der verschlafene Preisträger (00:42:12) Demian beim großen Bankett (00:46:50) Verabschiedung Wir freuen uns über Fragen, Anregungen und Feedback an podcast@spektrum.de. Die Idee für diesen Podcast hat Demian Nahuel Goos am MIP.labor entwickelt, der Ideenwerkstatt für Wissenschaftsjournalismus zu Mathematik, Informatik und Physik an der Freien Universität Berlin, ermöglicht durch die Klaus Tschira Stiftung. Links zur Folge Geschichten aus der Mathematik | Edward Frenkel: https://detektor.fm/wissen/geschichten-aus-der-mathematik-edward-frenkel-babelfisch ➡️ Artikel zum Nachlesen: https://detektor.fm/wissen/geschichten-aus-der-mathematik-genie-chaos-in-oslo-abelpreis

Seit über 20 Jahren wird in Oslo der Abelpreis verliehen. Nicht immer läuft bei der Verleihung auch alles glatt. Wir schauen auf amüsante Anekdoten der letzten Jahre. (00:04:32) Wer ist Robert Langlands? (00:12:26) Warum bat Langlands um Bedenkzeit? (00:18:59) Luis Caffarelli: Der Messi der Mathematik (00:24:52) Singularitäten und ihre Bedeutung (00:31:06) Michail Gromov: Revolution in der Geometrie (00:37:47) Der verschlafene Preisträger (00:42:12) Demian beim großen Bankett (00:46:50) Verabschiedung Wir freuen uns über Fragen, Anregungen und Feedback an podcast@spektrum.de. Die Idee für diesen Podcast hat Demian Nahuel Goos am MIP.labor entwickelt, der Ideenwerkstatt für Wissenschaftsjournalismus zu Mathematik, Informatik und Physik an der Freien Universität Berlin, ermöglicht durch die Klaus Tschira Stiftung. Links zur Folge Geschichten aus der Mathematik | Edward Frenkel: https://detektor.fm/wissen/geschichten-aus-der-mathematik-edward-frenkel-babelfisch ➡️ Artikel zum Nachlesen: https://detektor.fm/wissen/geschichten-aus-der-mathematik-genie-chaos-in-oslo-abelpreis

At many health centers, plain old telephone service (POTS) is still one of the primary forms of interaction with patients outside of office visits. In a recent interview with Tracy Causey, CEO of Capital Area Health Network, he shares how their three sites, servicing about 17,000 patients, used AI to streamline patient interactions.As an eClinicalWorks site, the health system decided to try healow Genie to handle calls. It was a simple integration, and worked "out of the box." Now healow Genie handles about 80% of all calls with no staff interaction, answering the phone within 14 seconds. The approximate 400 calls they get each day are spread out, as patients take advantage of 24/7 availability. And when a call does require staff, healow Genie gets a live agent person on the phone within a minute and 20 seconds.Learn more about Capital Area Health Network: https://cahealthnet.org/Learn more about eCW: https://www.eclinicalworks.com/Healthcare IT Community: https://www.healthcareittoday.com/

Was soll man groß zu dieser letzten Folge vor der Sommerpause sagen. Wir sind ein bisschen überarbeitet und brauchen diese Pause wirklich. Aber danach wird alles anders, besser, toller, visueller! Genießt den Sommer Leute. Wir melden uns! Lena & David

If you could add any player to the Pirates for the rest of 2026, regardless of contract and trade compensation, who would you pick? Skubal? Ohtani? PCA? Or still Mason Miller?

Jeff Hathhorn joins to discuss Brandon Eisert's woes, if Bubba Chandler could be traded, the replay review to end the game, more Mason Miller proposals. If you could add any player to the Pirates for the rest of 2026, regardless of contract and trade compensation, who would you pick? Skubal? Ohtani? PCA? Or still Mason Miller?

Barbara and Jacie continue their CIRSx 2026 recap with the medical talks, emphasizing that the episode should not be considered medical advice and encouraging listeners to consult their own CIRS practitioners. They summarize Dr. Ritchie Shoemaker's talk on “triple positives” on GENIE (AKT, RELA, tubulin) as an early risk pattern for neurodegeneration including Parkinson's, highlighting the promise of early intervention. Dr. Lacey Venanzi focused on gut-driven inflammation in CIRS via LPS, dysbiosis, permeability tied to low MSH, and discussed preferred microbiome testing. Dr. Kellen Milani shared a stalking experience and the need for practitioner boundaries. Louise Carter reviewed GENIE/HLA patterns, suggesting CIRS subtypes and vascular dysregulation. Dr. Eric Dorninger covered step five of the Shoemaker Protocol: androgen correction, especially DHEA, plus additional testing options like DUTCH. Dr. Scott McMahon critiqued overly broad test interpretation. Dr. Oseni discussed pulmonary manifestations of CIRS and spirometry, noting TGF-beta1 and fibrosis. Dr. Peg DiTulio reviews MARCoNS treatment data, maintenance strategies to prevent recolonization, and when to consider ENT/dental issues. For more information and support, join us at https://thecirsgroup.com TIMESTAMPS 00:00 Intro and disclaimer 01:00 Dr. Shoemaker on Triple Positives 03:34 Dr. Lacey Venanzi: Gut Endotoxins and Actinos 10:03 Dr. Milani: her story of being stalked by a patient 15:58 Louise Carder's talk on GENIE subtypes 20:46 Dr. Eric Dorninger's hormone support talk 24:52 Dr. Scott McMahon on proper medical terms 32:18 Dr. Oseni on testing lung capacity 35:57 Dr. Peg DiTullio: lessons from treating MARCoNs 42:59 Wrap Up Next Episode For more information and support, join us at https://thecirsgroup.com HELPFUL LINKS: Dr. Lacey Venanzi's website: https://www.drlacey.org/ Our interview with Dr. Lacey: https://youtu.be/baldT8N7nl4?si=A4SrJ6tmY_zr1u53 Dr. Lacey's new podcast with Dr. Christian: https://www.coherencedx.com/ Dr. Kellyn Milani's website: https://remedynaturalhealth.com/ Louise Carder's website (for UK based patients): https://www.colabeu.com/ Dr. Eric Dorninger's website: https://drdorninger.com/ Our interview with Dr. Anjali Noble about hormones and menopause: https://youtu.be/OStNHRNffqw?si=PUfXfmfcAoVbvFR1 Our episode on urinary mycotoxin tests: https://youtu.be/qWIIBaMybFA?si=5yoXqYTGvDSC6YG6 Our interview with Dr. Peg re: tick borne illness: https://youtu.be/czSjykCdOR4?si=P-oePTLGCG-ujip6 Dr. Peg DiTulio's website: https://regenixhealing.com/ Our episode all about MARCoNs: https://youtu.be/wOSmKcZr06s?si=7s7wJKQQa7EidDel Order Jacie's book! The 30 Day Carnivore Bootcamp: https://a.co/d/7MgHrRs The CIRS Group: Support Community: https://thecirsgroup.com Instagram: https://www.instagram.com/thecirsgroup/ Find Jacie for carnivore, lifestyle and limbic resources: Jacie's book on the Carnivore diet!  https://a.co/d/8ZKCqz0 Instagram: https://www.instagram.com/ladycarnivory YouTube: https://www.youtube.com/@LadyCarnivory Blog: https://www.ladycarnivory.com/ Find Barbara for business/finance tips and coaching: Website: https://www.actlikebarbara.com/ Instagram: https://www.instagram.com/actlikebarbara/ YouTube: https://www.youtube.com/@actlikebarbara Jacie is a Shoemaker certified Proficiency Partner, NASM certified nutrition coach, author, and carnivore recipe developer determined to share the life changing information of carnivore and CIRS to anyone who will listen. Barbara is a business and fitness coach, CIRS and ADHD advocate, writer, speaker, and a big fan of health and freedom. Together, they co-founded The CIRS Group, an online support community to help people that are struggling with their CIRS diagnosis and treatment.

René Higuita war kein Torwart wie alle anderen. Er verließ seinen Strafraum, dribbelte Gegenspieler aus, schoss Freistöße und Elfmeter – und veränderte damit die Rolle des Torwarts nachhaltig. Für die einen war er ein Genie, für die anderen ein Verrückter. In dieser Folge erzählen wir die Geschichte des Kolumbianers, der den Fußball revolutionierte. Wir blicken zurück auf seinen folgenschweren Fehler gegen Roger Milla bei der WM 1990, den legendären Scorpion Kick im Wembley-Stadion und die enge Verbindung zu Trainer Francisco Maturana, der ihm die Freiheit gab, das Torwartspiel neu zu denken. Gleichzeitig werfen wir einen Blick auf die dunklen Seiten seiner Karriere ... Dieser Podcast wird vermarktet von der Podcastbude.www.podcastbu.de - Full-Service-Podcast-Agentur - Konzeption, Produktion, Vermarktung, Distribution und Hosting.Du möchtest deinen Podcast auch kostenlos hosten und damit Geld verdienen?Dann schaue auf www.kostenlos-hosten.de und informiere dich.Dort erhältst du alle Informationen zu unseren kostenlosen Podcast-Hosting-Angeboten. kostenlos-hosten.de ist ein Produkt der Podcastbude.

The American Radicals Podcast covers ICE "surging" into NYC, gig work hits manufacturing, record student loan defaults, boomers up-sizing in house, and AI's takeover of the military industrial complex. Check us out on Spotify! https://open.spotify.com/show/09AZ2WuYnWbZ2941wsb6jW?si=76c005605dc64dc1 https://www.foxnews.com/politics/stephen-miller-trump-administration-pursuing-policy-debank-illegal-immigrants https://www.whitehouse.gov/presidential-actions/2026/05/restoring-integrity-to-americas-financial-system/  https://www.occ.gov/news-issuances/bulletins/2026/bulletin-2026-31.html#&_intcmp=fnc_politics_article_main-content_article-body_6_11  https://www.foxnews.com/media/tom-homan-warns-ice-surge-coming-new-york-city-mamdani-boasts-sanctuary-status  https://comptroller.nyc.gov/reports/nycs-federal-funding-outlook-under-trump/  https://www.cbsnews.com/news/student-loan-defaults-surge-covid-tied-payments-pause-expiration/  https://www.npr.org/2026/07/08/nx-s1-5876084/manufacturing-flexible-part-time-work  https://www.wsj.com/economy/housing/baby-boomer-home-sales-real-estate-879614be?st=bG5h4o&reflink=desktopwebshare_permalink  https://www.washingtonpost.com/technology/2026/07/20/how-armed-robots-could-become-military-weapon-choice/  https://www.nytimes.com/2026/07/20/opinion/ai-drones-modern-warfare.html  https://www.bbc.com/future/article/20260513-your-car-is-spying-on-you-its-about-to-get-worse  https://x.com/collinrugg/status/2078947054345408995 

Welcome back to YOU HAVE BEEN WATCHING: A BRITISH SITCOM PODCAST! Its a record breaking heatwave in the UK so why not join your ho-ho-hosts Robert and Alex as they celebrate Christmas In July! In this episode which is definitely a specially prepared Christmas in July episode and totally not a repackaged, unreleased Christmas episode , they discuss Peep Show's memorable Christmas episode as well as the often overlooked Richard Curtis sitcom adjacent comedy film Bernard and The Genie from 1993, staring Alan Cumming, Lenny Henry and Rowan Atkinson. So sit back, make some Christmas wishes and enjoy ... merry Chris-Mark everyone! Host/Editor Robert Turnbull Co-Host Alex Lynch Find Robert on Twitter: @Forducks Find Alex at: www.alexlynch.co.uk Find the podcast on Linktr.ee: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://linktr.ee/YHBWpod⁠⁠⁠ Join our Extra Laughs Discord server: https://discord.gg/MZjuZhwYMZ Support the Film Stories podcast network on Patreon: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/simonbrew⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter: @filmstories Facebook/Instagram/Threads: Film Stories Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.filmstories.co.uk⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Title music: Jumping Cricket (c) Birdies via epidemicsound.com Learn more about your ad choices. Visit megaphone.fm/adchoices

What if the greatest shift in how you lead, work, and live isn't about learning another skill, but about changing the way your brain experiences the world?Amy sits down with Dr. Dawson Church, award-winning science writer, researcher, and author of Spiritual Intelligence, to explore the growing body of neuroscience revealing that emotional regulation, attention, compassion, and presence are not fixed personality traits. They are trainable capacities that reshape the brain over time.For years, spirituality has often been dismissed as something personal, abstract, or impossible to measure. Dawson challenges that assumption by sharing the research behind the four neural circuits that support spiritual intelligence and why strengthening them can transform everything from workplace performance to relationships and overall wellbeing. Together, Amy and Dawson also unpack why traditional meditation doesn't work for everyone, how evidence-based practices like EFT tapping and Eco Meditation accelerate change, and why cultivating inner awareness may be one of the most practical leadership skills available today.If you've ever wondered whether there's a science behind feeling calmer under pressure, responding with greater compassion, or unlocking more creativity without pushing harder, this conversation offers a compelling new perspective on what becomes possible when we intentionally train the brain that shapes every experience of our lives.Download your free copy of Dr. Dawson Church's The EFT Mini-Manual and bonus EcoMeditation at https://dawsongift.com/Moments That Create Momentum:The Brain You Practice Becomes the Life You Experience: Discover why spiritual intelligence isn't a personality trait you're born with, but a set of neural pathways you can intentionally strengthen.The Four Circuits Behind Calm Under Pressure: Explore how emotional regulation, attention, compassion, and self-awareness shape the way you lead, respond, and connect long before a stressful moment arrives.Why Meditation Doesn't Work for Everyone: Learn why quieting your mind isn't the goal, and how science-backed practices can make inner transformation more accessible.Performance Begins Long Before Productivity: Understand why your greatest breakthroughs may come from changing your internal state instead of pushing yourself to work harder.Training the Brain for Something Bigger Than Success: See how developing spiritual intelligence can shift not only how you perform at work, but how you experience your relationships, purpose, and everyday life.About the Guest:Dr. Dawson Church is an award-winning science writer, researcher, and bestselling author whose work explores the intersection of neuroscience, psychology, and human potential. Through more than 100 published clinical studies and collaborations with researchers from institutions including Harvard Medical School, Duke, Emory, and Columbia, his research has examined how meditation, emotional regulation, and self-transcendent experiences can reshape the brain and improve wellbeing. He is the author of The Genie in Your Genes, Mind to Matter, Bliss Brain, and Spiritual Intelligence.Beyond his research and writing, Dawson is the founder of the National Institute for Integrative Healthcare and the Veterans Stress Solution, a nonprofit initiative that has provided free PTSD treatment to more than 22,000 veterans and their family members. He also developed EcoMeditation, an evidence-based practice designed to make meditation accessible to beginners, and continues to share research-backed approaches that help people reduce stress, cultivate resilience, and unlock their full potential.https://dawsonchurch.com/https://www.facebook.com/dawsonchurchhttps://www.youtube.com/@EftuniverseEFThttps://twitter.com/EFTUniversehttps://www.linkedin.com/in/dawson-church-68b4a8/https://www.instagram.com/theeftuniverse/About Amy:Amy Lynn Durham, known by her clients as the Corporate Mystic, is the founder of the Executive Coaching Firm, Create Magic At Work®, where they help leaders build workplaces rooted in creativity, collaboration, and fulfillment. A former corporate executive turned Executive Coach, Amy blends practical leadership strategies with spiritual intelligence to unlock human potential at work.She's a certified Executive Coach through UC Berkeley & the International Coaching Federation (ICF) In addition, Amy holds coaching certifications in Spiritual Intelligence (SQ21), the Edgewalker Profile, and the Archetypes of Change . In addition to being the host of the Create Magic At Work® podcast, Amy is the author of Create Magic At Work®, Creating Career Magic: A Daily Prompt Journal and the founder of Magic Thread Media™. Through her work, she inspires intentional leadership for thriving workplaces and lives where “magic” becomes reality.Connect with Amy:https://createmagicatwork.net/https://www.linkedin.com/company/create-magic-at-workhttps://www.facebook.com/112951637095427https://www.instagram.com/createmagicatworkhttps://www.youtube.com/channel/UCnEm4h3fUgaq8qgvZpz6dGgThanks for listening!Thanks so much for listening to our podcast! If you enjoyed this episode and think that others could benefit from listening, please share it using the social media buttons on this page.Do you have some feedback or questions about this episode? Leave a comment in the section below!Subscribe to the podcastIf you would like to get automatic updates of new podcast episodes, you can follow the podcast on Apple Podcasts or your favorite podcast app.Leave us an Apple Podcasts reviewRatings and reviews from our listeners are extremely valuable to us and greatly appreciated. They help our podcast rank higher on Apple Podcasts, which exposes our show to more awesome listeners like you. If you are enjoying the show, please leave us a review on Apple Podcasts.Mentioned in this episode:This show was brought to you in part by the Magic Thread Media Network. To learn more visit: https://magicthreadmedia.com/

Ginagawa mo bang genie si Lord minsan? Remember na si Lord ang mabuti sa ating buhay, hindi lamang ang tagadala ng mabuti ritoAll Rights Reserved, CBN Asia Inc.https://www.cbnasia.com/giveSupport the show

Genie Wiley was locked away for thirteen years, but rescue was only the beginning of one of psychology's most haunting cases. This episode follows the story of Genie Wiley, a child kept in near-total isolation, deprived of language, movement, comfort and the outside world until she was discovered at thirteen. Her case became a landmark in debates around language acquisition, childhood development and the critical period hypothesis — but behind the research papers was a vulnerable girl who needed care, safety and time. We trace Genie Wiley's childhood isolation and abuse, the Los Angeles welfare office discovery, and the scientists, linguists, psychologists and caregivers who became involved after her rescue. The case raises a deeply uncomfortable question: when does helping a child become studying her? And when does science start taking more than it gives? This is dark history, hidden histories and one of those strange true stories where the facts are already devastating enough. For listeners interested in psychology case studies, child neglect, developmental psychology, scientific ethics and what really happened after the headlines, Genie Wiley's story remains as heartbreaking as it is important. Topics Include Genie Wiley's childhood isolation and abuse The Los Angeles welfare office discovery The critical period hypothesis Language acquisition after childhood deprivation The scientists and caregivers who studied Genie The ethical line between care and research Resources and Further Reading Genie Wiley - Wikipedia The Mockingbird Don't Sing - IMDB Starved, tortured, forgotten: - The Guardian Wild Child Speechless After Tortured Life - ABC News Genie - Encyclopedia Britanica The Development of Language in Genie - UCLA Linguistics PDF Host & Show InfoHosts: Kyle Risi & Adam CoxIntro Music: Alice in dark WonderlandCommunity & Calls to ActionReview & follow on: Spotify & Apple PodcastsInstagram: @theCompendiumPodcastWebsite: thecompendiumpodcast.comSupport us: Sign up to PatreonShare this episode with a friend! If you enjoyed it, tag us on social media and let us know your favourite takeaway. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us Fan MailEpisode #81 – The Chlorine Genie: A Different Approach to Salt Chlorine GenerationWhat if you could enjoy the benefits of salt chlorination without adding thousands of parts per million of salt directly to the pool?In Episode #81, Lauren Broom sits down with Patrick Lajoie to explore the innovative Chlorine Genie system and how it differs from traditional salt chlorine generators. Learn how this unique technology generates chlorine from a separate brine tank rather than the pool water itself, helping reduce the amount of salt added to the pool while delivering a consistent chlorine residual. Patrick also discusses the system's self-cleaning design, simplified maintenance, pH management capabilities, and how steady chlorine production can improve water quality and reduce chemical fluctuations. Whether you're a pool professional or a homeowner interested in alternative sanitization technologies, this episode offers an in-depth look at a different approach to chlorine generation and the science behind it. Strategic Podcast Sponsor:  Pool Brain

 In this episode Rachel is joined by her friend Genie! The dating scene in Miami is nothing but shticky situation after shticky situation. Even when Genie dates guys from New York, they seem to think that dating a Miami girl means that the Jewish rules don't apply. Where in the Torah does it say that halachah doesn't apply in Miami? This is definitely an episode you don't want to miss!Join Maalot Mizrach for a singles Challah Bake - meet like-minded people, mingle, and prep challah. July 16th at 6:45PM on the Upper West Side. Sign up at https://dub.sh/maalot-mizrach?ref=shtick. Use code SHTICKY for $15 off of an individual ticket!Shticky Situations is sponsored by CoronaCrush. To find out more information about CoronaCrush visit their website and coronacrush.co. Also join the CoronaCrush Facebook group and sign up for speed dating events!Remember to like the Shticky Situations page on Facebook, follow @shtickysituationspod on Instagram, and follow @shtickysituationspod on Tiktok! And make sure to join the Shticky Situations Loop Group:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://loopmein.app.link/invite?uuid=7bd5252c-c1ff-47fc-938b-1c319c57354e⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Want to be a guest and hang out with Rachel and discuss your own dating stories? Apply today https://forms.gle/FhwZs74JBTJgGpw8A! Want to try your luck at dating Rachel or any of her guests? Also apply today https://forms.gle/J31HUQ5aYTzjz5Bv6! You can also send an email too shtickysituationspod@gmail.com or DM @shtickysituationspod on Instagram. Serious inquiries only.Shticky Situations is sponsored by Primrose Flower Shoppe! Primrose is located at 2922 Avenue M, Brooklyn, phone number 929-376-9815, and follow them on Instagram @primroseny.

Barbara and Jacie share “hot takes” after CIRSx 2026 in Fort Lauderdale. Barbara attended in person and Jacie watched virtually. They discuss feeling “weird vibes” and a more corporate yet conflicted identity, with misalignment about whether the conference is for providers, patients, or remediators, and concerns about limited innovation within the Shoemaker community. They encourage practitioners to seek innovation through CIRS Lab and mention Dr. Lacey Venanzi and Dr. Christian Navarro-Torres' work. They highlight standout talks from Brian Tinker on safe homes, Dr. Venanzi on endotoxemia/actinos in the gut, Mike Pelletier on patient lifestyle changes, Dr. Kellyn Milani on being stalked by a patient, Louise Carter on GENIE patterns, Jack Goetz on building a CIRS-conscious home, and Kayleigh Concannon on low-tox furnishings, while noting many other talks felt unhelpful and lacking actionable takeaways. For more information and support, join us at https://thecirsgroup.com TIMESTAMPS 00:00 Intro: CIRSx 01:11 Hot Takes explained 03:25 Medical Disclaimer 03:48 Weird Conference Vibes 06:30 Who is CIRSx for? 07:57 Innovation beyond Shoemaker 10:56 Gatekeeping and publishing pressure 14:53 Tell me why I'm watching this 18:22 Standout talks and teasers 30:00 Up next! For more information and support, join us at https://thecirsgroup.com LINKS MENTIONED IN THE EPISODE: If you're a health coach or practitioner in the CIRS space, join the practitioner membership at https://www.cirslab.com/ Our recent episode with Dr. Christian and Dr. Lacey: https://youtu.be/7H6CKEhIMSU?si=Dr18_Gg8fpZfkKsh Dr. Christian and Dr. Lacey's new podcast: https://www.youtube.com/@CoherenceDX Order Jacie's book! The 30 Day Carnivore Bootcamp: https://a.co/d/7MgHrRs The CIRS Group: Support Community: https://thecirsgroup.com Instagram: https://www.instagram.com/thecirsgroup/ Find Jacie for carnivore, lifestyle and limbic resources: Jacie's book on the Carnivore diet!  https://a.co/d/8ZKCqz0 Instagram: https://www.instagram.com/ladycarnivory YouTube: https://www.youtube.com/@LadyCarnivory Blog: https://www.ladycarnivory.com/ Find Barbara for business/finance tips and coaching: Website: https://www.actlikebarbara.com/ Instagram: https://www.instagram.com/actlikebarbara/ YouTube: https://www.youtube.com/@actlikebarbara Jacie is a Shoemaker certified Proficiency Partner, NASM certified nutrition coach, author, and carnivore recipe developer determined to share the life changing information of carnivore and CIRS to anyone who will listen. Barbara is a business and fitness coach, CIRS and ADHD advocate, writer, speaker, and a big fan of health and freedom. Together, they co-founded The CIRS Group, an online support community to help people that are struggling with their CIRS diagnosis and treatment.

Note to the listener: If it sounds like the recording abruptly stops at the 26 min mark, that's because it did. We had a recording glitch that cut short Dr. Nathan Vanhorn's examination of Roman's 9 as a hope for the reversal of a hard heart. Apologies. - Gandalf

Join us in another episode of Mad Chatters, where we discuss anything and everything when it comes to Disney, Universal, and more.Have topics that you want us to talk about? Make sure to join us at patreon.com/wtmhpodcast Also, make sure to check out our Discord!Enjoy the Show!Support the show

Send us Fan MailYou post a Disney itinerary because you're excited, and suddenly the comments act like you've committed a crime against theme parks. We lean into that chaos and break down the most common “your Disney trip is going to suck” takes the internet throws at people, especially in Facebook groups where everyone somehow knows the one true way to do Walt Disney World and Disneyland.We start with the easy targets: where you stay and how you get around. Value resorts, moderates, deluxe hotels, and even staying off-site all get dragged for different reasons, even though each option can be the perfect fit depending on budget, group size, and what you actually want from a vacation. Then we hit itineraries and gatekeeping, from “two park days isn't a real trip” to “you must visit every park,” plus the debate over Disney Springs, pool days, and how much downtime is “allowed.”From there we get into the choices people love to judge the most: dining and spending. We talk table service vs quick service, the “that restaurant is terrible” reflex, and why meal planning depends on weather, walking, and your pace. We also dig into souvenirs, buying ahead on Amazon, Lightning Lane decisions like Genie+ and single passes, and paid extras like tours and ticketed parties. The through-line stays the same: plan your trip for your priorities, leave room to pivot, and stop asking strangers to approve your fun.If you've ever been told your Disney plans are wrong, you'll feel seen. Subscribe, share this with your favorite Disney planner, and leave a review with the single piece of advice you ignore every time.

Adam, Hayden, Lottie and Nick discuss whether the show Pluribus (Apple TV) promotes a pro-animal rights message, including highlighting the “straw man” arguments against veganism in the show and in fan reactions to it. We also cover what we'd each do in the Pluribus scenario, not necessarily advocating for cannibalism, and the Pluribus as an analogy for communism while the main character Carol represents capitalism/rugged individualism.  Radiothon: Thanks again to everyone who has donated to our Radiothon campaign! With donations for our show via Givenow and the 3CR site combined, we have now reached over 90% of our $1,700 target. If you'd like to help keep our show and 3CR going, you can get us over the line here: https://www.givenow.com.au/cr/FreedomofSpecies We appreciate any support!  More Pluribus Analysis: Prestige TV Podcast – ‘Pluribus' Episode 3: Negative Nancy at the Ice Hotel: https://www.theringer.com/podcasts/the-prestige-tv-podcast/2025/11/14/pluribus-episode-3-negative-nancy-at-the-ice-hotel The Rich Eisen Show – Vince Gilligan on the Underlying A.I. Themes in Apple TV's ‘Pluribus': https://www.youtube.com/watch?v=05DXxA5ddDU Bias Sinha – Rhea Seehorn compares her ‘Pluribus' character to Walter White as she questions hate she received: https://www.msn.com/en-au/entertainment/tv/rhea-seehorn-compares-her-pluribus-character-to-walter-white-as-she-questions-hate-she-received/ar-AA25erLJ?ocid=msedgntp&pc=U531&cvid=6a28c527a97b45e2b637391cec4aa0be&ei=26  Music: Pluribus theme by Dave Porter. Genie in a Bottle by Christina Aguilera. What Doesn't Kill You Makes You Paranoid by The Beaches. Dead by Mia June.

BS Section and House Keeping Discord Server geekoholics.com/discord/ Whatcha Been Playing? Forza Horizon 6 Ninja Gaiden: Ragebound Starfox Remake: Demo Shantae: Half Genie Hero News: Cross Platform / PC / Misc The new handheld version of Banjo-Kazooie contains tons of unreleased music IO Interactive facing layoffs as it cuts ties with Project Fantasy publisher Xbox Onimusha: Way of the Sword release date moved forward in a crowded September Nintendo  New Switch 2 model spotted in the wild with updated screen PlayStation PlayStation announces the end of disc games, starting in January 2028 Sony is closing the PlayStation Store on PS3 and Vita Now Rockstar says GTA 6 'plays best on PS5' Sony to Delete Movies Owned by PlayStation Users, List Includes More Than 550 Digital Titles Xbox Ahead of expected mass layoffs, union says Xbox staff 'will not be treated as disposable' Amid huge expected cuts, Xbox claims: 'We're not reducing our investment in games' PSA's: Epic Games Store Freebies: I Have No Mouth, and I Must Scream, River City Girls 2 Free 4 All The Sheep Detectives Toy Story 5 Supergirl Intro/Outro Music: Geeky Beat – @johnsbernardo Help support the show: - Subscribe to our Twitch channel http://twitch.tv/geekoholics - Please review the show (bit.ly/geekoholics) on Apple Music, Apple Podcasts and to share with your friends. Reviews help us reach more listeners, and the feedback helps us to produce a better show. Join our Discord server: CLICK HERE

Things are certainly heating up in the pinball market, and it'snot just because summer's here. With hot new game launches, tons of sizzling code updates, some fiery new marketing ideas and even a tropical pinball cruise, make sure you join Jonathan and Martin for the latest June 2026 edition of the Pinball Industry News PINcast.After months of teases, Jersey Jack Pinball's long-rumoured Sonic the Hedgehog game finally launched on Sonic's 35th birthday.  A packed two-level playfield incorporates some truly innovative ideas, while the three model range tops out with Sonic battling Doctor Eggman above the Collector's Edition's backbox.After announcing last month that their next remake gamewould be Tales of the Arabian Nights, Pedretti Gaming revealed the game this month, with Legacy and 30th Anniversary editions offering a choice of artwork packages and powder coat finishes.  In addition to the classic ruleset, there's new enhanced code to increase the challenge to Battle the Genie, while the company has taken the opportunity to bolster their US support operations.After frustration from potential buyers at Winchester Mystery House selling out so quickly, Barrels of Fun have launched a new programme to give paid subscribers an extra week to get on-board future releases.There are other perks to their Barrels Reserve scheme too, including the opportunity to board a cruise ship for a six-night Pinball at Sea experience.  Find out what is involved to make sure you don't miss the boat.There are several job openings to report this month, including one organising Stern's presence at trade and consumer shows, another designing games for American Pinball, or maybe simply building them on the production line.  After all, everyone in pinball had to start somewhere.There are numerous code updates across multiple manufacturers, including the mass roll out of new system software for Stern Spike machines as predicted in last month's PINcast.  There is also news of shows taking place this weekend, and even late breaking news for Jaws pinball owners with a special 4th July Revenge mode now available.So, catch this and much, much more in the June 2026 editionof the Pinball Magazine and Pinball News Pinball Industry News PINcast.  Download or stream it right now from your favourite podcastsupplier.You'll also find it on YouTube and YouTube Music, or you canget it direct from Spotify right here. Also, don't forget you can subscribe to the PINcast absolutely free to guarantee you get the freshest episode delivered to you every month, the very moment it is released.With new games from multiple manufacturers coming out thismonth and many more expected shortly, join Jonathan and Martin each month to ensure you're fully up-to-date with what's happening in the pinball world.After all, it's the podcast the pinball industry listens to.

Shaq is back for #43 on the bad list for Kazaam! No, Shazaam featuring Sinbad isn't real, but Shaq's rap career is, and it takes up a lot of this film when you aren't watching a snarky kid try and reconcile with his sleazy dad and something something wishes magic comedy candy bars. We didn't exactly hate this one and one of us liked it better than Steel, and it may only be on the bad list because of flawed human memory and internet jokes. For whatever reason this ep is more NSFW than usual so don't bring the kids.Support us at our podcasting network, Podcastio Podcastius at https://www.patreon.com/podcastiopodcastius.  You'll get early episodes of this and out other podcasts, along with a live chat here and there.Speaking of our other podcasts - seriously, you could only listen to various other configurations of us:Luke Loves Pokemon: https://lukelovespkmn.transistor.fm/Time Enough Podcast (Twilight Zone): https://timeenoughpodcast.transistor.fm/Game Game Show (a game show gaming games): https://gamegameshow.transistor.fm/Occult Disney: https://occultdisney.transistor.fm/Podcast: 1999 (where Mark and Matt rap about 70's tv sci-fi): https://podcast1999.transistor.fm/And Matt makes music here:https://rovingsagemedia.bandcamp.com/Coming Soon: American History XCatwomanThe Lion King

Die KI-Aktien sind ins Rutschen geraten, viele haben zuletzt nervös ins Depot geschaut – und überall steht dieselbe Frage im Raum: Platzt hier gerade die größte Blase der Börsengeschichte? Weil Dietmar Deffner durch Kalabrien tourt, springt dieses Mal Doppelgänger-Host Philipp Klöckner als „Ersatz-Bulle“ ein. Und der hat eine überraschend klare Ansage: Er fühlt sich eher wie 1998 als wie im Jahr 2000 – vor dem großen Knall käme also erst noch eine Verdreifachung. Gemeinsam mit Holger Zschäpitz nimmt „Pip“ den großen KI-Trade auseinander: Warum OpenAI seinen Börsengang womöglich nie hinbekommt. Warum xAI mit mageren 12,5 Prozent Wachstum praktisch abgehängt ist, während Anthropic seinen Umsatz verzehnfacht. Wer das Rennen bei Verbrauchern, Profis und Konzernen macht, ob Meta und Amazon wirklich Schnäppchen sind und warum Software-Aktien zu stark abgestraft sein könnten und welche Titel hier Potenzial bieten. Dazu Zschäpitz' Bär über Uwe Bolls Machetenfilm „Citizen Vigilante“ und den Streisand-Effekt sowie eine bitterböse Abrechnung mit US-Handelsminister Howard Lutnick, der den amerikanischen Kapitalismus gerade Richtung Planwirtschaft dreht. Bulle und Bär – diesmal als Klöckner und Zschäpitz. Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte: https://linktr.ee/deffnerundzschaepitz DEFFNER & ZSCHÄPITZ sind wie das wahre Leben. Wie Optimist und Pessimist. Im wöchentlichen WELT-Podcast diskutieren und streiten die Journalisten Dietmar Deffner und Holger Zschäpitz über die wichtigen Wirtschaftsthemen des Alltags. Schreibt uns an: wirtschaftspodcast@welt.de Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutzerklärung: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

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

Today, we tackle a reoccuring question from lots of people in a feminization journey or sissy training program. It's about body hair. There are various ways to say it but this seems to sum it up: “Mistress, can I be a genuine sissy girl if I'm hairy?”Mistress Erika noticed something online that inspired the episode. Several of our girls posted photos in pantyhose. They looked stunning. Then they apologized for their hairy legs. That sparked a conversation in our adult social networking site, Enchantrix Empire.We knew we had to go deeper because many of our listeners were feeling the same conflict.Some of the topics include:Does a sissy always need to be smooth?Will a Femdom Mistress want every feminization journey to include shaving all body hair?What does shaving represent to you? Could this be a feminization ritual that helps to switch from male to female?What do we recommend if you have a partner who discovers your crossdressing secret and forbids the razor?What if you want to be femme but you love your beard?We use comments from ladies we know who share their thoughts, experiences, and worries about body hair. While we preach self acceptance, we know that can be difficult with pop culture and porn stereotypes about beauty standards. Our friend Genie delivers the truth: “Nothing on the outside changes the inside. You do not feel like a woman until you accept it internally.”Remember, we are available for personalized sessions on sissy training, feminization wisdom, and distance domination dynamics. Let's explore your transformation together!DISCORD:  LDWErika and LDWOliviaOlivia@EnchantrixEmpire.com  Ms Olivia's blog: Experienced MistressErika@EnchantrixEmpire.com  Ms Erika's blog: Intelligent Phone Fantasy

Akash Nigam has been building Genies since 2017 with a conviction that avatars will be the visual layer of the internet. As CEO of Genies, he's assembled IP partners including the NBA, MLB, Sanrio, and Kakao, with more major studios and agencies set to announce before the end of May. The pitch: every app, game, website, and celebrity is going to have an AI personality. Genies wants to be the framework that gives all of those personalities a face.What separates Genies is portability and scale. A character that took eight weeks in 2021 now takes ten minutes. Staying stylized rather than photorealistic isn't just aesthetic — it's what got Hollywood to the table. Talent doesn't want deepfakes. They want a Genie: trained on private IP data, capable of one-on-one fan relationships that make Instagram feel thin.AI XR News: Tim Cook stepped aside as Apple CEO with hardware chief John Ternus taking over. Humanoid robots ran a half marathon in Beijing while a Sony robot defeated professional table tennis players, opening a conversation about Chinese robotics capabilities and AI data infiltration risks the US is still underestimating.Key Moments:[00:06:45] Tim Cook steps aside: what the Apple leadership transition signals about wearable AI[00:12:00] Humanoid robots and table tennis: China's robotics flex[00:13:00] The data infiltration argument: open-source risk and a warning for the US[00:24:00] The IP land grab: NBA, MLB, Sanrio, Kakao, Naver Webtoon[00:28:00] From photo to avatar in 10 minutes: how Genies' generation pipeline scaled[00:32:00] Why Instagram feels thin and how Genies enables one-on-one fan relationships[00:49:00] 80 people, $150M raised, and why Bob Iger sees Genies as the future of DisneyIf AI personalities are going to be everywhere, what do they look like? Akash has been building the answer for nearly a decade. Q3 is when it goes live.Brought to you by Zappar and Mattercraft — the leading visual development environment for immersive 3D web experiences. Mattercraft now includes an AI assistant for design, code, and debugging in real time. Start building at mattercraft.io.Subscribe to the AI XR Podcast wherever you listen to podcasts, or watch on YouTube - https://youtu.be/Fs8h2KcJclQ Hosted on Acast. See acast.com/privacy for more information.

Last 4 days before regular tickets sell out at AI Engineer World's Fair - this is the single biggest gathering of AI Engineers, Founders, Leaders, and Researchers in the world. Attendees get >$5000 worth of sponsor credits and talk tracks are looking FANTASTIC. Join us!The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.It's not necessarily that xAI is uniquely incompetent (it's clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.While GPU access is a bottleneck, simply increasing CapEx won't automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.From building Discord's developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP's independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.We go deep on AMP's vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind's unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.We also discuss Anthropic's culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.We discuss:* Why 95% utilization was considered an outage at Google* Why AI infrastructure waste compounds at frontier-lab scale* Why “move fast and break things” does not work for AI data centers* How data center backlash, power grids, and community incentives shape AI scaling* AMP's vision for making FLOPs flow like megawatts* Why compute needs an independent system operator* How interruptible demand and dynamic prioritization worked inside Google* Why DeepMind research hoarding creates negative externalities* AMP's 1.2GW base-load ambition and the need for 6GW of spike capacity* Why end-of-life prediction could become one of AI's most important healthcare applications* Frontier Systems, output maxing, and full-stack alignment* Why APIs and abstraction layers become lossy as organizations scale* Superconductors, standards, and the dream of lossless systems* SF Compute, open protocols, and the future of compute marketplaces* Why non-NVIDIA chips can still benefit from NVIDIA's reference architecture* Trust boundaries and why chip startups need visibility into future model architectures* Why VCs often underestimate researchers as CEOs* Scientists as star athletes of the mind* Why great CEOs need to be confrontational up and down the stack* Why leading the frontier matters more than “winning”* How Anthropic cracked coding* Why culture is fragile, not a permanent moat* Why hardship was a feature, not a bug, for Anthropic* Why Anthropic's P0 was coding from day one* Periodic Labs, physics as the constraint, and technical reality* Silicon Valley mercenaries, missionary teams, and what happens after a breakthroughAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney* X: https://x.com/AnjneyMidhaAMP PBC* Website: https://amppublic.com/* X: https://x.com/amppublicTimestamps00:00:00 Introduction00:00:09 Why AI Compute Is Being Wasted00:03:17 Responsible Infrastructure and Data Center Backlash00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts00:12:41 Foundry, Frontier Labs, and Research Hoarding00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction00:24:08 Frontier Systems, Output Maxing, and Alignment00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs00:38:17 AI Coachella and First-Principles Thinking00:42:43 Leading vs Winning in Frontier AI00:45:54 How Anthropic Cracked Coding00:48:25 Culture, Hardship, and Anthropic's P000:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries00:56:26 Rishi Valley, Singapore, and Money as a Measure00:58:47 Closing ThoughtsTranscriptIntroduction: Anjney Midha, AMP, and Compute WasteSwyx [00:00:00]: We're in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.Compute Utilization: Node Allocation, MFU, and AlignmentAnjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you're measuring in these clusters. One is node allocation, and then the other's MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it's not at, 95%-Swyx [00:00:29]: There is no excuseAnjney [00:00:29]: There's no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that's one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? Fundamentally it's an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes theoretically they are, but in practice the number of people in the chain, the supply chain between, the capital and all the way to whoever's managing the cluster and then whoever's measuring what the output is, are just so many, degrees of separation away that, the, The Have you ever heard the radian metaphor, which is at the beginning of an arc, if you have two arcs that are two lines that are just off by a few degrees, that-Swyx [00:01:33]: It spreads outAnjney [00:01:34]: It spreads out, right? Or at scale. And I think what's happening is a lot of cluster implementations and infrastructure, a lot of frontier labs and other teams, that's what's happening, is they're, they initialize the plan, which is kind of like North Star with a team that wants to do good, but then they're, required to scale so fast instead of iteratively that the wastage just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups. If you spend time with people who've been in the semiconductor industry or the DSN industry for a long time, this is not new, and I don't think AI should be an excuse. Sure. Something What is new? Okay. We have a lot of new capabilities, but that doesn't mean just abandon common sense. Common sense should always be in fashion. ? AI scaling doesn't change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I'm, obviously I'm, I'm an investor, or I'm an investor by background. Over the last few years now we're running an AI infrastructure business called, AMP. And I think that it's okay to say this time is different on the capabilities front. We are genuinely getting capabilities at, of the, of a kind we haven't had before. That doesn't give you an excuse to say this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now, that's great for the startup mindset, but you remember this moment where Zuck went from saying, “Move fast, break things” to, move-Responsible Infrastructure and Data Center BacklashSwyx [00:03:10]: Fast and stable infrastructureAnjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who's the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let's call it, $4 an hour. If you're having to bring up a new data center in a new community, why not just say we're going to charge 4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash?” I can tell you as a customer of that compute, I would love that. I'd be happy to pay an additional 50 cents per hour at scale.Swyx [00:03:57]: Wow. Yeah.Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I'm going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.Swyx [00:04:13]: Of community backlash?Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.Swyx [00:04:19]: Wow. That's a huge number.Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it's a little bit of overstated. These things can get over-reported, but it-Swyx [00:04:27]: They don't just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there's a new AI deal. If we're bringing up a data center in your community, we're actually going to reduce the cost of your electricity bill. Okay, now we're talking. Right? The community's going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that's not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don't know when it's going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That's certainly not how we're procuring compute. Or we're, we're trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who've been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they're adults. I trust them.Swyx [00:05:44]: They can run LAN. They can run power.Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They've, they've had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there's a lot of short-term thinking going on in the compute layer, and it's going to catch up to us. It's not going to be good.AMP Grid: Making FLOPs Flow Like MegawattsSwyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?Anjney [00:06:28]: In systems design, right, there's, there's two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we're building here, which is basically a compute grid. We're trying to do for compute what the electric grid-Swyx [00:07:02]: PowerAnjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we're actually the opposite of a full stack integration like approach.Swyx [00:07:12]: Super horizontal.Anjney [00:07:13]: Where it's much more horizontal and it's, it's multi-cloud, it's multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There's stranded pools of compute all over the place and there's no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we're working on, it's extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I'm actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we'd like all of these folks to participate on the grid. There's, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they'll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what's called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren't competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what's called an ISO, an independent system operator of the grid. So that's how we see ourselves. Economically, that's what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-Swyx [00:09:28]: Did your schedulingAnjney [00:09:28]: They did that at Google. And, -Swyx [00:09:32]: And you have infra shops from Discord as well.Anjney [00:09:35]: I have some.Swyx [00:09:35]: I don't know, I don't know if Discord is like the primary identity, but what-whatever, I'm just kind of-Anjney [00:09:39]: No, D-Discord was-Swyx [00:09:40]: Choosing a well-known name.Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-Swyx [00:09:56]: It's the same thing, yeahAnjney [00:09:57]: It's the, it's the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-Swyx [00:10:08]: For the calls, yeah.Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world's 200 million plus monthly active gamers, right? And so that's, that's how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-Swyx [00:10:31]: Bundling and unbundlingAnjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-Swyx [00:10:36]: HorizontalAnjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world's best, research labs and so on. We're sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.Swyx [00:11:53]: Like priorities.Anjney [00:11:54]: It's a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who's saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That's a, that's a made up example.Swyx [00:12:15]: It's very real. Brain Marketplace was real. And, we've, we've covered this on the pod with David Luan, who was-Anjney [00:12:20]: Oh, great. OkaySwyx [00:12:20]: Was there. And the criticism is that, well, actually sometimes you need central command to go all in on a thing. And actually sometimes capitalism via credits doesn't work. Not, this is not a criticism of AMP. I'm just saying, this is a thing that has been tried, internally within Google, and it led to Google missing GPT.Foundry, Frontier Labs, and Research HoardingAnjney [00:12:41]: Like, we structured ourself essentially very similarly to Google. We are structured as a holdings company. So, Alphabet holdings is Alphabet holdings, and then they've got these subsidiaries called Google and-Swyx [00:12:51]: Other betsAnjney [00:12:52]: Other bets and so on. We've got, AMP holdings, and we've got our infrastructure business, and then we've got a capital business called Foundry that incubates new frontier AI labs or invests in them as venture capital, like Periodic. We put a few hundred million dollars into Anthropic from our fund earlier this year. So wherever we feel like teams are making progress, especially researchers and so on who've pushed the frontier inside of existing labs like DeepMind, I find, there comes a point where they feel misaligned with the dictatorship of Alphabet holdings. And at that point, sometimes the dictatorship doesn't want them anymore. And they're “Thank you. You've done your job here. You've kind of helped us through the zero to one phase, and for whatever reason, we're going to deprioritize your amazing, omni model or whatever it is, and instead we're going to prioritize coding.” And, I think that's a tragedy, but I get it. They're Sergey and team are running their own business there. But that doesn't mean we the rest of us should sit around waiting for that progress to get unlocked for the rest of the world and humanity. If you think about how much extraordinary research has happened inside of DeepMind over the last 10 years, I, Demis and Sergey and those guys did such a great job. But at the end of the day, so much of that has never seen the light of day?Swyx [00:14:00]: Or they're like papers only, but they never actually shipped it to production or-Anjney [00:14:03]: What's worse is the paper is actually not even being published anymore ‘cause there's a six-month embargo inside of DeepMind, right? We've heard about this where a paper comes out, and then I think there's a six-month embargo window where if anybody on the business team says, “This could be interesting” It's embargoed for life.Swyx [00:14:18]: Exactly. So the stuff that gets published is the stuff that's not good enough.Anjney [00:14:21]: There's an adverse selection problem, basically. Yeah. At this point-Swyx [00:14:25]: It's, it's a common complaint at NeurIPS, by the way, that's “Well, why would I look at the papers that are the trash of GDM?”Anjney [00:14:31]: Again, I think it's a tragedy. I get it. They're running their business, but the rest of the I think there's negative externalities of research being hoarded, and so that'there's a market failure. And somebody needs to unlock that research, and we can't do it on our own. We only have 1.2 gigawatts of compute. That's nothing. That's about $40 billion of cloud spend. We're going to need a lot-Gigawatt-Scale Compute and End-of-Life PredictionSwyx [00:14:51]: By the way, is that's a new number. I haven't, haven't come across that gigawatt number. That's huge.Anjney [00:14:56]: Yeah. And to be clear, we haven't secured all of it. That's how much demand we have started to secure. I think publicly we haven't actually confirmed how much we have for this year. In order-Swyx [00:15:04]: Where do you want to get to?Anjney [00:15:06]: I think the steady state would be that we have a base load pool Of 1.2 gigawatts at all times Of base load capacity. For spike capacity, right now my estimate is we need roughly six gigawatts over the next four years for all our teams to feel like they were able to keep moving the frontier, whatever they're working on, whether it's, like superconductor discovery over here. There's a new investment we're working on right now, which is in the end of life prediction space in healthcare. It's extraordinary how much you can, you can give this was actually my graduate school work. I went to grad school for bioinformatics at Stanford Med. And I know we-Swyx [00:15:40]: Econ, MCS, bio.Anjney [00:15:41]: So my-- I was this really weird cat where, I was never satisfied with my major options. So at one point I was an econ major, then I was a CS major, then I was a MCS major called mathematical computational science, and they decided they were going to end that major. So I took all that coursework, and I applied it to grad school, my graduate degree in bioinformatics, which was the master's program, and then I thought I was going to do a PhD. I never ended up doing it. I dropped out and went to work at Kleiner. But I was lucky enough to apprentice with this professor at, Stanford Med. His name is Nigam Shah, and he was working on end of life prediction. Stanford is one of the only research facilities in America that has a longitudinal patient data set that's larger at scale. I think it's at least 12 million patient lives. The only larger data set is at the VA, the Veterans Affairs, of America. And to do research, like do any deep learning and so on that data set, it was called the STRIDE data set at that time, you had to be a Stanford Med School affiliate, which is why I went and enrolled in the bioinformatics department. End of deep learning was early. Nigam Shah had the visibility-- the vision to see that, you could do end of life prediction to help palliative care. In America, the, over 30% of all Medicare, Medicaid spend, at least at that time, was spent on end of life care. And what's we grew up in Asia, so we all-- Yeah, at least I won't speak for you, but I have A very different relationship with death than I find folks who grew up in America do. In America, spiritually and culturally, especially in Western societies where Christianity, the Christian tradition sort of frames death as this terminal point, there's often a judgment day and so on. The way we view death is with a finality. In Indian culture, in Hindu culture, death is one-Swyx [00:17:35]: Also, he's Buddhist as well.Anjney [00:17:36]: You're Buddhist, yeah. So it's one, it's one step in a journey of many lives, right? And so, I grew up in this city called Chennai in the south of India, and when people die, you dance on the street. There's like a procession where your body is carried to be cremated and your family, like celebrates and there's drums and so on. It's this huge thing. And, It's because the idea is that you're going to be reincarnated. You've been liberated from the responsibilities of this life, and now you're onto your next. It's a new It's like going off to a new college or whatever, right? And so it was so alien to me when I got here as an undergrad- That the medical system works backwards from that assumption that we have to view death as this terminal thing and delay it, postpone it's a bad thing. And so at the time, clinical decision support in the United States was this very primitive field. Even to this day, physicians in the United States often will tell you when you have a terminal disease, this is your, we've diagnosed you, which is great. Our ability to diagnose you is extraordinary. You have somewhere between six months to six years to live. What do you do with that information? The error bars are so high that then you In times of uncertainty, we default to culture, and when the culture is let's-- this is a bad thing, I've got to prolong my life, then you start doing things like And just to, just sort of from a systems perspective, what's going on there is Physicians often feel like they need to provide such high error bars because there's always some uncertainty in end of life diagnosis, and if you provide the wrong Diagnosis or recommendation to your patient, you can be sued for medical malpractice. And then your license can be taken away. It can be catastrophic for your career. In contrast, if in countries where that's not the case, what you often observe is that patients, physicians are quite prescriptive with their recommendation. They say, “Hey, this is your condition. The literature says that you probably have this much time on Earth left. My expert opinion is that you are an outlier or whatever.” And they try to be more prescriptive, and that empowers a patient, right? ‘Cause then a patient can say, “I trust my doctor. They said on average, I have six months to live, but if I do these things, I may have a shot because of my particular predispositions or my genetic history or whatever.” And that empowers you to go about your life in a actually more scientific way than leaning on religion, culture, spirituality, and so on. In contrast, here, because of that medical malpractice sort of thing looming over your head, a physician never gives you a clear recommendation. So instead you say, “Okay, Doc, well, let's try it all.” And then you start a whole regime of drugs and therapies, and then you often spend weeks and weeks in the hospital, and that deteriorates your quality of life. And when that deteriorates your quality of life, you instead of spending your last few days doing the things you love with your family, you're spending it on a hospital bed. And that ends up being thirty percent of Medicare and Medicaid. So it's worse for the patients. The doctors feel terrible. The American taxpayer is paying a huge amount of money. And so this is why Nigam Shah, who was this professor at Stanford, said, “Anjney, if there's “ I kind of sat down with him. I was this young, I'd, I was twenty-one, and I was “I want to work on a big problem.” He's “The big problem is end of life care.” And so we tried to do deep learning to say, to-- So we started trying to run deep learning on these tried patient data sets to say, “Could you have an AI system make a recommendation that is orders of magnitude more precise about how much time you have left once you've been diagnosed with a terminal condition than a human?” And then if we can get that precision to be high enough, then you can empower the patient. And it turns out the tech works. Like it's-- Once you get the data set, like RL works. Honestly, even regression models work. You don't need to get that fancy. At the time, we were just trying, doing like very simple neural nets.Swyx [00:21:54]: Simple solutions, yeah.Anjney [00:21:54]: Today, what we can do with RL is extraordinary. The problem remains then and now is regulatory, because you actually can't shift the burden of the wrong clinical diagnoses from the physician to the AI system. And so at that time, I got quite disillusioned ten years ago for, twelve years ago where, ‘cause I felt I just didn't have the resources to influence regulation. Today, I'm very lucky. I'm in a different place. I've, I'm a lot older, and so I've been spending a lot of time on my next incubation, which is how can we unlock the, patient empowerment by training AI models to do end of life prediction much, with much more precision and ac-Swyx [00:22:37]: Oh, wow. You're still focused on this the whole time.Anjney [00:22:40]: The-- I haven't been able to get, this out of my mind a single day for the last fourteen years. This is the hill I want, I would like to die on. There's two, I would say. What? I actually, I'd prefer not to die.Swyx [00:22:51]: Yeah, exactly.Anjney [00:22:52]: But I think two bipartisan issues, I think two issues that should be bipartisan in America are how do we empower patients to make the right clinical decisions at the end of their life, such that we're reducing the taxpayer burden with science? It's just good old science, and AI can help here. And the second is, net positive data centers, ‘cause I think that's the biggest critical bottleneck on training and good enough AI models to help people at the end of their life. So there's sort of two sides of the, of the same scaling bottleneck curve, but those two, we formed AMP as a public benefit corporation. My wife and I, who you've met, you've met Viv. Her passion is education. Her family is a long line of educators and so on, and, of physicists. And so this class is my attempt to stop being the black sheep of the family and be a, an educator. But if I'm not educating, the thing I would be doing is working, on these two problems, whether on the political spectrum or as a researcher back at, in some lab. And my hope is if anyone's listening to this podcast, if they're passionate about either of those two topics, I'd love to hear from them. We'll, we'll we can share the contact in the show notes, but, we're looking for people to join both of those missions on the, on the political side as well as on the medical side, on the research side.Frontier Systems, Output Maxing, and AlignmentSwyx [00:24:08]: You said, this is a discipline that you want to form. You call it's called variously called Frontier System. It's variously called One Person Frontier Lab. What is the ideal name or shape of this? Like the, what is the mission?Anjney [00:24:24]: Of the class?Swyx [00:24:26]: Of the discipline that you're, exploring, right? I The class is called Frontier Systems. But like for me, maybe one phrase is you're, you're just anti-waste, right? Which is wasting GPUs, wasting in human and Medicare. But is there, is there a broader theme that I'm, that maybe you can encapsulate more succinctly?Anjney [00:24:45]: Yeah. The, from an engineering perspective, it's very simple. It's output maxing. It's the, it's the department of output maxing.Swyx [00:24:51]: Making the most of what we have.Anjney [00:24:52]: Exactly. I'm a huge believer in optimal outcomes. I think both in America and other countries, we are losing our appreciation for nuance, and this is the thing of And AI is the same case, right? Oh, the bitter lesson holds. Okay, fine. But that doesn't mean you just like throw 500 GB300, 500,000 GB300s at your suboptimal model scaling and you waste a bunch of compute. It also doesn't mean that, the most optimal is to have like 50 different architectures where there isn't enough standardization. One of the reasons Anthropic has had extraordinary sort of velocity is ‘cause they picked the transform architecture and said, “This is simple. Let's double down on it,” right? And now luckily there's enough investment going to the space that we can afford other architectures, but at the time, investment was just too fragmented into other architectures, so that arguably unlocked scaling. So I think there's a philosophy. I think we all owe it to ourselves to do output maxing with a new capability called AI on a global level. I think if I was starting a new department at Stanford, depending on how fuzzy or technical I wanted to be, I'd probably call it the Department of Alignment. Like-Swyx [00:25:59]: It's an overloaded termAnjney [00:26:01]: But it is, But alignment really Is a hard problem. And I think when you unlock it, full stack alignment is super hard in any organization and in any system. Like in a, in a venture capital firm, if you can have full stack alignment between your limited partners and your, the founders who are creating the value and ultimately the public that owns the IPO stock, that is a gift that keeps giving. And when you study the history of these systems, when they start off, they usually start out small scale where the feedback loop is actually so tight that there's alignment. And then the more you try to scale, the more division of labor happens, the more specialization happens, and at each step you add abstractions. And wherever there's an API interface, there's like loss. There's communication loss. And so I think a really cool thing would be for us to figure out is there a way for us to have our cake and eat it too as an engineering discipline? Is there a way to actually scale up and scale out Without losing any alignment, without lossy transmission?Swyx [00:27:01]: You mean standards?Anjney [00:27:02]: So standards is one way. The other way is you just have net new capabilities. So like what we're trying to do here is discover new superconductors. A room temperature superconductor would be a lossless transmission mechanism for energy. We would have flying cars. We are right within a few years of having a new room temperature superconductor. So I think those are the two. You either have to standardize On protocols or API specs that allow lossless communication, or you can come up with a whole new capability that unlocks so much abundance, the standardization doesn't matter ‘cause you just unlock net new capacity. This, the, so this is what I spend my days thinking about these days.Compute Markets, SF Compute, and Non-NVIDIA ChipsSwyx [00:27:38]: No, I think every infra person at, who wants scale and wants to output max does eventually end up thinking about this. We don't have time to go into it, but we have done an episode with SF Compute-Anjney [00:27:50]: Oh, coolSwyx [00:27:50]: That is trying to standardize The futures contract for compute. I don't, I don't know how that's going by the way, but like at some point this will be public.Anjney [00:27:57]: Oh, I think Evan is awesome and SF Compute is the kind of effort that I hope we can accelerate because what often happens is these exchanges are very hard to get, they, it's hard to bootstrap them, right? Because they often require-- There's many inefficiencies between parties. There's trust boundary inefficiencies in infrastructure because you don't trust, one part of the stack doesn't trust another part of the stack to give them visibility. There's capital markets inefficiencies, there's operational efficiencies. So if you can inject like a single shock to the system of a ton of compute demand or supply, then you can accelerate, these new flywheels. And so my hope is one day, or soon, if SF Compute needs extra like has excess capacity, they just hook it up to the grid and they get flooded with demand from us. And on the other side, if they have a ton of demand but they don't have supply, they just again hook up to the grid and it's a two-way protocol where they can just hook up to our capacity. And I don't think we're too far from that. Today our working implementation of it is mostly through a group of labs, universities, and a few sort of trusted parties who are, who all feel like they're in alignment to borrow an over sort of used word. But our hope is to just have it be an open protocol that anyone can hook up to on-Swyx [00:29:20]: Hook up for demand or hook up for supply? In primarily demand, it sounds like. Like you-Anjney [00:29:25]: No, bothSwyx [00:29:26]: You would want to offer demand.Anjney [00:29:27]: Both. Yeah. Unfortunately, what's happened in the last six weeks is, we thought we'd have a bunch of excess capacity by the end of this year. It's all gone.Swyx [00:29:37]: It's exploding.Anjney [00:29:38]: It, yeah. It's all gone. And so I have, my text messages are full of friends, we know many of these people, these are founders who've raised billions of dollars in San Francisco going, “Oh, any chance you have like 50 nodes in the next few weeks?”Swyx [00:29:51]: What is the scope for, non-Nvidia, right? You have Lisa Su coming and, Rainer Pope as well. And so There is a lot of demand for, more performance Alternative architectures and all that. At the same time, this hurts your standardization.Anjney [00:30:11]: I don't think so. So actually Rainer's a great example, right? Rainer is a CEO and founder of, MatX. I actually had him by for office hours in the class earlier today, and there was an insight he brought up that I hadn't considered before, which is when they decided to pick the standard For their data center, they picked the NVIDIA reference architecture. So the MatX chips Just plug in to any site that has an NVIDIA bring up planned. And, the-Swyx [00:30:42]: It's just software then. It's, it's not the-Anjney [00:30:44]: A-Swyx [00:30:44]: Hardware.Anjney [00:30:46]: Well, from an input and IO perspective It's the same footprint as an NVIDIA rack.Swyx [00:30:52]: That makes sense.Anjney [00:30:53]: Where they have done, innovated a bunch from what I can tell is on systems co-design. Which is where a lot of the gains are to be had. And so he picked He was “Anjney, we, there's just so much work to do when you're building a new chip company.”Swyx [00:31:08]: Can't fight every front.Anjney [00:31:08]: You just can't fight on every front. So my question to him was, “Well, you're working on this new chip. Their tape-out is next year. What, who are you going to partner with to host the chips?” And he said, “Whoever will host them. That's just not, that's not my focus.” And I said, “But how did you “ you decided back to our earlier systems design question, he decided that, he didn't want to be a full, fully integrated chip provider. The bottleneck they're focused on is the logic die, and they, he feels they can crank out a ton of performance gains through co-design there. But then that means you delegate, to our question earlier, it, you he's the data center provider is a different part of the stack, and so then he's dependent on that part of the ecosystem to host his chips to get the performance gains to the customer. So now you have another abstraction, and you might have loss. So I asked him, “How do you prevent loss?” And back to your point, he said, “I just picked the NVIDIA standard ‘cause I didn't want to Like I wanted to piggyback off of an existing protocol.” And that, what's great about NVIDIA is that reference architecture is known.Swyx [00:32:15]: Open.Anjney [00:32:15]: It's open. They've published it. So Jensen's actually enabled someone like Rainer to build a chip company like MatX, and I don't see them as competitive. The compute demand is so high. Like, I don't I think NVIDIA's not able to meet the demands of production, so we just need more chips. And I think it's very smart what MatX has done, which is say, “We're just going to we're not going to innovate on the data center design ‘cause actually, thank you, Jensen, you've done all the hard work. Where we can innovate is somewhere else.” And I think that's, that's very healthy. I think that's how we unblock new bottlenecks. And my view is these, the, chip teams like MatX, who have arrived at the insight that co-design is the way, The primary bottleneck for them is trust boundary. To do co-design well, you need visibility into the next model generation as soon as possible ‘cause it takes two years to tape out. So if by the time I bring my chip to market, your model architecture's changed, I'm host. Now, when he was inside Google, he was sitting next to the Gemini team. He was on Palm or whatever.Trust Boundaries, Co-Design, and Researcher CEOsSwyx [00:33:19]: His co-founder was the, was one, was one of the Palm guys, I think.Anjney [00:33:23]: Yes. Yes, exactly. So when you're inside the trust boundary of Google, then your systems co-design loop is super tight. When you leave as a founder, one of the biggest risks you take is now you're outside the trust boundary. And so what I love doing is helping chip teams who can help us unlock more capacity for the independent ecosystem access to trust. Because when I If I've been, involved with a lab from day one, and I was lucky enough to work with Anthropic, and then I'm on the board of Mistral and helped Black Forest Labs get started. I think at this point I'm on six or seven different teams.Swyx [00:33:57]: Only six? I feel like my mental number was going to be 13, but yeah, it's-Anjney [00:34:02]: No, I go deep with one at a time.Swyx [00:34:04]: You're founding CEO of Arena.Anjney [00:34:07]: Nah, that was an, that was an-Swyx [00:34:08]: Administrative CEOAnjney [00:34:09]: It was an administrative five-month gig where Whalen and Anastasios were graduating from their PhDs, and they didn't need a product team. So I helped recruit the head of engineering product and design. But Anastasios has always been the CEO of that company. I played a pinch-hitting I'm an intern. I was CEO intern For five months. -Swyx [00:34:33]: I interviewed him, and he's he's very well-spoken. I think he's a debate, former debate, champion. But also very quantitative and mathematical, which is-Anjney [00:34:41]: He-Swyx [00:34:41]: Such a unicorn.Anjney [00:34:43]: See, what's amazing about him? If you look at his output, he's an output maxer. By the time he was graduating from his PhD, which he only graduated last year, he had published more work with a citation count than, people twice his age. But at the same time, he'd already started a project called LLM Arena that was being used by millions of people As a side project. And time and time again, what I've realized is venture capitalists suck at seeing human beings as, dynamic agents where-Swyx [00:35:14]: They want to put you in a boxAnjney [00:35:15]: They want to put you in a box.Swyx [00:35:15]: This is your thing.Anjney [00:35:16]: So the first time I got introduced to Anastasios, somebody had told me “Oh, he's amazing, but he's a researcher.” I was “what? What do you mean he's a researcher?” That's what-Swyx [00:35:28]: Like he's not a CEO, not a founder.Anjney [00:35:29]: Not a CEO, exactly. I was “Are you crazy? Do you Have you met Dario?” Dario's a scientist. He's gone from zero to, what will soon be a trillion-dollar company in four years. Being a CEO, nominally speaking, is not that hard. Being a good CEO is hard. Being a great CEO actually requires a level of performance that scientists who have already published at the top of their field have accomplished. It is super hard to be a competitive scientist. To publish in academia over the last 20, 30 years, to make it to the top of your discipline at a place like Berkeley, you are a star athlete. Like, you are an athlete of the mind, and you perform at the highest levels. And to get there, whether you're, Anastasios or Whalen at Berkeley, or you are Robin, who-Swyx [00:36:23]: BFL, yeahAnjney [00:36:24]: With Black Forest, who created Stable Diffusion, or if you're, like Guillaume at Meta, who created Llama before he started Mistral. The amount of human leadership you have to demonstrate to get the resources, like get the trust of the organization, publish it, put it up. I would just fund researchers all day Right? If who have contributed already to the field. If they've, if they've put SOTA out there, they're, they're star athletes already. If they haven't done SOTA Look, they can still be good CEOs, but then I find the failure mode is that they just don't want to be CEOs, they primarily want to publish, and that's okay, too. One of the things we do with the AMP Grid is we donate excess compute. We have two nonprofits, like university labs. We carved out like a couple thousand H100s. But I do think there's extraordinary research being done on university campuses. My father-in-law's a physicist. He's a professor. Extraordinary work in physics, and we need that. But if you want to be a CEO, what you need to be willing To do is be super confrontational, outside of science. Like within the scientific community, some of the best researchers are very confrontational about their convictions, right? This architecture is right. To be a great CEO, you basically have to be willing to be confrontational up and down the stack.Swyx [00:37:41]: To your own team.Anjney [00:37:42]: To your own team-Swyx [00:37:43]: To customersAnjney [00:37:43]: Hiring, recruiting customers. Well, I would say, Yeah, pretty much to everyone Everybody. Of course-Swyx [00:37:50]: I see, I feel a little bit of that in my own work, but yeah, I can't imagine the stakes that Dario has had to go through. It's, it's pretty insane.Anjney [00:37:56]: No, I don't think the stakes are that different From how you're feeling it, right? Stakes are personal scaling vectors, right? The stakes that seem so low to you, like having this podcast where you can talk to somebody and just have a you're an extraordinary communicator, right? Like already in this conversation, you've pulled more out of me than most people, and I've been on 12 podcasts in the last two weeks.AI Coachella and First-Principles ThinkingSwyx [00:38:17]: I think I, we've just seen each other enough that there's some base trust.Anjney [00:38:20]: There's base trust.Swyx [00:38:20]: And I think, and I know that you, that I've done my homework and like I know that trust is a big deal for you, so.Anjney [00:38:27]: I think trust is about consistency, and you and I have seen each other In the community for years, right? Like, I remember the first time we met was at NeurIPS in New Orleans. I don't know if you remember that, luncheon.Swyx [00:38:38]: Oh my God.Anjney [00:38:39]: Reiko had set up this Reiko's amazing, and he set up this luncheon and-Swyx [00:38:43]: Yeah, I was “Who's this Discord guy?” I'm “Okay.” But-Anjney [00:38:45]: No, you weren't-Swyx [00:38:46]: You were just “You made some investments.”Anjney [00:38:47]: You were much less polite. You were “Who's this VC?” You're like-Swyx [00:38:51]: No, I Was I? Oh my God.Anjney [00:38:53]: It was-Swyx [00:38:53]: I'm so sorryAnjney [00:38:53]: It was visible on your face.Swyx [00:38:54]: I'm so sorry. But you weren't, you weren't The introduction was bad. I was I didn't know who you were.Anjney [00:39:00]: The, see, this is the thing about context, right? Like, but then I think I heard your accent. And I was “Are you-”Swyx [00:39:06]: Singapore, yeahAnjney [00:39:06]: “Are you Singaporean?” And you're “Yeah.” And I said, “I went to high school, JC, in Singapore.” And then the ice broke. But This is the there are in the scientific community, sometimes the stakes are very high for people who haven't had the emotional, what is called EQ Coaching and mentorship, right? Which is like to have scientific impact, you often need to be a extraordinary emotional, like emotionally in tune person with the folks you're trying to influence. And so what comes so naturally to you is actually a super high stakes thing to other people. And so I wouldn't assume that Dario's more stressed out than you. These things are you'd be surprised how similar and small sometimes the problems are to you That some of the world's biggest, leaders are facing. And that's what I've learned from this class. The guest speakers are Sam, Satya, Jensen.Swyx [00:40:01]: AI Coachella.Anjney [00:40:02]: Yeah. It's AI Coachella, right? So we got to get all the headliners, and they're I'm very lucky that some of these people have either mentored me over the years or I've done business with them. And when you, take the performative stuff out and any assumptions you may have about these people that you read in the press or on Twitter, We're all just humans. We're all trying to get along. And what's so special about this moment is AI is forcing, like scaling, the bitter lesson is forcing a lot of people to revise their assumptions for how the world works and go back to first principles or go and educate themselves. So the kind of people I was, I won't name who this person is, but I was at an event last week in Texas and, ran to somebody who said, “Anjney, I came across the class. What do you think about real time action prediction models?” And I was, don't know how happy it made me feel when they asked me that question. I know they've done the work. They've challenged themselves. I'm, they didn't ask me, “What do you think of world models?” They said, “What do you think of n-”Swyx [00:41:04]: Real time action predictionAnjney [00:41:05]: “action, real time action prediction models?” World models, don't get me wrong, are cool and everything, but you and I both know that is a layer of abstraction that is sometimes not usefully precise enough. Right? Ours-Swyx [00:41:16]: There's like four different kinds of world models.Anjney [00:41:17]: Yes, exactly.Swyx [00:41:18]: We've done the part with general intuition, by the way, which is very focused on, -Anjney [00:41:22]: Oh, cool. Yes. I love Pim. Pim is great. And this is what I love about people who've done that level of work. They realize they're not in competition with people who the rest of the world thinks they're in competition with.Swyx [00:41:34]: Because they're not in the category, they're in the specific thing they're trying to do.Anjney [00:41:37]: They're focused on their mission, and they have a systems understanding of the bottleneck they're trying to solve. And when somebody else says, “I'm working on real time, action prediction models too,” Pim goes, “Oh, I love that person. I want, I can learn from them.” But the minute they're “Oh, that person's a world model person,” it's “like which type of world model person?” But mostly they're just trying to figure out if it's a waste of their time, because we don't have enough time. So, Pim, for example, is super, loves this other company I work with we've talked about called Black Forest Labs. And he's mentioned to me multiple times that he's so, He thinks what Flux is doing is really cool. Andy Blattman came by and spoke in the class. And what I find over and over again is for people who do the work, who can be usefully precise enough about like what is actually going on in the world of frontier research, The sense of camaraderie is still well and alive, but it gets lost sometimes when you have to like abstract The technical complexities in, business terms And then the VCs are “How are you different from that world model?” I'm going to say Where do I even start to explain this stuff? And then the misalignment creeps in.Leading vs. Winning in Frontier AISwyx [00:42:43]: This is good. Yeah, I think, people listening get a sense of, what it is like to operate at a real level, like yourself, rather than at, the journalist level, where you have to sort of put everyone in, a rough category and create a narrative of competition, and who's winning today, who's behind.Anjney [00:42:58]: It-- this idea of winning is so Weird to me.Swyx [00:43:03]: You do want to win. You want you want competitiveness.Anjney [00:43:06]: No, I think you want to lead.Swyx [00:43:07]: You want SOTA.Anjney [00:43:07]: No, I think you want to lead. Yes, so you want to push the frontier. You want to push the SOTA. You want to do something that hasn't been done before. You want to capture value, but you don't want to capture so much value that, people think you're unaligned with your mission or trying to do what's best for the world. You want to capture enough value that you can keep innovating, right? And I think that people want to lead, they don't really This idea of winning and losing, again, I love Jensen. He's a, he's a leader. The mindset that he talked about on Dwarkesh's podcast, right? He's “I didn't wake up with a loser mindset.” I think that was awesome, right? Because he's, he's an engineer. Dwarkesh has done the work. So there's at least-- even though the, to me, it was very obvious they're talking about the same thing, they just passed each other. They just had to basically, Jensen has this, five-layer cake abstraction of how the industry works. And Dwarkesh had, I think from that podcast, had more of, a pre-training, mid-training, post-training systems loop concept.Swyx [00:44:04]: It's just a factor of who he talks to, right? Again, it's very clear.Anjney [00:44:06]: It's the systems It's the abstraction, the mental models, the It's the whole-- Dude, so much of the problem in the world is reasoning by analogy. And then the assumptions that are held invisibly.Swyx [00:44:19]: Yeah, I've, I've said, this is actually the best time in human history for first principles thinkers. Because everything you think will happen is actually now coming true.Anjney [00:44:28]: Correct. And the venture capital community is, notorious for this, where people look-- In times of uncertainty, they, cling to axioms that ended up being true from the previous era, and they kind of like proclaim them with confidence as if they're truths, but they're not. And it's very important to see the distinction between a heuristic and an axiom. An axiom can be proven-Swyx [00:44:55]: Like from internal consistency point of viewAnjney [00:44:56]: With internal consistency. A heuristic is a way you kind of a shortcut. And my God, the number of people I have had to put up with over the last few years who proclaim-- use heuristics As axioms to judge people, to judge which companies are going to succeed or the number of people who are “Oh, yeah, Anthropic, they're just training models right now,” but this one continue.Swyx [00:45:22]: Because that's a B2B SaaS?Anjney [00:45:23]: Yeah, the, like Which over the fullness of time, if you squint at it, maybe. But the way you arrive there is so important that you can-- you just, you can dismiss people. Here's what happened, right? What happened is Anthropic basically achieved takeoff in October of last year. That training run-Swyx [00:45:41]: Whatever, three seven?Anjney [00:45:42]: I forget the numbers now, but whatever that checkpoint was-Swyx [00:45:45]: We saw the cognition.Anjney [00:45:46]: Yeah. Right? You probably-- The, to those of us in the community, especially once post-training was done and it was released in December-Swyx [00:45:52]: Yeah. Can I sneak a sneaky question in there? I don't know if you have a perspective, maybe you don't, I just The number one question is how did Anthropic crack coding, right? Because Claude One, Claude Two, okay, like it was part of it, but it wasn't a big deal. And the leading hypothesis, it's a lucky dice roll that was then compounded, right? Like it was like Mildly better, but then they saw it and they were “Okay, let's really invest.”How Anthropic Cracked CodingAnjney [00:46:17]: I had this very annoying teacher. I went to this boarding school called Rishi Valley in India, which is like this, bird preserve. It's like three hundred and fifty acres of bird preserve in rural India, and there was no technology for seven years. There was this teacher, I won't name them, but they would have this-- I hated it every time he said this to me. He was “Luck fa-favors the prepared mind,” which is like a common saying, but the way he delivered it, always grated me, ‘cause he was always I was always one of those kids who got, a good grade without trying very hard. ‘Cause like high middle school is not that hard if you, if you're generally, paying attention and so on. And there was this one time where I-- But then I would get an eighty percent grade, and he would keep pushing me to say “The reason you didn't get the ninety-five plus percent is because you're not that lucky.” And I would say, “What do you mean?” ‘Cause I would think that I deserved that grade, and I would sometimes argue with him. And he'd say, “You didn't have a prepared mind. If you want to get lucky again “ There was basically one time where I got like ninety-five or ninety-six on this, on this subject, and I, now that I felt entitled. I was “Okay, I'm going to keep doing this,” and I didn't. And then he was “Luck favors a prepared mind. You got lucky last time, but you got to stay prepared.” And I didn't understand what he meant. Now, as I'm older, I'm okay, these adults actually knew a thing or two. Anthropic has been the most prepared company for four years. And so then when the right, context data comes in, the right developers start sending in, the right context diffs, Sure, you could say you got lucky, but if you ask me, they're pr-pretty damn prepared with paranoia for like four years. And you have to remember, it was so hard for them to get going early on that they had to do so much more with so much less that you just have to be prepared to be so efficient.Swyx [00:48:06]: Yes. There's numbers on their burn compared to OpenAI. I've, I've written about it, but they are so much more efficient in their, in their tech stack.Anjney [00:48:14]: It's not even It's not funny.Swyx [00:48:14]: Not even close.Anjney [00:48:15]: Yeah. But it's so clear, right? Like how to output max for the world. They have been prepared, and you could call that luck, but Luck favors the prepared mind.Culture, Hardship, and Anthropic's P0Swyx [00:48:25]: This is one of those things that I was going over some of your old lectures and, you were data, people think it's a moat and actually it's culture and actually it's team Actually. And I, it's-- there's different levels of moats, and this is the ultimate one that determines everything else. Which you can then compoundAnjney [00:48:43]: You're saying culture is the ultimate moat? Yeah. But the thing about culture is it's very fragile. So moats, I don't think they're-- there's very few moats I found that are actually moats. They're-- It's, it's a nice concept, but in reality, you have to replenish your culture. Ben Horowitz was, the speaker in CS153 on Tuesday, and I asked him this question about the culture bottleneck in teams because, there are several AI teams-Swyx [00:49:09]: His book, Hard Things About Hard ThingsAnjney [00:49:11]: Hard Thing About Hard Things. But more concretely, there are so many AI labs today that have all the cash they need, they have all the compute they need, and they're still not able to ship anything SOTA. And then you start seeing people leave and so on, and my diagnosis, it's, is it's the culture. And so I asked him, Ben, they're-- He's been one of the most aggressive investors in AI labs. He goes back to this thing which resonates in my mind a lot. It-- When I used to work at a16z, I would, book a conference room, and right outside the conference room, which is closest to the toilet ‘cause it was the fastest way for me to go use the bathroom between Zoom meetings-Swyx [00:49:45]: Oh my God, I'll put maxing my toilet optimization. Okay, never mind.Anjney [00:49:48]: It was not healthy in hindsight, but maybe this is TMI. But anyway, outside that conference on the wall was this quote that was printed that said, “Culture is not a set of beliefs, it's a set of actions.” And it's by Bushido, is this, Japanese philosopher. And if you stop taking the actions that demonstrate the mission alignment to what you've said to your team and to your-- the world matters to you, then your culture starts to fray. So it's not actually a moat, I would say. It's a very brittle, fragile thing that requires daily tending to like a garden. But if you figure out the system to keep that garden tended, which I think ultimately comes down to knowing yourself ‘cause you most naturally, if you're authentic and so on, you'll naturally make trade-offs that seem effortless to you, but that reinforce your culture. And then That becomes this very hard thing for other people to catch up to. And at Anthropic, from day one, there was this mission like-- missionary like zeal and belief that, hey, these capabilities will scale. These systems are stochastic, not deterministic. There will be error bars, and until we crack interpretability, there's risk. And at some point, people will go-- stop using Claude just for coding. They'll use it in some mission-critical context where there's-- it'll throw off a bug, and then people are going to come blame them, and they want to be on the right side of history where they said, “Yes, this is a powerful technology. We think it's going to change the world, And we want to be very measured and scientific about the fact that, ‘Hey, guys, these are stats models, statistical models.' That's how statistics works.” ultimately, when you're training neural nets, it is just a statistical system. And I think that Belief that safety is important and that it might seem toy-like in the early days, and sometimes, you could say, “Anjney, they totally over-exaggerated the risk,” like two years ago when they said, “Let's not launch Claude One,” or whatever. Well, okay, maybe in hindsight, but hindsight is twenty/twenty. And at the time, they didn't know how that model would be used, and to them it felt existential if somebody came and said, “You weren't responsible. It-- This wrote a bug.” The liability associated with that is massive. So how do you prevent against that? Well, day in, day out, you say safety. And when you start deviating from that, you have the team hold you accountable, you have the world hold you accountable, and I think that becomes a moat over time. At some point, that moat will get challenged and so on, and then it become fragile. I hope it endures because that's the beauty of having founders run the show, ‘cause they can make really hard trade-offs to do mission alignment. The hardest part is in the earliest days when you don't have a group of people who are going through difficulty, stress, crisis together, then your culture doesn't get defined sharply enough, and that's what I'm worried about right now, is there's so much money going to these labs. There's no hardship. There's no-Swyx [00:52:50]: To anyone who knowsAnjney [00:52:51]: There's no to anyone who knows. And that, in hindsight, was a feature, not a bug for Anthropic. The number of people who said no, the number of people who said, “Sorry, we're all doing investors in OpenAI,” that is competitive difference. It forces you to really understand, what is the hill you want to die on at the expense of everything else. What's the P zero? And there, P zero from day one was coding. The reason, the mechanism system there was if we crack coding, Then we will crack AGI. Our mission is AGI. We want to get there safely. If we focus on codin

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