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A founder can be burned out enough to want an exit and still not beready to let the business go.Juan Ignacio knows that tension from both sides. He built a B2B SaaSlending platform from Madrid, raised tens of millions in venturecapital along with a $100 million debt facility, and exited four yearsafter launching. The deal created relief, but it also exposed aquestion the transition could only postpone: What do I do with my lifenow?In this episode of Your NEXT, Juan joins Jerome Myers to unpack whathis own near distressed exit taught him about preparation, runway, andthe danger of waiting until the company needs a transaction. He alsoexplains how that experience led him to found L40, a sell side M&Aadvisory firm focused largely on B2B SaaS companies.Together, Jerome and Juan explore why founders sometimes sabotage thetransactions they say they want, how valuation expectations and markettiming affect the likelihood of a sale, and why “I just need themoney” may be a signal to examine the real problem before hiring anadvisor.Juan also explains the Rule of 40, what makes an advisor a genuinefit, and why a healthy company is not automatically a desirableacquisition target.The hardest part of an exit is not always finding a buyer.Sometimes it is helping the founder become willing to leave.Learn more about Juan and L40:https://www.l40.comTake the Exit Readiness Assessment:https://www.exittoexcellence.com/eraLearn more about Jerome Myers:https://www.exittoexcellence.comThank you,Jerome Learn more about your ad choices. Visit megaphone.fm/adchoices
This morning on the show, we had a wild ride with a Bruno Mars contest that had everyone on the edge of their seats. But it wasn't just the music that got our hearts racing - we also had a heartwarming conversation with Olivia, the founder of Runway for Recovery, an incredible organization that's been making a difference in the lives of women and families affected by breast cancer.This episode was a mix of laughter and tears, as we navigated a Bruno Mars contest that had some unexpected twists and turns. We also took a moment to pay tribute to the one and only Dolly Parton, who left an indelible mark on the music world. And in our topic time segment, we dived into some hilarious and relatable stories about dating and relationships.But what really stood out in this episode was the inspiring story of Olivia and her journey with Runway for Recovery. From its humble beginnings to its current success, this organization has been making a real difference in the lives of those affected by breast cancer. We also heard from a caller who won tickets to see Bruno Mars, and got to experience the excitement of the contest firsthand.If you want to hear more about the Bruno Mars contest, the inspiring story of Runway for Recovery, and the hilarious dating stories, tune in to this episode of the show. We're sharing some amazing stories and having a great time, and we can't wait to hear your thoughts.See omnystudio.com/listener for privacy information.
This is the Fear & Greed Afternoon Report - everything you need to know about what happened in the markets, economy and world of business today, in just a few minutes. ASX ends lower Runway near collision Steadfast to leave ASX ARN loss on Kylie, Jackie O implosion Walmart slumps Join our free daily newsletter here.Find out more: https://fearandgreed.com.au/See omnystudio.com/listener for privacy information.
This is the Fear & Greed Afternoon Report - everything you need to know about what happened in the markets, economy and world of business today, in just a few minutes. ASX ends lower Runway near collision Steadfast to leave ASX ARN loss on Kylie, Jackie O implosion Walmart slumps Join our free daily newsletter here.Support the show: http://fearandgreed.com.au/See omnystudio.com/listener for privacy information.
In episode 301 of the Simple Flying Podcast, your hosts Tom & Channing discuss:American Airlines to bring back seatback screensVietnam Airlines Boeing 787 overrun incident in MunichDelta Air Lines boosts premium seats on new aircraftLufthansa Airbus A380 lands short of runway in MunichAlaska Airlines Boeing 787 flight attendants overextended on long-haul flights
Good Vibe Tribe - Runway For Recovery full 383 Wed, 19 Aug 2026 12:47:00 +0000 TEXfvtV2z31ju2JW91QWpAIiGDUZq0Nx latest,wbmx,society & culture Karson & Kennedy latest,wbmx,society & culture Good Vibe Tribe - Runway For Recovery Karson & Kennedy are honest and open about the most intimate details of their personal lives. The show is fast paced and will have you laughing until it hurts one minute and then wiping tears away from your eyes the next. Some of K&K’s most popular features are Can’t Beat Kennedy, What Did Barrett Say, and The Dirty on the 30! 2024 © 2021 Audacy, Inc. Society & Culture https://player.amperwavepodcasting.com?feed-link=https%3A
What if having less could actually give you more—more time, more energy, more creativity, and more freedom?In this episode of Badass Basic Bitch, Brianna sits down with Courtney Carver, founder of Be More with Less and creator of the Project 333 Challenge, to talk about simplifying your life, letting go of what's weighing you down, and redefining what “enough” really looks like.Courtney's journey toward simplicity began after a life-changing health diagnosis forced her to take a closer look at the stress in her life. What started with addressing debt, clutter, and an overwhelming career eventually became a completely different way of living—and a movement that has inspired people around the world to rethink their relationship with their stuff.Courtney breaks down her famous Project 333 Challenge: dressing with just 33 items or less for three months. But as Brianna and Courtney explore, this conversation is about much more than cleaning out your closet.They dive into the emotional reasons we hold onto things, how shopping can become a way of coping with discomfort, the influence of social media and algorithms on consumerism, and the surprising mental freedom that comes from having fewer decisions to make every day.From designer handbags and kids' clutter to online shopping and decision fatigue, this episode will have you looking around your house—and maybe opening your closet—the second it's over.In This Episode, We Discuss:How Courtney's journey toward simplicity beganWhy reducing stress became the catalyst for changing her lifeThe creation of Be More with Less and Project 333How the Project 333 Challenge worksWhat is—and isn't—included in your 33 itemsWhy Project 333 isn't about deprivation or sufferingHow having less helps redefine what “enough” actually meansThe emotional attachment we develop to clothes, handbags, and possessionsA simple trick for letting go of items you're emotionally attached toBrianna's experience letting go of designer bags and simplifying her wardrobeHow shopping can become a coping mechanism when other parts of life feel outside our controlWhy clutter isn't always made up of things we dislikeThe challenge of managing clutter when you have kidsHow fewer choices can benefit children, tooReducing decision fatigue through a simplified wardrobeWhy fewer possessions can create more room for creativityManaging your energy instead of constantly trying to manage your timeUsing services like Rent the Runway without falling back into overconsumptionHow Instagram, TikTok, algorithms, and online shopping influence what we think we needWhy Courtney intentionally reduced her own social media consumptionThe surprising realization that most people aren't paying attention to what you wearHow simplifying one area of your life can inspire changes everywhere elseMemorable Quote“One of the biggest benefits is realizing that nobody notices what you're doing.” — Courtney CarverKey TakeawaysSimplicity Isn't About DeprivationProject 333 isn't designed to make you suffer through three months with nothing to wear. The challenge is an experiment in discovering how much you actually need.By choosing 33 items—including clothing, jewelry, shoes, and accessories—and living with them for three months, you get the opportunity to step away from the constant cycle of wanting more and discover what you genuinely enjoy wearing.Sometimes You Need Space Before You Can Let GoGetting rid of something can feel surprisingly emotional, especially when the item represents a memory, identity, accomplishment, or version of yourself.Courtney suggests removing items from sight before deciding whether to permanently let them go. Creating distance can weaken the emotional connection and help you recognize whether you actually miss the item—or simply felt uncomfortable getting rid of it.Clutter Costs More Than MoneyEverything we own requires something from us. We have to organize it, clean it, maintain it, store it, think about it, or decide what to do with it.Reducing what you own isn't simply about having a cleaner closet. It can reduce decision fatigue and free up mental energy for creativity, relationships, work, problem-solving, and the things that matter more.Shopping Can Give Us the Illusion of ControlWhen something painful or stressful is happening outside of our control, focusing on something we can control—like what we buy—can feel comforting.But that temporary relief can eventually create another source of stress through clutter, spending, and the constant pressure to acquire more. Recognizing why you're reaching for something can be just as important as deciding whether to buy it.Social Media Is Constantly Telling Us We Need MoreInstagram, TikTok, online shopping, and personalized algorithms have created an environment where we're constantly being introduced to the next thing we're supposed to want.Courtney shares why she's intentionally reduced her own time on certain platforms and why becoming more conscious about what we're consuming digitally can help change what we consume physically.Fewer Decisions Can Create More CreativityWhen you remove dozens of unnecessary decisions from your day, that energy doesn't disappear—it becomes available for something else.Courtney has found that simplifying areas like her wardrobe gives her more capacity for writing, business, problem-solving, and creativity. Sometimes creating more space in your life literally begins by removing things from it.Nobody Is Paying as Much Attention as You ThinkOne of the unexpected lessons from Project 333 is discovering how little other people notice what you're wearing.The fear that someone will notice you repeating an outfit or wearing fewer things is often much bigger in our minds than it is in reality. Letting go of that perceived judgment can create freedom far beyond your closet.Ready to Try Project 333?For three months, dress with 33 items or less, including:ClothingJewelryAccessoriesShoesItems such as underwear, sleepwear, workout clothing used specifically for exercise, and special-occasion pieces aren't included in the 33.The goal isn't to find the perfect 33 pieces. It's to experiment with less and pay attention to what changes—your mornings, your decisions, your spending, your energy, and potentially much more.Connect with Courtney CarverCourtney Carver is the founder of Be More with Less and creator of the Project 333 Challenge. Through her books, writing, courses, and The Simplicity Space Community, Courtney helps people live with less and let go of what's weighing them down.
"Lulujaru represents my connection to my family, and a representation of the land, skies and how we sort of move through Country" - Cissi Gore-Birch.
Senior Director of the Community Action Center Anika Rychner discusses tomorrow's annual Runway Revival, beginning at 6:30pm at the Weitz Center.
Hey Great Pop Culture Debate audience! We're taking a short break from new episodes this week, but don't worry—we'd never treat you like fashion roadkill. Instead, we're pulling a Hot Take from our Best Project Runway Design episode from Season 7.In this clip, our panelists are debating two unforgettable creations that prove fashion can be wearable art. In one corner, it's Christian Siriano and Chris March's unforgettable Avant Garde couture gown, a design that redefined what was possible on the Project Runway runway. In the other, it's Coral Castillo's breathtaking final runway look, a masterclass in craftsmanship, creativity, and signature style.If you enjoyed what you heard, be sure to check out the full Best Project Runway Design episode in our podcast feed for even more fierce fashion debates, or visit greatpopculturedebate.com for dozens of other pop culture showdowns.We'll be back next week with an all-new episode. Until then, remember: Everyone's entitled to their wrong opinion.EDITOR: Bob ErlenbackINTRO/OUTRO MUSIC: "Dance to My Tune" by Marc Torch#projectrunway #christiansiriano #chrismarch #coralcastillo #fashion #bravo #bravotv #realitytv #avantagarde #fashionweek #podcast #podcastclipsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Build an AI universe by compounding recognizable characters, consistent worlds, recurring stories, and creative assets instead of starting from zero. BLVCKL!GHT has spent thousands of hours doing the thing most AI creators accidentally avoid: sticking with one weird idea long enough for it to become an actual universe. Route 47 now spans 28 interconnected shows and characters, four seasons, recurring locations, expanding lore, and even a game in development. characters → worlds → stories → audience → distribution → monetizationDrew and Rory dig into:+ how creative consistency becomes leverage+ why cult audiences can beat virality+ and why every finished asset can make the next one easier to create.BLVCKL!GHT breaks down AI world-building, Route 47, original AI IP, character development, consistent AI art styles, audience compounding, and FAST TV distribution. Krea, Magnific, MiniMax H3, Seedance 2.5, Flux 3, Runway, RunComfy, Comfy Cloud, Higgsfield, and Luma Dream Machine frame the AI image and video workflow discussion. AI agents, creator economics, model costs, synthetic media, and creative ownership shape the broader conversation.---⏱️ Fast Hour 00:00 Who is Blvckl!ght?04:15 Why did Blvckl!ght change his mind on AI?09:06 Why does Gen Z hate AI but love his work?12:15 What shaped Blvckl!ght's visual style?16:19 What is Route 47?19:04 How do you build an AI universe?22:31 How do you stay creatively consistent?24:35 Which AI tools does Blvckl!ght use?26:42 Can AI agents make creatives slower?29:14 Why can a cult audience beat virality?30:15 How can AI creators monetize on FAST TV?32:44 How does AI content reach mainstream media?34:34 How do AI film projects get financed?38:14 What is “Hi-Fi Slop” and why does it fail?40:00 How do shorts become a real series?46:52 Why can obvious AI feel more authentic?47:57 What is AI's real creative opportunity?50:14 MiniMax H3 vs Seedance 2.553:38 Local AI video vs cloud generation54:39 How fast can one AI creator produce?55:55 What is Flux 3?56:57 How do you control AI generation costs?59:06 How do unlimited AI generations work?01:00:29 Brand equity vs capability in AI tools01:03:26 Why do creators stay loyal to AI tools?01:10:13 Where should AI creators draw the line?01:18:26 Should AI make personal decisions?01:27:01 Where should you start with Route 47?01:28:45 How can AI check story consistency?
Investigation launched after a near-miss between two planes on the Sydney airport runway; 20-thousand evacuated in Canada as wildfires bare down on British Columbia; New South Wales pushes back against possible relocation of the 2028 NRL Grand final.
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Heather and Natalie with Homeward Animal Shelter are in studio on Afternoons Live with Tyler Axness to tell you all about Pawject Runway on Aug 11.See omnystudio.com/listener for privacy information.
Do you open your closet every morning and feel like you have nothing to wear? You are not alone, and it is not your fault. Most of us were never taught the basics.Bridget Blacksten is a Los Angeles based advertising, editorial, celebrity, and personal stylist with seven years of high-end styling experience. She is the founder of Studio Bee, and she has worked on photo shoots, press events, commercials, and advertising campaigns for major brands. In this episode, she brings all of that expertise down to earth and breaks it down for the rest of us.We talk about the wardrobe essentials every woman and man should own, what stores actually deliver quality without breaking the bank, how to read clothing labels to know if something is worth buying, and what the biggest fashion don'ts are for both men and women.This one is fun, practical, and packed with tips you can use the next time you open your closet.00:00 Opening: The wardrobe basics every woman should own 01:00 Welcome Bridget Blacksten and her background in fashion 02:00 From musical theater and retail to Rent the Runway in New York 03:00 Seven years of high-end styling: photo shoots, commercials, and campaigns 04:00 How to stay grounded in a fast-paced glamorous industry 05:00 Style tips for everyday women on a real budget 06:00 Building your wardrobe from a foundation of basics07:00 The truth about department store mirrors and why they lie 08:00 Why taking a photo or video is better than trusting the mirror 09:00 Fast fashion versus quality: what to look for and what to avoid 10:00 Abercrombie and Fitch, Levi's, and the denim conversation 11:00 Why you should wash your jeans as little as possible 12:00 Banana Republic, Gap, and Old Navy: the underrated wardrobe trifecta 13:00 Sizing is not your fault: why you can be four different sizes at once 14:00 Necklines explained: V-neck, scoop neck, mock neck, and more 15:00 How to dress a bustier figure and feel supported and stylish 16:00 Pinterest and Google as your free personal style education 17:00 Learning fashion vocabulary so you can actually search what you want 18:00 The wardrobe essentials every woman needs 19:00 Blazers, suits, white button downs, and the little black dress 20:00 How accessories make or break an outfit 21:00 The one focal point rule for jewelry and accessories 22:00 Shoes: sneakers, heels, flats, and playing with materials 23:00 Men's wardrobe essentials: trousers, loafers, jackets, and more 24:00 Why a great jacket is worth the investment 25:00 Fashion don'ts for men: skinny jeans have to go 26:00 Fashion don'ts for women: prints, colors, and keeping it simple 27:00 How to read clothing labels and why 100% cotton matters 28:00 Sustainable fashion and what it actually costs 29:00 What working with Bridget looks like and every budget is welcome 30:00 Seasonal styling, closet refreshes, and making money from what you sell 31:00 Renting for events so you never have to own a gown you will wear once 32:00 How to book a free consultation with BridgetBook a free consultation: https://calendly.com/bridgetblacksten/30-minute-consultation-session-facetimeWebsite: https://www.bridget-blacksten.comInstagram: @bridgetblackstenTikTok: @babebythebeachFor more information about Dali or DaliTalksvisit us at https://www.DaliTalks.com/linktreeFollow me on Instagram @DaliTalkshttps://www.instagram.com/dalitalksFollow me on Facebook @DaliTalkshttps://www.facebook.com/dalitalkFollow me on LinkedIn:https://www.linkedin.com/in/dalitalks/Please like, share, subscribe to this channel, and comment below. THANK YOU FOR YOUR SUPPORT!
DMVKIKNIGHTS MONDAY NIGHT MADNESS BABYDOLL MULAN EDITION 8/3/26 DESTINY SUPREME x NEZUKO LOUBOUTIN x VJ You Da' Birthday Vol. II (Babydoll Edition) Date: Monday, August 3rd Location: E5L — 1230 9th St NW, Washington, DC 20001 MC: Destiny Supreme and Nezuko Louboutin DJ / Sounds: Icon VJ Supreme Dress Code: Must incorporate a bow (satin, lace, rhinestone, oversized, or mini) CATEGORIES: MC vs. MC Face Best Dressed vs. Streetwear Realness Perfect 10s Hands Performance Old Way vs. New Way Body vs. Sex Appeal Tag Team Performance & Runway
Daniel Mahncke and Shawn O'Malley take a deep dive into DLocal (NASDAQ: DLO), the first Uruguayan unicorn and the emerging markets payment provider for companies like Amazon, Uber, Spotify, Netflix, and many more. DLocal is trading at attractive multiples while growing payment volumes at over 70% and printing cash due to high operating leverage and a high-margin business model. That cash is given back to shareholders in the form of dividends and buybacks. Daniel and Shawn discuss whether the high customer concentration and the declining take rate justify the cheap valuation or whether the market is not understanding the full potential of this emerging market jewel. In the end, Daniel values the business and decides whether DLO deserves a spot in The Intrinsic Value Portfolio. IN THIS EPISODE YOU'LL LEARN: (00:00:00) Intro (00:02:04) How DLO became the leading player in emerging markets (00:06:16) What makes DLO's business model stand out (00:14:20) What two megatrends DLO benefits from (00:26:41) Whether there is a race to the bottom with take rates (00:50:37) How DLO compares to Western competition (00:56:58) How DLocal distributes cash to shareholders (01:13:26) Valuation discussion of DLO (01:16:05) Whether DLO is valued attractively (01:17:44) Whether Shawn and Daniel add DLO to the Intrinsic Value Portfolio Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Join the exclusive The Intrinsic Value Mastermind Community. Track The Intrinsic Value Portfolio. Learn more about how to join us in NYC for our Intrinsic Value Conference. Portfolio Review Submit Tool. Value Investors Club Pitch on DLO. Interview with the CEO, Pedro Arnt. DLocal Investor Relations Podcast. Founder and CEO Interview by Stratechery. Check out our previous Intrinsic Value breakdowns Uber, Nike, Reddit, Nintendo, Airbnb, AutoZone, Alphabet, Ulta, John Deere, Madison Square Garden Sports. Related books mentioned in the podcast. Ad-free episodes on our Premium Feed. NEW TO THE SHOW? Get smarter about valuing businesses through The Intrinsic Value Newsletter. Check out The Investor's Podcast Starter Packs. Follow our official social media accounts: X | LinkedIn | Facebook. Try our tool for picking stock winners and managing our portfolios: TIP Finance. Enjoy exclusive perks from our favorite Apps and Services. Learn how to better start, manage, and grow your business with the best business podcasts. SPONSORS Support our free podcast by supporting our sponsors: Fiscal.AI References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm
With election day 2026 just over three months away, lawmakers in Washington are facing the end of the runway for passing legislation they can take home to their districts before November. Farm state legislators in particular would like to finally have a completed Farm Bill to show for their efforts over the last 18 months, but the possibility of passing farm legislation in both houses and getting it to the President's desk in time is growing more unlikely with each passing day. And yet, hope springs eternal, and we have our very own, brand new DTN Ag Policy Editor Jake Zajkowski bringing us the latest on the efforts in D.C. He's here to tell us about current Farm Bill work and what to expect in the weeks ahead, but he'll also give us an update about the ongoing USDA reorganization, and how it might go on to impact farmers as harvest approaches. He'll then talk to us about the current trade picture, from USMCA reauthorization to the latest tariff announcement, and clue us in to happenings around farm aid, labor, and MAHA. We'll wrap up with a look ahead at what the outcome of the November midterms could mean for ag policy in the lame duck session. This episode was recorded on July 31, 2026. See DTN newsroom's coverage on the Senate farm bill markup on dtnpf.com.
Jean-Luc Brunel, a prominent French modeling agent and founder of MC2 Model Management, maintained a long-standing and deeply troubling relationship with Jeffrey Epstein. More than just a social acquaintance, Brunel was widely identified as one of Epstein's primary suppliers of young girls, using his modeling agency as a pipeline to traffic aspiring models—many of them minors—into Epstein's control. Survivors and witnesses have alleged that Brunel lured girls from disadvantaged backgrounds in Europe and South America with promises of modeling contracts, only for them to be flown to the United States where they were abused by Epstein and his associates. Brunel's agency reportedly received substantial funding from Epstein, who appeared to use the business as both a recruitment mechanism and a cover for the systematic exploitation of vulnerable young women.Allegations against Brunel include not only trafficking but also direct involvement in sexual abuse, with several survivors naming him in sworn testimony. He was accused of participating in the assaults alongside Epstein and facilitating access to girls who had little understanding of what they were being brought into. Despite being investigated in multiple jurisdictions, Brunel operated for decades with little interference, a fact that has drawn criticism toward international law enforcement and regulatory bodies for their failure to act. His relationship with Epstein was not incidental—it was structural, and essential to Epstein's ability to source victims under the radar of conventional oversight. When Brunel was finally arrested in France in 2020, it was viewed as long overdue, though he died in custody in 2022 before facing trial.to contact me:bobbycapucci@protonmail.comBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-epstein-chronicles--5003294/support.
This week on Driving Law, Kyla Lee and Paul Doroshenko discuss a major Supreme Court of Canada decision concerning privative clauses and the ability of governments to restrict judicial review of administrative decision-makers. They explain why the ruling could eventually matter for British Columbia drivers challenging decisions involving ICBC's Enhanced Care system and the Civil Resolution Tribunal. The discussion examines the unusually high standards imposed on people seeking court review and whether some of those restrictions could be constitutionally vulnerable. Plus, the Ridiculous Driver of the Week features a motorhome that crashed through the perimeter fence at Abbotsford International Airport and continued onto an active runway, forcing aircraft to abort their landings. Check out the Lawyer Told Me Not To Talk To You T-shirts and hoodies at LawyerToldMe.com and Sit Still Jackson at SitStillJackson.co.
Daniel Mahncke and Shawn O'Malley take a deep dive into DLocal (NASDAQ: DLO), the first Uruguayan unicorn and the emerging markets payment provider for companies like Amazon, Uber, Spotify, Netflix, and many more. DLocal is trading at attractive multiples while growing payment volumes at over 70% and printing cash due to high operating leverage and a high-margin business model. That cash is given back to shareholders in the form of dividends and buybacks. Daniel and Shawn discuss whether the high customer concentration and the declining take rate justify the cheap valuation or whether the market is not understanding the full potential of this emerging market jewel. In the end, Daniel values the business and decides whether DLO deserves a spot in The Intrinsic Value Portfolio. IN THIS EPISODE YOU'LL LEARN: (00:00:00) Intro (00:03:01) How DLO became the leading player in emerging markets (00:07:14) What makes DLO's business model stand out (00:19:08) What two megatrends DLO benefits from (00:28:18) Whether there is a race to the bottom with take rates (00:56:10) How DLO compares to Western competition (01:00:30) How DLocal distributes cash to shareholders (01:19:00) Valuation discussion of DLO (01:21:41) Whether DLO is valued attractively (01:23:19) Whether Shawn and Daniel add DLO to the Intrinsic Value Portfolio Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Join the exclusive The Intrinsic Value Mastermind Community. Track The Intrinsic Value Portfolio. Learn more about how to join us in NYC for our Intrinsic Value Conference. Portfolio Review Submit Tool. Value Investors Club Pitch on DLO. Interview with the CEO, Pedro Arnt. DLocal Investor Relations Podcast. Founder and CEO Interview by Stratechery. Check out our previous Intrinsic Value breakdowns: Visa, Amazon, Sea Limited, Mercado Libre, Shopify. Related books mentioned in the podcast. Ad-free episodes on our Premium Feed. NEW TO THE SHOW? Get smarter about valuing businesses through The Intrinsic Value Newsletter. Check out The Investor's Podcast Starter Packs. Follow our official social media accounts: X | LinkedIn | Facebook. Try our tool for picking stock winners and managing our portfolios: TIP Finance. Enjoy exclusive perks from our favorite Apps and Services. Learn how to better start, manage, and grow your business with the best business podcasts. SPONSORS Support our free podcast by supporting our sponsors: Plus500 Netsuite Shopify Plaud References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm
Natalie Draper and Sarah Aylward discuss the Runway Revival fashion show, happening on August 13th.
OB445: Runway Sharing and Separation Anxiety Released to show supporters on 6/24/2026 Public release scheduled for 7/29/2026 Have a great week, and thanks for listening to Opposing Bases Air Traffic Talk! ✈️ Real pilots. Real controllers. Real talk.
AI news: Sam Altman says we're IN the Singularity, GPT-6 rumors, and AI models literally broke out of their sandbox. What a week. On today's AI For Humans, we dig into the wild GPT-6 rumors (emphasis on RUMORS), Sam Altman's "I've been waiting for this my whole life" singularity moment, Ilya Sutskever's SSI scaling up with Nvidia, and the ongoing debate over whether Anthropic's Opus 5 is brilliant or just hard to love. Also: Flux 3 might be the best AI video model we've seen yet (wait until you see Stacked Plates Man), Runway teases Seedance 2.5, and the new Big Bang Theory has an AI controversy. Plus, THE SCARY STUFF: OpenAI's models exploited a zero-day and compromised Hugging Face during a security eval, the fight over open weights heats up as Kimi K3 goes open, and Chinese robots run military drills. THE SINGULARITY MIGHT BE HERE. BUT WE'RE NOT AFRAID // Show Links // GPT-6 rumors round-up (unconfirmed) https://x.com/TokenGremlin/status/2081493241795629464 Sam Altman full interview (Relentless Podcast) https://youtu.be/Vv3CEAS_w34?si=3y4SWBWxOVkqCEui The Return of Ilya: SSI scales with Nvidia https://x.com/ilyasut/status/2081732293161582930?s=20 Anthropic's Claude Opus 5 https://www.anthropic.com/news/claude-opus-5 Opus 5 Tower of Babel demo https://x.com/petergostev/status/2082071858367648035?s=20 Matt Shumer's zero-shot Counter-Strike clone https://x.com/mattshumer_/status/2081054356405731740?s=20 Black Forest Labs' Flux 3 announcement https://bfl.ai/blog/flux-3 Flux 3 split screen rendering https://x.com/umesh_ai/status/2081664138942529601?s=20 Flux 3 GPU migration documentary (Venture Twins) https://x.com/venturetwins/status/2081515687944822800?s=20 Flux 3 VHS-style recordings https://x.com/venturetwins/status/2081948871882911999?s=20 Stacked Plates Man https://x.com/gandamu_ml/status/2081956426801435060?s=20 https://x.com/gandamu_ml/status/2080871397371371823?s=20 Flux 3 pirate bass https://x.com/itspoidaman/status/2081651615493464406?s=20 Big Bang spinoff AI Controvesy https://x.com/sitcomcrave/status/2081152263481913774?s=20 Runway teases Seedance 2.5 https://x.com/runwayml/status/2082112674666529224?s=20 OpenAI on the Hugging Face security incident https://openai.com/index/hugging-face-model-evaluation-security-incident/ Jensen Huang on the Open Alliance https://x.com/JensenHuang/status/2080643682408321103?s=20 Anthropic has not signed (TechCrunch) https://techcrunch.com/2026/07/24/as-us-weighs-response-to-chinese-ai-industry-urges-against-broad-open-weight-restrictions/ Kimi K3 goes open weights https://x.com/scaling01/status/2081759521878270426?s=20 Chinese robot military drills https://x.com/ClashArchivist/status/2081499576373297562?s=20 Pentagon scales data centers on Army bases https://x.com/Polymarket/status/2082052445144826055?s=20 // Join the AI For Humans community // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow Follow AI For Humans on TikTok: @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Speaking and booking https://www.aiforhumans.show/
Anastasija Lynch pushed back the moment this episode was pitched to her as a burnout conversation. She is an executive coach and co-host of Beyond the Noise who spent a good chunk of her career in technology before moving into coaching senior leaders, and her correction was specific: the people she works with are capable, successful, and perfectly able to power through almost anything, which is exactly why the real problem stays invisible. They have been carrying too much for too long with nowhere honest enough to think it through, and she says that is a different problem with a different fix.In this conversation, we get into:- The sentence leaders repeat for months before everything hits crisis- Why the people closest to you quietly keep you exactly where you are- What one protected hour a week actually does, and what it will not do- The leader who changed one work decision and watched his home life settleThis is a defense of that distinction, tested against the obvious objections: why someone smart enough to run a company cannot simply think their way out alone, why a journal or a long run is not the same thing, and how you would know the difference between a conversation that changed something and one that only felt good. If you are the person everyone turns to and you have not had room to think about your own decisions in months, this episode gives you language for what is actually happening.
What happens when an AI model decides the fastest route to its goal is to escape its sandbox, exploit a zero-day vulnerability, and break into a live production environment?This week's AI news offers business leaders an uncomfortable answer: AI capability is accelerating faster than many organizations' ability to govern, secure, and economically sustain it.The smart response is not to panic—or blindly chase every new model. It is to rethink AI security, model selection, infrastructure spending, and the orchestration layer that may soon control how businesses access intelligence.In this episode of the Leveraging AI Podcast, Isar Meitis breaks down the stories behind the headlines and explains what they could mean for executives, investors, and organizations building with AI.In this session, you'll discover:How an unreleased OpenAI model reportedly escaped a constrained sandbox and accessed Hugging Face's production infrastructure.Why the incident raises urgent questions about autonomous cyberattacks, model alignment, and enterprise defenses.Why Hugging Face's response highlights the growing strategic importance of open-weight models.How AI routers are replacing the “one model for everything” approach.Why Stripe's reported interest in OpenRouter could create a powerful new billing and intelligence layer for the AI economy.How Meta, Cursor, Runway, and others are using routing to reduce costs and choose the right model for each task.Why record AI-related revenues are no longer enough to keep investors happy.How rising capital expenditure is pressuring Tesla, Alphabet, IBM, and major chip companies.Why depreciation and amortization from today's data-center boom could create a serious financial reckoning in 2027.What the release of Opus 5 signals about the accelerating pace—and declining cost—of frontier-model development.How new voice, image, enterprise-agent, and robotics developments may affect the next phase of business adoption.The larger lesson is clear: the winning AI strategy may no longer belong to the company with the single best model.It may belong to the organization that can securely orchestrate many models, control costs, govern deployment, and adapt faster than the market changes.About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!
Send us feedback/questions via TextToday Dave and Jim talk about the examples movies are providing by releasing trailers months in advance. We also talk about the loss of John C. Dvorak from the No Agenda Show.Video VersionSponsors:PodcastBranding.co - They see you before they hear youBasedonastruestorypodcast.com - Comparing Hollywood with History?Video Version (unedited)Mentioned In This EpisodeSchool of Podcastinghttps://www.schoolofpodcasting.com/joinPodpagehttp://www.trypodpage.comHome Gadget Geekshttp://www.theaverageguy.tvFix My Podcasthttps://www.fixmypodcast.comI Play Rocky TrailerChapters:00:00 Introduction01:17 Sponsor: PodcastBranding.co https://www.podcastbranding.co02:23 Sponsor: Based On a True Story Podcast https://www.basedonatruestorypodcast.com03:34 Promotions Are Months Not Days or Weeks06:32 Daves Workflow for an Episode09:27 One Comment Can Change Everything12:07 John C. Dvorak Has Passed https://www.noagendashow.net21:57 Dave's Newsletter Article https://podcastingobservations.com/p/impacting-your-audience23:27 Streamdeck Controlling Lights28:55 Thank You For Your Support https://www.askthepodcastcoach.com/awesome29:33 Join the School of Podcasting https://community.schoolofpodcasting.com29:44 Build Your Podcast Website with Podpage http://www.trypodpage.com29:59 Home Gadget Geeks https://www.homegadgetgeeks.com30:19 Podindy.com Use the Coupon Code: pacer http://www.eventbrite.com/e/1987753467129/?discount=pacer31:17 Featured Supporter: Shane from Spybrary https://www.spybrary.com32:24 Please Support the Show and Give Back https://podcastcoach.supercast.com32:49 People are Still Writing Books34:40 AMP and the new podcast definition46:43 Number one mistake new podcasters make49:15 First steps starting from zero56:16 One piece of advice to reach 1,000 listenersFeatured Supporter: Jodi KrangleCheck out her show: Audio Branding the Hidden Gem of Marketing Leave Your QuestionGo to askthepodcastcoach.com/voicemail and leave your message to be answered on the next show.PodMatchPodMatch Automatically Matches Ideal Podcast Guests and Hosts For InterviewsSupport the showBE AWESOME!Thanks for listening to the show. Help the show continue to exist and get a shout-out on the show by becoming an awesome supporter by going to askthepodcastcoach.com/awesome want a one time donation? Buy Dave a Coffee.
Sapphire Ventures' Jai Das talks with TITV Host Akash Pasricha about the big tech pushback against open-weight AI regulation and OpenAI's recent sandbox exploit. We also talk with Runway Chief Product Officer Anthony Maggio about their new AI media model router, and we get into the nuclear energy boom powering AI data centers with The Information's Laura Mandaro and Nick Wingfield.Articles discussed on this episode: https://www.theinformation.com/newsletters/applied-ai/openais-hugging-face-ai-hack-spooked-employeeshttps://www.theinformation.com/articles/nuclear-startup-valar-atomics-talks-6-billion-valuation-power-milestonehttps://www.theinformation.com/articles/silicon-valley-unites-anthropic-chinese-ai-restrictionsSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Tech Titans Push Back on Open-Weight AI Regulation11:08 - OpenAI Model Breach & Enterprise Security Guardrails16:53 - Runway CPO Anthony Maggio on AI Media Model Routers27:43 - The Editor's Cut: VC Bets on Nuclear Power for AI Data Centers
This NASA solution for sliding aircraft became a global standard for highway and pedestrian safety.
Most founders think their business determines its valuation. The 1 hidden valuation driver costing founders millions is often the founder themselves. By the time a buyer expresses interest, much of your valuation has already been established. Systems, leadership, and operational independence aren't built during due diligence—they're revealed by it. Waiting until an offer arrives often means negotiating from a position that took years to create, but only weeks to evaluate. The bigger risk isn't always EBITDA or revenue growth. Buyers are also assessing whether the business can thrive without the founder, whether transition expectations are aligned, and whether hidden dependencies will create pressure on valuation after the deal begins. Those conversations can quietly reshape enterprise value long before the purchase agreement is signed. Cece Lung from Rich & Sassy Wealth Strategies shares why founders often become the biggest hidden valuation driver in their own business—and why waiting until buyer interest appears can quietly cost millions before negotiations even begin. Learn more about your ad choices. Visit megaphone.fm/adchoices
This week Ben, Brian, and Ted get into IFR currency: what it actually takes to stay instrument current and ready. Six approaches, holds, tracking and intercepting nav aids, and the clock that never stops running. Foggles versus a safety pilot versus the real thing, why the foggles are "the worst approximation of the instrument experience," and whether an IPC is the smarter move than chasing six approaches every six months. Plus a full tape machine of your voice memos and a whole lot of Oshkosh anticipation.From the tape machine: Ash (flying_wheelies) on flying as a wheelchair pilot and sport pilot in his Paradise P1, and more of your feedback.Mentioned on the show:Brian's THE LONG WAY: https://www.makesmallcorrections.com/Frequency Change Aviation, Nashville: https://frequencychangeaviation.com/EP36 with CFI & flight school owner Jeff Ramsey: https://podcasts.apple.com/us/podcast/ep36-cfi-flight-school-owner-jeff-ramsey-on-how-to/id1591463789?i=1000615289723Ash's Flying Wheelies: https://www.instagram.com/flying_wheeliesParadise P1: https://en.wikipedia.org/wiki/Paradise_P1_LSAICP Savannah: https://en.wikipedia.org/wiki/ICP_SavannahWhen Do You Need An IPC (Boldmethod): https://www.boldmethod.com/learn-to-fly/regulations/when-do-you-need-an-ipc-instrument-proficiency-check/1DullGeek's "I Can't See the Runway!!": https://www.youtube.com/watch?v=ZoIQv7IsfCw
Your goal might be real and deeply meaningful, but the timeline you attached to it could be the thing draining your energy.I'm sharing one quote that has repeatedly helped me drop the stress of trying to achieve something and return to motivation and peace.When I hold that frame in mind, I stop treating progress like a verdict on my worth and start treating it like a journey I can adjust.We dig into a distinction I picked up from How to Live a Meaningful Life: the transactional world versus the flow world.Doing VS Being. How we need both and how to balance between them - to get more and feel good.Transactional mode is about getting things done, hitting targets, and moving on.Flow mode is about being present, noticing your experience, and actually living your life while you pursue big outcomes.The problem isn't doing.The problem is living only in doing, even turning “spiritual” practices like meditation or prayer into another task.I talk through how these two modes work best as polarities you use intentionally, not extremes you choose between.If you're working on long-term goals like fitness, health, building a business, creating impact, growing a career, or improving relationships, this mindset episode gives you a simple framework to reduce overwhelm - with a simple quote/mantra and a question to come back to every time your goals feel heavy or life feels like a chain of frustrations and setbacks.If it helped you - share it with one person, and leave a review to help me reach and help more people around the world.Text Me Your Thoughts and IdeasSupport the showBrought to you by Angela Shurina Certified Health, Sleep, Performance & Executive Coach 360 with 18 years of experience helping people change to feel, be and do their best.
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AI is making creative production faster while increasing the value of human craft, imperfection, and hands-on creative control.Drew and Rory start with Netflix's 300 AI-assisted programs and somehow end up defending Blockbuster, boxy cars, greasy roommates, and the radical act of making creative work harder on purpose. Between the usual intellectual potholes, they uncover why invisible AI succeeds, why perfect outputs are becoming exhausting, and why human-made work may become the premium signal.Covered in this episode:Netflix generative AI workflows span concept development, pre-visualization, visual effects, post-production, and release. Suno, Udio, Strudel, Foley artistry, AI music licensing, Runway visual storytelling, Claude, ChatGPT, Figma shaders, Photoshop retouching, creative consistency, nostalgic design, imperfect aesthetics, and hybrid human-AI production define the broader creative shift.---⏱️ Fast Hour00:00 Why are guests returning to Fast Hours?03:45 Why does summer trigger nostalgia?10:49 How is Netflix using generative AI?20:39 Could Blockbuster have become Netflix?24:04 What AI tools has Netflix open-sourced?28:36 What did the Suno breach reveal?32:21 Should AI be used to make music?43:31 Breaking down Runway's lamp film—hy does it work?51:43 How many movie story arcs exist?53:32 Does AI increase the value of human craft?57:53 Why are creators rejecting AI perfection?01:06:16 Why is nostalgic design returning?01:17:09 Why do simple stories feel better?01:23:09 How do AI projects maintain consistency?01:25:43 Who should Fast Hours interview next?#GenerativeAI #AIFilmmaking #AIMusic #CreativeProcess #FastHours
7-19-26 Faith Is the Runway | Exodus 4:1–17 | Pastor Joshua Kennedy Faith Is the Runway Pastor Joshua Kennedy Take Flight Series
Movie of the Year: 2006The Devil Wears Prada (feat. Katie Walsh!)The Devil Wears Prada Podcast Episode: Fashion, Power, and the Best of 2006Welcome to The Devil Wears Prada podcast episode from Movie of the Year: 2006. This week, the Taste Buds enter the gleaming offices of Runway magazine. Furthermore, they bring backup. Film critic Katie Walsh joins Mike, Greg, and Ryan to decide whether David Frankel's fashion-world comedy can survive the 2006 bracket. Along the way, the panel debates feminism in the workplace, the myth of the suffering artist, and the eternal question of Andy's terrible friends. Additionally, the episode features a full Anne Hathaway Career Retrospective bracket. Consequently, this one runs deep on both style and substance.About the FilmThe Devil Wears Prada arrived in the summer of 2006 and promptly stole it. Director David Frankel adapted Lauren Weisberger's bestselling novel about a young journalist grinding through an impossible job. Anne Hathaway stars as Andy Sachs, an aspiring writer who lands a position at Runway magazine. Her boss is Miranda Priestly, the most feared editor in fashion, played by Meryl Streep in an Oscar-nominated performance. Moreover, Emily Blunt and Stanley Tucci deliver career-launching supporting turns. The film earned over $300 million worldwide and near-universal acclaim. Notably, Roger Ebert praised Streep's icy, understated Miranda as the engine of the whole picture. For more background, the Wikipedia entry on The Devil Wears Prada covers its production and legacy in detail. The film now faces its toughest challenge yet: the Movie of the Year 2006 bracket.Guest Panelist: Katie WalshThe Devil Wears Prada 2006 podcast episode welcomes a genuine authority to the panel. Katie Walsh is a Los Angeles-based film critic who reviews weekly releases for the Tribune News Service and the Los Angeles Times. Additionally, she serves as Vice President of the Los Angeles Film Critics Association. Her writing has appeared in GQ, Vanity Fair, Rolling Stone, Vulture, Slate, and The Playlist. She co-hosted the podcast Miami Nice and has guest-hosted Switchblade Sisters. Furthermore, she frequently appears on KCRW's Press Play and has taught film criticism at Chapman University's Dodge College of Film and Media Arts. In practice, that means the Taste Buds must actually defend their takes this week. Someone in the room knows what she is talking about.Feminism and the WorkplaceIs Miranda Priestly a feminist icon or a cautionary tale? The panel digs into how the film frames women, power, and ambition. Miranda runs an empire, yet the film punishes her with a collapsing marriage. Meanwhile, Andy gets scolded by nearly everyone for taking her job seriously. The episode asks whether the movie critiques workplace sexism or accidentally reproduces it. Notably, Katie Walsh brings a critic's perspective to how 2006 audiences read Miranda versus how we read her now. Two decades of think pieces have flipped the film's villain into its hero. Ultimately, the panel debates whether that reversal says more about the movie or about us.Can Art Only Come from Suffering?Andy suffers for a job she claims to hate, all in service of her writing dreams. Consequently, the Taste Buds tackle a bigger question: does great art require misery? The film suggests that paying dues at Runway makes Andy a better journalist. However, it also shows the cost. She loses friends, a boyfriend, and nearly herself. The panel weighs the romantic myth of the starving artist against the reality of creative work. By contrast, some of the best art comes from stability, support, and cerulean-blue sweaters chosen by someone else. Above all, this segment asks what we actually owe our ambitions.Andy's Friends: The Worst People in FashionEvery rewatch of The Devil Wears Prada produces the same realization: Andy's friends are monsters. They mock her job, demand free designer swag, and play keep-away with her work phone. Meanwhile, her boyfriend Nate pouts because she missed his birthday party for her career. Therefore, the panel litigates the great question of the film's second act. Are these people grounding Andy in reality, or are they sabotaging her? Specifically, the group ranks the friends from least to most insufferable. Nevertheless, at least one Taste Bud attempts a defense of Nate. It does not go well.Anne Hathaway Career Retrospective: The BracketSpecial segment time. The Devil Wears Prada podcast crew builds a full bracket to determine Anne Hathaway's most iconic role. The field spans her entire career. The Princess Diaries, Brokeback Mountain, Rachel Getting Married, Les Misérables, The Dark Knight Rises, and Interstellar all enter the arena. Additionally, deep cuts and dark horses make surprise appearances. Seeding arguments get heated. Upsets happen. As a result, friendships within the panel are tested. Does Andy Sachs herself take the crown, or does an Oscar-winning turn steal it? You will have to listen to find out. Check Anne Hathaway's full filmography on IMDb and play along at home.Why The Devil Wears Prada Still MattersTwenty years later, The Devil Wears Prada refuses to fade. The film remains a Rosetta Stone for conversations about work, ambition, and the price of excellence. Miranda's cerulean monologue is still the definitive speech about how culture actually functions. Moreover, the movie launched Emily Blunt, redefined Anne Hathaway, and gave Meryl Streep one of her signature roles. A 2026 sequel proved the appetite never left. In addition, the film's questions have only grown sharper in the era of hustle culture and quiet quitting. Is Miranda a monster or simply held to a different standard? Ultimately, that debate keeps the film alive, and it makes for a fierce competitor in the Movie of the Year 2006 tournament.Related Episodes from Movie of the Year: 2006Catch up on the season so far, from the 2006 season introduction to the music that defined the year:Movie of the Year 2006: Intro Part 1The 2006 Bracket Reveal: Meet the Sweet 16Tristram Shandy: A Cock and Bull StoryThe 2006 Mixtape, Part IAll Movie of the Year episodesFAQ: The Devil Wears Prada Podcast and FilmWhat is this episode of The Devil Wears Prada podcast about?The Taste Buds and critic Katie Walsh debate whether The Devil Wears Prada deserves to advance in the Movie of the Year 2006 bracket. Topics include workplace feminism, art and suffering, Andy's awful friends, and a full Anne Hathaway career bracket.What is The Devil Wears Prada about?Aspiring journalist Andy Sachs takes a job assisting Miranda Priestly, the tyrannical editor of Runway magazine. Consequently, Andy must decide how much of herself she will trade for success.Who directed The Devil Wears Prada?David Frankel directed the film. He later directed Marley & Me and worked extensively on Sex and the City. Aline Brosh McKenna wrote the screenplay from Lauren Weisberger's novel.Who stars in The Devil Wears Prada?Meryl Streep, Anne Hathaway, Emily Blunt, and Stanley Tucci lead the cast. Streep earned an Academy Award nomination for playing Miranda Priestly.More Questions About The Devil Wears PradaIs Miranda Priestly based on Anna Wintour?Widely, yes. Author Lauren Weisberger worked as an assistant to Vogue editor Anna Wintour, and Miranda is broadly understood as her fictional counterpart. However, Streep has said she modeled the performance on powerful men she knew rather than on Wintour.Who is the guest on this episode?Film critic Katie Walsh joins the panel. She reviews films for the Tribune News Service and the Los Angeles Times and serves as Vice President of the Los Angeles Film Critics Association.What is the Anne Hathaway Career Retrospective?It is a bracket-style special segment. The panel seeds Anne Hathaway's most famous roles against each other and votes round by round until one iconic performance remains.Why does The Devil Wears Prada still matter?The film remains the defining movie about ambition, mentorship, and toxic workplaces. Moreover, its cultural conversation keeps evolving, as the 2026 sequel proved.
Welcome back to Behind the Win. We're talking about how a city positions itself for growth, visibility, and long-term impact without losing what makes it special. And a big part of that conversation right now is Ogden, especially with the momentum around the Ogden Airport and new commercial service, including Breeze Airways. Joining us today is Casey Sanders, the Airport Marketing and Business Recruitment Manager for Ogden-Hinckley Airport, where he's leading efforts to bring in new service and drive economic growth through aviation. He also serves as an honorary commander with the Hill Air Force Base, giving him a unique perspective on the connection between the airport, the defense community, and the broader region.
Local fashion designers took high fashion to the streets last month to showcase clothes characterized as "rasquachic."
Episode Description A goal without a launchpad is just a daydream without a deadline. Today we build the structure that makes your Mars mission real before you move a single inch. I think SMART goals are stupid, at least the time-based, realistic part. Ask Elon how long Mars should take. A big goal doesn't fit in a tidy box. What it needs is one thing. A decision to begin, a date on the calendar, and a few visible markers so you can feel yourself moving. Build it to pull you forward, not push you. Let's build your launchpad. Featured Story I'm recording this on Monday the 29th, right before I head out on vacation. I've got a goal that splits the year in two, the first half from the second. So every single thing I'm doing today and tomorrow is cleanup. Clearing stuff out, tying off loose ends, getting it all off my plate. I'm not white-knuckling it. I'm building momentum on purpose. When July 1st hits and I kick into the next stage, the obvious next step is already carrying me there. That's a launchpad. The decision is made, the date is set, and the runway is clear. All I have to do then is light it up. Important Points A goal without a launchpad is a daydream without a deadline. Make it real before you move with a date and a structure. The decision to begin is the one move that fuels everything after it. Decide first, and the motivation shows up next. Lay small visible markers between here and your goal. They prove you're moving so you don't get lost in the dark middle. Memorable Quotes A goal without a launchpad is a daydream that doesn't have a deadline. Your daydream without a deadline is nothing. There is one thing that gives you the motivation to continue your goal, and that is the decision to begin it. Decide. You decide to go, you go. Your commitment is automatic if you're motivated enough to reach the freedom on the far side. Scott's Three-Step Approach Start by making one clear decision to begin, because that single choice is what fuels every step that comes after it. Next, make it real before it's real, naming a date, putting it on the calendar, and saying the goal out loud to someone. Then lay visible markers down the runway, benchmarks that prove your momentum so you never stall in the dark middle. Chapters 1:53 - A goal without a launchpad is just a daydream 2:42 - Why SMART goals are stupid for a big goal 4:15 - The decision to begin is your only motivation 5:31 - Make it real with a date you say out loud 5:58 - Anticipation is the fuel that pulls you forward 6:37 - Designing the next step to be totally obvious 7:29 - Runway markers that prove you are moving Connect With Me Search for the Daily Boost on YouTube, Apple Podcasts, and Spotify If you enjoy the Daily Boost, you might like Notes From Scott. A few mornings each week, I send a short note with something I've been thinking about or noticing lately. Sometimes those ideas turn into podcast episodes later. You can sign up at https://notesfromscott.com. Email: support@motivationtomove.com Main Website: https://motivationtomove.com YouTube: https://youtube.com/dailyboostpodcast Instagram: https://instagram.com/heyscottsmith Facebook Page: https://facebook.com/motivationtomove Facebook Group: https://dailyboostpodcast.com/facebook Learn more about your ad choices. Visit megaphone.fm/adchoices
We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li
Brady goes on an aerobatic stunt flight - and Tim is annoyed by the theme tune from Roger Ramjet. Plus loads of other stuff.A few snippets from the flight can be seen on the YouTube video for this episode - https://youtu.be/YQeBXNE868MAn extended video of the flight with multiple camera angles and all the audio can be seen on our Patreon - https://www.patreon.com/unmadeFM/posts/163054917With the thanks to Cabaero Aviation and pilot Mark Hooton - https://cabaeroaviation.com/Today's Request Room - https://www.patreon.com/unmadeFM/posts/163143083Support us on Patreon - https://www.patreon.com/unmadeFMDiscuss the episode on our subreddit - https://www.reddit.com/r/Unmade_Podcast/USEFUL LINKSRunway 23 - https://www.runway23.fm/Runway 23 on Apple - https://podcasts.apple.com/us/podcast/runway-23/id1896917264Runway 23 on Spotify - https://open.spotify.com/show/033x15ndR31wM6YujhTv4wPeriodic Videos (Brady's chemistry videos) - https://www.youtube.com/periodicvideosTim's downloadable doodles (or noodles) - https://www.patreon.com/unmadeFM/posts/here-are-guitar-161608346John and his gold card (and other pictures from the episode) - https://www.unmade.fm/episode-181-picturesSywell Aerodrome - https://www.sywellaerodrome.co.uk/Port Arthur - https://portarthur.org.au/The Port Arthur Massacre - https://en.wikipedia.org/wiki/Port_Arthur_massacreRoger Ramjet intro - https://www.youtube.com/watch?v=E7SqSNQeAFMToday's Request Room - https://www.patreon.com/unmadeFM/posts/163143083
Designing runways for hot air and heavy planes, inspecting pavement cracks with measuring wheels, and protecting vicious little owls from construction crews with Eileen Vélez-Vega, a civil aviation engineer in Puerto Rico. What does the color of runway lights reveal to a pilot? And what's a "spall"?WANT MORE EPISODE SUGGESTIONS? Grab our What It's Like To Be... "starter pack". It's a curated Spotify playlist with some essential episodes from our back catalogue.GOT A COMMENT OR SUGGESTION? Email us at jobs@whatitslike.comFOR SPONSORSHIP OPPORTUNITIES: Email us at partnerships@whatitslike.comWANT TO BE ON THE SHOW? Leave us a voicemail at (919) 213-0456. We'll ask you to answer two questions:1. What's a word or phrase that only someone from your profession would be likely to know and what does it mean?2. What's a specific story you tell your friends that happened on the job? It could be funny, sad, anxiety-making, pride-inducing or otherwise.We can't respond to every message, but we do listen to all of them! We'll follow up if it's a good fit.
Fashion visionary Law Roach joins Obsessed to talk about bringing his signature honesty to Project Runway, why he calls himself an "image architect," and the psychology behind creating iconic celebrity style. He reflects on his longtime collaboration with Zendaya, the evolution of method dressing, the lessons he learned from Celine Dion, and why fashion has become one of Hollywood's most powerful marketing tools. Plus, Law shares behind-the-scenes stories from his career, his approach to judging reality TV, and the style trends he'd happily leave behind. Follow Kevin Fallon on Instagram @kpfallon Follow Matt Wilstein on Instagram @mattjwilstein New episodes every Thursday, and Saturday; early drops on YouTube. Learn more about your ad choices. Visit podcastchoices.com/adchoices
Lameka Fox is an international fashion model whose career has taken her around the world. But in 2018, she became the target of a stalker. What began as unwanted contact escalated into years of harassment, surveillance, threats, and repeated invasions of her privacy. Despite reporting the behavior, she struggled to get the protection she needed. Today, Lameka shares her story of surviving stalking, how it changed her life, and how she helped advocate for New York's CREEP Act to strengthen protections for stalking victims. Share Your Story on the Show: strictlystalkingpod@gmail.com Our Sponsors Boll and Branch Get twenty percent off your first order, plus free shipping during the Memorial Day sale at bollandbranch.com/strictly with code STRICTLY. Exclusions apply. Delete Me Today get 20% off your DeleteMe plan when you go to JoinDeleteMe.com/STALKING and use promo code STALKING at checkout. Quince Go to https://www.quince.com/strictly for free shipping on your order and 365-day returns. Shopify shopify.com/strictlystalking for a one-dollar-per-month trial period! Solace You can start a free session in under a minute at solaceconcierge.ai/strictlystalking. REMI shopremi.com/strictly to get 50% off your new night guard with code STRICTLY Whatnot Download the Whatnot app today and get free shipping on your first order. Just search W-H-A-T-N-O-T— Whatnot — in the app store and start scoring amazing deals. Progressive Insurance Press play on comparing auto rates. Get your auto quote at Progressive.com to join the over 28 million drivers who trust Progressive. Guest Links: Lameka Fox IG: https://www.instagram.com/lamekafox/ Creep Act IG:https://www.instagram.com/thecreepact/ Related Podcasts lovelustfear | with Jake Deptula Listen & Subscribe Here: lovelustfear The Last Trip - Podcast - hosted by Jaimie Beebe Listen & Subscribe Here: The Last Trip Instagram @strictlystalkingpod @feathergirl77 @jaked3000
The second half of the year isn't a continuation, it's a new season. In this episode, I'm sharing my own mid-year reset and what my clients are doing differently to finish the year strong, because the most powerful people know something the rest of the world doesn't: the second half is where the year is actually won or lost. Consider this your wake-up call and your runway. Now let's walk. I am taking on a few people for 2nd half advisory, consulting & coaching, subscribe to the newsletter list and reply for details www.KellyLynnAdams.com Will I see you virtually on Wednesday, July 1st's our monthly virtual networking, connected circles event, did you RSVP yet? Link in my Instagram bio. Last month's meeting was powerful. If you haven't joined the waitlist for The Curated Table Events, message EVENTS. If you haven't subscribed to the newsletter for exclusive events, offerings and announcements make sure you are on the newsletter here: www.KellyLynnAdams.com
Fresh off a 10-day trip with my kids in Cabo and LA, I'm catching you up on everything: from Disney adventures and quality time with my favorite people to one of the biggest moments for Uncommon James yet: our Miami Swim Week runway debut.After getting flooded with questions about how I prepared to walk the runway, I'm sharing exactly what I did (and didn't do) leading up to Miami. We're talking alcohol, workouts, sleep, gut health, inflammation, supplements, saunas, spray tans, coffee enemas, and why I think feeling your best has way more to do with overall health than chasing a number on the scale.A word from my sponsors:Salt and Stone: Try Salt and Stone's discovery set to find your signature scent — Go to https://SaltandStone.com/HONEST and use code HONEST at checkout for 15% off your first order.Nutrafol: See thicker, stronger, faster-growing hair with less shedding in just 3-6 months with Nutrafol. For a limited time, Nutrafol is offering our listeners $10 off your first month's subscription and free shipping when you go to https://Nutrafol.com and enter the promo code HONESTDraftKings Casino: Download the DraftKings Casino app and sign up with code HONEST to claim your Flex Spins and experience Cashingo—the feature you can't play anywhere else! The Crown is Yours. In partnership with DraftKings Casino. Gambling problem? Call one eight hundred GAMBLER. In Connecticut, help is available for problem gambling call eight eight eight seven eight nine seven seven seven seven or visit CCPG.org. Please play responsibly. Twenty-one plus. Physically present in Connecticut, Michigan, New Jersey, Pennsylvania, West Virginia only. Void in Ontario. Eligibility restrictions apply. Non-withdrawable Spins issued as fifty spins per day for twenty days, valid for select games only and expire each day after twenty four hours. See terms at casino.draftkings.com/promos. Ends July 22, at 11:59 PM Eastern Time.LMNT: Right now LMNT is offering a free sample pack with any purchase, That's 8 single serving packets FREE with any LMNT order. This is a great way to try all 8 flavors or share LMNT with a friend. Get yours at https://DrinkLMNT.com/HONEST.Lululemon: Go to https://lululemon.com right now. New styles drop all the time and the colors go fast, so don't wait. And if something doesn't work for you, free returns, always. Ladder: If you have an iPhone, head to https://ladder.fit/HONEST and take a quick quiz to find your perfect Ladder plan. Use my link and get a free 7-day trial with NO credit card, and $10 off your first month if you join.Armra: Go to https://armra.com/HONEST or enter HONEST to get 30% off your first subscription order.Hiya: Receive 50% off your first order. To claim this deal, you must go to https://hiyahealth.com/HONEST.For more Let's Be Honest, follow along at:@kristincavallari on Instagram@kristincavallari and @dearmedia on TikTokLet's Be Honest with Kristin Cavallari on YouTubeProduced by Dear Media.This episode may contain paid endorsements and advertisements for products and services. Individuals on the show may have a direct or indirect financial interest in products, or services referred to in this episode.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
If you're still wondering how you can give back to the community this Pride season, we have the answer! Keep our little indie pod afloat by joining our Patreon at the $5/month tier or higher and unlock our growing library of full-length ad-free bonus episodes, ad-free weekly episodes, mp3 downloads of all our original songs, exclusive Discord access to hang out with us, and more! Get an automatic discount on your membership by signing up for an annual subscription. Welcome back to Lez Hang Out, the podcast that fell in love with the shape of a woman long before Gaga made it cool. This week, Leigh (@lshfoster) and Ellie (@elliebrigida) hang out with Lez Hang Out's very own production assistant and professional mermaid, Kristin Murison (@therealksparkle), and talk about why the 2026 blockbuster hit, The Devil Wears Prada 2, should've been gay. If you missed our Should've Been Gay episode on the original The Devil Wears Prada film we highly recommend listening to it first for the perfect podcast double feature. You could tell us that the entirety of this nearly 2-hour long sequel takes place in Emily Charlton's head as she recovers in the hospital after being hit by a taxi in the first movie and we would believe you. It's simply too gay of a script for there to be any other explanation aside from ‘lesbian fever dream'. Whether you ship Mirandy, Sachston, or a secret third thing (we suggest Lucy Liu and literally any other woman on screen), you will come out of DWP2 extremely well-fed. Although it has been 2 decades since Miranda Priestly (Meryl Streep) graced our screens, her red heels remain firmly on the necks of lesbians everywhere. Not even the decline of print journalism, the rise of fast fashion, and a full-on scandal can dethrone the queen of Runway. By reputation alone, she gets Lady Gaga to perform what is undoubtedly her gayest song since Born This Way. Yet, even Miranda and Lady Gaga's chemistry isn't the gayest thing about DWP2. That title is shared by the Emilys– Andy “I froze my eggs and have never hidden a feeling in my entire life” Sachs (Anne Hathaway) and Emily “I'm divorced and visibly repulsed by any man I have to get physically close to” Charlton (Emily Blunt), who spend the entire film openly ogling one another and bickering like an old married couple while wearing increasingly more masc outfits. They're pretty much canonically dating by the end, bonding over a plate of shared carbs as Emily confesses to having called Andy all those years ago (all but admitting that she has been holding on to the disappointment of Andy not calling her back for literally 20 years). If that's not a lesbian fever dream, we don't know what is. We know one thing for sure, The Devil Wears Prada 2 Should've Been Gay. Give us your own answers to our Q & Gay on Instagram and follow along on Facebook, TikTok, YouTube and BlueSky @lezhangoutpod. Email us @lezhangoutpod@gmail.com. Connect with us individually: Ellie Brigida (@elliebrigida). Leigh Holmes Foster (@lshfoster). Support the pod by shopping small for your Pride #ootd at bit.ly/lezmerch & picking up our Lez-ssentials songs on Bandcamp. Learn more about your ad choices. Visit megaphone.fm/adchoices
I am so thrilled to be joined on today's Juicy Scoop by the hilarious Jamie Lee! She is an Emmy winning writer for the hit show Ted Lasso, and you also know her incredible work as an actress and writer on Crashing and so many other amazing projects. Today, Jamie is here to dive into her latest one-woman show, where she investigates the surprising death of a friend from when she was 20 years old and sets out to solve the mystery of what actually happened. Plus, we are getting into the future of television writing and how AI might impact the industry, breaking down the drama surrounding Brad Pitt's kids dropping his last name (and whether he'll start a new family with his younger girlfriend), and dissecting why celebrities weren't allowed to wear heels on the runway at the Sports Illustrated fashion show. Subscribe to my new show Juicy Crimes!: https://bit.ly/juicycrimes Stand Up Tickets and info: https://heathermcdonald.net/ Subscribe to Juicy Scoop with Heather McDonald and get extra juice on Patreon: https://bit.ly/JuicyScoopPod https://www.patreon.com/cw/juicyscoop Watch the Juicy Scoop On YouTube: https://www.youtube.com/@JuicyScoop Shop Juicy Scoop Merch: https://juicyscoopshop.com/?srsltid=AfmBOopTZFUvAeokrJJ6dQ5wuAW1T3nssO6pHk47u7KymJUBtBgKCvfX Follow Me on Social Media: Instagram: https://www.instagram.com/heathermcdonald/ TikTok: https://www.tiktok.com/@heathermcdonald YouTube: https://www.youtube.com/@HeatherMcDonaldOfficial Learn more about your ad choices. Visit podcastchoices.com/adchoices
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