Podcasts about Poolside

This Week In Startups is made possible by: DigitalOcean https://do.co/twist Every.io https://every.io PayPal https://paypal.launch.co Today's show: *Nvidia agreed to buy Hugging Face for $12.9 billion, just days after quietly licensing Poolside's coding model for $6 billion+. On TWiST, Jason lays out why this is actually the biggest AI story of the year. Nvidia plans to offer enterprises unmetered, unlimited on-prem compute, pulling them away from reliance on Anthropic and OpenAI's frontier models and products. PLUS we're chatting with Halter founder & CEO Craig Piggott about how his company helps farmers optimize their land use via solar-powered AI-driven cattle collars. AND Hop Aero CEO and co-founder Matija Milenovic shows us Rook, his autonomous hypersonic cargo rocket that can launch from a shipping container and deliver cargo up to 450 miles away in about 15 minutes. Finally, Jason and Lon explain why you never post Slop on the timeline. Guests: Craig Piggott on X: https://x.com/craig_piggott Halter: https://www.halterhq.com/ Matija Milenovic on X: https://x.com/mat_milenovic Hop Aero: https://hopaero.com/ Relevant Links: OpenAI Open Letter: https://openai.com/collective-cyberdefense/ OpenAI Hugging Face report: https://openai.com/index/hugging-face-incident-and-the-road-ahead/ OpenAI Jalapeño report: https://openai.com/index/jalapeno-first-results/ Dario Amodei-Gavin Baker tweet thread: https://x.com/DarioAmodei/status/2088758816376807762 TechCrunch: Nvidia-Hugging Face deal: https://techcrunch.com/2026/08/26/nvidia-closes-in-on-hugging-face-acquisition/ TechCrunch coverage of Halter: https://techcrunch.com/2026/04/04/unpacking-peter-thiels-big-bet-on-solar-powered-cow-collars/ Dealroom: Sir Peter Back on Rocket Lab: https://app.dealroom.co/news/note/sir-peter-beck-on-rocket-lab-s-no-money-so-we-have-to-think-playbook-from-a-60k-new-zealand-start-up-to-an-end-to-end-space-company WSJ: Druckenmiller op-ed: "Let the Bond Market Speak": https://www.wsj.com/opinion/let-the-bond-market-speak-81529d74 NOTUS: Druckenmiller's WSJ op-ed was written with AI: https://www.notus.org/media/stanley-druckenmillers-wsj-op-ed-bessent-ai WaPo: Wall Street Journal defends Druckenmiller op-ed: https://www.washingtonpost.com/business/2026/08/25/wall-street-journal-says-ai-generated-op-ed-didnt-breach-its-standards/ Photon Matrix Laser Mosquito Killer: https://photonmatrixlab.com/ "The Wizard of the Kremlin" trailer: https://www.youtube.com/watch?v=U7ctYp3zQVA "King & Conqueror" trailer: https://www.youtube.com/watch?v=anS9xQEPTsU "Lanterns" trailer: https://www.youtube.com/watch?v=7UIBOsuUwc4 "Rebel Ridge" trailer: https://www.youtube.com/watch?v=gF3gZicntIw Alpaka Bravo Sling Max 2 bag: https://alpakagear.com/products/bravo-sling-max-v2 No Agenda podcast: https://www.noagendashow.net/ THR: John C. Dvorak obit: https://www.hollywoodreporter.com/business/business-news/john-dvorak-dead-tech-journalist-no-agenda-inside-track-1236657317/ Timestamps: 0:00 The 100+ company open letter on cyberdefense 9:48 DigitalOcean - Want to see what building on a true AI-native platform looks like? Head to https://do.co/twist to start building on DigitalOcean's AI-Native Cloud today — and cut your AI workload costs by up to 50%. 16:46 Why some models are "unsafe at any latency" 19:44 Craig Piggott of Halter joins the show 21:03 Every.io - For all of your incorporation, banking, payroll, benefits, accounting, taxes or other back-office administration needs, visit https://every.io 26:09 How do you add cows to Starlink? 31:26 PayPal - Pay zero processing fees on your first $100K in eligible PayPal payment volume. Learn more at https://paypal.launch.co 47:52 Founder-led hyper-local sales motion 53:15 Remember John C. Dvorak 58:27 Matija Milenovic of Hop Aero joins 59:53 Sending out autonomous swarms in the Indo-Pacific 1:11:26 Nvidia's mega-Hugging Face deal 1:16:32 Nvidia's "unmetered" play against Anthropic and OpenAI 1:29:47 How letting AI write for you is like lip syncing 1:33:44 Laser-guided mosquito defense system 1:36:32 Lon's streaming picks   Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis   Check out all our partner offers: https://partners.launch.co/   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

Ruthie Polinsky & Katilin Sharkey talk Bears with us poolside bonus 1138 Fri, 28 Aug 2026 22:48:11 +0000 za9AAvumsZlTSyf8C3tDRss88VCNfnbW sports Spiegel & Holmes Show sports Ruthie Polinsky & Katilin Sharkey talk Bears with us poolside Matt Spiegel and Laurence Holmes bring you Chicago sports talk with great opinions, guests and fun. Join Spiegel and Holmes as they discuss the Bears, Blackhawks, Bulls, Cubs and White Sox and delve into the biggest sports storylines of the day. Recurring guests include former Bears coach Dave Wannstedt, former Bears center Olin Kreutz, Cubs manager Craig Counsell, Cubs second baseman Nico Hoerner and MLB Network personality Jon Morosi. Catch the show live Monday through Friday from 2 p.m. to 6 p.m. CT on 104.3 The Score, the exclusive audio home of the Cubs and the Bulls, or on the Audacy app. © 2026 Audacy, Inc. Sports https://player.amperwavepodcastin

Ruthie Polinsky & Katilin Sharky talk Bears with us poolside (Hour 1) bonus 2035 Fri, 28 Aug 2026 23:03:00 +0000 XXPs2SsGew0pr26sQfaZI48HBeIxcv29 sports Spiegel & Holmes Show sports Ruthie Polinsky & Katilin Sharky talk Bears with us poolside (Hour 1) Matt Spiegel and Laurence Holmes bring you Chicago sports talk with great opinions, guests and fun. Join Spiegel and Holmes as they discuss the Bears, Blackhawks, Bulls, Cubs and White Sox and delve into the biggest sports storylines of the day. Recurring guests include former Bears coach Dave Wannstedt, former Bears center Olin Kreutz, Cubs manager Craig Counsell, Cubs second baseman Nico Hoerner and MLB Network personality Jon Morosi. Catch the show live Monday through Friday from 2 p.m. to 6 p.m. CT on 104.3 The Score, the exclusive audio home of the Cubs and the Bulls, or on the Audacy app. © 2026 Audacy, Inc. Sports https://player.amperwavep

AGENDA:  00:00 NVIDIA Launches a Three-Deal Blitz Across the AI Stack 04:31 NVIDIA's $12BN Poolside Deal Exposes Frontier AI's Capital Wall 15:39 NVIDIA Moves to Back Mercor at $20BN and Perplexity at $30BN 26:55 OpenAI Confirms 2027 IPO as Anthropic Seizes the Lead 40:42 Hugging Face Draws $13BN Takeover Interest as Open Models Boom 44:56 Citadel Cashes Out 80% of Its Leopold Aschenbrenner AI Trade 51:51 AI Boom Is "Less Than One-Third Over"—But Token Addiction Has a Price 01:04:07 Stripe Reaccelerates to 41% as AI Reshapes Software's Leaderboard 01:07:45 Instinct Security Scare Exposes Why AI Agents Still Cannot Be Trusted 01:23:55 VCs Name AI's Biggest Bubbles: Support, Defense, Humanoids and Roll-Ups  

Nvidia will report earnings this Wednesday amid growing challenges for the AI industry, including the financial vulnerability of some of its biggest customers. WSJ's David Uberti breaks down what the chip giant is doing to shore up those potential weaknesses across the market. Plus, WSJ's Callum Borchers explains how companies are using AI to monitor worker productivity – and shares tips for evading the sneaky trackers. Isabelle Bousquette, a reporter for the Wall Street Journal Leadership Institute, hosts. Sign up for the WSJ's free Technology newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A.M. Edition for Aug. 24. Trade talks between Canada and the U.S. have broken down, with Mark Carney betting no deal is better than a bad deal. WSJ trade reporter Gavin Bade details the new tariffs. Plus, Paramount begins settlement talks with several Democratic states today in a bid to avert an antitrust lawsuit over its takeover of Warner Bros. Discovery. And Nvidia, not satisfied with producing the computing power for AI, spends billions of dollars on developing open-weight AI models to compete with China. Luke Vargas hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hugging Face explored a sale at $13B+, keeping the AI M&A wave rolling. Trump scolded towns that reject data centers while Abbott said the industry dug its own grave, Fable 5 spending plateaued, and Apple cut 200+ jobs. Links Sources: Hugging Face is exploring a sale that could value it at $13B+, up from $4.5B in 2023, and has been working with a bank to evaluate bidders' interest (Business Insider) Delangue has said Hugging Face is close to profitability and barely touched its 2023 round, and it turned down a $500M Nvidia investment at a $7B valuation earlier this year (TechCrunch) President Trump says communities that oppose data centers are "making a mistake" as they create "tremendous amounts of jobs and money", amid bipartisan backlash (Axios) Texas Gov. Greg Abbott says data center companies "dug their own grave" and deserve the backlash, after ordering an audit that has stalled roughly 1,800 projects (Fortune) Ramp data: Fable 5, launched in June, has plateaued at ~11% of spending on Anthropic tools, as companies shift to cheaper models; Opus 5 surpassed Fable 5 (Financial Times) Nvidia plans to use its $6B licensing deal with Poolside to build one of the world's most powerful open-weight models, to compete with DeepSeek and Kimi K3 (The Wall Street Journal) Sources: Apple is cutting 200+ jobs, including ~100 positions from the Vision Pro unit and another 100 from the Siri team, as it focuses on new devices and AI (Bloomberg) Subscribe to the ad-free feed.

A quickie video for the Youtube Channel.  An experiment.Johnshares quick thoughts on a few topics: they disliked the Ted Lasso season premiere, arguing the show needs more reality and that it's unbelievable Ted—after finishing second in the Premier League—would be working part-time at a supermarket instead of coaching, doing TV, or taking another high-level job, and they also can't buy the idea he'd return to the UK to coach a second-division women's team. They then say “Alley Cats” was very bad despite liking Ricky Gervais, feeling it was mostly cursing and quitting a couple minutes in. Finally, they react to Ranker's best sitcoms poll (1.3M votes, 101K people) naming Friends #1, calling it insane and arguing it wasn't even better than Seinfeld, while also mentioning The Brady Bunch's cultural impact and that Everybody Loves Raymond is underrated. Ted Lasso Season Premiere Rant, Ricky Gervais' “Alley Cats” Letdown, and Why Friends Isn't #1 Poolside, the host shares quick thoughts on a few topics: they disliked the Ted Lasso season premiere, arguing the show needs more reality and that it's unbelievable Ted—after finishing second in the Premier League—would be working part-time at a supermarket instead of coaching, doing TV, or taking another high-level job, and they also can't buy the idea he'd return to the UK to coach a second-division women's team. They then say “Alley Cats” was very bad despite liking Ricky Gervais, feeling it was mostly cursing and quitting a couple minutes in. Finally, they react to Ranker's best sitcoms poll (1.3M votes, 101K people) naming Friends #1, calling it insane and arguing it wasn't even better than Seinfeld, while also mentioning The Brady Bunch's cultural impact and that Everybody Loves Raymond is underrated. Poolside, the host shares quick thoughts on a few topics: they disliked the Ted Lasso season premiere, arguing the show needs more reality and that it's unbelievable Ted—after finishing second in the Premier League—would be working part-time at a supermarket instead of coaching, doing TV, or taking another high-level job, and they also can't buy the idea he'd return to the UK to coach a second-division women's team. They then say “Alley Cats” was very bad despite liking Ricky Gervais, feeling it was mostly cursing and quitting a couple minutes in. Finally, they react to Ranker's best sitcoms poll (1.3M votes, 101K people) naming Friends #1, calling it insane and arguing it wasn't even better than Seinfeld, while also mentioning The Brady Bunch's cultural impact and that Everybody Loves Raymond is underrated. 00:04 Ted Lasso Rant00:25 Reality Check Complaints01:27 Alley Cats Letdown01:47 Best Sitcom Debate  Become a supporter of this podcast: https://www.spreaker.com/podcast/daily-comedy-news-with-johnny-mac--4522158/support.Daily Comedy News is hosted by Johnny Mac and releases every weekday. Subscribe on Spotify, Apple Podcasts, or your favorite app. Part of the Caloroga Shark Media network. For transcripts and show notes visit www.dailycomedynews.comDaily Comedy News with Johnny Mac is a daily podcast covering comedians, stand-up comedy, late night television, and the comedy industry. New episodes every morning. Follow on Apple Podcasts, Spotify, or wherever you listen.  Contact John at John@thesharkdeck dot com For Uninterrupted Listening, use the Apple Podcast App and click the banner that says Uninterrupted Listening.  $4.99/month John's Substack about media is free. 

For the second hour of Stokely and Evans without Mark Schlereth, they rag on Stink for being gone and tell the tale of Poolside. Stoke shows off his 8-year-old phone. Sean Payton spoke about Mike Shanahan yesterday and it prompts the guys to share their stories of the greatest coach the Broncos have seen. They project how much playing time the starters will play in the first preseason game and hear what Sutton had to say about they WR room. The Morning Crew wrap up the second hour in 4 Down Territory where Jonah Coleman figures to have a limited role, load management comes to the Broncos, Riley Moss is developing his ball skills, and Drew Brees' top 10 case. 

Episode DescriptionPlastChicks Lynzie Nebel and Mercedes Landazuri sit down with Wendy and Steve Hoenig poolside at the Plastics Pioneers Association (PPA) and Plastics Hall of Fame (PHoF) Spring 2026 Networking/Conference in Sarasota, Florida to explore their remarkable journey through the plastics industry as partners in both life and business.After distinguished careers at Dow, Wendy, a 2024 Plastics Hall of Fame inductee, and Steve combined their expertise and entrepreneurial spirit to launch H&H Business Development, where Wendy serves as Founder and CEO and Steve serves as Chief Technology Officer.They share insights from their trailblazing careers at Dow, discussing how they met and built successful careers in plastics, transitioned from corporate life to entrepreneurship, and discovered the unique advantages of running a business together.The conversation explores how their complementary management styles strengthen both their company and home life, how they navigate disagreements, capitalize on each other's strengths, and support one another within their blended family. They also reflect on some of the proudest moments of their careers and the lessons they've learned along the way.Join us for an inspiring discussion about partnership, leadership, innovation, and the power of building a life and business together.About the GuestsWendy Hoenig is Founder and CEO of H&H Business Development, a consulting firm focused on developing new technologies and businesses.  She has over 40 years of experience in new business development in Fortune 100 corporations, venture capital, private equity, and start-ups. In 2024, she was inducted into the Plastics Hall of Fame in recognition of her leadership and significant contributions to the plastics industry through her work at Dow and H&H Business Development.Steve Hoenig is Chief Technology Officer of H&H Business Development, where he helps organizations develop and commercialize innovative technologies and business opportunities. He is an experienced technology leader in the plastics and flooring industries. Drawing on his extensive expertise in the plastics industry, Steve works alongside Wendy to help companies accelerate growth and innovation.NotesSee Wendy's 2024 induction into the Plastics Hall of Fame.Watch the PlastChicks podcast on the SPE YouTube Channel.PlastChicks is sponsored by SPE-Inspiring Plastics Professionals and the Plastics Industry Association. Look for new episodes on the first Friday of every month.

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

Hey friend — if you're planning a pool day and want to bring something better than boxed rosé (but still chill and easy), this episode's for you. We're big fans of low-drama, high-fun batched drinks: think margaritas and mojitos as the MVPs. They're simple to scale, super tweakable, and they won't leave you with a sad, flat, sugary mess after a few hours in the sun. Quick PSA: don't bring Long Island iced tea to the heat. Also, please don't do blue motherfuckers. Learned that the hard way — way too much alcohol with way too much artificial sugar and a color that makes you question life choices. If you're making something for a group, pick something that stays pleasant in a pitcher. Real talk on batched cocktails: start simple. Margaritas are the perfect base — tequila, lime, and a touch of agave. Mojitos are great too, just keep the soda separate so it stays fizzy when you serve. For fruit-forward sweetness, use ripe fruit (watermelon, strawberries, peaches) and muddle it in the pitcher for natural flavor instead of dumping in extra sugar. Sugar + sun = rough morning, so let the fruit do the work. Want to get fancy? Try spicy margaritas with jalapeño, or play with citrus like blood orange or a lemon-lime-orange blend. If you want bubbles, batch the base and top each poured drink with Prosecco or soda — don't pre-mix the fizz. Also, remember large-batch ratios sometimes need a little tweaking from your single-drink recipe, so taste and adjust before you serve. Another favorite hack: make a few different lemonades (spicy watermelon lemonade, triple-citrus lemonade, ginger-peach lemonade) and let folks pick their liquor — rum, vodka, tequila, gin — so people can control strength and sweetness. It's great for guests who don't drink much; they still get a delicious refresher without the buzz. Bottom line: keep it fresh, keep it simple, and don't over-sweeten. If you try one of these batched ideas (or invent your own twist), tell us how it went — we love hearing your variations. Hit us up at truecrimesagainstwine@gmail.com or on Instagram. We'll send you some swag if it's cute enough. Cheers and enjoy the pool!

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1^Kevin DuRard v. Tommy Farrow - Welcome to Summer Poolside v. Blessings (Joy)2^Madonna v. EDX - One Step Away v. TCL (Kevin DuRard Mash-Up)3^Maesic f. John P - Yawa No Dey (Sistek Extended Remix)4^Hugel + Dawty f. Preston Harris - Loosen Up (BENJAMIN BLUES Remix)5^Sistek f. Carla Morrison - Cuando Te Veo (Extended)6^Ranny - I Think I Like You (Extended)7^Harry Styles - Aperture (Sadrican Afro House Remix)8^Nico de Andrea f. Imad - Around 9 (Extended Mix) 9^Becky Hill - what do i have to do? (Extended Mix)10^Hugel + Imael Angel f. Ultra Nate - Movin' To The Sun (Extended Mix) 11^David Guetta + Hugel f. French Montana + Aidan Martin - Shine (Extended Mix)12^Maiba - Love On My Mind (Andrey Exx Remix)13^KC Lights f. Niki + the Dove) - THIS IS FOR YOU14^Nora En Pure - Tibet (Extended Mix) 15^Meduza f. Rani - Silence (Extended Mix)16^Madonna v. SAFARIS - Good for the Soul v. Zephyrus (Kevin DuRard Mash-Up) 17^John Summit f. Feid - CHICA 305 (Extended Mix)18^EDX f. Julia Temos - Atmosphere feat. Julia Temos (Extended Mix)19^AR/CO + Sistek - Sound Of The Sunrise (Extended Mix)20^ThereWereTwo + Elyonn + Böhm f. Gabriel Paris - Underneath It All (Extended Mix)

Back in October 2024, Poolside was an early AI star. Cofounded by former Github CTO Jason Warner, the startup had raised $500 million on a $3 billion valuation to build coding agents for governments and large companies. But over the next 18 months, Poolside largely disappeared from view, while OpenAI and Anthropic ballooned to nearly trillion-dollar valuations with a crop of Chinese labs building open source models nipping at their heels.  Now Poolside is back with a new model called Laguna that on public benchmarks beats its American and Chinese open source competition — with the very notable exception of Chinese lab Moonshot's latest AI model, Kimi K3.  "As an American company building for the West, we'll be the most capable open model in the West,” Warner, Poolside's co-CEO and cofounder, tells Forbes. “Globally, in this weight class of the 118 billion parameter model, we are the leader.” Warner claims that the startup spent those 18 months where it went quiet building the infrastructure for a “model building factory” that could pump out Laguna and continue with new and more powerful iterations every five weeks. "The classic notion of model building is an artisanal process…We've built an industrial model-building process,” he says. By Iain Martin, Forbes Staff Learn more about your ad choices. Visit megaphone.fm/adchoices

Poolside host Johnny Mac covers fan reactions to Conan O'Brien's slicked-back “Greaser Conan” hair on his podcast with Molly Shannon, including jokes likening him to a drug lord. He reports on David Letterman's Montreal Comedy Festival appearance featuring Paul Shaffer, a big Canadian flag, “O Canada,” jokes apologizing for American leaders, remarks that Canada is not for sale, guest Will Arnett (SmartLess, Arrested Development, BoJack Horseman), a Will Arnett-themed top 10 list, and a closing singalong of “Takin' Care of Business” including French lyrics. Mac notes Netflix's Kevin Hart film “72 Hours,” Hart's barbs at LeBron James, and Cosmo/Hola interviews with Ben Marshall and Marcello Hernandez about the movie and comedy. He previews Weird Al's tour setlist and the Just for Laughs schedule, makes Comedy Stock Market “buys,” and plugs upcoming Rory Scovel and Whitmer Thomas specials plus Brian Bates' new special. 00:19 Greaser Conan Hair01:25 Letterman Montreal Night02:58 Kevin Hart 72 Hours03:53 Ben Marshall Profile05:55 Marcello Hart Impressions06:58 Weird Al Tour Preview09:11 JFL Schedule Rundown10:36 Comedy Stock Market Buys14:14 Why Buy Scovel Thomas15:48 Maher Netflix Skim16:10 Leslie Jones HGTV16:44 Brian Bates Special Become a supporter of this podcast: https://www.spreaker.com/podcast/daily-comedy-news-with-johnny-mac--4522158/support.Daily Comedy News is hosted by Johnny Mac and releases every weekday. Subscribe on Spotify, Apple Podcasts, or your favorite app. Part of the Caloroga Shark Media network. For transcripts and show notes visit www.dailycomedynews.comDaily Comedy News with Johnny Mac is a daily podcast covering comedians, stand-up comedy, late night television, and the comedy industry. New episodes every morning. Follow on Apple Podcasts, Spotify, or wherever you listen.  Contact John at John@thesharkdeck dot com For Uninterrupted Listening, use the Apple Podcast App and click the banner that says Uninterrupted Listening.  $4.99/month John's Substack about media is free. 

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

Thank you for joining us!#GladTidings #WelcomeToTheFamily #WeAreGTJoin us for service in person or online every Wednesday at 7pm (EST) and Sundays at 9am & 11am(EST)2009 Fullers Cross Rd. Ocoee, Fl 34761If you would like to get connected to what God is doing at Glad Tidings Church, text GUEST to 407-993-2496 If you would like to support GT financially you can give through the OcoeeGT app, or online through our website by clicking here http://www.ocoeegt.com/giving. Text ‘WEAREGT' to 73256 to give using your mobile device.For more information about Glad Tidings Church, visit ocoeegt.com face or follow us on our social media platforms below.Instagram- https://www.instagram.com/wearegt.church/Facebook- https://www.facebook.com/GladTidingsChurchOcoee

Welcome to Nikky After Dark, where your naughtiest confessions come to life. I'm your host Nikky and tonight we are exploring delicious first-time cravings, filthy shared fantasies, and steamy summer nights that turn into unforgettable group adventures.First up, imagine being the center of a married couple's hungry attention… their hands, mouths, and eyes all over you as you finally live out that threesome fantasy that's been making you soaked for years.Then, picture your husband and his new jack-off buddy stroking to pictures and videos of you… getting rock-hard while they talk dirty about your body, only for him to come home and fuck you senseless while he recounts every filthy detail.And later, feel the warm Miami night air on wet skin as a casual pool hang with your girlfriend and a sexy new friend turns into a dripping, moaning, full-on FMF threesome under the stars.Stay tuned, because these stories are dripping with tension, risk, and raw lust.Support The Show:Apple: https://podcasts.apple.com/us/channel/dear-nikky/id6442972835Spreaker: https://www.spreaker.com/podcast/dear-nikky-hidden-desires--6316414/support Join us over on Discord: https://discord.gg/uqqxsCSDfw Featured Release: Dear Nikky: Sex Confessions From People Just Like You is out now! Dive deeper into the raw, unfiltered stories you love.Contact:Email: Nikky@dearnikky.com (or dear.nikky.confessions@gmail.com)Website: DearNikky.com/confessionsSocials: Twitter/X (@DNikky162), Instagram (@DNikky162), Facebook (@DearNikky)Content Warning: This episode contains explicit sexual content, including graphic descriptions of ass play, butt plug sex, bisexual encounters, threesomes, public/exhibitionist sex, and consensual adult fun. Stories depict enthusiastic consent. Listener discretion advised; 18+ only. Submissions involving bestiality, incest, underage role-play, rape, non-consensual content, or racial slurs are not aired.Get Involved:Submit Your Story: Got a secret fantasy or steamy confession? Write to Nikky at Nikky@dearnikky.com or submit anonymously at DearNikky.com/confessions. By submitting, you certify you're 18+, the sole creator, and that the content meets our guidelines.Say Hello: Have a burning fantasy or just want to chat? Reach out on socials or email — Nikky loves hearing your naughtiest thoughts!Support the Show: Leave a review on Apple Podcasts, Spotify, Spreaker or your favorite platform. Your support helps new listeners discover the heat!

Trust us, we have marriage all figured out…. Daffnee and Torri are diving headfirst into the wild, unfiltered reality of marriage as a millennial: what it actually looks like ten-plus years in versus what you thought it would be at 25. They get real about why "let me check with my spouse" is not the controlling red flag your single friends think it is, whether marriage is actually overrated or just misunderstood, and the exact moment you walk in the door and can feel your partner's mood before they say a word. Plus, they name the specific brand of grumpy, huffy, silent-treatment energy every husband seems to get when he's stressed but won't say why (you'll know it when you hear it). One of Daffnee's personal favorites. Buckle up. Recommended LINKS: Better with Daffnee podcast homepage: https://www.daffneecohen.com/podcast/ Connect on Instagram: @Daffnee Learn more at and get tons of freebies at daffneecohen.com What We're Reading: Daffnee's pick:   A Fine Balance by Rohinton Mistry — a 600-plus-page deep dive into life in India during a chaotic era, discovered through a book influencer. Daffnee says it's the kind of hidden-gem, non-trendy read that asks something of you, and she's fully immersed. Torri's pick:  A Court of Thorns and Roses (re-read, book four) — her self-declared "book junk food" era. Poolside, no shame, plowing through the series again because, in her words, she has Swiss cheese brain, so it's basically a new book every time. Bonus reco: Salman Rushdie — prompted by Daffnee's Fine Balance pick, Torri throws in a highly-recommended nod to Rushdie's work for anyone wanting to go even deeper into literary fiction.

From poolside to night out - this is Beachhouse RADIO, July 2026 #BHR68

Thank you for joining us!#GladTidings #WelcomeToTheFamily #WeAreGTJoin us for service in person or online every Wednesday at 7pm (EST) and Sundays at 9am & 11am(EST)2009 Fullers Cross Rd. Ocoee, Fl 34761If you would like to get connected to what God is doing at Glad Tidings Church, text GUEST to 407-993-2496 If you would like to support GT financially you can give through the OcoeeGT app, or online through our website by clicking here http://www.ocoeegt.com/giving. Text ‘WEAREGT' to 73256 to give using your mobile device.For more information about Glad Tidings Church, visit ocoeegt.com face or follow us on our social media platforms below.Instagram- https://www.instagram.com/wearegt.church/Facebook- https://www.facebook.com/GladTidingsChurchOcoee

Fluent Fiction - Hungarian: From Poolside Sketches to Heartfelt Connections Find the full episode transcript, vocabulary words, and more:fluentfiction.com/hu/episode/2026-07-12-07-38-19-hu Story Transcript:Hu: A Széchenyi Fürdő forró nyári napja zsúfolt volt.En: The hot summer day at the Széchenyi Fürdő was crowded.Hu: Rengeteg ember élvezte a napot és a meleg víz gyógyító hatását.En: Many people enjoyed the sun and the healing effect of the warm water.Hu: Levente, a csendes programozó, épp a medence szélén pihent.En: Levente, the quiet programmer, was resting on the edge of the pool.Hu: Szüksége volt egy kis kikapcsolódásra a munka feszültségei után.En: He needed a little relaxation after the work stress.Hu: A nap lassan lemenni készült az égen, amikor Levente meglátta Esztert, aki a közelben ült, és egy vázlatfüzetbe merült.En: The sun was slowly setting in the sky when Levente saw Eszter, who was sitting nearby, absorbed in a sketchbook.Hu: Az ölében levő papír tele volt életvidám rajzokkal, melyek az őt körülvevő fürdő hangulatát tükrözték.En: The paper in her lap was full of lively drawings that reflected the atmosphere of the surrounding bath.Hu: Eszter élénk természetű grafikushallgató volt, aki új ötletekért jött a Széchenyi Fürdőbe.En: Eszter was a vivacious graphic design student who had come to the Széchenyi Fürdő for new ideas.Hu: Rajzai elbűvölőek voltak, de tele volt kétségekkel önmagát illetően.En: Her drawings were enchanting, but she was full of self-doubt.Hu: Barátja, Árpád, vidáman mesélte neki a legfrissebb egyetemi pletykákat, de Eszter figyelme inkább a rajzaira és az ottani emberekre koncentrálódott.En: Her friend, Árpád, was cheerfully sharing the latest university gossip with her, but Eszter's attention was more focused on her drawings and the people around her.Hu: Levente kezdett bátorságot gyűjteni ahhoz, hogy megszólítsa Esztert.En: Levente was starting to gather the courage to speak to Eszter.Hu: Nem volt könnyű dolog, hiszen általában magában szokott maradni, de azok a rajzok... szinte odahúzták hozzá.En: It wasn't an easy thing, since he generally kept to himself, but those drawings... they practically drew him to her.Hu: Végül összeszedte magát, és odament Eszterhez.En: Finally, he pulled himself together and went over to Eszter.Hu: „Szia,” mondta bátortalanul.En: “Hi,” he said shyly.Hu: „Nagyon tetszenek a rajzaid.En: “I really like your drawings.Hu: Inspirálóak.”En: They're inspiring.”Hu: Eszter meglepődött, de örömmel fogadta a dicséretet.En: Eszter was surprised but welcomed the compliment with joy.Hu: „Köszönöm!” válaszolta mosolyogva.En: “Thank you!” she replied with a smile.Hu: „Még dolgozom rajtuk, de igazán jólesik ezt hallani.”En: “I'm still working on them, but it feels really good to hear that.”Hu: Ahogy beszélgettek, Levente és Eszter megosztották egymással álmaikat és félelmeiket.En: As they talked, Levente and Eszter shared their dreams and fears with each other.Hu: Levente arról mesélt, mennyire nehéz néha új emberekkel ismerkedni.En: Levente talked about how hard it can be to meet new people sometimes.Hu: Eszter pedig bevallotta, hogy bizonytalan a munkájával kapcsolatban.En: Eszter admitted she was uncertain about her work.Hu: De ahogy a nap lassan eltűnt az égbolton, valami megváltozott közöttük.En: But as the sun slowly disappeared from the sky, something changed between them.Hu: Egy mély megértés és bizalom szövődött.En: A deep understanding and trust was formed.Hu: Árpád visszatért a medencéből, vidám mosollyal az arcán, készen arra, hogy újabb történeteket osszon meg.En: Árpád returned from the pool, a cheerful smile on his face, ready to share more stories.Hu: De amint meglátta Levente és Eszter közt a kialakuló kapcsolatot, elmosolyodott és nem zavarta őket.En: But he saw the budding connection between Levente and Eszter and smiled, refraining from interrupting them.Hu: A nap végén Eszter és Levente telefonszámot cseréltek.En: At the end of the day, Eszter and Levente exchanged phone numbers.Hu: Mindketten érezték, hogy valami különleges kezdődött el.En: Both felt that something special had begun.Hu: Levente megnyílt, és már nem félt új embereket megismerni.En: Levente opened up, and no longer feared meeting new people.Hu: Eszter pedig magabiztosabban tekintett a művészi útjára.En: Eszter looked more confidently at her artistic path.Hu: A Széchenyi Fürdő forró nyári napja így lett a kezdet egy új barátságnak, talán valami többnek is.En: The hot summer day at the Széchenyi Fürdő thus became the beginning of a new friendship, perhaps something more.Hu: A folklór és a modern élet találkozása, kellemes melegben és kávéillatú hangulatban, átmenet a múlt és a jövő között.En: A meeting of folklore and modern life, in the pleasant warmth and coffee-scented ambiance, a transition between the past and the future. Vocabulary Words:crowded: zsúfoltprogrammer: programozóhealing: gyógyítórelaxation: kikapcsolódássketchbook: vázlatfüzetlively: életvidámatmosphere: hangulatvivacious: élénk természetűgraphic design: grafikusenchanting: elbűvölőself-doubt: kétségekgossip: pletykákcourage: bátorságinspiring: inspirálóakcompliment: dicséretuncertain: bizonytalanunderstanding: megértéstrust: bizalominterruption: zavartaexchange: cseréltekfear: féltconfidently: magabiztosabbanartistic: művészifolklore: folklórtransition: átmenetmodern: modernambiance: hangulatpotentially: képes lehetbudding: kialakuló

For the second hour of Stokely and Evans without Mark Schlereth, Mike educates Stoke on the origin of the “Poolside” moniker. The Nuggets waived Big Val, saving $8M, what is next move for the Nuggets? The Broncos lost JFM in the trenches, so who’s set to replace him on the line? Mike and Stoke pick out a successor. They discuss why Sean Payton struggles to commit to running the football and they wonder if Davis Webb, a young coach trying to make a name for himself, will be any better. What’s Trending? Valanciunas getting waived, mixed messages from LeBron, a flash in the pan, and the youth sports pipeline killing Team USA’s ceiling.

Fluent Fiction - Japanese: Poolside Tanabata: A Troubled Host's Tale of Friendship and Fun Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ja/episode/2026-07-07-07-38-19-ja Story Transcript:Ja: 記録的な暑さの中、太陽が輝くある夏の日。ハルトは庭のプールを見つめました。En: On a summer day with record-breaking heat and the sun shining brightly, Harto gazed at the pool in the yard.Ja: 彼は友達を招待して、タナバタにプールパーティーを開く予定でした。En: He planned to invite friends over and host a pool party for Tanabata.Ja: しかし、彼の心には不安がありました。昨晩、彼はうっかりしてプールのフィルターを壊してしまったのです。En: However, he was feeling anxious because he had accidentally broken the pool filter the night before.Ja: 今、水のレベルがどんどん下がっていました。En: Now, the water level was steadily decreasing.Ja: ハルトは汗をぬぐいながら、問題を解決しようと思いました。En: Wiping the sweat from his brow, Harto thought about how to solve the problem.Ja: しかし、自分ひとりではどうにもならないことを感じ、隣のアキコに助けを求めに行きました。En: However, realizing he couldn't handle it alone, he went next door to ask Akiko for help.Ja: アキコは頭の回転が速く、どんなトラブルも解決するのが得意な友達です。En: Akiko is a quick-thinker and a friend who excels at solving any trouble.Ja: 「アキコ、お願い助けて！プールの水がどんどん減ってるんだ。」ハルトは焦って言いました。En: "Akiko, please help! The pool water is draining away fast," Harto said hurriedly.Ja: アキコは少し考えてから言いました。「まず、どこが壊れているか見てみよう。」En: Akiko thought for a moment before replying, "First, let's check where the damage is."Ja: その間、ハルトのおおらかな友達のサトシは、早くも到着していました。En: Meanwhile, Harto's easygoing friend Satoshi had already arrived.Ja: 彼は状況を面白く見ており、ゲストを笑わせることに専念していました。En: He found the situation amusing and focused on making guests laugh.Ja: 「プールの中で宝探しゲームでもどう？」と、彼はおどけて言いました。En: "How about a treasure hunt game in the pool?" he joked.Ja: アキコは手早くプールのフィルターを調べ、何とかなる方法を考え始めました。En: Akiko quickly examined the pool filter and began thinking of a way to fix it.Ja: 「ハルト、あそこにあるガムテープと古いホースを持ってきて。」En: "Harto, bring that duct tape and old hose over there."Ja: 「これで何とかなるの？」ハルトは半信半疑でしたが、アキコを信じることにしました。En: "Will this really work?" Harto was skeptical but decided to trust Akiko.Ja: 一方、サトシはみんなをプールから庭に移して、タナバタの短冊に願い事を書く提案をしました。En: Meanwhile, Satoshi proposed moving everyone from the pool to the yard to write wishes on Tanabata strips.Ja: 「願い事を書いて星に祈ろう！」と、サトシは明るく言いました。En: "Let's write our wishes and pray to the stars!" Satoshi said cheerfully.Ja: アキコは見事に修理を終え、フィルターの応急処置をしました。En: Akiko skillfully finished the repair and made a temporary fix to the filter.Ja: 水の減少は止まり、ハルトは安心しました。En: The water stopped decreasing, much to Harto's relief.Ja: 「ありがとう、アキコ。本当に助かった。」ハルトは心から感謝しました。En: "Thank you, Akiko. You really saved the day," Harto expressed his heartfelt gratitude.Ja: パーティーは成功しました。En: The party was a success.Ja: 子供たちは楽しく泳ぎ、願い事を書いて夜空に祈りました。En: The children swam happily and wrote their wishes to pray to the night sky.Ja: ハルトはこの経験から、皆で協力する大切さを学びました。En: From this experience, Harto learned the importance of working together.Ja: 「仲間がいるって、いいね。」ハルトは微笑みました。En: "Having friends around is great," Harto smiled.Ja: タナバタの夜、満天の星空に、彼らの願いが輝いていました。En: On the night of Tanabata, their wishes shone brightly in the star-filled sky.Ja: そして、ハルトは新たな友情の力を胸に刻みました。En: And Harto etched the power of newfound friendship into his heart. Vocabulary Words:record-breaking: 記録的なanxious: 不安steady: どんどんgazet: 見つめましたbrow: 汗solve: 解決problem: 問題hesitant: 半信半疑temporary: 応急処置excels: 得意hurriedly: 焦ってamusing: 面白くgrim: おどけてexplore: 調べproposal: 提案quick-thinker: 頭の回転が速くexpressed: 心からcherished: 刻みましたtreasure: 宝探しstrip: 短冊gratefully: 心から感謝shine: 輝くdoubtful: 半信半疑alone: ひとりでoccurred: 起こりましたrelief: 安心しましたguest: ゲストamusing: 面白くetched: 刻みましたprayed: 祈りました

Poolside 2026 - Tracklist: 1. Dual Sessions - I Always Think of You 2. Dual Sessions, Mandy Jones, Ronan - WILDFLOWER (Rooftop Mix) 3. Stereo Dub, G-Spliff - The Fate of Ophelia (Big Beat Instrumental Mix) 4. Bellestar, Urban Love, Tom Polo - A New Day (Deep House Mix) - Urban Love & Bellestar 5. Urban Love - Your Heart 6. Bellestar - The Sound Of Silence 7. Bellestar, Andy Stone - Let Somebody Go 8. Urban Love - Out In The Dark (House Music) - Urban Love 9. Klub Rider - Runaway 10. Groove Messengers - Sparks 11. Os Novos do Rio, Ronan - The Look Of Love (Ronan Remix) 12. Stereo Dub, Nikko Mad - Perfect Mistake 13. Mia Olsen, Nikko Mad - Paper Hearts 14. Stella Starlight Trio, Blue System, Ronan - People From Ibiza (Ronan Remix) 15. Groove Da Praia, Anakelly, Ronan - The Rhythm of the Night (Ronan Remix) 16. Eve St. Jones, Ronan - I Ain't Worried (Ronan Remix) 17. Gavin Moss, Urban Love, Tom Polo - Follow You, Follow Me (House Remix) x Urban Love & Tom Polo 18. Nick Prosen, Julie Benson - Something Strong 19. General Soundbwoy, Urban Love, Nikko Mad - Be Easy (Nikko Mad Mix) 20. Bellestar, DJ Leao - Young Folks 21. Michelle Simonal, Ronan, Nikko Mad - Can't Get Enough of Your Love, Babe 22. Boisterous Men - So Good To Me 23. Lizette, BossArt Ensemble, Ronan - Dance Hall Days (Ronan Remix) 24. Freedom Dub - Four to the Floor (DJ Style Mix) 25. Elian Voss - That's My Middle Name 26. Bellestar - Love Will Tear Us Apart (Ronan Remix) 27. Ituana - Learn To Fly (Ronan Remix) 28. Ronan, James Farrelli, Sarah Menescal - Eye In The Sky (Ronan Remix) 29. Ronan Esteban Portela - The Other Side 30. No.oN - Somebody That I Used To Know

PlastChicks Lynzie Nebel and Mercedes Landazuri interview Tim and Barb Womer poolside at the Plastics Pioneers Association (PPA) and Plastics Hall of Fame (PHoF) Spring 2026 Networking/Conference in Sarasota, Florida. They speak to Tim and Barb about how they met, balancing work and personal life, raising a family, his plastics industry career journey through various engineering and executive roles to owning his own business, how Barb's business expertise is critical to their mutual professional success, and the deep satisfaction of living life with a shared vision. Tim Womer is the owner of TWWomer and Associates, LLC, has had numerous patents issued on his inventions in screws, screw mixers and other products and processes, and has been inducted into the Plastics Hall of Fame.Watch the PlastChicks podcast on the SPE YouTube Channel.PlastChicks is sponsored by SPE-Inspiring Plastics Professionals and the Plastics Industry Association. Look for new episodes on the first Friday of every month.

Welcome to Poolside Confessions, my summer series where I sit by the water, slow down, and share what I'm actually living and thinking about. Today's confession is a celebration and an honest look back at 12 years of showing up. 500 episodes. I still can't believe it. My first podcast was published on August 8, 2014, and I have been showing up consistently – week after week – ever since. Over coffee, I pulled out my journal and wrote down every lesson this journey has taught me. In this episode, I'm sharing the lessons from 500 episodes – what consistency has really cost me, what it's changed in me, and what it's given me. I'm also sharing something very hard for me to say out loud: what's coming next. Here's what we cover: Why consistency builds a body of work before it creates a result Your voice isn't something you find; it's something you develop Why you're never going to feel ready, and what to do instead How the work itself changes you from the inside out Why repetition isn't a weakness, it's actually needed The importance of letting yourself evolve publicly and proudly Why your podcast, your blog, your content is an archive of your own becoming How to be devoted to your dreams while staying detached from the outcome Why the more you create, the less precious you become How consistency teaches you to deal with criticism Why creativity lives in the living, not in sitting at home trying to figure out what to say Why the real reward isn't the number, but the woman you become by reaching it What it means when your truest essence asks for a new expression What's next after 500 episodes Did you enjoy this episode? Subscribe to the podcast and leave a 5-star review! You can also listen to this show on YouTube and on all your favorite podcast platforms. How to Connect with Tonya Leigh Website: https://schoolofselfimage.com/ Instagram: https://www.instagram.com/tonyaleigh Facebook: https://www.facebook.com/TonyaLeighOfficial/ LinkedIn: https://www.linkedin.com/in/tonyaleighofficial/ Pinterest: https://ph.pinterest.com/tonyaofficial/ Twitter: https://x.com/tonyaleigh YouTube: https://schoolofselfimage.com/yt-tl #schoolofselfimage #becomeunrecognizable #selfimagetransformation  

Welcome back to the Dear Nikky Podcast, where we read your steamiest, most honest confessions and explore all the naughty corners of desire. I'm Nikky, and tonight we're diving deep.Three short teasers:First up — a tropical vacation turns into an unexpected audience when two college girls catch a couple in the middle of passionate pool sex… and one of them flashes back.Next — a seemingly innocent shopping trip at Frederick's of Hollywood becomes a scorching threesome in the changing room with a very hands-on saleswoman.And later, a shy fiancée finally gives in to her man's fantasy and gets taken hard right in front of their brightly lit bedroom window for the whole neighborhood to potentially see.Stick around… these stories are delicious.Join us over on Discord. https://discord.gg/uqqxsCSDfw Support Nikky:Patreon: Unlock exclusive confessions, bonus thoughts, and steamy Q&As at Patreon.com/DearNikky. Join the inner circle for extra spice!Featured Release: Dear Nikky: Sex Confessions From People Just Like You is out now! Dive deeper into the raw, unfiltered stories you love.Contact:Email: Nikky@dearnikky.comWebsite: DearNikky.com/confessionsSocials: Twitter (@DNikky162), Instagram (@DNikky162), Facebook (@DearNikky)Content Warning: This episode contains explicit sexual content, including graphic descriptions of nudity, public sex, infidelity, and boundary-pushing consensual fantasies. Stories are fictional and depict enthusiastic consent. Listener discretion advised; 18+ only. Submissions involving bestiality, incest, underage role-play, rape, non-consensual content, or racial slurs are not aired.Get Involved:Submit Your Story: Got a secret fantasy or steamy confession? Write to Nikky at Nikky@dearnikky.com or submit anonymously at DearNikky.com/confessions. By submitting, you certify: You're the sole creator of the submission. You're 18+ and legally able to submit erotic material. No prohibited themes. Names/identifiable info may be changed. You release all rights to the submission.Say Hello: Have a burning fantasy or just want to chat? Email Nikky@dearnikky.com or connect on Twitter (@DNikky162), Instagram (@DNikky162), or Facebook (@DearNikky). Nikky wants to hear your naughtiest thoughts!Support the Show: Love these private peeks into filthy lives? Leave a review on Apple Podcasts, Spotify, Spreaker or your favorite platform to help new listeners discover the heat. Your support keeps the conversation sizzling!Become a supporter of this podcast: https://www.spreaker.com/podcast/dear-nikky-hidden-desires--6316414/support.

In this episode, Stephanie and the crew talk about Trump's latest blunders, including the reflecting pool debacle. As reports of vandalism and bizarre claims swirl, they dissect the absurdity of Trump's assertions, including his insistence that the pool would be the best in history. Bella shares her bittersweet news about leaving the show for a new job in Hollywood, sparking a heartfelt discussion about growth and change. The team also tackles the ridiculousness of MAGA supporters falling for satirical posts about Antifa, revealing the depths of delusion in the world of Trump. Guests Bob Cesca and Malcolm Nance.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Welcome to Poolside Confessions, my summer series where I sit by the water, slow down, and share what I'm actually living and thinking about. Last year, I felt a very particular kind of tiredness. Not physical. Emotional fatigue paired with restlessness, and I couldn't figure out why. Was it my hormones? The time of year? What I discovered was something else entirely: I was tired of performing. I don't think of myself as a performer. I live close to my values. I know who I am. And even still, I had been performing. In this episode, I share what I found when I got honest with myself – and why breaking up with your own success strategies might be the most important thing you do this year. Here's what we cover: What success strategies are – and why even your best ones expire Why the things that carried you this far might not take you all the way The moment you realize you're at a three, not a ten Why it's harder to let go of what's working than what isn't The question that cuts through everything Why the fatigue you're feeling has nothing to do with supplements What it actually means to be fully alive Did you enjoy this episode? Subscribe to the podcast and leave a 5-star review! You can also listen to this show on YouTube and on all your favorite podcast platforms. How to Connect with Tonya Leigh Website: https://schoolofselfimage.com/ Instagram: https://www.instagram.com/tonyaleigh Facebook: https://www.facebook.com/TonyaLeighOfficial/ LinkedIn: https://www.linkedin.com/in/tonyaleighofficial/ Pinterest: https://ph.pinterest.com/tonyaofficial/ Twitter: https://x.com/tonyaleigh YouTube: https://schoolofselfimage.com/yt-tl #poolsideconfessions #becomeunrecognizable #selfimagetransformation

Send us Fan MailHello and welcome to our show. Grab your favorite drink and join us poolside for a fun and honest conversation on this episode of Just Talkin Outloud!We talk about one of life's biggest challenges—finding time to relax. Where do you go to unwind? What helps you escape the stress of everyday life?We also dive into a question that hits close to home: Have you ever missed a special moment because you were buried in your phone? From family memories to everyday experiences, we share our thoughts on balancing technology and living in the moment.And to lighten things up, we laugh about common phrases people have been saying wrong their entire lives. Some of these mistakes are hilarious—and you might be guilty of a few yourself!So pull up a chair, relax, and join us for another fun episode of Just Talkin Outloud!#JustTalkinOutloud #PoolsidePodcast #Relaxation #PhoneAddiction #FamilyTime #FunnyPhrases #MarriagePodcast #LifeConversationsSupport the showFacebook     https://www.facebook.com/justtalkinoutloudTwitter         https://twitter.com/just_outloudWebsite       https://justtalkinoutloud.buzzsprout.comEmail             justtalkinoutloud@gmail.com https://www.buzzsprout.com/1925628/supporters/new  https://www.buzzsprout.com/?referrer_id=1907869https://www.speakpipe.com/justtalkinoutloud

Welcome to Poolside Confessions, my summer series where I sit by the water, slow down, and share what I'm actually living and thinking about. Today's confession is something I've been sitting with for a while. I started thinking about the seasons of my life when anxiety showed up the loudest. Some of those seasons made sense from the outside – things were hard. But others were confusing. Things were going well. Success was real. And the anxiety was still there. Sitting with all of it, something became clear: anxiety is never really about what's happening outside of you. So what is it about? In this episode, I share what I've come to understand – and the practice that has quietly shifted how I live. Here's what we cover: Why anxiety is never about your circumstances What always accompanied my most anxious seasons – and why I didn't see it at the time How broken promises to yourself create low-grade anxiety you can't quite name What happens when you stop trusting your intuition Why the scary stories you tell yourself about the future are working against you The practices that have built real self-trust in my life Did you enjoy this episode? Subscribe to the podcast and leave a 5-star review! You can also listen to this show on YouTube and on all your favorite podcast platforms. How to Connect with Tonya Leigh Website:  https://schoolofselfimage.com/ Instagram:  https://www.instagram.com/tonyaleigh Facebook:  https://www.facebook.com/TonyaLeighOfficial/ LinkedIn:  https://www.linkedin.com/in/tonyaleighofficial/ Pinterest:  https://ph.pinterest.com/tonyaofficial/ Twitter:  https://x.com/tonyaleigh YouTube:  https://schoolofselfimage.com/yt-tl #poolsideconfessions #becomeunrecognizable #selfimagetransformation  

This week we're sharing our Poolside book recommendations as well as Elsie's favorite hot takes. Thank you to this week's sponsor: Get at least 15% off any annual membership at masterclass.com/abeautifulmess Get 10% off + free shipping at Tumbleliving.com/mess Elsie's Pollside Books: The Paper Palace By Miranda Cowley Heller The Wedding People by Alison Espach The Wishing Game by Meg Shaffer Emma's Poolside Books: The Thursday Murder Club by Richard Osman Things you Save in a Fire by Katherine Center The Vacationers by Emma Straub You can support us by leaving us a couple of 5 star recipe reviews this week at abeautifulmess.com Have a topic idea for the podcast? Write in to us at podcast@abeautifulmess.com or leave us a voicemail at 417-893-0011.  

This is your curated guide to the best summer reads!Patreon https://www.patreon.com/talkbookishpodcastInstagram https://www.instagram.com/talkbookishpodcast/Merch https://www.bonfire.com/store/talkbookishpodcast/

Welcome to Poolside Confessions, my summer series where I sit by the water, slow down, and share what I'm actually living and thinking about.  Today's episode is one I've been wanting to record for a while. People assume aging well is a formula. Find the right peptides. Count the right macros. Follow the right protocol.  I'm not here to tell you what to take or track. I'm here to tell you what I've actually come back to, again and again, over the years – the four things that have made the most difference in how I feel, how I move through the world, and how I see myself at 50. Some of it might not be what you expect. Here's what we cover: Why aging well starts with your emotional life, not your physical one The love affair you must have with yourself if you want to enjoy getting older Why women with "arrival energy" are missing the point  and how to stay curious The dictator vs. the wild child: finding the self that leads you through life True pleasure: what it is, what it isn't, and how to tell the difference The overlooked physical practice that changes how you move, walk, and carry yourself Did you enjoy this episode? Subscribe to the podcast and leave a 5-star review! You can also listen to this show on YouTube and on all your favorite podcast platforms. How to Connect with Tonya Leigh Website:  https://schoolofselfimage.com/ Instagram:  https://www.instagram.com/tonyaleigh Facebook:  https://www.facebook.com/TonyaLeighOfficial/ LinkedIn:  https://www.linkedin.com/in/tonyaleighofficial/ Pinterest:  https://ph.pinterest.com/tonyaofficial/ Twitter:  https://x.com/tonyaleigh YouTube:  https://schoolofselfimage.com/yt-tl #poolsideconfessions #becomeunrecognizable #selfimagetransformation  

If you've ever convinced yourself you were dying only to discover you just forgot your morning coffee, congratulations—you and Moon have something in common.This episode begins with Moon's dramatic weekend health crisis, which included headaches, body aches, sweating, canceled plans, and a genuine belief that he had caught the flu. After missing parties, skipping events, and suffering through a soccer match, the shocking diagnosis arrived: accidental caffeine withdrawal. One decaf mistake later, Moon was spiraling. Two rose lattes later, he was ready to conquer the world, write albums, and possibly become mayor of Paris.Meanwhile, the crew breaks down one of the most unexpected party surprises in recent memory when former Blues star Jamie Rivers decides the perfect pool-opening gift for his fiancée Ashley is... live monkeys. Not monkey decorations. Not monkey-themed cupcakes. Actual monkeys. Naturally, the monkeys arrive during a crowded backyard party packed with guests, children, music, and enough chaos to make everyone question several life choices. The result is equal parts adorable, confusing, and mildly terrifying.The conversation somehow escalates into monkey behavior analysis, party planning mistakes, surprise animal logistics, and the realization that getting bitten by a monkey in a bikini was probably not on anyone's weekend bingo card.The gang also recaps King Scott's massive baby shower, complete with mountains of gifts, bacon, desserts, and the looming anticipation of the show's upcoming gender reveal. There are discussions about weird party foods, mysterious hot-dog cake creations, and why some recipes should maybe stay inside family cookbooks.As if that wasn't enough, Rafe conducts what can only be described as investigative journalism by revisiting a local Hooters. What follows is an unexpectedly deep exploration of restaurant culture, paper plates, silent dining rooms, forgotten glory days, and whether a restaurant can accidentally become an existential experience. It's part food review, part sociology experiment, and part cry for help.The crew also tackles one of life's toughest questions: what's the saddest food to eat alone? Cake? Ice cream? A blooming onion? The answers get surprisingly personal as stories of lonely desserts, spaghetti mishaps, old promotional cakes, and questionable life decisions come flooding out.From caffeine dependency and monkey business to restaurant nostalgia and emotional food debates, this episode delivers exactly the kind of beautiful nonsense that makes this daily comedy show what it is. If you're looking for a daily comedy show that can seamlessly connect French coffee, poolside monkeys, hot-dog cake, and Hooters trivia without ever making sense, you've found your people.Follow The Rizzuto Show → linktr.ee/rizzshow for more from your favorite daily comedy show.Connect with The Rizzuto Show Comedy Podcast online → 1057thepoint.com/RizzShow.Hear The Rizz Show daily on the radio at 105.7 The Point | Hubbard Radio in St. Louis, MO.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today, Pastor Andrew explores some of the core elements of our life together in Christ. Though commonly mentioned, they are often overlooked because of their frequent use in Christian culture. Yet, when these elements are truly implemented in our lives, they bring joy to our hearts and to others. Let's sit down and have a simple poolside chat about them. Facebook:   / grovechurchmj   Instagram:   / grovechurchmj    Visit us online at http://grovechurchmj.com

PlastChicks Lynzie Nebel and Mercedes Landazuri interview Norm and Sue Fowler poolside at the Plastics Pioneers Association (PPA) and Plastics Hall of Fame (PHoF) Spring 2026 Networking/Conference in Sarasota, Florida. They speak to Norm and Sue about how they met, their relationship and home life, his induction into the Plastics Hall of Fame in 2025, her professional career, his career at Xerox, mentoring, contributions to the plastics industry, leadership roles in SPE-Inspiring Plastics Professionals, and life in Key West, Florida. Watch Norm Fowler's induction into the Plastics Hall of Fame (YouTube).Watch the PlastChicks podcast on the SPE YouTube Channel.PlastChicks is sponsored by SPE-Inspiring Plastics Professionals and the Plastics Industry Association. Look for new episodes on the first Friday of every month.

Welcome to Poolside Confessions, my summer series where we gather around the pool and I confess the things I've been thinking, feeling, and experiencing – in hopes of normalizing these conversations and helping you know you're not alone. Today's confession is one that almost every successful woman I've ever talked to has felt at some point. The fear of losing it all. When I first started my business, my fears looked different. Am I good enough? Will I make it? What will people think? And in the back of my mind was this big fantasy of arrival – that when I finally hit a certain number, built a certain life, I'd feel safe. Secure. Like I'd finally made it. You get there. And a new fear is waiting. The bigger the building you've built, the harder the ego whispers about the fall. So many women end up creating a life their younger selves would have been so proud of – and they can't even enjoy it because they're too busy white-knuckling it. Too afraid it's all going to be taken away. In this episode, I'm walking you through the practices that have set me free from this fear – and that I've watched set other women free, too. Here's what we cover: Why the fear of losing it all shows up the moment you finally build something you love The thought pattern that keeps successful women exhausted and unable to enjoy what they've created How to entertain the worst-case scenario… so you can finally make peace with it The compassion practice I use with the scared little girl inside of me Why getting clear on what is enough brings relief How to stop building a prison out of your own success The truth about freedom… and why it was never about the money   Did you enjoy this episode? Subscribe to the podcast and leave a 5-star review! You can also listen to this show on YouTube and on all your favorite podcast platforms. How to Connect with Tonya Leigh Website: https://schoolofselfimage.com/ Instagram: https://www.instagram.com/tonyaleigh Facebook: https://www.facebook.com/TonyaLeighOfficial/ LinkedIn: https://www.linkedin.com/in/tonyaleighofficial/ Pinterest: https://ph.pinterest.com/tonyaofficial/ Twitter: https://x.com/tonyaleigh YouTube: https://schoolofselfimage.com/yt-tl #poolsideconfessions #becomeunrecognizable #selfimagetransformation  

The enduring hopefulness of Christianity is grace. And by Christianity, I don't mean systems, theories, books, and buildings—no, I mean Christ… Jesus the Christ. If we're not careful, we will reduce grace to a cliché that fits on a bumper, a verse posted on social media, or a happy (but fleeting) feeling. Do that, and you will miss out on the enduring hopefulness you were meant to have. And who doesn't want enduring hopefulness? That's what a poolside encounter with Jesus teaches us.   First time listening to our podcast? We'd like to get to know you! Do you have any prayer request? Send us a message.  Connect with Us:

(0:00) Mark Dondero & Andrew Callahan - in for Zolak & Bertrand - begin the hour discussing Drake Maye as he enters Year 3 in the NFL. Will Maye make a leap or stay around the same level?(11:09) Which Patriot will make the biggest sophomore leap in Year 2? Plus, more lingering thoughts on the QB and how to build around him. Does Campbell remain the team's Starting LT?(24:43) Phil Perry declares certain compensation too pricey for AJ Brown. Dondero and Callahan discuss whether the impending AJ Brown acquisition puts them over the top or not. They each have opposing views on the matter.(34:57) The guys conduct The Sports Hub Poolside Playlist Draft and offer 3 rounds of summertime jams.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

A customer tells you “no” on a repair you know matters. Now what? We dig into the uncomfortable but real part of running a pool service business: handling resistant customers who don't want to fix equipment, don't want to spend money, or don't understand the risk you're trying to prevent. I walk through how I think about customer service in a safety-driven trade, and why the phrase “the customer is always right” can be dangerously incomplete when you're dealing with pressurized systems, electricity, and chemicals.We start with a scenario that every pool pro eventually faces: a cracked pool filter. What looks like a small crack can become a serious hazard under pressure, and I share a simple analogy that helps homeowners finally grasp the stakes. We also talk about the practical side of repairs, including why swapping only the top or bottom of a filter can be a bad bet, and why sometimes the only responsible recommendation is a full replacement.Not every customer refusal requires you to walk away, so we contrast safety-critical issues with more flexible ones, like a dead salt cell on a saltwater chlorine generator. If the customer won't replace it, we cover how to convert to a chlorine pool, what to watch for with salt levels and total dissolved solids, and how setting expectations up front can prevent surprises later. Then we zoom out to route management: the one-for-one rule, how to gracefully drop a difficult account, and why trees, debris, and ancient equipment can quietly destroy your schedule.If you want clearer boundaries, better client conversations, and a stronger pool service route, hit play. Subscribe, share this with a pool pro who needs it, and leave a review with your toughest customer pushback story.• why “the customer is always right” breaks down in pool service, plumbing, and electrical work• how to explain a cracked pool filter as a serious safety hazard• why replacing only the filter top or bottom often makes little sense• when customer refusal forces you to discontinue service• how to handle a dead salt cell by converting to a chlorine pool• how to set expectations when selling a saltwater system, including salt cell lifespan and replacement cost• using the one-for-one rule to drop unworkable accounts• how untrimmed trees can make weekly maintenance unrealistic• when old pumps and filters turn a pool into a time sink Send us Fan MailSupport the Pool Guy Podcast Show Sponsors! HASA https://bit.ly/HASAThe Bottom Feeder. Save $100 with Code: DVB100https://store.thebottomfeeder.com/Try Skimmer FREE for 30 days:https://getskimmer.com/poolguy Get UPA Liability Insurance $64 a month! https://forms.gle/F9YoTWNQ8WnvT4QBAPool Guy Coaching: https://bit.ly/40wFE6y

From poolside to night out - this is Beachhouse RADIO, May 2026 #BHR67

Send us Fan MailOnce Upon a Time at Secrets Part 2: Poolside Chaos and Party Nights Gone Wild | Episode 242In part two of their takeover recap, Dan and Lacy are back to break down even more unforgettable moments from their wild week at Secrets Hideaway. From high-energy pool parties and brand-new Motorbunny games turning up the heat, to their mission of meeting and connecting with as many people as possible, the vibes stay electric. Friday night brings a Magic Kingdom twist—but what follows is a chaotic group experience that doesn't quite go as planned, thanks to sunburn struggles, emotional moments, and a series of unexpected curveballs. They wrap things up with Saturday's epic pool party and the Villains & Vixens club night, where they trade playtime for the dance floor and end up having one of their favorite nights yet. It's real, raw, and a reminder that not every moment has to go perfectly to still be unforgettable.BOOK BLISS CRUISE - The Swing Nation - Main Website Quick Navigation Website: -- (Find all our social media links & more!)- Swinger Society - Our Website to meet, connect & events Swinger Society Discord Our Facebook Group- Swinger Websites -Kasadie 90 day free trialUsername: TheSwingNation SDC 14 day free trial Username: TheSwingNation** Use code 36313 for 14 days free! **- Merch & More -Order Your Merch Here!- Lacy's Fun Links -VIP OnlyFansPREMIUM OnlyFans-- THANK YOU TO OUR SPONSORS --IKNOWMYSTATUS: Test Like a Porn StarUse Code LifeStyle and get 15% OFFShameless Care: ED MedicationUse Code TSN at checkout for $15 off your order!Promescent® Make Love Longer, It's Time for Great SexUse Code SwingNSupport the show- Thank you for the support! -