Podcasts about Pure

Everyone thinks they know what makes a great hoagie… until they come to Hoagie Throwdown. Last year, we watched Philadelphia's most iconic, old-school hoagies go head-to-head with the city's most creative chefs, who completely reimagined what a hoagie could be.   This year, Hoagie Throwdown is back on Sunday September 27th at Other Half Brewing. One of last year's contenders, Chef Chad Durkin, already has his eyes on the championship! He tells what's new at Porco's Porchetteria, Small Oven Pastry Shop, and Breezy's Deli, regales us with stories of sailboats and Cake Boss, and calls out other Philly chefs to rise to the challenge of Hoagie Throwdown… Get tickets at DeliciousCityPodcast.com to throw down with all the hoagies, pro wrestling, and craft beer you can handle. (00:00) Hoagie Throwdown is back! How will we top last year?? (07:37) Chef Chad Durkin is fired up for the competition (14:24) Tips for Hoagie Throwdown: throw your roll into the ring (34:33) Whatcha Been Eatin': from Atlantic City to Ardmore, everything we love (45:30) The Sauce: what's opening and closing in Philly  And of course, we could not do this without our amazing partners who are as passionate about food and drink as we are: If your restaurant or company wants to be in the headlines for all the right reasons, click here to discover how Peter Breslow Consulting and PR can take your business to the next level Social media and digital content are two of the most important things you can create for your brand. Check out Breakdown Media, a one stop shop for all of your marketing needs. Listen to us on Spotify Listen to us on Apple Watch us on YouTube Connect with us on Instagram, and share your eating adventures by tagging us in your posts so we can talk about them on air.

You have to hear this to believe it! Is this officially the worst national anthem performance of all time?!

Åren runt sekelskiftet 1900 var en motstridig epok. En tid för sexuella experiment, men också djup misogyni och antisemitism. Motsägelserna beskrivs tydligast av Colette, menar Ulrika Kärnborg. Lyssna på alla avsnitt i Sveriges Radios app. ESSÄ: Detta är en text där skribenten reflekterar över ett ämne eller ett verk. Åsikter som uttrycks är skribentens egna. Först sänd: 2022-02-22.Det är få författare från förra sekelskiftet som fortfarande har förmågan att uppröra. Sidonie-Gabrielle Colette, här för enkelhetens skull kallad Colette, är en av dem.Pocketutgåvorna av hennes böcker står i min bokhylla, tummade och med spruckna ryggar, prydda av sliskiga omslag som fortfarande gör mig genererad. Titlar som "Vagabonden", "I bojor", "Sido" och "Chéri" väcker associationer till mörka budoarer och åldrade kokotter som fläktar sig med dyrbara solfjädrar. Alla är de skrivna i en stil som jag trots det upplever som omedelbart tilltalande: fräsch, passionerad och engagerad."Kärlek är ingen hedervärd känsla". Så lyder en av Colettes mer kända sentenser. Av allt att döma är den en krass sammanfattning av hennes första äktenskap med den notoriskt otrogna journalisten och författaren av bortglömda skandalböcker, M. Willy. Med denne tvivelaktige, medelålders person flyttar Colette som tjugoåring till Paris. Där presenterar han henne för modernistiskt sex och låter henne träffa alla som "betyder något", det vill säga folk som Balzac, Marcel Proust, Claude Debussy, Jean Cocteau, men också legendariska kurtisaner som la belle Otero.Genom Willys försorg får hon stifta bekantskap med hela spektrumet av sexuella avvikelser. Priset blir högt – Colette har sagt att Willy för alltid korrumperade hennes förhållande till kärleken – men i gengäld vinner hon insikter som ska visa sig användbara. Framför allt lär han henne om sinnlighet och hur hon ska förmedla sina egna intryck i skrift. Colette är nämligen redan från början starkt påverkad av hur ting och människor ser ut och doftar, hon äger en överkänslighet som gör att hon kan höra ett löv falla; hon faller i extas över hur en tomat smakar eller de rosa skiftningarna på småkrabbornas ryggsköldar. Hos Colette är driften alltid sublimerad, omdirigerad till en nästan mystisk upplevelse av samhörighet med tingen, växterna och djuren – men nästan aldrig med männen.I fyrtioårsåldern sätter hon sig ner och påbörjar ett slags sexuellt bokslut, "Pur et l'impur", enligt henne själv det närmaste hon kom en uppriktig självbiografi. "Det rena och det orena", blir en upprörande och subversiv bok om de eskortflickor, prostituerade bögar, flamboyanta lesbiska och professionella förförare som hon själv umgicks och låg med under sina första stormiga Paris-år. Jag förstår varför den inte fick någon svensk förläggare, innehållet är rena dynamiten. Romanen är skriven i dialogform där kärleken reduceras till ett spel, en affärstransaktion, där det gäller att inte tappa masken. Kvinnorna som Colette respekterar mest är cynikerna, de som försvarar sin kurtisanmoral och skryter med att de aldrig någonsin sagt vad de egentligen kände. Allt är skildrat med den sensuella pragmatism som är Colettes signum och anledningen till att jag alltid återvänder till hennes författarskap.En av de mest slående aspekterna av Colettes liv och verk är hur hårt hon kämpade för att hitta sitt eget uttryck. Hon föddes i obemärkthet på landsbygden 1873 och dog världsberömd i Paris 1954. Efter succédebuten skrev hon en lång rad romaner, noveller, essäer och självbiografier. Som journalist skildrade hon allt från kvinnomisshandel och mode till gastronomi och fejkade orgasmer. Hon var en av de första kvinnorna som rapporterade direkt från fronten under första världskriget. Samtidigt var hon oavbrutet sysselsatt med att spränga genregränserna. Hon skapade stumfilmsintriger och skrev librettot till en opera av Ravel. Vid sidan av gjorde hon koreografier till pantomimteater – och extraknäckte själv framgångsrikt som mimare och dansare.Den konservative författaren och kulturpersonligheten Henry de Montherlant kallade Colette "den största naturliga franska författaren". Suzanne Brøgger, den nordiska författare som mest liknar Colette, har beskrivit hennes språk som såpbubble-fjärilvingelätt: "Som om skrivandet var det naturligaste av allt för denna språkgourmet, alltid på jakt efter ett bättre ord, utan att syntaxen röjer det hårda slitet, de oändliga rättelserna och omskrivningarna". Även om det hon skrev om, alla interiörerna från Paris undre värld, kunde te sig skandalöst höll hon sig stilistiskt innanför det tolerablas gränser. Det gällde inte i lika hög utsträckning hennes erotiska testamente.Colette blev tvingad att revidera "Det rena och det orena" när hon var i sjuttioårsåldern. Boken kom ut 1941, i ett kulturklimat som på grund av kriget skilde sig kraftigt från det som en gång format Colettes konstnärskap. Ändå är framför allt om ungdomens könsroller och umgänge hon vill berätta. Därför är det inte rösterna från mellankrigstidens radikala kulturliv som når oss, utan det mer avlägsna sorlet från la belle époque. Det var en era då den europeiska misogynin nådde en höjdpunkt. Männen kände sin auktoritet och ställning hotad, inte bara av den skrämmande varelse som gick under namnet "Den nya kvinnan", utan även av två andra mytologiska väsen. "Juden" och "den homosexuelle" var två kulturella projektioner som underminerade den patriarkala ordningen.Även om Colettes feministiska kritik inskränker sig till det som pågår i sängkammaren, framstår hon som en pionjär. Och hon blir, skriver den amerikanska kritikern och författaren Judith Thurman, en av de första som sanningsenligt skildrar den nyligen frigjorda kvinnans situation i ett mansdominerat samhälle. Genom sin osentimentala, för att inte säga iskalla blick på relationer och sexuella identiteter, vågar hon visa att "kvinnligt" och "manligt" är kulturella konstruktioner. Alla samhällen som delar upp individer i sådana som "ger" och sådana som "tar emot" sexuell njutning, fördärvar sina unga. De flesta, oavsett biologiskt kön, föds med behov av att dominera såväl som att underkasta sig, säger Colette med en blinkning. Ur förtrycket av de naturliga drifterna föds perversionerna, och det är de som Colette med en sådan framgång har lyckats skildra. Hennes skarpa iakttagelser av det som en gång kallades "onatur" gör att "Det rena och det orena" numera ses som ett viktigt bidrag till HBTQ-litteraturen.Men författare lever som vi alla vet sällan som de lär. Judith Thurman skriver i sitt förord till the Pure and the Impure att Colette ironiskt nog skrev "Det rena och det orena" under en period då hon själv, efter att i flera decennier ha levt i olyckliga relationer, hade funnit en stadig partner. Äkta maken nummer tre, Maurice Goudeket, tycks ha skänkt Colette den trygghet hon letat efter. Och även om deras förhållande var stormigt och de fick uthärda rejäla smockor från båda hållen, var det ändå en relation mellan jämlikar.Och som Judith Thurman sammanfattar det:"'Det är inte förrän man blivit frisk som man upptäcker att man varit sjuk', skrev Colette en gång till Maurice. En sanning som bekräftas av hennes egen lovsång till ett sårat och splittrat Eros, författat av en kvinna som för första gången kände sig hel".Ulrika Kärnborg, författare

This week's adventure took me up the historic Merryman Trail on Mount Douglas, one of the mountain's oldest and steepest routes, where every step was a reminder of the early settlers and the Indigenous people who called this place home long before them. The relentless climb quickly turned into a power hike, with heart rate soaring, fallen trees to navigate, and just enough pauses to appreciate the forest and swap a few words with fellow hikers. I reflected on old races, trail history, and how these days I'm just as happy sharing personal thoughts as I am chasing miles, even if the audience is a little smaller. Reaching the summit was hard-earned, but the reward was fresh views, a few photographs, and the satisfaction of conquering one of Mount Doug's toughest ascents. From there, it was all smiles and an easy downhill cruise on Churchill Drive, another memorable Jackal adventure safely in the books.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-running-jackal--500980/support.

The Fork In Your Ear Ep#218 Jimonthy Crickets! - Podcast Show Notes & Summery 8-1-26 Quick Summary Tim and Nate open with the usual technical chaos—Nate's ancient camera older than his kids, USB hub murders, Windows being Windows, and a new meshy chair—before Tim scrapes himself off the floor with minimal coffee. The big gaming bombshell is Nintendo snagging a FromSoftware exclusive (Dustbloods) led by Miyazaki himself: Bloodborne-vibes, industrial revolution setting, up to eight-player PvE, and Tim is currently in the closed NDA network test. Microsoft brings four original Xbox classics to PC (Fusion Frenzy, Crimson Skies, Conker's Live and Reloaded), sparking preservation vs. "just sell it again" debate. Nate digs into the new Halo campaign on PC while Tim's wrist is still sidelining him. Entertainment covers Spider-Man: Brand New Day (Tim is all-in), American Psycho after a four-and-a-half-hour serial-killer museum visit, and the couple literally dodging the Bite of Seattle shooting by having a fight and going home. Life wraps with raccoon babies, the local "Jimmathy" meme, and Nate's wife getting back on two wheels with a nearly-new 2025 Triumph Speed Twin 900. Classic Fork energy from start to duck-call finish. Detailed Show Notes

Introduction of the message of ICAM, Polygynist/Plural Marriage announcement, Pure worship series 7, Heavens agency Bible school donations

Réécoutez FG Underground avec Nora en Pure du mercredi 29 juillet 2026

This week, we're revisiting one of our most popular episodes from last year with Gregg Braden, best-selling author, scientist and pioneer in the emerging paradigm bridging science, social policy and human potential, where we discuss humanity's future, his theories on the transhumanist agenda, and what he considers the urgent fight to preserve our human potential.In this eye-opening conversation, Braden warns we may be the last generation of pure humans as artificial intelligence and digital surveillance race ahead, threatening to sever us from our consciousness, our DNA, and even our souls.He unpacks the manipulation of media algorithms, emotional programming, and disinformation campaigns designed to divide humanity and dull our innate heart-brain coherence. From simulation theory and the Mandela Effect to his groundbreaking fractal time calculator that forecasts future global patterns, Braden dives deep into the spiritual and scientific underpinnings of this global shift.He also shares profound personal insights, including his near-death experience, interdimensional encounters, and his understanding of the spiritual meaning behind dementia. As society stands at a tipping point, Gregg Braden invites us to awaken our inner technology, reclaim ancient wisdom, and remember that love, not AI, is the most powerful force in the universe.Make your summer wardrobe feel easier. Go to https://www.quince.com/breakdown for free shipping on your order and 365-day returns.Go to https://helixsleep.com/breakdown to receive 20% off sitewide, 25% of Luxe Mattresses and 30% off Elite Mattresses.Upgrade your kitchen with Our Place today. Visit fromourplace.com/BREAK and use code BREAK for 10% off sitewide.Follow us on Substack for Exclusive Bonus Content: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bialikbreakdown.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BialikBreakdown.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube.com/mayimbialik⁠⁠⁠See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Ron swears he looks like young Johnny Depp (everyone else sees Benjamin Franklin). Tony puts his dog down and immediately starts calculating the housework savings. A cop dresses as a tree to catch speeders, Neil deGrasse Tyson ruins Top Gun, and Batman somehow doubles subway chivalry. Add turkey-bacon lies, fruit-sticker rage, lasagna-noodle warfare, and the annual “is the Earth flat?” round. Pure, unfiltered FU Friday. They somehow turn Yoko Ono's ass, a 20-year rotting fish-sauce stench, and whether “schmear” is a scam into legitimate conversation topics. Come for the chaos, stay because you can't look away.

PURE comedy!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Welcome to Science Quest!

The boys are BACK introducing our new cohost Tom Calautti, talking Providence new head coach, his press conference, Poitras, Brunet, Lohrei++ PLENTY more . Make sure to follow us on twitter @OnlyBruinsPod @DowntownBoosy2 @BrettHoward_ @BobbieBrewski @TCalauttis. Follow us on tiktok @onlybruinsFollow us on instagram @OnlyBruins_Follow us on Youtube @OnlybruinspodcastMake sure to check out our Pure hockey link and get the best hockey gear out there! https://alnk.to/bisa9vc

Pure driving energy and relentless peak time vibes. Built for dark rooms, heavy sound systems, and late-night sweat

Costa Rica Part 2 is here. This episode gives us the absolutely historic Kardashian moment of Khloé saying to Scott, ‘if you want to get your dick wet so bad, then get your f*cking dick wet,' before throwing her water on him. Pure mf cinema. ShopMy: https://shopmy.us/shop/commentsbycelebsCodes: Get 15% off your first order plus free shipping at BollAndBranch.com/comments with code commentsHellobatch.com/CBC and use code CBC at checkout for 30% off sitewide - applies to subscriptions tooQuince.com/cbc for free shipping on your order and 365-day returnsStart your free trial at SHOPIFY.COM/commentsLolaBlankets.com and use code CBC to get 40% OFF your orderSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Opie's sugar-free diet ends in non-explosive “bloops,” a toothpaste-tube bathroom struggle, and his wife casually leaving an enema on the sink. Meanwhile the cable company trenches through the neighbor's prize flower bed and tomatoes… and still doesn't fix the Wi-Fi. Plus Fauci pleading the Fifth on his tie color, Katy Perry trapped in a giant bottle, Erik's one-year SNL stories, and Ron's Mount St. Helens pimple. Pure unfiltered chaos.

Calm your body and mind with 12 hours of deep brown noise, designed to improve sleep quality and help you drift off naturally.https://distrokid.com/hyperfollow/brownnoisesleepsounds/brown-noise-sound-3Buy me a Coffee Support me here ☕ Thank you!buymeacoffee.com/BrownNoiseSleepSounds

If you were to sit in a quiet room with no distractions and honestly answer the question — what is your number one priority in life — what would you say? It is the kind of question most of us prefer not to sit with too long, because the honest answer is often more complicated than we would like. Children, careers, spouses, finances: these things are not wrong in themselves, but God does not compete for position in our lives. He simply waits, with long-suffering patience, for us to return to Him as our first love. The widow of Zarephath had reached the end of everything. One last meal for herself and her son, and then nothing. When the prophet Elijah arrived with what must have sounded like an absurd request — give me food first, before you feed yourself and your child — her decision to obey was an act of profound, costly faith. She put the man of God first, and as a result, the flour and oil did not run out for the entire duration of the famine. Her obedience was not a transaction. It was trust. Matthew 6:33 captures the principle her life illustrated: seek first the kingdom of God and His righteousness, and all these things will be added to you. Putting God first is not about earning blessings. It is about living in honest acknowledgment that everything we have comes from Him, and that He alone is worthy of the first and best of what we have to offer — our time, our finances, our attention, and our hearts. Today's Bible Verse "But seek ye first the kingdom of God and His righteousness, and all these things shall be added unto you."— Matthew 6:33, KJV Ponder Today God does not compete for first place — but He deserves it. Every good thing in our lives is a gift from Him. Giving Him priority is not a burden He imposes on us. It is the natural and right response to who He is and what He has done. Obedience is the practical expression of putting God first. The widow of Zarephath's faith was demonstrated not in words but in action. Putting God first shows up in how we spend our mornings, our money, our time, and our attention. Our motives for seeking God matter deeply. We do not put God first in order to receive blessing upon blessing. We put Him first because He gave His only Son for flawed, undeserving humans. Pure motives honor Him far more than calculated obedience. Putting God first means removing compartmentalization from our lives. God is not meant to occupy one section of our schedule or one category of our budget. He is meant to be first in every area — no exceptions, no separation. The goal is to hear "well done, good and faithful servant." True success in this life is not measured by what we accumulate but by whether we sought first His kingdom and His righteousness. That eternal perspective changes how we order our daily priorities. A Prayer for You Today Our Father and our God, thank You for Your goodness that extends from generation to generation. There is no one like You, and no one deserves the honor and glory that is Yours alone. Help me to put You first in every area of my life. May my love for You grow each day as I think of Your sacrifice. Remind me that You are always with me, so that when I win it is You, and when I stumble You are there. Help me to seek first the kingdom of God, and help me to die daily to my flesh so that I do not idolize myself or anything You have given me. Each day You give, I choose to honor You by making You first priority. In Jesus' name, Amen. Don't Miss an Episode If today's prayer helped you reorder your priorities around the One who deserves first place in your life, we'd love to stay connected. Subscribe to the LifeAudio newsletter at LifeAudio.com for daily prayers, devotionals, and more content to keep your heart and your life oriented toward the kingdom of God every day. If you like this podcast, be sure to check out our sister podcast, Your Nightly Prayer - an evening Christian prayer podcast to help you end your day in conversation with God. https://www.lifeaudio.com/your-nightly-prayer/ Discover more Christian podcasts at lifeaudio.com and inquire about advertising opportunities at lifeaudio.com/contact-us.

Send us Fan Mail80s post disco funk R&B band... completely overlooked; by me anyway... KLEEER!

Ross Jackson, the host of the "Locked on Saints" podcast, joined Sports Talk. Jackson broke down the start of the Saints' training camp journey. He shared his thoughts on Tyler Shough, Jordyn Tyson, and the Saints' backup running backs.

From the March 2025 7-Day Sesshin at Yokoji Zen Mountain Center

Opie and Tony P roast LeBron chasing one last ring for chump change in Philly (while the fans sharpen their knives), then dive into the wildest weight-loss side effect yet—people paying thousands to inject processed dead-body fat back into their sunken “Ozempic faces.” Also, they rank guilty pleasures from taint itches to post-BBQ shit-talk rides and still somehow make room for nude-beach stories.Pure chaotic comedy gold.Donating to the show helps us make more!https://www.paypal.com/ncp/payment/JANCGHFW7GJHATHANK YOU

Titus 2:1-10 (ESV)Andrew and Edwin discuss sound doctrine and healthy teaching to produce healthy families and, therefore, healthy congregations.Read the written devo that goes along with this episode by clicking here.    Let us know what you are learning or any questions you have. Email us at TextTalk@ChristiansMeetHere.org.    Join the Facebook community and join the conversation by clicking here. We'd love to meet you. Be a guest among the Christians who meet on Livingston Avenue. Click here to find out more. Michael Eldridge sang all four parts of our theme song. Find more from him by clicking here.   Thanks for talking about the text with us today.________________________________________________If the hyperlinks do not work, copy the following addresses and paste them into the URL bar of your web browser: Daily Written Devo: https://readthebiblemakedisciples.wordpress.com/?p=26309The Christians Who Meet on Livingston Avenue: http://www.christiansmeethere.org/Facebook Page: https://www.facebook.com/TalkAboutTheTextFacebook Group: https://www.facebook.com/groups/texttalkMichael Eldridge: https://acapeldridge.com/ 

Hearts on Fire: Clean Hands & Pure Hearts Week 2 | Pastor Aaron Simmons Mission, Vision & Core Values Our Mission is To reveal the goodness of God to everyone everywhere. Join us at 10 am every Sunday Morning or for our Livestream worship service at 10 am on Facebook and at UpperRoomOhio.com Find us on Facebook: www.facebook.com/UpperRoomOhio/ Follow us on Instagram: www.instagram.com/upperroomohio Give us a call: 937-667-5585 Address 648 N. Hyatt St. Tipp City, OH 45371

The Blessed Life According to the Son | Part 4Blessed are the Merciful and PureMatthew 4:17, 23-25, 5:1-8 | King's Chapel Live StreamWhat does a life transformed by the Gospel actually look like?As Jesus continues the Beatitudes, He reminds us that the blessed life is not something we earn. It is the life that grows in those who have already been made citizens of the Kingdom of Heaven through Christ.Because we have received God's mercy, we become people who show mercy. Because Christ has given us a new heart, we begin to live with an undivided devotion to Him.In this message, we explore what it means to be merciful toward those who are hurting, broken, and in need. Just as the Good Samaritan showed compassion to a stranger, Christians are called to reflect the mercy of Jesus by restoring dignity, extending kindness, and pointing others to the hope found in Christ.We also consider what Jesus means when He says, "Blessed are the pure in heart." A pure heart is not a perfect heart. It is a heart that has been made new by God's grace and is wholly devoted to Him. Through Christ, God replaces our hearts of stone with hearts that desire Him above everything else.The Beatitudes are not a list of qualifications for entering God's Kingdom. They are the beautiful evidence of what God is doing in the lives of those He has already redeemed.If you long to become more like Christ and to reflect His mercy in a divided world, this message points us back to the transforming power of the Gospel.Connect with King's Chapel in Longwood, FL - ▶️ www.kingschapelfl.com▶️ https://www.facebook.com/KingsChapelfl▶️ https://www.instagram.com/kingschapelfl/For the GLORY of our Great GodFor the GOOD of our NeighborMatthew 5:7-8 sermon, blessed are the merciful, blessed are the pure in heart, Sermon on the Mount, Beatitudes explained, mercy in the Bible, pure in heart meaning, Good Samaritan sermon, Christian discipleship, King's Chapel Longwood FL

More Amanda legbeard stories: https://www.youtube.com/playli... In this episode of r/LegbeardStories we encounter Amanda... This legbeard story has a lot to unpack, and plenty of parts to get through. In this legbeardstory my sympathy is thrown out pretty quick. It should be fun! It doesn't matter what your background is, you always need to treat people like people and not use them simply to get off. Neckbeards seem to learn this lesson particularly slow and it really does make my blood boil... For your fill of neckbeard stories we've got you covered with the freshest weeaboo, niceguy, and neckbeard happenings on reddit. Stick with ReddX for your daily dose of cringe with a side-dish of relatability. You might even feel good for dessert. ------------------------------------------------------------ #reddit #legbeard #dishonest Join me on Discord dude: https://discord.gg/Sju7YckUWu Check out the Twitch streams: https://www.twitch.tv/daytondo... One-time PayPal donation: https://www.paypal.me/daytondo... Support this channel on Patreon: http://patreon.com/daytondoes Stalk me on the Twitter! http://www.twitter.com/daytond... Visit me over on Facebook: https://www.facebook.com/ReddX... Got a story? I got a subreddit: https://www.reddit.com/r/ReddX... Here's an Amazon link to my microphone: https://amzn.to/3lInsRR Wanna rock the ReddX merch? https://teespring.com/stores/r... Character animations are by: https://twitter.com/DarkleyStu... Check out my other channel: https://www.youtube.com/dayton... Wifey's channel is right over here: https://www.youtube.com/channe... ------------------------------------------------------------ Playlists: Full neckbeard story compilations: https://www.youtube.com/playli... All of our neckbeard stories: https://www.youtube.com/playli... All of our legbeard stories: https://www.youtube.com/playli... All of our RPG Horror Stories: https://www.youtube.com/playli... All of our weeaboo tales: https://www.youtube.com/playli... ------------------------------------------------------------ Podcasts: Spotify: https://open.spotify.com/show/... Soundcloud: https://soundcloud.com/reddxy iTunes: https://podcasts.apple.com/us/... Google Podcast: https://podcasts.google.com/fe... Spreaker: https://www.spreaker.com/show/... Podchaser: https://www.podchaser.com/podc... Deezer: https://www.deezer.com/us/show... Podcast Addict: https://podcastaddict.com/podc... JioSaavn: https://www.jiosaavn.com/shows... We are working on getting listed on Castbox, Audible, and iHeartRadio ASAP! Have you ever met a neckbeard or a nice guy? They are frustrating to deal with, but luckily you aren't alone! These r/neckbeardstories from Reddit are among the top posts of all time and include some of the funniest Reddit stories ever posted on the neckbeard stories subreddit! rSlash NeckbeardStories have all kinds of funny neckbeards in them, but especially the nice guy. And the weeaboo. There is a wide spectrum of neckbeards, and this is but a small slice of it. Listening to ReddX's neckbeard stories playlist is a great experience! These neckbeard stories Top Posts of All Time from Reddit are made for you to enjoy any time you feel like it, so be sure to save my rSlash neckbeard stories playlist to your favorites! While there are many rslash channels that read r/neckbeard stories and r/prorevenge from reddit, each channel has their own way of performing them. Some of the top rSlash entitled parents channels I recommend checking out are the original rSlash, Redditor, fresh, r/Bumfries, VoiceyHere, Mr Reddit, Storytime and Darkfluff. These Reddit story channels inspired me to start my own Reddit story channel, with a focus on Entitled Parents stories and at times going into the r/pettyrevenge and r/choosingbeggars subreddit as well. Because most of my audience prefers Entitled Parents stories of Reddit, I tend to just stick with reading the r/EntitleParents Top Posts of All Time. But I also enjoy getting up close and personal with neckbeards and weeaboos from time to time. Subscribe to ReddX for the freshest daily Reddit content. I post relatable readings of Reddit posts and Reddit stories every single day! Journey with me as I relate these amazing Reddit stories to my personal life journey. I'm greatly inspired by the top reddit posts of all time videos and reddit stories on YouTube which is why I started doing them myself. YouTube: https://www.youtube.com/channe... Discord: https://discord.gg/Sju7YckUWu Twitch: https://www.twitch.tv/daytondo... PayPal: https://www.paypal.me/daytondo... Patreon: http://patreon.com/daytondoes Twitter: http://www.twitter.com/daytond... Facebook: https://www.facebook.com/ReddX... Merch: https://reddx-shop.fourthwall....

Episode 470 – 80s & 90s Remixes | Arabic Violin Fusion | House & Dance MixStep into Episode 470, where legendary '80s and '90s hits are reimagined with fresh house, dance, and electronic remixes. This exclusive DJ mix blends timeless classics with modern production, creating an energetic soundtrack for driving, workouts, parties, or simply relaxing.A signature highlight of this episode is the fusion of Arabic violin melodies with iconic retro tracks, delivering a unique East-meets-West sound that combines nostalgia with contemporary club vibes.If you love 80s music, 90s music, dance remixes, house music, retro classics, electronic music, Arabic fusion, violin remixes, DJ mixes, and nostalgic playlists, Episode 470 is made for you.

Website: In His Name Counseling, LLC | Dr. Donna PotterWebsite: Pure YL Organic Salon | organic hair color | 260 Forest Avenue suite 5, Laguna Beach, CA 92651, USA

Am I the Genius? is the show where you get real answers to questions you've always wondered but didn't think to ask. Subscribe on YouTube - youtube.com/@amithegenius?sub_confirmation=1 Am I the Jerk? on Instagram - instagram.com/amithegenius Am I the Jerk? on Spotify - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://open.spotify.com/show/0uEkxvRMpxLuuHeyPVVioF?si=b279dadfe593432b⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ x.com/amithejerk facebook.com/amithejerk SUBMIT YOUR OWN STORIES HERE ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://amithejerk.com/submit⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Mint Mobile - Get this new customer offer and your 3-month Unlimited wireless plan for just 15 bucks a month at MINTMOBILE.com/AITJ Quince - Keep it classic and cool — with long-lasting staples from Quince. Go to Quince.com/AITJ for free shipping on your order and three hundred and sixty-five -day returns. EveryPlate - Dig into these flavor-packed meals your household will love. New customers can enjoy this special offer of only $1.99 a meal. Go to everyplate.com/podcast and use code AITG199 to get started. Green Chef - Head to Greenchef.com/50AITJ and use code 50AITJ to get fifty percent off your first month, then twenty percent off for two months with free shipping. Lola Blankets - Get 35% off your entire order at Lolablankets.com by using code AITJ at checkout. Uncommon Goods - To get 15% off your next gift, go to UncommonGoods.com/AITJ Don't miss out on this limited-time offer. Uncommon Goods. Learn more about your ad choices. Visit megaphone.fm/adchoices

WCR Nation Ep. 476 | A Window Cleaning Podcast Water-fed poles changed the window cleaning industry forever... but are you using yours to its full potential? In this episode of WCR Nation, Jersey gets real about water-fed window cleaning. From common mistakes and bad habits to the techniques that actually save time and make money, this is an honest conversation for anyone using—or thinking about using—a water-fed pole. Whether you're just getting started or you've been cleaning with pure water for years, there's a good chance you'll pick up a tip that makes your next job faster, easier, and more profitable. We cover: • Common water-fed mistakes • When water-fed is the right choice (and when it isn't) • Brush selection and technique • Pure water myths • How to become faster and more efficient • Real-world lessons from years in the field Need supplies? Let me know! I would love to do that for you! Text/Call: 862-312-2026 https://windowcleaner.com/?sca_ref=3020234.dl0aAoVJ1A #WCRNation #WindowCleaning #WindowCleaner #WindowCleaners #WaterFedPole #PureWater #WindowCleaningBusiness #WindowCleaningPodcast #SmallBusiness #BusinessGrowth #Entrepreneur #ResidentialWindowCleaning #CommercialWindowCleaning #HomeServiceBusiness #WindowCleaningTips #ServiceBusiness #XERO #TradWindowCleaning #ProfessionalWindowCleaner #CleaningIndustry

Titus 1:10-16 (NKJV)Andrew, Isack, and Edwin discuss how there can be pure people if we've already learned how sinful we are.Read the written devo that goes along with this episode by clicking here.    Let us know what you are learning or any questions you have. Email us at TextTalk@ChristiansMeetHere.org.    Join the Facebook community and join the conversation by clicking here. We'd love to meet you. Be a guest among the Christians who meet on Livingston Avenue. Click here to find out more. Michael Eldridge sang all four parts of our theme song. Find more from him by clicking here.   Thanks for talking about the text with us today.________________________________________________If the hyperlinks do not work, copy the following addresses and paste them into the URL bar of your web browser: Daily Written Devo: https://readthebiblemakedisciples.wordpress.com/?p=26289The Christians Who Meet on Livingston Avenue: http://www.christiansmeethere.org/Facebook Page: https://www.facebook.com/TalkAboutTheTextFacebook Group: https://www.facebook.com/groups/texttalkMichael Eldridge: https://acapeldridge.com/ 

We highly suggest that you READ the TEXT at the link below, as you listen to the audio above. https://www.gracegems.org/2001-1/Pure,%20unalloyed,%20perfect%20truth.htm Feel free to FORWARD this gem to others!

Donald Macleod explores the life and music of George Frideric Handel through the patrons, employers and institutions that helped shape his career. From the aristocratic circles of Italy and the court of Hanover to royal favour in Britain and the charitable culture of Dublin, Handel proved adept at navigating the worlds on which composers depended. Yet he remained unusually independent, moving between courts, theatres and concert halls while steadily building a career on his own terms. Along the way, patronage could offer opportunity, security and prestige, but it also brought rivalries, competing loyalties and political complications. Through sacred music, opera, anthem and oratorio, this podcast traces how Handel responded to the people and places that supported his work.Music featured includes extracts from:Dixit Dominus, HWV 232 Tu fedel? Tu costante?, HWV 171a: Recitative and aria Rodrigo, HWV 5: Overture; recitative and aria Il trionfo del Tempo e del Disinganno, HWV 46a: Overture Il trionfo del Tempo e del Disinganno, HWV 46a: "Pure del cielo" La Resurrezione, HWV 47 Eternal Source of Light Divine, HWV 74 Nine German Arias, HWV 202–210: No. 6, "In den angenehmen Büschen" Rinaldo, HWV 7a: Overture Silla, HWV 10: Overture Il pastor fido, HWV 8a: "Sol nel mezzo risona del core" Water Music Suite No. 2 in D major, HWV 349 Acis and Galatea, HWV 49a: "Love sounds th'alarm" As Pants the Hart, HWV 251b Acis and Galatea, HWV 49a: Sinfonia Esther, HWV 50a Coronation Anthem: Zadok the Priest, HWV 258 Coronation Anthem: My Heart Is Inditing, HWV 261 Riccardo Primo, HWV 23: Act III Chorus Atalanta, HWV 35: Overture Alexander's Feast, HWV 75 Funeral Anthem for Queen Caroline, HWV 264 Messiah, HWV 56: Sinfonia L'Allegro, il Penseroso ed il Moderato, HWV 55: "Sweet Bird" Imeneo, HWV 41 Messiah, HWV 56: Part III Foundling Hospital Anthem, HWV 268Presented by Donald Macleod Produced by Ellie Ajao for BBC Audio WalesFor full track listings, including artist and recording details, and to listen to the pieces featured in full (for 30 days after broadcast) head to the series page for George Frideric Handel (1685-1759): https://www.bbc.co.uk/programmes/m002yxkn.And you can delve into the A-Z of all the composers we've featured on Composer of the Week here: http://www.bbc.co.uk/programmes/articles/3cjHdZlXwL7W41XGB77X3S0/composers-a-to-z.

Are we all technically arseholes, whether by accident or just by default?Joining Seán to discuss is Jennifer Horgan, who has been writing about this in the Irish Examiner.

We highly suggest that you READ the TEXT at the link below, as you listen to the audio above. https://www.gracegems.org/2001-1/Pure,%20unalloyed,%20perfect%20truth.htm Feel free to FORWARD this gem to others!

Erik Marino details his brutal vertigo battle (inner ear crystals spinning him out, Epley maneuver gone wrong, and possible mouse-related Hanta fears), while Ron the Waiter gets grilled for his suddenly darker “beach hair” that definitely isn't dye. The guys spiral into Blue Steel impressions, movie quote wars, Monica turning 53, and a fortune cookie that swears December will be chill. Pure unfiltered chaos.Thanks for dontaing to the show https://www.paypal.com/ncp/payment/JANCGHFW7GJHA helps me to keep this going.  Peace!

Maggie Haberman has been on the Trump beat for decades. Now she has written—with co-author Jonathan Swan—a tour de force on how the president is remaking our government in his image. Haberman gives Harry an inside look at a White House in thrall to a president who's never been so uninhibited. She shares a swath of insights about Trump's health, his reluctance to help Republicans in the midterms, just how close he came to invoking the Insurrection Act, and more. Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us Fan MailNaughty N'awlins Part 2: Parades, Parties & Pure Chaos | Episode 255Naughty N'awlins Part 2 is here—and Dan and Lacy are wrapping up their unforgettable trip to New Orleans with even more wild stories, meaningful moments, and nonstop partying.They begin with the Sexual Freedom Parade, sharing why the experience is about so much more than costumes and celebration. Surrounded by a community proudly embracing freedom, acceptance, and authenticity, the parade becomes one of the most emotional and moving moments of the entire event.From there, the energy turns up at the Glow Night Party, where Dan and Lacy connect and play with some new couples before preparing to host their own party the following day at The Boot Scoot'n Rodeo. Between mechanical bull rides, line dancing, great music, and a packed crowd, their party brings a little country chaos to Naughty N'awlins.But the night is only getting started. Immediately after, they head to the Secrets Party, where things get even crazier with shots galore, wet T-shirt contests, crowd surfing, and plenty of unforgettable moments. Finally, everyone gets dressed up for the Mardi Gras Ball before Dan and the other DJs officially shut down Naughty N'awlins with an epic after-party lasting from 2:00 to 4:30 in the morning.Pour yourself a drink and join Dan and Lacy as they recap the final days of Naughty N'awlins—filled with connection, community, new experiences, and just the right amount of debauchery.Get Tickets to Electric Pleasures- 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! ** SLSUsername: NorthernGuynSouthernGirl- Merch & More -Order Your Merch Here!- Lacy's Fun Links -VIP OnlyFansPREMIUM OnlyFans-- THANK YOU TO OUR SPONSORS --Wisp : Making sexual healthcare inclusive, cost-effective, and accessible—for everyoneUse Code SWING at checkout for 15% off your oder!Shameless Care: ED Medication and at home STD testingUse Code TSN at checkout for $30 off your order!Promescent® Make Love Longer, It's Time for Great SexUse Code SwingNation for 5% off!Support the show- Thank you for the support! -

Enjoy uninterrupted white noise designed to block distractions, calm your mind, and promote deep, restful sleep.https://distrokid.com/hyperfollow/brownnoisesleepsounds/white-noise-sound-machineBuy me a Coffee Support me here ☕ Thank you!buymeacoffee.com/BrownNoiseSleepSounds

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

Episode #227 of the PricePlow Podcast takes us back to the NNB Nutrition booth at IFT First 2026 in Chicago, where Shawn Wells sat down with Mike and Ben for the first of two conversations recorded on-site. This one skips the usual business updates (that’s what part two, with Dustin Elliott and Jake Mulder, covers) and goes straight for the theoretical side of things: where Wells thinks the next several years of supplement innovation are headed, and the problems NNB’s science team wants solved before anyone else notices they exist. Shawn Wells’ Working Theories, and Where NNB Nutrition Takes Them Wells has built a working framework around what he calls “GLP-1 Drag”, three linked theories covering muscle loss, mitochondrial fatigue, and dopamine flattening in people using GLP-1 medications. From there, the conversation covers precision fermentation’s takeover of the herbal extract market, a buccal pouch platform built around fast-absorbing nootropics and stimulants, and an unplanned detour into flush niacin and nicotine pouches. Subscribe to the PricePlow Podcast on your favorite platform and sign up for NNB Nutrition news alerts before diving in. https://blog.priceplow.com/podcast/shawn-wells-glp-1-drag-227 Video: GLP-1 Drag, Precision Fermentation, and What’s Next for NNB Nutrition https://www.youtube.com/watch?v=1YN6_wqnDkA Detailed Show Notes: Shawn Wells on GLP-1 Drag and the Future of NNB Nutrition (0:00) – Introductions (1:15) – NNB’s Unicorn Growth and the Shift to Pure, Potent, Precise (3:15) – Why Precision Fermentation Is Replacing Herbal Extracts (7:45) – Turning Competitors Into Customers (9:15) – Adaptogens Over Stimulants (11:45) – What Shawn Wells Calls “Mitochondrial Drag” (13:45) – GlucoVantage: A Post-GLP-1 Solution (17:45) – Titrate Down: Why Quitting GLP-1s Cold Backfires (19:15) – Dopamine Flattening and the Post-GLP-1 Comedown (20:45) – Ben and Mike on Their Own GLP-1 Experiences (25:45) – DL185 Dileucine and the Muscle-Sparing Stack (31:45) – Inside the Buccal Pouch: Paraxanthine, Alpha-GPC, and Huperzine A (38:15) – AmeriPouch, Troches, and the Nicotine Pouch Boom (41:45) – Flush Niacin as a Possible Nicotine Off-Ramp (44:15) – What’s Next: GAA, 4-Hydroxyisoleucine, and Pure 6-Paradol (48:45) – The Case for Mood and “Shift in State” Beverages (52:45) – Blue Light Glasses, Health Transformations, and Hyrox Goals Where to Follow and Learn More Connect with Shawn Wells and NNB Nutrition LinkedIn: Shawn Wells Instagram: @shawnwells NNB Nutrition Website Instagram: @nnbnutrition LinkedIn: NNB Nutrition NNB Nutrition on PricePlow – Sign up for news alerts Resources Mentioned Episode #048: Shawn Wells Upcoming Stimulant & The Energy Formula … Read more on the PricePlow Blog

What pairs perfectly with John Carpenter's most underrated supernatural horror? Pure, unfiltered heat, courtesy of our sponsor Torchbearer Sauces! In this episode, Tony Wash stops by to give us the scoop on what to keep a lookout for on SCREAMBOX in August and trust us, you're going to want to hear this. Then Tony joins Zena and Shelby for a deep dive into John Carpenter's IN THE MOUTH OF MADNESS (1994), featuring brand new sauces from Torchbearer Sauces! And instead of the usual round of trivia, the three switch things up in a way that will DEFINITELY keep you on your toes. You're going to want to tune in for this one. IN THE MOUTH OF MADNESS follows an insurance investigator who visits a small town searching for a popular horror novelist who has mysteriously vanished. But the deeper he digs, the more he realizes the author's books have an impact far beyond inspiration and reality itself may not be what it seems. Have YOU "lived" any good books lately?

David Rothkopf joins Joanna Coles to discuss Donald Trump's awkward World Cup appearance. A moment complete with loud boos and a bewildered walk across the championship stage, sparks a broader conversation about a president increasingly disconnected from reality at home and abroad. They examine Trump's rambling public appearances, the mounting toll of his foreign policy decisions, and why his critics argue his presidency is becoming defined by ego, chaos, and an alarming lack of empathy. David and Joanna delve into the growing influence of the “Manosphere” inside MAGA, the arrest of Andrew and Tristan Tate, the administration's controversial priorities, and what they reveal about the culture surrounding Trump. From E. Jean Carroll collecting her judgment to fears over election integrity, international legal exposure, and the expanding web of political corruption, they connect the week's biggest stories into a sobering portrait of an administration consumed by loyalty, image, and self-preservation—and explain why the consequences of today's headlines may be felt long after Trump's presidency comes to an end. If you're ever injured in an accident, you can check out Morgan & Morgan. You can start your claim in just a click without having to leave your couch: https://ForThePeople.com/DAILYBEAST #ad Learn more about your ad choices. Visit podcastchoices.com/adchoices

Full Show only on Patreon: www.patreon.com/dopeypodcast Episode Teaser Summary: Dave is back in the garage with Heart Attack Doug for a hilarious and chaotic Dopey Tuesday reunion! The episode opens with a fresh Surrealismo remix of the Dopey theme, sparking an immediate debate over Beatles remixes, Rocky Raccoon, and who actually wrote the hits (with Dave campaigning hard for Izzy Stradlin). Doug brings the fire with savage voice memos and an all-time great impression of a whiny, anti-Semitic Dave, while the duo dives headfirst into classic meeting drama — someone stole from the collection basket and now the boys argue about gossip vs. playing cop, benefit of the doubt, and trusted servant votes. Doug drops sensible recovery wisdom, Long Island sprinkler guy corruption stories, and his famous “bragging or complaining” diagnosis. They also tease Dave's vacation stories, Canadian wildfire smoke horror, Kratom opinions (and Dave's upcoming Rolling Stone piece), and read spicy Spotify comments praising Ray Brown while dragging Doug. It all wraps with a raw live musical moment and classic Dopey banter. Pure unfiltered chaos, laughs, and real talk — classic Dave & Doug energy. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Bonsoy Blitzed Presents... Pure. Quewey. Nerdism. The opening Challenger Series event of the year was held in Ballito Durban. In the men's CT pedigree shone through with big Col Houshmand taking the win. In the women's Teresa Bonvalot took out an all Portugese final over Francisca Veselko. There was also plenty to like from the Australians' performance. See omnystudio.com/listener for privacy information.

Zafra Lerman is a scientist, educator and humanitarian. She received a Ph.D. in Chemistry from the Weizmann Institute of Science, Israel and conducted research on Isotope Effects at Cornell and Northwestern Universities in the U.S. and the ETH, Zurich, Switzerland. She developed an innovative approach of teaching science through art, music, drama, dance, and rap which was successful with underprivileged students around the globe. She worked on human rights cases in the former Soviet Union, Russia, China, Guatemala, Cuba, Peru, and South Africa and was successful in preventing executions, releasing prisoners from jail and bringing dissidents to freedom. She is the President of the Malta Conferences Foundation, which uses science diplomacy as a bridge to peace in the Middle East by bringing scientists from all countries in the Middle East together with Nobel Laureates. This five-day conference puts scientists under the same roof to develop collaborations and friendships that overcome the chasms of distrust and intolerance. She received over forty international awards, including the Presidential Award for Mentoring from President Clinton (1999), AAAS Award for Science Diplomacy (2015), American Physical Society Andrei Sakharov Prize for Human Rights (2016), Peace and Justice prize from the UN NOVUS summit (2016), Distinguished Women in Chemistry or Chemical Engineering Award from the International Union of Pure and Applied Chemistry (2017), and she was nominated for the Nobel Peace Prize by a member of the US Congress and a member of the French Parliament (2017, 2018, 2019, 2020, 2021, 2023, 2025). Her book “Human Rights and Peace: A Personal Odyssey” was published in August 2024. On March 6, 2025 she received in New York the International Advocate for Peace Award from the Cardozo Journal of Conflict Resolution. Previous awardees include Bill Clinton, Jimmy Carter, Archbishop Desmond Tutu, and Paul McCartney.Chapters:00:19 - Innovative Approaches to Teaching Science01:05 - The Journey into Education and Science20:49 - Innovative Teaching Methods in Chemistry36:46 - Human Rights and Science Education44:33 - Focusing on the Middle East: A New Directionhttps://zafralerman.comhttps://www.linkedin.com/in/zafralerman

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