Podcasts about Arrival

Multiple storms will bring rounds of snow, ice and rain from the Midwest to the Northeast as Arctic air briefly surges south, raising risks of slick travel, ice jams and urban flooding into midweek. Also, two dogs rescued on Monday during a blizzard in Babylon, New York have been returned home. The man who rescued them said, “it could have been a tragedy. The number of people who stopped to help, it shows that people do care.” Learn more about your ad choices. Visit podcastchoices.com/adchoices

In the Electrek Podcast, we discuss the most popular news in the world of sustainable transport and energy. In this week's episode, we discuss Tesla's Cybercab being dead on arrival, Donut Lab's miracle battery, Waymo expanding, and more. The show is live every Friday at 4 p.m. ET on Electrek's YouTube channel. As a reminder, we'll have an accompanying post, like this one, on the site with an embedded link to the live stream. Head to the YouTube channel to get your questions and comments in. After the show ends at around 5 p.m. ET, the video will be archived on YouTube and the audio on all your favorite podcast apps: Apple Podcasts Spotify Overcast Pocket Casts Castro RSS We now have a Patreon if you want to help us avoid more ads and invest more in our content. We have some awesome gifts for our Patreons and more coming. Here are a few of the articles that we will discuss during the podcast: Elon Musk threatens to halt Tesla Giga Berlin expansion over union vote Tesla Cybercab program manager exits ahead of launch Tesla adds 64 Megacharger locations to map, revealing Semi truck charging routes Used Tesla prices rise 4.3% while rest of EV market drops after tax credit ends Donut Lab's ‘miracle' solid-state battery confirms 0-80% charge in 4.5 min — but there's a catch BYD to unveil 1,500kW EV charger that can add 2km of range in 1 second Lucid (LCID) announces ‘step-change' in Q4 as it aims to build 25,000 to 27,000 EVs in 2026 Waymo adds 4 more cities to its robotaxi service, now 10 total (Tesla: still 0) Here's the live stream for today's episode starting at 4:00 p.m. ET (or the video after 5 p.m. ET: https://www.youtube.com/live/8u-7fZpN36M

Post-Gazette Penguins insider King Jemison takes a look at the team's trade of Tristan Jarry and how Kyle Dubas' return keeps looking better and better after their 4-1 win over the New Jersey Devils on Thursday night. This show is presented by FanDuel. First, what does Sidney Crosby's injury mean moving forward? Can Evgeni Malkin and the rest of the team continue to put in solid performances like Thursday's? How was Dubas able to turn Jarry's "untradable" contract into Stuart Skinner, Sam Girard and multiple draft assets? How good was Arturs Silovs against the Devils on Thursday? Can he stay dialed in? Is he the starting goaltender, or is Skinner? Does it matter? How important will the penalty kill led by guys like Blake Lizotte and Noel Acciari be for the rest of the season? King tackles those topics and more, plus tips his cap to some individual brilliance from Egor Chinakhov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

We are going on an adventure! Love The Lord of the Rings? Why not read along with us as we consider the books from the writer's point of view! Taking it chapter by chapter, novelist Julia Golding will reveal new details that you might not have noticed and techniques that will only go to increase your pleasure in future re-readings of our favourite novel. Julia also brings her expert knowledge of life in Oxford and English culture to explain some points that might have passed you by.(00:00) Beginning Book Four and Returning to Frodo and Sam(04:30) A Landscape of Confusion and Pathetic Fallacy(11:40) The Cliff Descent and the Elven Rope(16:30) Storm, Nazgûl, and Threads Connecting the Wider Story(22:45) The Arrival of Gollum(29:50) Pity Versus Precious and the Moral Turning Point(36:30) Frodo’s Authority and Gollum’s Oath(38:20) A Frustrating Chapter That Changes EverythingFor more information on the Oxford Centre for Fantasy, our writing courses, and to check out our awesome social media content visit: Website: https://centre4fantasy.com/website Instagram: https://centre4fantasy.com/Instagram Facebook: https://centre4fantasy.com/Facebook TikTok: https://centre4fantasy.com/tiktok

Note: Video of this episode can be found on Youtube and Spotify! Coming soon to Apple Podcasts. In this episode, hosts Tom Kim and Trevor Mountcastle recount Trevor's recent New Years trip to Australia that included visits to Brisbane, Sydney and Melbourne. They discuss the challenges of booking flights using points and miles, the varying experiences with American Airlines and Qantas business class, and the highlights of their stay in Brisbane, including the Lone Pine Koala Sanctuary. The conversation also covers their New Year's Eve celebrations in Sydney, exploring local attractions like the Opera House and The Rocks, and the unique culinary experiences they encountered. The hosts also discuss Melbourne and her beautiful markets and the Australian Open. They discuss the challenges of booking travel during peak times, the logistics of traveling with family, including limited mobility considerations, and how we chose specific flight experiences to mitigate those logistical challenges, such as the importance of non-stop flights. Key Points From This Episode: 00:00 Introduction to the Australian Adventure 00:47 Planning the Trip: Points and Miles Challenges 04:55 Flight Experience: Comfort and Crew Dynamics 11:27 Arrival in Australia: First Impressions 17:55 Hotel Experience: Hyatt Regency Brisbane 21:21 Wildlife Encounters: Lone Pine Koala Sanctuary 26:37 Exploring Brisbane: Markets and Local Culture 27:47 Traveling to Gold Coast: A Budget-Friendly Choice 31:04 Accessibility Challenges in Air Travel 31:22 Packing Light for a Long Trip 34:56 Choosing Accommodations: From Hyatt to Apartments 36:28 New Year's Eve in Manly: A Family Celebration 44:11 Transitioning to Hyatt Regency: A Familiar Stay 48:10 Exploring Sydney: New Experiences and Local Markets 50:49 Cultural Experiences: A Visit to the Opera House 51:40 Experiencing the Sydney Opera House 52:46 Traveling to Melbourne: A New Adventure 53:52 Hotel Experiences and Upgrades 55:32 Booking Strategies for Travel 59:07 Exploring Melbourne: Markets and Local Culture 01:00:38 Weather Challenges in Melbourne 01:04:34 Dining Experiences and Service Culture 01:11:07 Unexpected Adventures at the Australian Open 01:16:56 Experiencing Sporting Events with Low Friction 01:19:07 Navigating Travel Logistics and Flight Changes 01:22:33 Dining Experiences in Airports 01:26:31 First Class Lounge Experience at Qantas 01:27:30 Reflections on Long-Haul Flights 01:34:15 Desire to Return to Australia and Future Travel Plans 01:36:51 The Value of Nonstop Flights

France vs Italy in the Guinness Six Nations was an incredible game and we talk about it and we look ahead to the biggest game of the 6 nations in Rome against England! 00:00 - Welcome 02:28 - Italy vs France Match Recap19:40 - Italy's Arrival as Test Match Players37:28 - Looking Ahead: Italy vs England45:53 - What Made Coach Mad54:38 - Benetton Head Coach News Hosted on Acast. See acast.com/privacy for more information.

Hadi Choopan has officially landed in the United States! In this episode of Heavy Muscle Radio, Dave Palumbo and Chris Aceto break down the massive news that the "Persian Wolf" has secured his visa and is already training on U.S. soil just two weeks out from the 2026 Arnold Classic. The duo dives deep into the high-stakes battle for the $750,000 top prize, analyzing the fierce competition between Choopan and the towering Andrew Jacked. In this episode: Hadi's Arrival & Security: The logistics of Hadi's visa and the buzz surrounding his training at an undisclosed U.S. location [01:06]. The Arnold Classic Preview: Detailed analysis of the top contenders, including Samson Dauda, Nick Walker, and James Hollingshead [19:11]. Derek Lunsford Update: Dave shares insights from his recent interview with the reigning Mr. Olympia, discussing Derek's mental state, his new gym in Tampa, and his strategy for the 2024 season [29:57]. Bodybuilding "Meccas": Is Tampa, Florida, officially taking over as the new global hub for pro bodybuilding? [24:24]. Olympia History: A nostalgic look back at the most exciting Mr. Olympia contests of all time, from Ronnie Coleman's 1998 upset to the Kai Greene vs. Phil Heath rivalry [07:32]. Watch the full episode for expert analysis on the upcoming Arnold Classic and the latest "Truth in Bodybuilding." Follow RxMuscle: Instagram: @RXMuscle Website: RXMuscle.com

Carlton small forward Ben Ainsworth has opened on his move to the Blues from the Gold Coast Suns in the off-season.See omnystudio.com/listener for privacy information.

Carlton small forward Ben Ainsworth has opened on his move to the Blues from the Gold Coast Suns in the off-season.See omnystudio.com/listener for privacy information.

Psalm 122 reveals the joy we should feel about gathering with God's people, challenging us to move from a 'have to' mentality to a 'get to' mentality about worship. Casual Christianity prevents spiritual growth and keeps us from the mountains God calls us to climb. We need fellow believers as companions on our faith journey, just as the Israelites traveled to Jerusalem together. Regular worship serves multiple purposes: giving glory to God, practicing obedience, guarding against spiritual drift, being formed into Christ's image, and strengthening our faith through God's Word. When believers are truly united, we become impenetrable to enemy attacks and naturally seek peace and blessing for others.

Former Palace defender Martin Kelly joins us on the 10th anniversary of THAT goal at Tottenham in the FA Cup to discuss that goal, joining the day Tony Pulis left and tonnes more. Get tickets to Jim's 2026 UK Tour here: ⁠⁠jimdalycomedy.com/tour⁠⁠ April 23rd LONDON (Last few tickets) April 30th BRIGHTON May 3rd MANCHESTER May 23rd CHESHAM June 7th BIRMINGHAM June 13th LIVERPOOL Join the FYP Clubhouse for extra episodes, match previews, post match reviews, early access to live podcast tickets and more: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠patreon.com/fyppodcast Chapters Chapters 00:00 Life After Football: Martin Kelly's Retirement Journey 02:56 Transitioning to Palace: The Move and Early Days 06:05 Navigating Managerial Changes: Pulis to Pardew 09:00 Playing Under Pressure: Experiences Against Liverpool 11:46 The Impact of Key Players: Jason Puncheon and Marouane Chamakh 28:54 Pardew's Arrival and Initial Impressions 31:01 Team Dynamics and Early Success 33:35 Challenges and Squad Morale 34:59 Transfer Rumors and Loyalty to Palace 36:33 Memorable FA Cup Moments 40:10 The Impact of Celebrations and Team Spirit 41:54 The Road to the FA Cup Final 44:17 Reflections on the Final and Its Aftermath 48:06 Emotional Connections to the Club 49:47 The Aftermath of a Tough Season 54:09 The Importance of Warm Weather Training 57:33 Mamadou Sakho's Impact on the Team 01:02:15 The Pressure of Relegation Battles 01:08:31 Challenges Under Frank de Boer 01:13:32 Roy Hodgson's Management Style 01:19:25 Transitioning to the Next Generation of Players facebook: FYPFanzineinstagram: @fypfanzinebluesky: @fiveyearplan.bsky.socialtiktok: @fiveyearplanpodcasttwitter: @fypfanzine⁠⁠⁠⁠⁠⁠⁠ ⁠⁠email: ⁠contact@fypfanzine.uk Learn more about your ad choices. Visit podcastchoices.com/adchoices

Giants ace, Logan Webb joins the show to reveal the origin of his warm‑up music, what it meant to represent his country in the World Baseball Classic, and his early impressions of new manager Tony Vitello. A must‑listen conversation from Spring Training.See omnystudio.com/listener for privacy information.

Giants ace, Logan Webb joins the show to reveal the origin of his warm‑up music, what it meant to represent his country in the World Baseball Classic, and his early impressions of new manager Tony Vitello. A must‑listen conversation from Spring Training.See omnystudio.com/listener for privacy information.

In this powerful interview, JJ Brighton joins Emilio Ortiz on the Just Tap In Podcast to explore the growing awareness that humanity may be approaching a pivotal convergence point — one that echoes ancient cycles tied to Atlantis, timeline bifurcation, and the emergence of what many traditions call the Golden Timeline. Rather than fear-based predictions, this episode dives into consciousness mechanics: how collective choice, coherence, and awareness determine which future humanity stabilizes into. Together, they unpack recurring archetypal events, planetary memory, and why so many people are suddenly feeling a deep sense of remembrance, urgency, and responsibility at this moment in history.✦ LIVE IN-PERSON: Join JJ and Emilio at the Conscious Life Expo in Los Angeles (February 21st, 2026) to explore the rise of crystalline children and what it means for humanity's future | https://consciouslifeexpo.com/?ref=ymrhndyThis conversation bridges ancient knowledge and modern awakening, touching on Atlantis, multidimensional timelines, quantum choice points, collective consciousness, disclosure, and the role of embodied humans during periods of planetary transition. JJ shares insights on why certain “events” appear to repeat across civilizations, how they can be consciously navigated, and what it truly means to align with a higher evolutionary trajectory. This episode is not about predicting catastrophe — it's about understanding the deeper invitation humanity is being offered right now.___________________PODCAST CHAPTERS00:00 – JJ Brighton Intro2:25 – The Global Era of Change 4:38 – The Golden Timeline & Harmonizing Shadow and Light7:31 – Soul Convergence & Awakening the Inner Master8:24 – Children Carrying Multidimensional Templates12:20 – Breaking the Illusion of the Generation Gap15:27 – Ancient Initiations & Remembering Past Lives16:38 – Atlantis, Egypt & the Concept of the Ancient Future19:14 – The True Wound of Atlantis29:16 – Spiritual Bypassing & Forcing Ascension Today34:16 – Nervous System Capacity & DNA-Level Remembrance35:42 – Sovereignty, Integration & Gentle Awakening37:39 – Anchoring the Codexes & the Arrival of 3I/ATLAS38:47 – The Diamond Sapphire Codex Explained42:55 – Light Language, Non-Verbal Codes & Translation48:47 – Cosmic Roots & Communicating Beyond Humanity52:48 – Language, Time & Rewriting Reality59:12 – Telepathic Children & Image-Based Communication1:05:24 – Conscious Life Expo & Live In-Person Activation1:15:26 – Unlearning Perfection to Meet the New Children1:17:07 – Stillness as the Most Powerful Human Choice___________________Guest: JJ Brighton | Activations with JJ ✦ Website | https://www.activationswithjj.com/✦ Courses | https://www.activationswithjj.com/offerings✦ Retreats & Excursions | https://www.activationswithjj.com/retreats-excursions✦ Activations with JJ Podcast | https://www.activationswithjj.com/podcast✦ Instagram | https://www.instagram.com/activationswithjj/✦ Linktree | https://linktr.ee/activationswithjj Host: Emilio Ortiz✦ IG | https://www.instagram.com/iamemilioortiz/✦ Subscribe to Channel | https://www.youtube.com/EmilioOrtizSpecial Offerings to Support the Show:✦ Make a One-Time or Recurring Donation on PayPal

(0:00) Fred, Hardy and Wallach reflect on Scott Zolak's escapades from yesterday. Producer, Jorge is a late arrival for his scheduled shift. The show hosts take their shots poking fun of the two.(20:09) RYAN JOHNSTON is the voice of the Boston Bruins on the 98.5 Sports Hub Bruins Radio Network and joins Toucher & Hardy to talk all things hockey! (34:09) (34:09) Jorge, The People's Champ, makes his highly anticipated late arrival to the show before exchanging pleasantries and excuses for his tardiness.Please note: Timecodes may shift by a few minutes due to inserted ads. Because of copyright restrictions, portions—or entire segments—may not be included in the podcast.CONNECT WITH TOUCHER & HARDY: linktr.ee/ToucherandHardyFor the latest updates, visit the show page on 985thesportshub.com. Follow 98.5 The Sports Hub on Twitter, Facebook and Instagram. Watch the show every morning on YouTube, and subscribe to stay up-to-date with all the best moments from Boston's home for sports!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This episode is a special crossover from The Next Wave podcast, hosted by Matt Wolfe and featuring a deep-dive conversation with marketing and business expert Joe Fier. The duo breaks down the five most interesting developments in AI from the past week, with a focus on SeedDance 2.0—an advanced video model from ByteDance that's dominating headlines for its realistic visuals and flawless lip syncing. They discuss how SeedDance is changing the game compared to heavyweights like Veo and Sora, and why its approach to copyright and training data might give it a global edge.Along the way, Matt Wolfe and Joe Fier demo tools live, including GPT-5.3 Codex Spark and Google's Gemini DeepThink, showing how these models can create websites, apps, and even solve scientific problems at lightning speed. The episode also explores the ethical and business ramifications of AI's rapid evolution—from ads in ChatGPT to the potential impact on jobs and creativity—making it a must-listen for anyone eager to stay ahead in the AI landscape.Topics DiscussedSeedance 2.0's Arrival & ImpactDemos & Real-World ExamplesThe Future of AI Video in Marketing & AdvertisingAI and IP/Copyright ChallengesUltra-Fast Coding ModelsHuman Creativity vs. AIAI Advertising & MonetizationRapid AI Advancement & Staying AheadResources MentionedThe Next Wave Podcast: https://www.thenextwave.showMatt Wolfe: https://www.youtube.com/@mreflow Seedance 2.0: https://www.seedance.com/ByteDance: https://www.bytedance.com/CapCut: https://www.capcut.com/Veo: https://deepmind.google/models/veo/Runway: https://runwayml.com/ChatGPT Codex: https://chatgpt.com/codexMatt Schumer's Viral Article: https://www.mattshumer.com/blog/ai-changes-everythingSuper Bowl Claude Commercial:

I PAY FOR BOAT TRIP WITH GAMBLE. Head to Ollie.com/CRAYCRAY, tell them all about your dog, and use code CRAYCRAY to get 60% off your Welcome Kit when you subscribe today! Head to Marley⁠⁠Spoon.com/⁠⁠offer/CRAYCRAY for 45% off your first order and free delivery.    Sign up for our premium podcast feed with 3x the content! Just go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.realitycraycray.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for a 30 second sign up for as little as $5, or if you already have a Patreon account, go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://patreon.com/realitycraycray⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.  Other Links: Instagram ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://realitycraycray.com/instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Leave us a review: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://realitycraycray.com/review-us⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Gift a Subscription: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://realitycraycray.com/gift⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

Night Heron is sexy, smokey, smoldering, sartorial (insert favorite 'S' superlative) Portland lush-pop nurtured to life by frontman and guitarist Cameron Spies—along with a murderer's row of musicians from the vanguard of Portland music. Cameron joined the fellas this week to chat on finding freedom within a defined sonic palette, the producer's ear vs. the creator's ear, wound packing (pro or con?), KEXP gratitude, Boss horniness, and the authorial voice of High Times. Plus killer music!Music this week:"Stronger Than Me" by Night Heron (23:01)"LEECHES (PLAY DEAD)" by Suzie True (43:24)"Arrival" by Night Heron (52:48)"Get Me" by Seance Crasher (68:05)"Si Te Vas" by Sotomayor (88:44)

Today on Ascend: The Great Books Podcast, Dcn. Harrison Garlick and Dr. Donald Prudlo, the Warren Chair of Catholic Studies at the University of Tulsa, discuss the Ante-Purgatory, the foot of Mount Purgatory (Cantos 1-5).Check out our guide on Dante's Purgatorio (out soon!)Visit Dr. Jason Baxter's website and use "Ascend" in the promo code for 20% off his Purgatorio audiobook.Thanks for the Center for Beauty and Culture at Benedictine College for their support!The conversation with Dr. Prudlo and Deacon Garlick on Cantos 1–5 of Purgatorio opens with the dramatic shift from the despair of Inferno to the hope and refreshment of Purgatory.In Canto 1, Dante and Virgil emerge from Hell onto the shores of Mount Purgatory at Easter dawn, where Dante humbly invokes Calliope, the Muse of epic poetry, signaling his project as “the Christian epic” (Dr. Donald Prudlo). They meet Cato the Younger, a pagan suicide saved by special grace, who embodies the four cardinal virtues and serves as Purgatory's guardian. Prudlo emphasizes the shock: “Cato the pagan, the suicide is going to heaven. And we have got to confront that or we're going to miss so much of what Dante has to tell us here” (Dr. Donald Prudlo). The ritual of washing with dew and girding with the humble reed contrasts the broken plants of the suicides in Hell and symbolizes the beginning of true humility and ascent.Cantos 2–5 introduce the late-repentant souls and the mountain's structure. In Canto 2, an angelic boat ferries souls singing “In exitu Israel de Aegypto,” a psalm of liberation that Prudlo calls “a multifaceted song” evoking Exodus, baptism, and community (Dr. Donald Prudlo). Casella's song of Dante's own poetry enchants the group until Cato rebukes their idleness.Cantos 3–5 explore excommunicated sinners like Manfred (“even under a curse like mine, no one's ever so lost that eternal love cannot come back, as long as hope has any sprouts of green” – Manfred via transcript) and the slothful Belacqua, who banters with Dante like old friends. Prudlo highlights the power of last-minute mercy and intercession: “Mary is the last refuge of sinners” (Dr. Donald Prudlo). The cantos teach that Purgatory is a place of communal hope, where grace reaches even the unlikely, and purification begins with humility, prayer, and rightly ordered love—setting the stage for the active ascent through the terraces.Chapters00:00 Introduction to Dante's Purgatorio04:42 The Importance of Reading Purgatorio08:02 Themes of Emancipation and Freedom10:57 The Role of Cato in Purgatorio13:49 Cato's Significance and Political Implications17:00 Cato as a Precursor to Christ19:51 Dante's Literary Techniques and Inspirations22:56 Contrasting Ulysses and Dante25:36 Cato's Death and Its Symbolism28:52 The Nature of Purgatory and Salvation31:51 Cato's Virtues and Their Relevance34:49 The Relationship Between Cato and Christ37:48 Conclusion and Reflections on Purgatorio50:03 Understanding Cato's Role in Purgatorio52:43 The Heartbreaking Choice of Cato54:39 Rituals and Purification in Purgatory01:00:18 The Arrival at Purgatory01:06:34 The Significance of Water in Salvation01:12:09 Virgil's Role and the Nature of Guidance01:24:57 Manfred: A Case of Late Repentance01:29:38 The Role of Intercessory Prayer in Purgatory01:34:00 Understanding Mount Purgatory and Its Significance01:40:15 The Character of Belacqua and Themes...

This is part 2!  Emma comes clean on her birthday. Forrest questions who he can trust. Birkan opens up to Laura during a romantic boat trip. Lisa confronts Daniel about another woman. Jovon heads to Anna's hometown. Stig reassesses his relationship with Aviva. --- You can gift the gift of gay all year round!  ⁠https://www.patreon.com/RealityGays/gift⁠ JOIN RealityGays+  + Patreon ⁠⁠ ⁠⁠⁠⁠https://www.patreon.com/RealityGays⁠⁠ or  + Supercast ⁠⁠ ⁠⁠⁠⁠https://realitygaysmulti.supercast.com/⁠⁠  + Apple Subscriptions https://podcasts.apple.com/us/podcast/reality-gays-with-mattie-and-poodle/id1477555097  +Watch us on video ⁠⁠www.youtube.com/@RealityGays⁠⁠ Click here for all things RG!  ⁠⁠⁠https://linktr.ee/RealityGays⁠ COME at Mattie on Cameo!  https://v.cameo.com/e/jnrS9iCLi0b To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Emma comes clean on her birthday. Forrest questions who he can trust. Birkan opens up to Laura during a romantic boat trip. Lisa confronts Daniel about another woman. Jovon heads to Anna's hometown. Stig reassesses his relationship with Aviva. --- You can gift the gift of gay all year round!  ⁠https://www.patreon.com/RealityGays/gift⁠ JOIN RealityGays+  + Patreon ⁠⁠ ⁠⁠⁠⁠https://www.patreon.com/RealityGays⁠⁠ or  + Supercast ⁠⁠ ⁠⁠⁠⁠https://realitygaysmulti.supercast.com/⁠⁠  + Apple Subscriptions https://podcasts.apple.com/us/podcast/reality-gays-with-mattie-and-poodle/id1477555097  +Watch us on video ⁠⁠www.youtube.com/@RealityGays⁠⁠ Click here for all things RG!  ⁠⁠⁠https://linktr.ee/RealityGays⁠ COME at Mattie on Cameo!  https://v.cameo.com/e/jnrS9iCLi0b To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

KB is BACK and boy oh boy Phillies Spring Training is here and it is MESSY! KB addresses Bryce Harper's comments about Dave Dombrowski's comments earlier in the offseason, Nick Castellanos signing with the Padres and his comments to the San Diego media, and why it's okay to criticize Dave Dombrowski for this offseason. KB also addresses an absolutely hysterical Twitter troll interaction and why you shouldn't try and get in a words battle with him... Onboarding Form: forms.gle/mZYnkiQcGv1ZxBSg9 Voicemail Line: speakpipe.com/UndergroundSportsPhiladelphia Support Our Sponsors! The City of Vineland: Visit www.vinelandcity.org/ and stay connected with the community and learn about important announcements, programs, and services offered by the city! Vineland, New Jersey... Where It's Always Growing Season! '47 Brand Shop for your favorite sports fan and get FREE SHIPPING on ALL orders with '47 Brand! 47.sjv.io/e1Nyor Kenwood Beer Visit kenwoodbeer.com/#finder and see who has Kenwood Beer on tap in YOUR area and crack open an ice cold Kenwood Beer to celebrate the good times! (MUST be 21+ to do so and PLEASE drink responsibly.) Merch & Apparel: www.phiapparel.co/shop + Use Code "UNDERGROUND" for 10% off! FOCO Get your Phillies overalls and shortalls with our pals at FOCO! https://foco.vegb.net/0ZyLgV Biñho Get 10% off your next purchase with code BINHOBENNETT62 from our pals at Biñho! binhoboard.com?bg_ref=pDJkDdNO1y Follow Us! Twitter: twitter.com/UndergroundPHI Instagram: www.instagram.com/undergroundphi/ TikTok: tiktok.com/@undergroundphi KB: twitter.com/KBizzl311 Watch LIVE: YouTube: www.youtube.com/@UndergroundSportsPhiladelphia FB: facebook.com/UndergroundSportsPHI Twitch: twitch.tv/UndergroundsportsPHI Intro Music: Arkells "People's Champ" Outro Music: Arkells "People's Champ" #fyp @NickCastellanos #Phillies #BryceHarper #DaveDombrowski #RingTheBell #MLB #podcastcharts #download #review #subscribe #UndergroundIndustries

Chris dives deep on today's episode of Before the 90 Days. Emma comes clean on her birthday. Forrest questions who he can trust. Birkan opens up to Laura during a romantic boat trip. Lisa confronts Daniel about another woman. Jovon heads to Anna's hometown. Stig reassesses his relationship with Aviva.— Vanity Farah is a fun and femme coded pop culture recap podcast with Chris Farah, an actress, writer, comedian who is obsessed with pointing out the aesthetic choices and beauty trends she sees in reality shows like 90 Day Fiancé and Love is Blind. We deeply and sassily examine the choices that reality subjects make in the pursuit of love, and relate hard to the humanity on display, from questionable eyebrow shapes to the profound loneliness that plagues us all. If you want a smart yet silly friend to talk about dumb, escapist things with, subscribe, follow, and give 5 stars. Follow Chris in all her platforms! https://www.youtube.com/@ChrisFarah instagram.com/chrislfarah https://www.tiktok.com/@chrislfarah https://substack.com/@chrislfarah https://www.patreon.com/chrisfarah/Become a supporter of this podcast: https://www.spreaker.com/podcast/vanity-farah-with-chris-farah--6618122/support.

Amye is joined by Amanda to chat about 90 Day Fiance: Before the 90 Days S8:EP10 Prepare for ArrivalEmma comes clean on her birthday. Forrest questions who he can trust. Birkan opens up to Laura during a romantic boat trip. Lisa confronts Daniel about another woman. Jovon heads to Anna's hometown. Stig reassesses his relationship with Aviva.Get BONUS content and ad-free episodes! Sign up at:www.littlemissrecap.com/supportOr go directly to Patreon at:www.patreon.com/littlemissrecapListen to my true crime podcast: Murder She Watched at www.murdershewatchedpod.comGet in touch with us:Facebook: https://www.facebook.com/groups/littlemissrecapFacebook group: https://www.facebook.com/groups/littlemissrecapInstagram: @littlemissrecap Voicemail: www.littlemissrecap.comEmail: amye@littlemissrecap.comYoutube: www.youtube.com/@littlemissrecapSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AD 70 and the Arrival of the Kingdom of Christ

Three Takeaways: ·     God is central in our theology, and he is involved in our pain.·     Painful times cannot be avoided. They shape who we are.·     Show up for people in pain. Don't ignore them.--------DAILY DEVOTIONAL WITH RON MOOREGet Ron's Daily Devotional to your inbox each morning; visit biblechapel.org/devo.CAREGIVINGDo you have a need we can pray for? Do you need someone to walk alongside you? Do you know of another person who needs care? Let us know at caregiving@biblechapel.org.GROWTH TRACKWe all have a next step - what's yours? To learn more about our Growth Track and to take your next step, biblechapel.org/connect.

Scott and Julian discuss an airline worse that Ryan air, in the Twilight Zone. If you enjoy this, please check out our massive back catalogue of reviews and try our patreon for more bonus content www.patreon.com/20cgmedia‍ ‍

Beloved,Happy Valentines Day. I have a LOVE STORY that you will SEE YOUR OWN STORY within. We all feel it.A vigilance for justice and new resurging freedom in the coming year as the Fire Horse takes the lead.The blaze ignites suppressed collective forces and pushes survival systems past their breaking points. We are entering a new era where authority shifts from systems and structures to the visceral intelligence of the spirit intelligence. So as we mark the end of survival as the organizing principle and the beginning of heart opening aliveness…….ultimately we are:Choosing Love Out LoudRight now, more than ever, we are collectively craving one thing—safety.Not just physical safety, but the kind that lets us exhale fully. The kind that allows our shoulders to drop, our hearts to soften, and our nervous systems to rest.We are living in a world that feels uncertain. The division, the pace, the pressure— all rising. And in the midst of it, many of us are quietly asking: Where can I feel safe? Where can I return to myself?For me, this question has shaped everything.My life's work has been about nurturing this very feeling—-creating spaces, -rituals, and deep connections that help others remember ….in what it feels like to truly rest inside the light within ourselves.And recently, something sacred arrived in my life, a presence I call Love—and I want to honor her arrival with the reverence she deserves.But before I introduce you to her, I want to share a bit about the journey that brought us together. I believe it holds a mirror to so many of our own paths.The Power of the PathTwo years ago, I put my name on a waitlist for a litter of puppies.At the time, I didn't realize what I was actually signing up for.It would take two full years to finally be granted first pick.Two years of waiting.Two years of wondering if it would ever happen.Looking back, I see that those two years were not a delay—they were a divine invitation.My angels and guides used that time to do deep, quiet work within me.To clear out old stories.To help me soften into the truth of who I am.I wasn't just waiting for a puppy.I was being prepared to receive Love.Choosing a Different Kind of LifeDuring that time, I was also navigating a life that doesn't follow the traditional script.No marriage.No children.Just me, my work, and the sacred space I hold for others.It's a path that has asked me to shed layers of perfectionism and comparison.To stop apologizing for what my life doesn't look like.And instead, to honor what it is: creative, spiritual, intentional.I've learned that we often shrink what we love in order to stay safe.But that shrinking is an illusion.Real safety comes from expansion.From saying, This is what I love. This is what I'm building - the Kingdom of God in a stolen world - offering us a opportunity of lifetimes.Meeting LoveAnd now, she's about here.Five more weeks.A small, grounded being with soft eyes and a quiet strength.She's not here to selfishly fill a emptiness - She's here to be.To embody what I've been learning.I've had many visions of us channeling together.To walk with me through the streets, into my salon, into the hearts of those we meet.She is Love, made visible.And she is a reminder that one regulated nervous system—just one—can shift the energy of a room.Of a day.Of a life.Anchors for the JourneyAre you working through uncertainty right now?If so, I want to offer you a few anchors that have helped me:Keep Your Vision Bright.Your imagination is sacred. It's not a distraction—it's a direction. Follow it.Serve with Boundaries.You can love people deeply without abandoning yourself.Evolve at Your Own Pace.The world will try to rush you. Don't let it. Go slow. Go deep.Stay Cheerful Inside.Not toxic positivity—but a quiet cheerfulness. A kind of spiritual defiance that says, I still believe in beauty.There Is No Finish Line.There is only today. Only the practice. Only the breath.Love as a FrequencyLove is a resonance that needs no words.It's not weakness.It's alignment.It's power.When we choose to become love—not just give it, but become it—it meets us back in ways we could never have planned.That's what's happening for all of us - now.❤️ I love you. May you find the safety to soften.May you choose Love, out loud.And may you FEEL the Love choosing you right back.❤️ Love, KassandraOOOH! PS: On Christmas Eve, I surprised my parents by putting money down for their own little puppy to enter their lives. To my delight, they said yes! This week, they welcomed little Albert. I'm SO in love with my parents, and now I have another love in my life as we welcome this new addition to their home, filling it with even more joy. He's just so adorable.PPS: If you have a dog or also in the pursuit…….please hit reply, as I am creating a epic journey to support the sacred naming process.Yup. I've already started a SACRED DOG naming journey (or meaning to a current dog's name). Here's a sneak peek of how it curates the energy and spiritual connection with “LOVE.” Origin & EtymologyDerived from Old English lufu, and rooted in the Proto-Germanic lubō, “Love” is a word that has transcended language to become a universal vibration. It is not bound by culture, creed, or species—it is the essence that binds all.Core MeaningUnconditional affection, divine union, and the highest vibrational field available to sentient beings. Love is the frequency that dissolves fear, heals wounds, and magnetizes abundance.How “Love” Filters the Energy of the Heart and HomeThe name “Love” acts as a harmonic tuning fork. Each time it is spoken, it initiates a subtle recalibration of the home's energetic field—transmuting conflict, anchoring presence, and inviting softness. Your dog becomes a walking reminder of your soul's true north.This name is especially potent in spaces where healing, forgiveness, or heart-centered leadership are needed. It is not passive—it is powerful.The Seed Sound: Why “Love” ResonatesPhonetically, “Love” is a single-syllable seed sound. The soft “L” opens the heart chakra gently, while the “V” vibrates through the throat and solar plexus, encouraging expression and emotional courage.Spoken slowly—“Luhv”—it becomes both a mantra and a medicine.The Name Decree (The Scroll)✦ ✦ ✦By the power of presence, and in honor of the sacred bond between human and guardian soul,I hereby decree the name of this Spiritual Ambassador to be:✦ LOVE ✦May this name be spoken with reverence,May its frequency ripple through every corner of our home,And may its essence awaken truth, tenderness, and transformation.This name is not given lightly—it is offered as a gift, a vibration, a vow.So it is spoken. So it shall be.✦ ✦ ✦The Light Between is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit thelightbetween.substack.com/subscribe

Prince William's three-day visit to Saudi Arabia was always going to be diplomatically significant but it unfolded under the shadow of intensifying headlines back home. In this special episode, Andrea is joined by Hello!'s editor and royal correspondent Emily Nash, fresh from the trip, with behind-the-scenes insight into what the tour was really designed to achieve: trade talks, cultural diplomacy, and the long-term relationship-building that comes with being a “king-in-waiting.” 00:00 Intro01:36 Welcome + what you'll learn (Saudi visit + wider royal context)02:30 Why William's Saudi trip mattered diplomatically (trade/defence/culture)03:24 Why Saudi is sensitive and why this visit raised eyebrows04:18 Two “kings in waiting”: William & MBS dynamic05:12 The Epstein file release: what's driving renewed scrutiny07:02 Why William & Kate's statement mattered (and the focus on victims)08:08 Arrival in Saudi: heritage welcome + private audience & dinner09:01 Human rights questions: what can be raised publicly vs privately10:02 Buckingham Palace statement: “ready to help police” and why that's major13:38 Public reaction, heckling, and how the royals read the “national temperature”18:01 On the ground in Saudi: modernisation and what surprised Emily19:02 Women's sport shift: girls' football, national team ambitions, equal pay22:18 AlUla & the desert day: wildlife reserve, Arabian leopard, soft power23:34 The changed programme: walking the old town instead of the cultural hub visit24:40 What happens next + half-term breathing space26:18 Wider Europe: Norway/Sweden royal scrutiny in the Epstein files29:29 Closing thoughts: weathering the storm + sign-off Learn more about your ad choices. Visit megaphone.fm/adchoices

World demand down How's the weather looking for the mid-crop season? What about surplus? What's the recent news from Ghana? Arrival numbers have been hard to come by; now we know why! Current market price and recommendations for coverage going forward Happy Valentine's Day! Host: Eric Thornton, VP Expert: Marilyn Adutwum, Data Analyst

Bomani Jones is joined by Yahoo Sports' Tom Haberstroh.  First, they discuss Victor Wembanyama's insane game against the Los Angeles Lakers and why he is already an MVP-level player.  Later, they discuss the brawl between the Pistons and the Hornets & why the NBA needs more players like Isaiah Stewart.  Finally,  they react to the league-wide tanking epidemic and postulate that the NBA needs to get rid of the draft once and for all. Learn more about your ad choices. Visit megaphone.fm/adchoices

Ballsy heel turns and a LOT of juvenile humour spotlighted WWE television brand in '06. Gareth Morgan presents 18 Things You Learn Binge Watching WWE In 2006...ENJOY!Follow us on Twitter:@GMorgan04@WhatCultureWWEFor more awesome content, check out: whatculture.com/wwe Hosted on Acast. See acast.com/privacy for more information.

Thousands of people have turned out to welcome a group of monks on a Peace Walk to Washington, DC.

A group of Monks have finally reached the nation's capitol after miles of walking. AP's Lisa Dwyer reports.

HEADLINE: Arrival: Entering Lunar Orbit and the Grey World. GUEST AUTHOR: Bob Zimmerman. SUMMARY:Apollo 8 successfully enters lunar orbit using the SPS engine, allowing the crew to witness the moon's desolate, cratered surface and confirm its impact origins.

It is a hit or it isn't. But this one was dead on arrival. What we thoughtwas a blow to America, became a tributeto America. The blow back works. It's not taken lightly. We should always push back. 

Understanding Ephesians: A Journey Through Identity and Faith In this episode, we transition from discussing new beginnings to embarking on a six-week series on the Book of Ephesians. We'll delve into each chapter week by week, exploring the historical context of Ephesus, a powerhouse of trade, intellectual ideas, and religious practices under the Roman Empire. Ephesians, written by the Apostle Paul, is analyzed to reveal its timeless relevance, examining how ancient challenges of identity, fear, and societal fragmentation mirror our current struggles. Discover the profound impact of being 'in Christ' and understand how Paul's message of identity and grace was both revolutionary then and remains so today. Join us as we uncover the theological richness, practical lessons, and transformative power of Ephesians. 00:00 Introduction to the Series on Ephesians 01:13 Historical Context of Ephesus 06:59 Paul's Arrival and Impact in Ephesus 10:10 Paul's Letter from Prison 11:11 The Core Message of Ephesians 14:13 Identity in Christ 17:07 Paul's Revolutionary Approach 21:23 Spiritual Blessings in Christ 24:49 Paul's Reminder of Holiness and Blamelessness 26:21 The True Meaning of Adoption in Roman Culture 30:19 The Concept of Civil Death and New Identity 34:36 Redemption and Its Economic Implications 37:23 Living from Acceptance, Not for Acceptance 40:59 The Importance of Identity in Christ 42:20 Concluding Prayer and Reflection

This podcast description was blatantly written by AI... In today's episode of The Clint, Meg, and Dan podcast, the gang discusses the recent Super Bowl 60 with hosts offering their takes on Bad Bunny's and Charlie Puth's performances. Meg critiques Dan's saucy re-imagining of a listener's meet-cute story at a kebab shop. The team explores the '6/7 Dating Trend' where people seek more stable relationships by dating partners deemed less attractive. Listeners call in to share their bizarre cringe injuries, and there's a $10,000 Easy Money giveaway. Plus, a chat with John Aiken from Married at First Sight Australia. Don't miss the laughs, cringes, and insightful conversations! 00:24 Meg's Sleep-In and Parenting Woes01:07 Romantic Fiction and Saucy Stories04:10 Meg's Arrival and First Call of the Day29:56 Romantic Fiction: Dan's Creative Take35:44 Super Bowl Halftime Show Controversy39:30 Banned from Using the Microplane40:00 Cringe-Worthy Injury Stories48:01 The Super Bowl Recap55:12 Reward Stories and Lost Pets01:06:58 Dating Trends: The 6-7 Theory

Acts Series

Welcome to The DMF — Discovering Meaning in Film and Acting. I'm Justin Younts, and in this episode we explore the evolving landscape of women in film — particularly in non-traditional and physically demanding roles — with filmmaker Jaze Bordeaux.In our conversation, Jaze shares his journey championing stories centered around women in combat sports and mixed martial arts — a concept that, when first pitched in 2016, was met with skepticism from distributors and industry decision-makers. At the time, many questioned whether audiences would support films featuring women in these kinds of physically intense, traditionally male-dominated roles.Despite the resistance, Jaze believed in the cultural and cinematic potential of these narratives and continued pushing forward. Today, the industry has shifted dramatically, with a growing wave of films highlighting women in combat sports and other unconventional spaces.We discuss:Women in non-traditional film rolesMixed martial arts films and combat sports cinemaIndustry skepticism vs audience demandProducing female-driven sports storiesIndependent film challenges during the pandemicFilm festival recognition and awards circuitsJaze also reflects on the success of his projects, which have earned over 30 awards — including honors at the prestigious Palladino D'Oro International Sports Film Festival. While accolades were never the goal, they stand as validation of the passion and perseverance behind these films.We also touch on the broader momentum building around stories like Queen of the Ring and Halle Berry's MMA-focused work, signaling a larger industry shift toward inclusive, boundary-breaking storytelling.Whether you're an actor, filmmaker, or film enthusiast, this episode highlights how persistence, vision, and advocacy can reshape what kinds of stories get told — and who gets to lead them.Join us as we explore the rise of women in combat sports cinema and the future of non-traditional roles in film.Check out these links:⁠⁠⁠http://jazebordeaux.com/⁠⁠⁠⁠⁠⁠https://www.facebook.com/jazebordeaux/⁠⁠⁠⁠⁠⁠https://www.instagram.com/jazebordeaux/⁠⁠⁠⁠⁠⁠https://www.threads.net/@jazebordeaux⁠⁠⁠⁠⁠⁠https://www.tiktok.com/@jazebordeaux⁠⁠⁠⁠⁠⁠https://x.com/jazebordeaux00:00:00 - Introduction00:00:05 - Advocacy for Women in Non-traditional Roles00:00:44 - Initial Resistance to Women's Mixed Martial Arts00:01:50 - Market Reception and Success of Women's Mixed Martial Arts00:04:20 - Accolades and Awards for the Film00:07:40 - Experience at the Palladino Doro International Sports Film Festival00:13:09 - Discussion on Short Film 'Starry Eyed'00:15:08 - Character Development and Casting Decisions00:16:32 - Involvement of Film School Graduates00:17:35 - The Evolution of Storytelling and Directing00:18:42 - Supporting Other Filmmakers and Directors00:19:55 - Introduction to 'Let Go'00:24:52 - Success of 'EFC' and Future Projects00:26:06 - Introduction to 'Genesis'00:26:13 - The Concept and Evolution of 'Genesis'00:30:25 - Clarification on 'The Arrival' Reference00:31:09 - Vision for 'Genesis'00:31:17 - The Timelessness of 'Genesis'00:32:17 - Advice for Storytelling00:32:31 - Closing Remarks

Mattes Kries | 10 Percent True | EP81 – Part 1In this episode, Mattes Kries—a former Luftwaffe Tornado IDS pilot and weapons instructor—traces his career from a hard-won start in NATO jet training through frontline Tornado operations, weapons school, and senior tactics leadership. He explains how Germany's Tornado force evolved from Cold War low-level nuclear strike toward conventional, medium-altitude employment; how lessons from U.S. and NATO exercises reshaped German tactics; and why culture, risk tolerance, and bureaucracy matter as much as hardware.Along the way, Mattes offers rare, candid insight into weapons school innovation, COMAO command without Link 16, live weapons integration, and the realities of training for combat in a force defined by safety-first constraints—grounded in vivid anecdotes and hard-earned lessons.Timestamps00:00 – The Greek instructor teaser01:58 – Welcome Mattes & Phil's subscriber questions: inspiration and most exhilarating mission12:05 – Matthew's subscriber question: history and pride in the modern Luftwaffe23:40 – Attachment to the past among today's Luftwaffe personnel29:10 – Starting out in the Luftwaffe34:02 – F-4 ambitions—and why fate had other (good) ideas41:28 – T-37 challenges (and the Greek instructor)49:00 – Turning early struggles into long-term success51:15 – Arrival on the Tornado at Büchel56:40 – Tornado IDS: roles, weapons, and mission sets1:05:35 – SIOP and nuclear strike planning1:10:40 – The MW-1 weapon system1:20:19 – Why the MW-1 was never fitted for training—and the power of German accountants1:29:30 – Staying on the boom: tanker planning as a weapons school student1:35:08 – Avoiding the KC-135 by design?1:36:35 – Responding to Starbaby's criticism of ECR capabilities vs decision-maker mindset1:54:25 – Part 2 incoming

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

The crisis of meaning among young people gets a lot of press; but a quieter crisis of calling afflicts every generation today. Dr. Arthur Brooks says the causes are the same: not knowing what our life is really and ultimately for. In his talk at The Heights Forum Convivium 2025, Dr. Brooks shares the facts about calling—where neuroscience, psychology, and theology all agree, and how he (finally) found his. He goes on to say that helping young people to discover the true Christian purpose of life and then one's personal calling is the missionary work of teachers. Chapters: 00:04:12 Teaching: a missionary field 00:06:15 Crisis of meaning among the young 00:07:35 Crisis of calling among more than the young 00:14:23 Sanctifying ordinary work 00:18:42 The marshmallow experiment: not all it seems 00:24:40 High achievers with no calling 00:27:55 Three tests for goal setting 00:36:12 Four profiles for career trajectory 00:44:31 Success addiction: when love feels conditional 00:48:33 Arrival fallacy: when the goal doesn't satisfy 00:51:34 Posture of submission to find your calling Links: Arthur Brooks: The Science of Happiness, Work & Life, personal website The Meaning of Your Life: Finding Purpose in an Age of Emptiness by Arthur Brooks, pre-order for March 2026 "How to Follow the Right Star" by Arthur Brooks, The Atlantic Also on the Forum: Choosing a College—Or Not featuring Alvaro de Vicente Rethinking College: Why go? How? When? featuring Arthur Brooks Featured Opportunities: The Art of Teaching Boys Conference at The Heights School (May 6-8, 2026)

NFL Network Chief National Reporter Steve Wyche joins to discuss Mike MacDonald rebuilding a winning culture in Seattle, the best NFL coaching hire this cycle, and an expanded international slate starting next season.

Join Mike and Scott today as we answer your awesome Listener Questions! Today we discuss thoughts on your arrival day for a big runDisney race weekend! We also talk give some suggestions for your visit to GEO-82, making the most of your first few hours on a DCL ship, recommendations for dinner at Palo, celebratory snacks after a race at Disneyland and much more! (..and yes, the pools are heated!) Come join the BOGP Clubhouse on our Discord channel at www.beourguestpodcast.com/clubhouse!  Thank you so much for your support of our podcast! Become a Patron of the show at www.Patreon.com/BeOurGuestPodcast.  Also, please follow the show on Twitter @BeOurGuestMike and on Facebook at www.facebook.com/beourguestpodcast.   Thanks to our friends at The Magic For Less Travel for sponsoring today's podcast!

Do you ever feel like joy is just out of reach? Is it something you have to work for, fake, or wait for some future version of yourself to finally experience? Heather Creekmore gets it—and she’s opening up in this episode with honesty about her own struggles to feel true joy, even while doing “all the right things” as a Christian. In this episode, Heather Creekmore shares her journey through perfectionism, body image battles, and chasing joy through achievements, life changes, and even motherhood, only to find that joy can’t be manufactured through striving, positivity, or “arriving.” What actually blocks our joy, and how do we finally break free? You’ll hear: The difference between happiness and joy—why this matters more than you might think The three big “joy blockers” and how they can sneak into anyone’s life (even when you know all the right answers!) Vulnerable storytelling about wrestling with body image, marriage, and motherhood, and what happens when joy doesn’t show up as you hoped Practical, faith-based strategies for actually growing in joy—no matter what your circumstances Encouragement to stop chasing “just over yonder” happiness and instead, rest in the truth of where joy is really found A free resource just for you: The “Obsessed to Blessed” mini-course at improvebodyimage.com/obsessed-to-blessed, helping you bookend your search for joy and freedom from body image struggles Whether you’re feeling joyless, weighed down by shoulds, or just yearning for something real and lasting, this conversation is a hug for your soul and a nudge towards real hope. If you’re ready to stop comparing and start living, this episode is a must-listen. Resources Mentioned: Free Mini-Course: “Obsessed to Blessed” – learn more and sign up at improvebodyimage.com/obsessed-to-blessed Work with Heather and her team to find your joy. Learn more here: https://www.improvebodyimage.com/christian-body-image-courses-and-coaching Don’t miss out—press play and rediscover the joy that’s closer than you think! Subscribe, share, and join the community as we break free from comparison and uncover the joy that lasts. Discover more Christian podcasts at lifeaudio.com and inquire about advertising opportunities at lifeaudio.com/contact-us.

Dylan Syer speaks publicly for the first time about the high-profile accusations that upended his life, including allegations of fraud, identity theft, and grand larceny stemming from his work as a celebrity personal shopper. Prosecutors claimed Dylan used access to clients' finances to make unauthorized luxury purchases, accusations he firmly denies. In this episode, Dylan explains what he says really happened, how the charges caused his business to collapse overnight, and what it was like being sent to Rikers Island while fighting to clear his name. He also opens up about the emotional toll of the case, navigating the criminal justice system, and ultimately being sentenced to one year of weekends at Rikers — a punishment that kept him trapped between freedom and incarceration. _____________________________________________ #RikersIsland #PrisonStories #TrueCrimePodcast #CelebrityLifestyle #LifeAfterPrison #JusticeSystem #RedemptionStory #lockedin _____________________________________________ Thank you to AVA & GOLD DROP SELTZERS for sponsoring this episode: AVA: Take control of your credit today. Download the Ava app and when you join using my promo code LOCKEDIN, you'll get 20% off your first year—monthly or annual, your choice. _____________________________________________ GOLD DROP SELTZERS: Head to https://www.thedryoak.com/ and use promo code LOCKEDIN at checkout for 10% off your order. _____________________________________________ Connect with Dylan Syer: Instagram: @SireConsultant TikTok: @SireConsultant _____________________________________________ Hosted, Executive Produced & Edited By Ian Bick: https://www.instagram.com/ian_bick/?hl=en https://ianbick.com/ _____________________________________________ Shop Locked In Merch: http://www.ianbick.com/shop _____________________________________________ Timestamps: 00:00 Police Raid, Fraud Charges, and Everything Falling Apart 01:55 Dylan Sire's Case: The Allegations and Why This Story Matters 02:59 Childhood, Family Values, and Early Influences 05:53 Discovering Luxury Fashion and the High-End World 09:47 Breaking Into High-End Sales and Designer Retail 14:12 Launching His Own Boutique and Becoming an Entrepreneur 22:14 Building a Celebrity Personal Shopper Business 27:47 Industry Shifts, Competition, and Cracks in the Model 33:53 Big Clients, Luxury Spending, and How the Business Worked 38:18 Financial Pressure, Business Trouble, and Legal Red Flags 42:38 Account Freezes, Chargebacks, and Sudden Collapse 47:17 Arrest, Booking, and Arrival at Rikers Island 54:00 Inside Rikers Island: Survival, Fear, and Reality 01:01:56 Bail, Media Attention, and Public Fallout 01:09:01 Court Proceedings, Charges, and Legal Strategy 01:16:20 Sentenced to Weekends at Rikers: How It Worked 01:23:30 Relationships, Trust, and Personal Consequences 01:32:00 Rebuilding After the Case: Frugality, Growth, and Lessons Learned 01:36:00 Final Reflections, Accountability, and Moving Forward Learn more about your ad choices. Visit megaphone.fm/adchoices

Guest: Dan Flores. Flores introduces his book Wild New World, discussing North America's deep evolutionary history, the arrival of carnivorous humans, and the resulting interactions with ancient fauna.1860 GRIZZLY BE HUNTER

On a weekend edition of the Orange and Brown Talk podcast, Mary Kay Cabot, Ashley Bastock and Dan Labbe start by discussing Twitter post that shook Berea -- new head coach Todd Monken's arrival and his enthusiastic greeting of quarterback Shedeur Sanders, reigniting the conversation about the team's future under center. The crew analyzes the potential quarterback competition between Sanders and a healthy Deshaun Watson, exploring how Monken, a renowned quarterback whisperer, might unlock the best in either player. Lance Reisland also joined the show. As a long-time admirer of Monken's work, Reisland provides analysis on his coaching philosophy, breaking down his flexible, player-focused schemes and why he's a great fit in Cleveland. They discuss which offensive players stand to benefit the most from the new system and what the Browns' biggest offseason needs are, from the offensive line to skill positions. Then Lance and Dan do a draft of snow day activities. Follow us: On X: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://x.com/orangebrowntalk⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ YouTube: h⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ttps://www.youtube.com/@ClevelandBrownsonclevelandcom⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/orangeandbrowntalk/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Music credits: Ice Flow by Kevin MacLeod Link: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://incompetech.filmmusic.io/song/3898-ice-flow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ License: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://filmmusic.io/standard-license⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices