Podcasts about Valley

This week on The Valley Persian Style, Sky and Tannin apologize but do not make up, Reza sends a text to Tommy about reconciling, Bamshad defends his wife even though he doesn't want to and more!Follow me on social media, find links to merch, Patreon and more here! Hosted on Acast. See acast.com/privacy for more information.

To follow along, please visit https://dailyliturgy.com.Epiphany - Proverbs 31, 1 Corinthians 12:1-11, Psalm 137Writers: Mike Kresnik, Bob Thune, Darby Whealy, Tyler AndersonNarrators: Charlotte Bertrand, Gary Nebeker, Bob Thune, Darby Whealy, Kevin HuddlestonMusic: Lens Distortions - https://lensdistortions.comProduction: Mike Kresnik, Bethany Gilbert, Zach LeeSources: The Worship Sourcebook; The Valley of Vision; The Book of Common Prayer; + original contributions by the authors.To follow along, please visit https://dailyliturgy.com.

One of the interesting things about the Guthrie story is that it is so deeply rooted in Arizona. Obviously it's taking place here; Guthrie's home is north of Tucson, and the desert landscaping has played a part in how the case is progressing It just feels like an Arizona story. Because it is. This week on Valley 101, longtime the Republic reporter who has been covering the story, talks about the Guthrie case, the frustrations of making slow progress and the Arizona ties. ⁠Submit your question⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ about Arizona! Follow us on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Guests: Richard Ruelas Host: ⁠Bill Goodykoontz⁠⁠⁠⁠⁠⁠⁠ Producer: ⁠⁠⁠⁠⁠⁠⁠⁠Amanda Luberto⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Sermon Scripture: Matthew 17:1-9 (14-20)We love mountaintop experiences, but you have to come off the mountain at some point and the process of descent is hard, both physically and emotionally. You have to learn how to let that mountaintop experience integrate into your daily life, where it will be truly put to use for the good of others. The most important moment in the story for us this week is not the blinding light, the voice from the cloud, or the vision of the witnesses, it is the touch of Jesus and the comforting words, "Get up. Do not be afraid."

Send us your feedback — we're listening“Psalm 23:4 — Jesus, Walk With Me When the Path Feels Uncertain” Psalm 23:4 (NIV): “Even though I walk through the valley… You are with me.” John 10:11 (NIV): “I am the good shepherd…” Live from London, England with Reverend Ben Cooper Brazil • United States • India • Portugal • Chile Jesus, there are moments when life narrows into difficult paths and the heart feels unsure of what lies ahead. Yet Your Word meets me gently with the promise that even in the valley, even in the tightening of circumstances, I am never abandoned. Across Brazil, Portugal, the United States, India, and Chile, many whisper this same quiet prayer tonight: Senhor, guia-me em cada passo. You draw near to every one, steadying the mind and calming the weight of fear. You walk beside us, not from a distance, but with deep compassion that restores the soul. You reveal Yourself as the Good Shepherd, the One who knows every turn, every shadow, every unseen pressure. You guide carefully, You protect faithfully, and You hold each heart with tenderness. Jesus, remind me that valleys do not define me; Your presence does. Let Your peace settle over the places where anxiety tries to rise. Let Your guidance shape each decision, each breath. When loneliness presses close, let Your nearness become my quiet strength. When uncertainty stirs, let Your voice lead me gently forward. Tonight, Senhor Jesus, dá-me paz no caminho difícil. As nations rest, as others begin their day, Your love spans every hour and every time zone. You carry the weary, reassure the worried, and lift those who feel unseen. Help me trust the steady rhythm of Your leading. Let this journey become a place where courage grows, where hope takes root, and where Your presence becomes the light guiding me into tomorrow. Jesus, walk with me through every valley and bring strength into every step ahead.  Psalm 23:4 devotional, Jesus my shepherd, comfort in fear, John 10:11 prayer, Portuguese Christian devotional, USA faith prayer, India night prayer, Brazil hope message, walking through valleys with Jesus, emotional reassurance Psalm 23, John 10:11, valley, shepherd, comfort, Jesus, devotional, Portuguese, India, USA, fear, reassuranceSupport the showFor more inspiring content, visit RBChristianRadio.net — your home for daily devotionals, global prayer, and biblical encouragement for every season of life. We invite you to connect with our dedicated prayer hub at DailyPrayer.uk — a place where believers from every nation unite in prayer around the clock. If you need prayer, or would like to leave a request, this is the place to come. Our mission is simple: to pray with you, to stand with you, and to keep the power of prayer at the centre of everyday life. Your support through DailyPrayer.uk helps us continue sharing the gospel and covering the nations in prayer. You can also discover our ministry services and life celebrations at LifeCelebrant.net — serving families with faith, dignity, and hope. If this devotional blesses you, please consider supporting our listener-funded mission by buying us a coffee through RBChristianRadio.net. Every prayer, every gift, and every share helps us keep broadcasting God's Word to the world.

Episode 177. This week on Balancing Act, Kristen is joined by bestie, Zack Wickham, to talk all about their friendship — and what you don't see on The Valley. They share how they met, their first impressions, the early VPR days, and how their bond grew behind the scenes. Zack opens up about moving to LA and finding his chosen family, while Kristen reveals her favorite things about him. They also talk skincare secrets, surprising fun facts, and clear up misconceptions about their friendship. Plus, listener questions and a rapid-fire round to wrap it all up. Sponsors: Arey: For a limited time, our listeners get 15% off at Arey by using code KRISTEN at Arey.com. Learn more about your ad choices. Visit megaphone.fm/adchoices

To follow along, please visit https://dailyliturgy.com.Epiphany - Proverbs 30, 1 Corinthians 9:16-27, Psalm 37:23-40Writers: Mike Kresnik, Bob Thune, Darby Whealy, Tyler AndersonNarrators: Charlotte Bertrand, Gary Nebeker, Bob Thune, Darby Whealy, Kevin HuddlestonMusic: Lens Distortions - https://lensdistortions.comProduction: Mike Kresnik, Bethany Gilbert, Zach LeeSources: The Worship Sourcebook; The Valley of Vision; The Book of Common Prayer; + original contributions by the authors.To follow along, please visit https://dailyliturgy.com.

Racing with Honor features two remarkable veteran paracyclists whose journeys through injury, resilience, and reinvention come to life at the Valley of the Sun Stage Race. Alongside their powerful stories, we highlight this week's announcements—including TriDot Pool School and G2G Velocity live ride sessions—plus our Get Gritty Tip, Workout of the Week, and a fun segment celebrating the heart of endurance sport. Supported by our show sponsor Vespa Power and Ask A Coach sponsor TriDot, this episode brings listeners inspiration, practical training insight, and a deeper understanding of purpose-driven performance.#Grit2Greatness #CoachingTips #Ask A Coach #TriathlonCoach #TriathlonPodcast #303Endurance #TriDot #EnduranceAthlete #SwimBikeRun #GetGritty #TriathlonTraining #CyclingLife #RunningCommunityWebsite - Grit2Greatness Endurance CoachingFacebook - @grit2greatnessenduranceInstagram - @g2genduranceGet Started with Grit2Greatness -Getting Started with Grit2Greatness - Google FormsCoach Contact Info:April.spilde@tridot.comTriDot Signup - https://app.tridot.com/onboard/sign-up/aprilspildeRunDot Signup - https://app.rundot.com/onboard/sign-up/aprilspildeCoach Lauren BrownLauren.brown@tridot.comTriDot Signup - https://app.tridot.com/onboard/sign-up/laurenbrownRunDot Signup - https://app.rundot.com/onboard/sign-up/laurenbrownCoach Rich SoaresRich.soares@tridot.comRich Soares CoachingTriDot Signup - https://app.tridot.com/onboard/sign-up/richsoaresRunDot Signup - https://app.rundot.com/onboard/sign-up/richsoaresGet Gritty Sponsor: Vespa PowerVespa Power Endurance helps you tap into steady, clean energy—so you stay strong, focused, and in the zone longer. Vespa is not fuel, but a metabolic catalyst that shifts your body to use more fat and less glycogen as your fuel source. Vespa comes in CV-25, Junior and Concentrate.Less sugar. Higher performance. Faster recovery.Home of Vespa Power Products | Optimizing Your Fat MetabolismUse discount code - 303endurance20

This week on the Unscaled Travel Show, we're heading just northeast of Las Vegas to Valley of Fire State Park — Nevada's oldest state park and one of the most visually wild landscapes in the Southwest.We're breaking down what the park actually is, how it's laid out, the essential stops and hikes to prioritize, and how to plan a visit without rushing through the best parts or underestimating desert realities. From Fire Wave and White Domes to ancient petroglyphs and the ghost town of St. Thomas nearby, this is your clear, first-time visitor guide to doing Valley of Fire the right way.____________________________________S04 Ep148____________________________________Connect with us on social media: Instagram: @unscaledtravelshowTwitter: @fullmetaltravlrFacebook: @fullmetaltravelerWebsite: ⁠⁠https://www.unscaledtravelshow.com/

Eine unheimliche Horror Story: Ich erinnere mich, dass ich eines morgens früh angerufen wurde. Es hatte eine Art "geologisches Ereignis" gegeben und jeder Inspekteur wurde vom Schreibtischdienst abgezogen. Wir brauchten eine sofortige Bauabnahme in der Umgebung des Greenbrier Valley. Wir sollten unser Vorgehen mit einem zentralen Kommando in Greene County koordinieren. An diesem Morgen schien Chaos zu sein. In den Lokalnachrichten war von einem entflohenen Mörder die Rede und wir bekamen Anrufe, dass ein See trockengelegt wurde. Das örtliche Krankenhaus brauchte dringend Blutspenden, konnte uns aber nicht sagen, warum. Alles in allem stimmte da etwas gewaltig nicht. Und niemandem gefiel es. Aber wir mussten es akzeptieren._______________________________________Verfasst von: Saturdead / D.D.WikmanÜbersetzung: Sab_84Quelle: https://www.reddit.com/r/nosleep/comments/13svnug/there_was_something_under_the_oak_valley_grain/

On The Valley Persian Style, there's a slip n slide party where Bamshad has the chance to defend his wife. Since his wife is Sky, he doesn't get too far without being shot down. Meanwhile, the ambush lunch with Sky and Tanin fails and GG tries to come up with more ways to make up with her dildo king so he'll pay for her new mansion. To watch this recap on video, listen to our bonus episodes, and get ad free listening, go to Patreon.com/watchwhatcrappens. Find bonus episodes at patreon.com/watchwhatcrappens and follow us on Instagram @watchwhatcrappens @ronniekaram @benmandelker Hosted on Acast. See acast.com/privacy for more information.

Who is more insufferable to watch? Sky or Tommy?? Teddi and Tamra think its hard watching Sky fight with everyone and not showing any accountability. Then there is Tommy, who seems like he’s only doing this because Mercedeh wanted to do the show again… And how many times is he going to burp on tv?! The ladies question why Golnesa thinks she should get alimony if she was only married to Dennis for one week. Plus, has Teddi crossed paths with Sky before?? Did she almost buy her jewelry? See omnystudio.com/listener for privacy information.

Happy Valentines Day (Eve), Jurors! We deep dive RHOBH where The A Team gets to go to the Hamptons and The B Team goes to the Manifestation Moment Dinner at Amanda's. Are the women coming too hard for Amanda? Why are we trying to make Amanda not inviting Dorit a storyline when she literally couldn't attend the party because she was living her best life in the Hamptons? Did Mo do a good job being PK's “soldier”? Is Dorit keeping the kids from him or is he a BIG LIAR?! Over in The Valley, Sky continues to hold a grudge, GG needs Dennis to pay $3k/month of her rent, Reza texts Tommy and MJ may slowly be checking out of her marriage. Come judge with us!You can find us:Podcast: ACast, iTunes, Spotify, wherever you listen!Instagram & Threads: @twojudgeygirlsTikTok: @marytwojudgeygirls // @courtneytjgFacebook: www.facebook.com/twojudgeygirlsMerch: www.etsy.com/shop/twojudgeygirlsPatreon: www.patreon.com/twojudgeygirls LTK: @marytwojudgeygirls // @courtneytjg Hosted on Acast. See acast.com/privacy for more information.

Zack Wickham and Kristi Cook of Spill Sesh join When Reality Hits to break down the biggest pop culture moments of the week. From how Kristi built her massively popular YouTube tea channel (and why she stayed anonymous for five years) to influencer scandals, The Valley teaser drama, and the never-ending Blake vs. Justin Hollywood mess, nothing is off limits. Plus, they dive into Taylor Swift and Travis Kelce, celebrity friendships, and what really happens when fame, PR, and lawsuits collide.Please support the show by checking out our sponsors!Rula: Rula patients typically pay $15 per session when using insurance. Connect with quality therapists and mental health experts who specialize in you at www.rula.com/Realityhits #rulapodO Positiv: Take proactive care of your health and head to OPositiv.com/REALITY or enter REALITY at checkout for 25% off your first purchase.ARMRA Colostrum: Go to armra.com/REALITY or enter REALITY to get 30% off your first subscription order.Quince: Go to Quince.com/realityhits for free shipping on your order and 365 day returns.Discover Your New Home at apartments.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Christopher O'Brien returns for part 2 of our conversation on Stalking the Herd. We get into deeper aspects of the Cattle Mutilation phenomenon, and you can follow us down the rabbit hole. From 1992 to 2002 Christopher O'Brien investigated over one thousand paranormal events reported in the San Luis Valley—located in south-central Colorado/north-central New Mexico. Working with law enforcement officials, ex-military, ranchers and an extensive network of skywatchers, he documented what may have been the most intense wave of unexplained activity ever seen in a single region of North America. His ten-year investigation resulted in the three books of his “mysterious valley” trilogy: The Mysterious Valley, Enter the Valley, and Secrets of the Mysterious Valley. His meticulous field investigation of UFO reports, unexplained livestock deaths, Native American legends, cryptozoology, secret military activity and the folklore, found in the world's largest alpine valley, has produced one of the largest databases of unusual occurrences gathered from a single geographic region. He is currently working with a team of specialists installing a high-tech video surveillance and hard-data monitoring system in and around the San Luis Valley. He has also authored Stalking the Tricksters which is published by Adventures Unlimited Press. This controversial book distills his years of field investigation and research into an ingenious unified paranormal theory that is sure to create intense interest and controversy. Hosted on Acast. See acast.com/privacy for more information.

To follow along, please visit https://dailyliturgy.com.Epiphany - Leviticus 19:1-2, 9-18, Mark 4:1-20, Psalm 37:1-22Writers: Mike Kresnik, Bob Thune, Darby Whealy, Tyler AndersonNarrators: Charlotte Bertrand, Gary Nebeker, Bob Thune, Darby Whealy, Kevin HuddlestonMusic: Lens Distortions - https://lensdistortions.comProduction: Mike Kresnik, Bethany Gilbert, Zach LeeSources: The Worship Sourcebook; The Valley of Vision; The Book of Common Prayer; + original contributions by the authors.To follow along, please visit https://dailyliturgy.com.

On today's episode of AllAboutTRH, Roxanne and Shantel sit down with The Valley: Persian Style star Golnesa Gharachedaghi AKA GG for a candid, unfiltered conversation. GG shares where things currently stand with Dennis, breaks down the fallout with Sky, and opens up about why she feels Mercedes crossed a line regarding her sister + so much more Want to jump straight to the interview? GG joins us at the 36-minute mark. Before the interview, we recap the latest Southern Charm drama, unpack Sally's cringe-worthy thirstiness, and dig into the conflict between Sally, Molly, and Venita that led to yet another dramatic walk-off. Subscribe to 'AllAboutTRH' on ⁠Apple Podcast⁠ ⁠https://podcasts.apple.com/us/podcast/allabouttrh-podcast/id1554996153⁠ Follow 'AllAboutTRH' on ⁠Spotify ⁠ ⁠https://open.spotify.com/show/79BLlV7530ggskem3tAvjp?si=3ea7024174324d3c⁠ Follow AllAboutTRH On ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow AllAboutTRH On ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow AllAboutTRH On ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Join Rox & Shantel of AllAboutTRH on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon ⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

LAPD’s West Valley Division gets honored today — a shout-out to the officers and staff serving the Valley. Dean Sharp, “The House Whisperer” (custom home designer and host of HOME on KFI AM 640 — Saturdays 6–8am, Sundays 9am–noon) joins Conway for Romancing Your Home on Valentine’s Day weekend: the most unusual homeowner requests, “open door dumps,” and easy ways to level up your home’s romance factor. More with Dean on pre-fab/manufactured homes — why they can save a shocking amount of materials, how well-built they’ve become, and why ADUs are exploding across SoCal. And a sad local loss: Sandy Steers, executive director of Friends of Big Bear Valley, has passed away. See omnystudio.com/listener for privacy information.

Kathy Clugston and the GQT team are in Tyne Valley, Newcastle.Kathy's joined by Dr Chris Thorogood, Bethan Collerton and Matthew Wilson, who tackle a variety of questions from troubled Aspidistra, yellowing Choisya and planting for winter colour. The team also discusses beech hedges, how to tackle the issue of invasive Japanese knotweed and unlikely exotics thriving in northern gardens.Later in the show, Bunny Guinness enlightens us on the benefits and use cases of grow lights within greenhouses. Producer: Dan CockerAssistant Producer: Suhaar AliA Somethin' Else production for BBC Radio 4To view the plant list, please go to the Gardeners' Question Time and open this week's episode page.

This week, a special road trip episode featuring award-winning Owensboro, Kentucky bluegrass singer/songwriter Rick Faris and his band recorded live at the 2024 Walnut Valley Festival in Winfield, Kansas. The annual Walnut Valley Festival, now in it's 53rd season, is one of the oldest and most respected acoustic music festivals in the world. Held at the Winfield, Kansas fairgrounds, more than 30 musical acts will perform on four separate stages, presenting over 200 hours of live music. Also, there is a dedicated contest stage where contestants vie for national and international championships in Finger Style Guitar, Flat Pick Guitar, Bluegrass Banjo, Old Time Fiddle, Mandolin, Mountain Dulcimer, Hammered Dulcimer, and Autoharp. There is a juried arts and crafts fair, exhibits by renowned instrument makers and music shops, family activities, a bevy of food vendors, a farmer's market and even a pub! An unusual aspect of Walnut Valley is its campground tradition. Campsites are not reserved and campers line up to claim a choice campsite during the "Land Rush.” Walnut Valley Festival goers often bring their own musical instruments to participate in the sometimes all night campground jam sessions. Bands like Old Sound and Sally & The Hurts that began as "Jam Bands" in the campgrounds, have even been invited to perform at the festival. Rick Faris is a Kansas Music Hall of Fame Member who was recently awarded the “Songwriter of the Year” at the 2024 International Bluegrass Music Association Awards making him an 8-time IBMA Award winner. In addition Rick won the coveted “New Artist of the Year” in 2022. Faris also spent 11 years with Special Consensus while the band earned two GRAMMY nominations before embarking on his chart topping solo career. The Rick Faris Band, is an International touring Bluegrass outfit playing in the US, Mainland Europe, the British Isles and Canada. They bring sibling harmony and comedic relief with brother JimBob Faris on bass and a youthful snap to their original brand of music with a couple of bluegrass thoroughbreds, Henry Burgess (who grew up with fiddle legend Byron Berline) and Gibson Davis (who is a third generation bluegrass musician following father Chris Davis and his Grandfather Danny Davis). Rick recently moved to Owensboro, Kentucky the Bluegrass Music Capital and has opened his Faris Guitar Co. - https://rickfaris.com/press-kit In this week's “From the Vault” segment, OHR producer Jeff Glover offers a 1988 archival recording of gospel music legends The Chuck Wagon Gang performing the 1934 J.R. Baxter song “After the Sunrise,” from the Ozark Folk Center State Park archives. In his segment “Back in the Hills,” writer, professor and historian Dr. Brooks Blevins explores the storied history of early gospel music publishing in the Ozark and Quachita Mountains.

Former Valley AD Brad Rose joins Trent with a look into High School Sports today, plus Cyclone talk with Eugene Rapay

One candidate in the Republican primary for Arizona governor is calling it quits. Our Friday NewsCap panelists analyze that and the rest of the week's top stories. Plus, a playlist of songs about love of all kinds from bands around the Valley.

Plus Dylan Kendall is running for LA City Council against Hugo Soto Martinez, clap that up!See omnystudio.com/listener for privacy information.

We finally get to see the meeting between Sky and Tanin, and while some apologies are given, they still are not in a good place. We get some other interaction of everyone else's reactions as well as some scenes with Mercedeh and Tommy before everyone comes together at Natasha and Amir's pool party.If you enjoyed this episode please share it with your Bravo friends and follow us on Instagram at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@taglinetwinks⁠⁠

Hello Bravo Bosses! You can watch this episode on YouTube! Today we are breaking down Season 15 Episode 9 of The Real Housewives of Beverly Hills and Season 1 Episode 7 of The Valley Persian Style. Joining me today is Caleigh Rykiss from the In Three Podcast. We have the best most thoughtful chat so check out my conversation with Caleigh! Love you BBs! Follow In Three and Caleigh on Instagram and Listen to In Three! Join the Patreon for $5 a month to get 4 extra episodes a month! ad free episodes! early episodes! and bonus content! Join the fun at patreon.com/thebravoinvestigatorpodcast YouTube  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Threads⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook NOTE: No claims have been verified and all information today is alleged, speculation, and is intended purely just for fun. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Asgaard's founder Tor Martinsen's death sends shockwaves through Wolf Valley but Lena is not convinced it was suicide. As she unravels Asgaard's tangled finances and covert deals with Russian backers, the dark legacy of Valborg Academy overshadows it all. Rose's recovered GoPro deepens the mystery, revealing a final dive, a motor, and a violent impact. And when ten-year-old Oscar Vikstad vanishes into the forest on the eve of a storm, the investigation becomes a race against time.The penultimate episode in a Nordic noir, where shocking crimes and long-simmering feuds threaten a remote mountain valley.LENA - Amrita Acharia AKSEL - David Menkin MAGNUS - Eirik Knutsvik PAUL - Raj Ghatak INGRID - Eva Eklöf HENRIK - Øystein Lode EVA - Ingvild Lakou ROSE - Stephanie MacGaraidh SUSANNA - Ingrid Werner ANNETTE - Sarah Whitehouse VIDAR'S MUM - Eva Eklöf SARA - Ronja Haugholt LENA'S MUM - Ingvild Lakou YOUNG LENA - Mackensie SutherlandAll other parts played by the castWritten by Charlotte Melén Composer - Marcus Aurelius Hjelmborg Singer - Johanne Baadsgaard Lange Sound Design - Louis Blatherwick, Steve Bond Director - Charlotte Melén Producer - Eleanor Mein Assistant Producer - Chloe Sackur Script Consultant - Lauren Shippen Development Producer - Saskia Black Executive Producers - Charlotte Melén, Celia de WolffAn Almost Tangible production for BBC Radio 4

Former Valley AD Brad Rose joins Trent with a look into High School Sports today, plus Cyclone talk with Eugene Rapay

Remember God loves you so much he sent his Son Jesus Christ to take the punishment for your sins. You are of great value. Jesus loves you and He is just a prayer away! 

Welcome back to The Viall Files: Reality Recap!  It's an incredible episode today, as the one and only Valley girl Golnesa "GG" Gharachedaghi joins us to get into the Valley: Persian Style! Meanwhile, we cover the Love Is Blind premiere, Summer House, Below Deck and more. Plus, the following questions are answered: Are Vans slip-ons true to size? Is it embarrassing for your cheating ex boyfriend to have a Bronze Olympic medal? Stay tuned to find out!! "You knuck if you buck…"  The Viall Files is going LIVE with the new cast of Temptation Island on May 6th! Tickets are on sale NOW! For more information, please visit netflixisajokefest.com.  Want ad free episodes and incredible bonus content?  Start your 7 Day Free Trial of Viall Files + here: https://viallfiles.supportingcast.fm/  HEY! YOU! DO YOU NEED DATING AND RELATIONSHIP ADVICE?  Email asknick@theviallfiles.com and be a part of future Ask Nick episodes! Subscribe to The ENVY Media Newsletter Today: https://www.viallfiles.com/newsletter  Listen to Humble Brag with Cynthia Bailey and Crystal Kung Minkoff now!  Listen on Apple: https://podcasts.apple.com/us/podcast/humble-brag-with-crystal-and-cynthia/id1774298881  Listen on Spotify: https://open.spotify.com/show/4NWA8LBk15l2u5tNQqDcOO?si=3b868996930347e8  Watch on YouTube: https://www.youtube.com/@humblebragpod Listen To Disrespectfully with Katie Maloney and Dayna Kathan now! Listen on Apple: https://podcasts.apple.com/us/podcast/disrespectfully/id1516710301 Listen on Spotify: https://open.spotify.com/show/0J6DW1KeDX6SpoVEuQpl7z?si=c35995a56b8d4038 Watch on YouTube: https://www.youtube.com/channel/UCCh8MqSsiGkfJcWhkan0D0w To Order Nick's Book and/or learn more about the show, go to: https://viallfiles.com THANK YOU TO OUR SPONSORS: IQ Bar - IQBAR is offering our special podcast listeners 20% off all IQBAR products—including the Ultimate sampler pack—plus FREE shipping. To get your 20% off, text FILES to 64000. CarGurus - Buy or sell your next car today with Car Gurus at https://cargurus.com  Coop - Let Coop help you show up feeling rejuvenated and ready to go. Get 20% off your first order and try Coop risk-free with a 100-night sleep-better guarantee at https://coopsleepgoods.com/viall  Wayfair - Get organized, refreshed, and back on track this new year for WAY less. Head to https://wayfair.com right now to shop all things home. Betterwild - Right now, Betterwild is offering our listeners up to 40% off your order at https://betterwild.com/viall  To advertise on this podcast please email: ad-sales@libsyn.com or go to: https://advertising.libsyn.com/theviallfiles   Timestamps: 00:00 - Intro 08:35 - Household Headlines 16:45 - Love is Blind 28:40 - Summer House 35:05 - The Valley: Persian Style 39:18 - Below Deck 45:00 - Golnesa Joins 1:11:35 - Outro Episode Socials: @viallfiles @nickviall @nnataliejjoy @gg_golnesa @ciaracrobinson @justinkaphillips @leahgsilberstein @the_mare_bare

How do you begin to disrupt the toxic family patterns you experienced as a child? What do you do with the dissonance between the outward appearance of your life and the reality of what was?For Amy Duggar King, life was lived in two very different worlds. One appeared happy and carefree, marked by regular appearances on her extended family's reality TV show, **19 Kids and Counting**. The other was dark and frightening, shaped by toxic narcissistic patterns and abuse that she and her mother endured at home with her father. As Amy grew older, she began to recognize that the abuse she experienced in her family of origin was not isolated. Some of her cousins—who seemed to be living the peaceful, picture-perfect life she longed for—were navigating their own pain behind the scenes. Over time, Amy came to understand that what meets the eye is not always the full story. Through deep personal processing, she began breaking generational cycles and dismantling toxic family patterns. She shares her story in depth in her book, Holy Disruptor: Shattering the Shiny Facade by Getting Louder with the Truth.In this episode, Davey and Amy discuss what narcissism can look like in families, the lasting effects of spiritual abuse, and what it means to become a Holy Disruptor—someone who courageously breaks cycles and patterns in the light of God's truth.If you've ever wondered how to undo the toxic patterns you grew up in, this conversation will encourage you to shatter the shiny facade and bring holy disruption to the places that need it most. Instagram: https://www.instagram.com/amyrachelleking Book: Holy Disruptor: Shattering the Shiny Facade by Getting Louder with the Truthhttps://amzn.to/46zzh5W Stories matter. They inspire, uplift, and remind us we're not alone in our pain. Hope in the Valley: 42 Days of Healing Through the Psalms After Loss, Grief, and Tragedy is a new devotional featuring real stories from the Nothing Is Wasted community—offering strength, comfort, and hope in life's hardest moments. Order your copy today at: www.nothingiswasted.com/hopeinthevalley Looking for help in navigating the valley of pain and trauma? Our Nothing is Wasted coaches can help: www.nothingiswasted.com/coaching Want a pathway through your pain? The Pain to Purpose Course can lead you through all you've been through: www.nothingiswasted.com/paintoppurpose Learn more about your ad choices. Visit megaphone.fm/adchoices

To follow along, please visit https://dailyliturgy.com.Epiphany - Proverbs 29, 1 Corinthians 8:1-13, Psalm 34Writers: Mike Kresnik, Bob Thune, Darby Whealy, Tyler AndersonNarrators: Charlotte Bertrand, Gary Nebeker, Bob Thune, Darby Whealy, Kevin HuddlestonMusic: Lens Distortions - https://lensdistortions.comProduction: Mike Kresnik, Bethany Gilbert, Zach LeeSources: The Worship Sourcebook; The Valley of Vision; The Book of Common Prayer; + original contributions by the authors.To follow along, please visit https://dailyliturgy.com.

Join Frank for another instalment of Wondrous Lands, a live play series of Earthborne Rangers. This is the first campaign, Lure of the Valley, solo. As this is recorded live, slips and errors are sure to creep in, so please extend me your understanding - I hope you enjoy the journey! If you want more of an explainer of the game, check out the first Wondrous Lands episode, where I offer more of a rules explainer.  You can view the map here, the campaign log here and my deck here. Timestamps: 0.00 intro 2.58 moments on the path / spoilers / couple of small corrections / the elder's book of uncommon wisdom 7.05 a question to the listener  7.39 setup and gameplay begins 27.30 what are aspirations? 37 or so: I fail the Remember test (0 vs 1) and still draw a card, which is wrong - I don't get to draw if I don't pass. It doesn't ruin the day, but definitely would've warped the final parts of it! 50.00 let's go travelling!  1.03.00 I have no clue why I kept in my coughing on the recording - but I can't edit it out without losing explaining what I'm doing. Sorry about that!  1.11.02 she's *not* an atrox. Derp! It's kind of obvious about three seconds later... Amazing logo courtesy of this guy Join Drawn to the Flame on Patreon: www.patreon.com/drawntotheflame Email us on drawntotheflamepodcast@gmail.com | Twitter is here and Facebook is here. Thank you for listening and subscribing.

Valley of Vision | The Upward Call by Benjamin Botkin | The Boulder and the Flume by Ralph Albert Blakelock American | Find more at www.ryanbush.org

Join the guys for some discussion on the wild weekend that was. Going to be a banger, bring your anger.

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

In this episode of the Destigmatize Mental Health: Hope In the Valley series, we are joined by Lorelei Punsalan, DNP, MSN, APRN, FNP-C, CNRN, CHSE, a powerhouse leader in clinical practice and community advocacy.Supported by the MAT Access Grant, this conversation dives deep into the unique challenges faced by the Filipino community in California. Dr. Punsalan brings her extensive expertise as a Doctor of Nursing Practice to explore the vital intersection of cultural identity and substance use recovery. From the weight of "saving face" and cultural stigma to the power of faith-based and family-centered engagement, we discuss how culturally grounded care is the key to breaking barriers in stimulant and opioid use treatment.

Today, say “yes” to the grace of God and “no” to sin—say no to those bad habits and dark thoughts. -------- Thank you for listening! Your support of Joni and Friends helps make this show possible.     Joni and Friends envisions a world where every person with a disability finds hope, dignity, and their place in the body of Christ. Become part of the global movement today at www.joniandfriends.org   Find more encouragement on Instagram, TikTok, Facebook, and YouTube.

From a major "Silver Lining" regarding Corbin Carroll's injury to a high-intensity "Who's Got the Beef?" segment on the Suns' home-court meltdown, Episode 636 analyzes the state of the Valley. We break down Mike Hazen's accountability standard, Jordan Ott's playoff roadmap, and the discipline gap that has Dillon Brooks staring at a suspension.In this episode:00:00 - Welcome15:14 - D-backs: The Carroll Crisis & The Road Back The Update: Andrew Saalfrank speaks to the media and provides his timeline for return to the bullpen.The National Stage: Torey Lovullo joins MLB Network to address the mindset shift in Spring Training and the reality of the Corbin Carroll injury.The Silver Lining: We react to Nolan Arenado's perspective on Carroll's injury—why he believes a proper rehab keeps this from being a season-ending disaster and why this specific injury is more common than fans think.32:30 - WHO'S GOT THE BEEF? The 27-Point Chain Reaction The Beef: I break down why the Suns didn't just lose to the Thunder; they lost because they couldn't close the door on the Mavericks.The "Squint" Factor: We react to Jordan Ott's viral interaction with Duane Rankin regarding the 44-to-9 free throw disparity and how that officiating logic forced a workload that gassed the starters for the OKC blowout.43:20 - Suns: The Post-Break Roadmap Thunder Post-Mortem: A deep dive into the 136-109 loss at home. We look at the "Trust Gap" and the fatigue of losing 4 of the last 5 at the Mortgage Matchup Center.The 27-Game Sprint: Jordan Ott speaks on the opportunity the Suns have post-All-Star break to solidify a playoff spot.The Discipline Gap: Dillon Brooks addresses his 16th technical foul. Unless rescinded, the Suns will be without their enforcer for the Spurs matchup on the 19th. We analyze the cost of "playing on the edge."Follow The Valley Verdict:Facebook: [@thevalleyverdict]Instagram: [@thevalleyverdictpodcast]YouTube: [@thevalleyverdict]

(Feb 12, 2026) People with disabilities and their advocates are gathering in Albany this week to press state lawmakers on key priorities; New York schools are a year away from electric bus requirements, but school officials say it's going to be a challenge; and we get a preview ahead of the Empty Bowls fundraiser in Keene Valley this weekend. 

After 7 officer involved shootings in the Valley in just a week, we talk to the head of the Police Association about the concerns surrounding law enforcement. 

Geoff, Gavin and Andrew talk about did you deserve to live today, Gudge Geoff, the everyman, egg sandwich, chicken sandwich, bottom pillow, Big Mac, top hat, stream starting soon, pleasantries, twitch, doctor, notifications, The Valley of Interest, Andrew's pop filters, thick desk, firmware update, Andrew's chair, food not food draft, plastic covering, thunder nuggets, other wings, Geoff's community interaction, anniversary, Fram, reboot, sitting, and LaZBoy office setup. Support us directly at https://www.patreon.com/TheRegulationPod Stay up to date, get exclusive supplemental content, and connect with other Regulation Listeners. Learn more about your ad choices. Visit megaphone.fm/adchoices

To follow along, please visit https://dailyliturgy.com.Epiphany - Genesis 45:3-15, Matthew 5:38-48, Psalm 17Writers: Mike Kresnik, Bob Thune, Darby Whealy, Tyler AndersonNarrators: Charlotte Bertrand, Gary Nebeker, Bob Thune, Darby Whealy, Kevin HuddlestonMusic: Lens Distortions - https://lensdistortions.comProduction: Mike Kresnik, Bethany Gilbert, Zach LeeSources: The Worship Sourcebook; The Valley of Vision; The Book of Common Prayer; + original contributions by the authors.To follow along, please visit https://dailyliturgy.com.

From the January/February 2026 edition of The Scottish Rite Journal.  Any accompanying photographs or citations for this article can be found in the corresponding print edition.Make sure to like and subscribe to the channel!  Freemasons, make sure you shout out your Lodge, Valley, Chapter or Shrine below!OES, Job's Daughter's, Rainbow, DeMolay?  Drop us a comment too!To learn how to find a lodge near you, visit www.beafreemason.comTo learn more about the Scottish Rite, visit www.scottishrite.orgVisit our YouTube Page: Youtube.com/ScottishRiteMasonsJoin our Lost Media Archive for only $1.99 a month!https://www.youtube.com/channel/UCv-F13FNBaW-buecl7p8cJg/joinVisit our new stores:Bookstore: https://www.srbookstore.myshopify.com/Merch Store: http://www.shopsrgifts.com/

Cover up — your desperation is showing! This week, the girls are exposing your thirstiest confessions after recapping their very polar-opposite vacations. Listeners wrote in with stories that range from unhinged to downright impressive: stalking a love interest, flying across the world just to “accidentally” run into someone, faking sleepwalking for a late-night peek, and even staging serious health scares that end with ambulances being called. If this episode proves one thing, it's that Valley girls will truly stop at nothing to get what they want

The Valley Persian Style Episode 6 We are only 6 episodes in and already getting a bestie fall out between Sky & Tannin! I'm not mad at it. JOIN THE SHE'S SPEAKING PATREON! https://www.patreon.com/shesspeaking SUBSCRIBE TO MY YOUTUBE CHANNEL -  https://www.youtube.com/channel/UCxspMsBruMQjN265ZGNoV1A BUY ME A COFFEE - https://www.buymeacoffee.com/shesspeaking FOLLOW ME ON SOCIAL: @shesspeakingwithemilyhanks Instagram - https://www.instagram.com/shesspeakingwithemilyhanks Threads - https://www.threads.net/@shesspeakingwithemilyhanks Learn more about your ad choices. Visit megaphone.fm/adchoices

To follow along, please visit https://dailyliturgy.com.Epiphany - Jeremiah 17:5-13, 1 Corinthians 6:12-20, Psalm 149Writers: Mike Kresnik, Bob Thune, Darby Whealy, Tyler AndersonNarrators: Charlotte Bertrand, Gary Nebeker, Bob Thune, Darby Whealy, Kevin HuddlestonMusic: Lens Distortions - https://lensdistortions.comProduction: Mike Kresnik, Bethany Gilbert, Zach LeeSources: The Worship Sourcebook; The Valley of Vision; The Book of Common Prayer; + original contributions by the authors.To follow along, please visit https://dailyliturgy.com.