Podcasts about Sams

In this episode, hosts Major DJ Taylor and Major Evelyn Payne sit down with Major General (Ret.) James "Spider" Marks — decorated Army intelligence officer, Georgetown University professor, and CNN military analyst — for a wide-ranging conversation on operational planning, intelligence, and leadership.General Marks draws on over 30 years of service, including his roles as Senior Intelligence Officer during the 1992 LA Riots and Operation Iraqi Freedom, to share hard-won lessons on anticipating threats, bridging the gap between tactical and strategic objectives, and navigating the complexity of modern military operations. He also tackles the growing role of AI on the battlefield, the challenge of controlling the narrative in an information-saturated environment, and what it truly means to keep it simple as a planner and leader.Whether you're a SAMS student, a field grade officer, or a student of strategy, this episode delivers candid, experience-driven insight into the art of operational thinking.Timestamps0:00 — Producer intro / Episode overview1:01 — Show intro & Guest bio (MG Spider Marks)2:27 — Advising operational leaders in a volatile world5:10 — AI, screens, and staying in the loop8:01 — Managing complex organizations and decision-making10:18 — Linking political objectives to tactical goals15:16 — What he'd do differently as a planner17:43 — Understanding the operational environment with limited time (AI, outside experts)22:09 — Formalizing informal relationships25:50 — DSCA & the LA Riots36:28 — How to see around corners today39:20 — Controlling the narrative in the age of instant media42:50 — Who influenced him most46:18 — Parting advice to SAMS students

Col Valerie Sams, MD is an Air Force trauma surgeon, surgical critical care expert, and the Director of the Center for Sustainment of Trauma and Readiness Skills (C-STARS) at the University of Cincinnati. Her path to the operating room was anything but ordinary.   Before medical school, she served as an Air Force line officer in logistics and fuels, learning how the operational side of the service actually works at the flight line. That bilingual fluency in operations and medicine now shapes how she advocates for resources, leads hospitals, and prepares the military health system for the next fight.    In this conversation, she walks through her two tours as the trauma czar at the Bagram role three hospital straight out of fellowship, where she was responsible not only for clinical excellence but for leading every nurse, emergency medicine physician, and surgeon doing trauma care across the theater. She talks honestly about the weight of that role, especially during her second deployment with junior surgeons on their first downrange experience, the rise in U.S. casualties, the green-on-blue threat, and her work standing up Medic-X as a force multiplier for limited deployed medical crews.     Col Sams makes a powerful case for the strategic importance of military-civilian partnerships like C-STARS, the only Air Force critical care air transport advanced training course, and explains how the Air Force, Army, and Navy are converging through the Joint Trauma System, the Mission Zero Act, and the American College of Surgeons Blue Book to professionalize military-civilian integration. She is direct about the skill sustainment crisis inside military treatment facilities, the shift from 65 percent beneficiary care to 20 percent, the urgency of the Military Unique Curriculum, and the need to train outside-the-tent skills deliberately rather than by accident.   Dr. Sams lays out a clear-eyed vision for large-scale combat operations: faster trauma registry feedback loops, autonomous and decision support tools, closed-loop control ventilation, ECMO projected forward, and a hard end to the wax pencil and TCCC card as battlefield documentation. She closes with what should remain the center of gravity for every military medicine decision — the warfighter — and the conviction that they deserve the best clinical care available anywhere in the country.     Chapters (00:47-05:47) From Fuels Officer to Trauma Surgeon (05:47-12:49) Two Tours as Trauma Czar at Bagram (12:49-24:46) ECMO Forward, C-STARS, and the Skill Sustainment Crisis (24:46-35:42) Joint Military-Civilian Integration and the Military Unique Curriculum (35:42-49:26) LSCO Readiness, Force Multiplication, and Battlefield Technology (49:26-58:30) Female Leadership, Clinical Excellence, and Legacy     Chapter Summaries (00:47-05:47) From Fuels Officer to Trauma Surgeon Col Sams describes her unconventional path from Air Force line officer in logistics and fuels to general surgery and trauma fellowship. She credits her operational background with giving her a bilingual fluency between line and medical worlds that strengthens how she advocates for resources, leads hospital operations, and earns credibility with non-medical commanders.   (05:47-12:49) Two Tours as Trauma Czar at Bagram She unpacks the weight of deploying as the trauma czar at the Bagram Role 3 immediately after her fellowship and the lessons that came from leading mass casualty events, debriefing young teams, and dealing with the green-on-blue threat. She explains the stand-up of Medic-X under Lt Gen Hogg as a deliberate force multiplier for limited deployed medical crews.   (12:49-24:46) ECMO Forward, C-STARS, and the Skill Sustainment Crisis Col Sams details her work projecting ECMO capability into austere environments and around the globe, then explains the mission, history, and structure of the three original C-STARS programs. She is direct about the skill sustainment crisis, with beneficiary care in military treatment facilities dropping from roughly 65 percent to 20 percent over two decades.   (24:46-35:42) Joint Military-Civilian Integration and the Military Unique Curriculum She describes the progress driven by the Mission Zero Act, the Joint Trauma System military-civilian work group, and the American College of Surgeons Blue Book. She makes the case for a robust Military Unique Curriculum that develops both surgical fundamentals and the outside-the-tent skills that today's young military surgeons need before they take their first leadership role downrange.   (35:42-49:26) LSCO Readiness, Force Multiplication, and Battlefield Technology Col Sams turns to large-scale combat operations and the blind spots that the counterinsurgency generation may carry into the next fight. She calls for faster trauma registry feedback, autonomous decision support tools, closed-loop ventilation, ECMO projected forward, and a hard end to the TCCC wax pencil as the primary battlefield documentation tool.   (49:26-58:30) Female Leadership, Clinical Excellence, and Legacy She offers candid advice to young female military surgeons on imposter syndrome, unconscious bias, and the discipline of staying clinically excellent. She closes with the conviction that patient-centered leadership, lifelong learning, and protecting clinical talent are the foundations of how military medicine should remember her work.     Take Home Messages Operational Fluency Strengthens Medical Leadership: Time spent on the line side of the military — understanding logistics, fuels, and how the operational force actually fights — builds credibility with non-medical commanders and sharpens advocacy for resources. Surgeons who speak the operational language sit at the right tables and make better decisions for their teams and their patients.   The Trauma Czar Role Demands Leadership Before Stride: Being responsible for an entire theater of combat casualty care immediately after fellowship is a heavy and unforgiving assignment. Clinical excellence is the floor; the real work is leading nurses, emergency medicine physicians, and surgeons through mass casualty events, debriefs, and the green-on-blue threat with junior teammates who have never deployed before.   Skill Sustainment Requires Military-Civilian Partnership: Military treatment facilities now deliver only a fraction of the beneficiary care they once did, and that volume cannot sustain combat-ready trauma teams. Embedded military-civilian partnerships like C-STARS, supported by the Mission Zero Act and the American College of Surgeons Blue Book, are the realistic path to keep wartime skills sharp.   Outside-the-Tent Skills Must Be Deliberately Trained: Today's young military surgeons need more than technical readiness. They need a deliberate Military Unique Curriculum that develops the non-clinical leadership skills required to run a theater trauma system, manage resources, and lead teams under pressure. Picking those skills up on the fly is no longer good enough.   LSCO Will Not Wait on the Wax Pencil: The next fight will not give the medical force three years to figure out what changed or seven years to update clinical practice guidelines. Force multiplication through MedicX, autonomous decision support tools, closed-loop ventilation, ECMO projected forward, and modern battlefield documentation are non-negotiable investments now, before large-scale combat operations force the lesson.   Col Valerie Sams, MD Biography    Colonel Valerie Sams is the Director of the Center for Sustainment of Trauma and Readiness Skills (CSTARS) Cincinnati and serves as Critical Care Air Transport Team (CCAT) Training cadre. Originally from Georgetown, KY, she was commissioned into the Air Force in 2000, initially serving as a supply and logistics officer, which included a deployment supporting Stabilization Forces in the Balkans.    Transitioning to medicine, she earned her medical degree from St. George's University in 2008. Col Sams completed her General Surgery Residency at the University of Tennessee Medical Center (2013) and a Trauma Critical Care fellowship at Brooke Army Medical Center (2015).    As a trauma surgeon and ECMO physician, Col Sams deployed twice as the Trauma Czar for Bagram Airfield, Afghanistan. Her extensive leadership roles include Trauma Medical Director, Assistant Chief of Trauma and Surgical Critical Care, Ground Surgical Team Pilot Unit Leader, and director of various military trauma research programs.   Episode Keywords WarDocs, military medicine, military trauma surgery, combat casualty care, trauma czar, Bagram role three, Air Force trauma surgeon, C-STARS Cincinnati, critical care air transport, CCATT, Joint Trauma System, military civilian partnership, Mission Zero Act, military unique curriculum, large scale combat operations, LSCO, prolonged casualty care, MedicX, ECMO in combat, battlefield documentation, TCCC card, closed loop ventilation, military medical leadership   Hashtags #MilitaryMedicine, #WarDocs, #CombatCasualtyCare, #TraumaSurgery, #JointTraumaSystem, #LSCOReadiness, #CSTARS, #MilCivPartnership   Honoring the Legacy and Preserving the History of Military Medicine    WarDocs exists to honor the legacy of Military Medicine, preserve its history, and inspire every generation — across all Services, Corps, and Ranks — to serve with excellence and pride. Through mentorship, coaching, and education, we equip those considering, entering, and serving in military medicine with the knowledge, connections, and community they need to thrive. We celebrate Who we are, What we do, and, most importantly, How we serve Our Patients, the DoW, and Our Nation. Find out more and join Team WarDocs at https://www.wardocspodcast.com/ Check our list of previous guest episodes at https://www.wardocspodcast.com/our-guests Subscribe and Like our Videos on our YouTube Channel: https://www.youtube.com/@wardocspodcast Listen to the “What We Are For” Episode 47. https://bit.ly/3r87Afm   WarDocs- The Military Medicine Podcast is a Non-Profit, Tax-exempt-501(c)(3) Veteran Run Organization run by volunteers. All donations are tax-deductible and go to honoring and preserving the history, experiences, successes, and lessons learned in Military Medicine. A tax receipt will be sent to you. WARDOCS documents the experiences, contributions, and innovations of all military medicine Services, ranks, and Corps who are affectionately called "Docs" as a sign of respect, trust, and confidence on and off the battlefield, demonstrating dedication to the medical care of fellow comrades in arms.   Follow Us on Social Media Twitter: @wardocspodcast Facebook: WarDocs Podcast Instagram: @wardocspodcast LinkedIn: WarDocs-The Military Medicine Podcast YouTube Channel: https://www.youtube.com/@wardocspodcast  

The springs - also know as Pupu springs - hold some of the clearest water in the world.

Seven clubs. One Europa dream. Zero guarantees. With the season in its final stretch, the Sams break down the most unpredictable European race on the card. Bournemouth, Brighton, and Brentford are making their case loud and clear. Chelsea and Everton? Running out of road.   Arsenal is closing in on the title with City in pursuit, but the Gunners look like a team that knows the moment is theirs. At the other end of the table, West Ham and Spurs are scrambling to survive — and the margin for error is gone.   Off the pitch: Salah is eyeing the exit, and Bruno Fernandes is chasing something historic. The Sams get into all of it.   And to pour out the new era: the Gin Binge begins. First up — Hendrick's. Rose petals, cucumber, and football. Obviously.   Grab a drink and listen   Sunderland 3 - Everton 1 Brentford 2 - Palace 2 Leeds 1 - Brighton 0 Man City 3 - Palace 0 Arsenal 1 - Burnley 0 Villa 4 - Liverpool 2 Newcastle 3 - West Ham 1 Man United 3 - Forest 2 Wolves 1 - Fulham 1     www.Dufootballshow.com Facebook @DUfootballshow Instagram @DUfootballshow TikTok @DUfootballshow YouTube @DUfootballshow Support the bar tab and get extra content: https://www.patreon.com/dufootballshow www.DUdripshack.com 

Scripture: 1 Samuel 7:3-12Resources for a life of following Jesus, every day, everywhere, with everyone.

heres this weeks offering of older classix and new hooj choonas lol share far and wide click on the like button please or leave a comment always nice to hear from you all more next week see you then 1/evelyn champaign king love come down the ezel and dj spen reproduction 2/edwin star contact dr packer remix 3/antonello ferrari tell me 4/adina howard read my mind opolopo remix 5/narada michael walden divine emotions save the robots 6/narada michael walden tonight im alright save the robots 7/evelyn champaign king get loose 8/the ritchie family one and only str rejig 9/ heatwave to hot to handle mike maurro remix 10/heatwave the grooveling mike maurro remix 11/hi gloss youll never know nu soul remix 12/702 star (main) 13/total contrast takes a little time ff edit 14/oddesey inc wayne soul avengerz special love 15/soulista ft tracey hamlin like nobody else 16/michael gray and tatiana owens my world 17/soul 2 soul keep on movin copyright salsa mix 18/the velvet directions midnight groove affair philly soul classic 19/loose ends hanging on a string hylnur solvi remix 20/atlantic starr circles hardsoul mix 21/marchio bosso magic water frank blythe remix 22/tony momrelle fly 23/evelyn champaign king back to love 24/tony momrelle spotlight 25/frankie knuckles whistle song eric kupper remix 26/change hit or miss soul persona remix 27/gap band big fun 28/shalia prospere expectations georgie b remix 29/cerrone new high ft adi oasis 30/memi p feal the rhythm 31/soul dharma flower dave lees boogiefied edit 32/incognito ft maya all for you funkatomic and francis hylton remix

Get the full, ad-free episode here:10 Percent True Memberships10PCT EP87 Marty “Pappy” Brogli – A-10 Desert StormMarty “Pappy” Brogli joins 10 Percent True to tell the story of flying the A-10 Warthog in Operation Desert Storm.From Cold War preparation and the long deployment to the Gulf, to tank killing, SAMs, AAA, and losing friends in combat, Pappy delivers an extraordinarily candid account of what it was really like to fly the Hog in one of the most dangerous air wars in history.This is not the sanitised version of Desert Storm. Pappy talks honestly about fear, humour, squadron culture, survival, morality, and the reality of combat at low level in an aircraft built to take punishment – and sometimes barely survive it.Along the way, he shares remarkable stories of crippled aircraft, manual reversion landings, near disasters, and the unique camaraderie of the A-10 community.If you've ever wondered what Desert Storm looked and felt like through the eyes of an A-10 pilot, this is one you won't want to miss.

Vi skal tilbage til 1500-tallet, til Samsø, til et sted hvor havet ikke bare er natur, men bærer på uforudsigelige ting, der kan dukke op og ændre et menneskes liv. Vi skal tale om 'Rasmus Nielsøn' af Rasmus Daugbjerg. Her møder vi en fæstebonde, der møder en havfrue, der kommer med en profeti. Den er fyldt med historie, eventyrlig mystik og stiller spørgsmål om kærlighed og samhørighed. Velkommen til Poptillæggets Bogklub, en bogklub for dem der vil læse og dem der bare vil lytte. Lone Nikolajsen, journalist og kritiker på Dagbladet Information Benedicte Gui de Thurah Huang, forfatter og kritiker på Politiken Vært: Lucia Odoom.See omnystudio.com/listener for privacy information.

" I also learned the hard way. I did pay the price for several years. I had a lot of injuries, and I was in the stress fracture cycle for seven years. And that was a wake-up call. It really showed me you're not invincible and, and you're gonna have to really learn how to do this differently," shares longtime Lane 9 supporter and community member, Amy Sams.  Amy reached out to Lane 9 last Fall seeking support from a dietitian in our Directory, finally ready to fueling adequately as she approached the California Internation Marathon in December (2025), with the goal to break 2:50 for the first time. And, a little spoiler: She did it!  Amy has spent decades navigating her history with an eating disorder, struggling with fueling and rest, and multiple bone stress injuries. She has run 25 marathons, but now feels like she's really tapping into her potential. Meanwhile, she reminds herself, she's "allowed to be a work in progress." Build your sport healthcare team—support with fueling, injuries, mental health, and medical support— by going to Lane9Project.org/directory Follow Lane 9 on Instagram @Lane9Project, and subscribe to our newsletter at Lane9project.substack.com

In this episode of Bottled Up, Andrew Allen interviews Will Sams, owner and head chef at Pearl restaurant in Fairhope, Alabama. Will Sams shares his journey from working his first deli job in Starkville after Hurricane Katrina, through culinary school at the Culinary Institute of America in New York, to cooking in New Orleans and Orange Beach. He explains how he developed Pearl's seafood-focused menu, sources fresh ingredients from local and regional suppliers, and maintains a family atmosphere at his small but popular restaurant. He also talks about the challenges of building a business, keeping quality high, and retaining employees. The episode wraps up with recommendations for first-time Pearl diners and where to find the restaurant online. 3 Key Listener Takeaways 1. Diverse Culinary Journey and Education   Will Sams shared his path from his beginnings at Bulldog Deli in Starkville after Hurricane Katrina, through finishing his business degree at Southern Miss while working nights in restaurants, to attending and graduating from the Culinary Institute of America in New York. His hands-on experience, including working both front and back of house during school, built the foundation for his later success. 2. The Importance of Adaptability and Understanding Local Markets   Throughout his career, Will Sams highlighted the significance of adapting to customer needs and local demographics. While building menus for restaurants like Playa, he learned to balance creative themes with customer expectations, such as including well-loved staples like burgers even in themed spaces, and always considering what will actually sell. 3. Community and Relationships Drive Business Success   Will Sams emphasized the role of strong relationships, both with local ingredient purveyors and with staff, in the ongoing success of his restaurant, Pearl. He described leveraging existing relationships with suppliers for quality ingredients and running a hands-on, supportive, and family-like work environment to retain employees and provide great customer experiences.  

The Sams are back and the title race just got interesting. City handled business with a clean sheet against Brentford to set the weekend's tone — then Arsenal answered late to keep the pressure on. With the Gunners right on their heels, the race to the top is far from settled.   Meanwhile, the bottom of the table is getting chaotic in the best possible way. Six clubs are scrapping for Champions League spots, and don't sleep on Bournemouth and Brighton — the Cherries and Seagulls are making a serious case for that final berth.   Everyone else? Draws. Lots of draws. Villa and Burnley traded blows in a 2-2 barnburner, Everton and Palace couldn't be separated, and Forest held Newcastle to a point. Sunderland and United played out a snooze fest, and Leeds and Spurs managed to cancel each other out. There were goals — just not necessarily winners.   The crew wraps up in style, finishing off the Whisky Advocate Top 20 countdown right at the top: #1 goes to Elijah Craig 12-Year Barrel Proof Rye. Was it worth the wait? You already know the answer.   All that and more — pour something good and press play.   Man City 3 - Brentford 0 Arsenal 1 - West Ham 0 Bournemouth 1 - Fulham 0 Brighton 3 - Wolverhampton 0 Liverpool 1 - Chelsea 1 Sunderland 0 - Man United 0 Villa 2 - Burnley 2 Everton 2 - Palace 2 Forest 1 - Newcastle 1 Leeds 1 - Spurs 1   www.Dufootballshow.com Facebook @DUfootballshow Instagram @DUfootballshow TikTok @DUfootballshow YouTube @DUfootballshow   Support the bar tab and get extra content: https://www.patreon.com/dufootballshow www.DUdripshack.com 

Scripture: Judges 2:1-15Resources for a life of following Jesus, every day, everywhere, with everyone.

Cain Sams Coaches Show of South Oldham Dragons !! Cain becomes the Member of Primo Status Today! If you don't know what that means, He is FAMILY NOW to the Podcast, the Brotherhood we create is Real! Win or Lose we will always have his back because we know the work and leadership comes from a Place of Love, the true reason why we do what we do. With the addiction of my Guy Brandon Meredith this was a fun episode as we just catch up with how this 2026 season season s looking and touch on what word Cain used to describe the 2025 season. Also Coach Meredith this was fun I MSSED YOU BUBBA! Coach Meredith took last year off and he discusses how he feels being back in the Grind of a high school football season Thank you Cain & Brandon for the Hospitality I truly appreciate the Brotherhood we have created BEST OF LUCK THIS SEASON !!! See you boys Week 4 !! #c2wpnetwork #cleats2whistlepodcast #coachesshow #kentuckyhighschoolfootball #Primostatus

THIS WEEKS PODCAST ( 41 ) BY DEMAND LOL , SORRY ITS A BIT LATE I BEEN DOUBLE BUSY IN THE GARDEN BUT ITS HERE NOW READY FOR YOU TO PLAY/ DOWNLOAD AND SHARE WITH FRINDS , DONT FORGET ANY ISSUES WITH PLAYBACK ON HERE THIS AT GO TO APPLE PODCASTS SEARFCH PAUL SAMS AND BOBS YA UNCKLE MORE NEXT WEEK X 1/mirko and meex seduction 2/block and crown move on up vintage groove 3/funkarama its our prime 4/jamiroquai space cowboy michael grays good vibe zone 5/down to the bone get up and dance 6/glenn jones meet me halfway there 7/liora twani ill find my way again 8/inherit the wind soulful mix remake 9/brooke combe tears wont lie 10/full flava ft donna gardier the glow of love rob hardt remix 11/aint no sunshine dj bones , bill withersv james brown mos def and fresh prince 12/gq make my dream a reality 13/kid creole and the coconuts im a wonderful thing baby mike mfn nu disco edit 14/bridge don't hold your love back chas summers remix 15/mark lower ft mishel ivon light it up 16/mirko and meex I feel love 17/stacey kidd I love you soulful mix 18/boogie fm just you and I 19/ t connection at midnight dr packers pianola dubstramental 20/stevie wonder another star 21/maze that look in your eyes 22/maze you pete le freq refreq 23/jestofunk when the world stood still 24/william bell headline news chas summers remix 24/julian jonah ft sugar rainbow tell me 25/save the robots playing macho man ( chilites changing for you ) remake 26/level 42 something about you str rejig 27/sister sledge thinking about you soulful jazzy house mix 28/i will survive afro soul 29/where you are rohaan paterson/maze mix 30/reel people bsic ft jl love it 31/tony momrelle all the things louie vega remix

Moderní překlad publikace Ajahna Brahma „Word of the Buddha“, kterou Ajahn používá k výuce buddhistických sútt. 00:29 – Čtyři ušlechtilé pravdy: Proč jsme uvězněni v samsáře 01:54 – První ušlechtilá pravda o utrpení (Dukkha) 02:11 – Druhá ušlechtilá pravda o příčině utrpení (Tanhá) 02:27 – Třetí ušlechtilá pravda o zániku utrpení 02:43 – Čtvrtá ušlechtilá pravda o cestě (Osmidílná stezka) 03:01 – Hloubka Dharmy a obtížnost jejího pochopení 04:31 – Brahma Sahampati žádá Buddhu, aby učil 05:01 – Co všechno je utrpení: Od zrození po smrt 09:17 – Pět složek existence (Khandhy)  17:48 – Tři charakteristiky bytí (Aničča, Dukkha, Anattá) 25:37 – Trojí varování: Stáří, nemoc a smrt 27:19 – Koloběh života (Samsára): Nekonečné putování a oceány slz 29:40 – Odpor k samsáře a cesta k vysvobození V roce 1907 vydal německý mnich, ctihodný Ñānatiloka, anglickou verzi knihy Buddhova slova. Je charakterizována jako „Přehled Buddhova učení slovy pālijského kánonu“. Obsahuje výběr autentického učení ze sútt, které pojednávají o hlavním buddhistickém učení čtyř vznešených pravd (včetně vznešené osmidílné stezky).  Více než 25 let používá Ajahn Brahm tuto publikaci jako učebnici k uvedení svých mnišských studentů do problematiky buddhistických sútt. Každý anagárika (adept přijatý k přípravě) a sámanera (začínající mnich) musí absolvovat tento kurz základního buddhistického učení, předtím než je mu uděleno vyšší vysvěcení jako bhikkhu (plně vysvěcený mnich).  Ajahn Brahm vytvořil pro účely výuky svých mnišských adeptů nový typ překladu — ani ne tak pro vědecké experty, spíše pro ty, kteří se chtějí ve svém životě ponořit do tohoto učení. Řídil se bystrou radou profesora A. K. Wardera: „Věta je přirozenou jednotkou sdělení a představuje minimální jednotku, která má přesný, plně vyjádřený význam. Pro účely studia musíme slovům přiřazovat přibližné významy a zařazovat je do slovníků, avšak zobecňující významy slov jsou velmi vágní, zatímco věty poskytují přesnější význam. Při tvorbě překladu lze nalézat ekvivalenty blízké jednotlivým větám, zatímco u jednotlivých slov je to často nemožné.“ Ajahn Brahmavamso ("Adžán"), autor knihy „Kráva, která plakala“, absolvent Univerzity v Cambridge, je buddhistický mnich a duchovní superstar. Přitom stále skromný, laskavý, autentický a vtipný. Žije v malé jeskyni uprostřed kláštera Bodhinyana nedaleko Perthu v Austrálii. Říká o sobě, že je "mnich, který má rád legraci" (fun loving monk). Český překlad: Radan Kuča, 2026 Dabing vytvořen pomocí AI. Originální publikace Ajahna Brahma The Word of the Buddha: https://www.wordofthebuddha.com/ More teachings in English: https://bswa.org/teachings/ Obrázek Buddhy: zdroj pixabay.com, upraveno pomocí AI

His People – 04-28-2026Jonathan Sams on good uses and dangers of artificial intelligence use in the church

What do a brave hobbit and legendary Final Fantasy heroes have in common? More than you might think! Join us as we explore the top 10 Final Fantasy characters who embody the unwavering loyalty and courage of Samwise Gamgee.The Final Fantasy franchise is filled with heroes who remind us of Samwise Gamgee of Lord of the Rings. The steadfast companion who cannot carry the burden but can carry the hero, these are our picks for the Top 10 Sams of FF.

Early bird discounts for the San Francisco World's Fair, the biggest AIE gathering of the year, end today - prices will go up by ~$500 tonight so do please lock in ASAP!From near-universal AI tool adoption inside Shopify to internal systems for ML experimentation, auto-research, customer simulation, and ultra-low-latency search, Mikhail Parakhin joins us for a deep dive into what it actually looks like when a 20-year-old, $200B software company goes all-in on AI. We cover why Shopify has become much more vocal about its internal stack, what changed after the December model-quality inflection, and why the real bottleneck in AI coding is no longer generation, but review, CI/CD, and deployment stability.We also go inside Tangle, Tangent, SimGym, which are three major AI initiatives that Shopify is doing to make experimentation reproducible, optimization automatic, customer behavior simulatable, and search and catalog intelligence faster and cheaper at scale. Along the way, Mikhail explains UCP, Liquid AI, and why token budgets are directionally right but often measured badly, why AI-written code can still increase bugs in production, what makes Shopify's customer simulation defensible, and what he learned from the Sydney era at Bing.We discuss:* Mikhail's path from running a major Microsoft business unit spanning Windows, Edge, Bing, and ads to becoming CTO of Shopify* Why Shopify is talking more publicly about AI now, and why staying at the frontier has become necessary for the company* Shopify's internal AI adoption curve, the December inflection, and why CLI-style tools are rising faster than traditional IDE-based tools* Why Jensen Huang is directionally right on token budgets, but raw token count is still the wrong way to evaluate engineering output* Why the real unlock is not more agents in parallel, but better critique loops, stronger models, and spending more on review than generation* Why AI coding can still lead to more bugs in production even if models write cleaner code on average than humans* Why Shopify built its own PR review flow, and why Mikhail thinks most off-the-shelf review tools miss the point* How PR volume, test failures, and deployment rollback are becoming the real bottlenecks in the agent era* Why Git, pull requests, and CI/CD may need a new metaphor once code is written at machine speed* What Tangle is, and how Shopify uses it to make ML and data workflows reproducible, collaborative, and production-ready from the start* Why Tangle is different from Airflow, and why content-addressed caching creates network effects across teams* What Tangent is, and how Shopify is using auto-research loops to optimize search, themes, prompt compression, storage, and more* Why Tangent is becoming a democratizing tool for PMs and domain experts, not just ML engineers* Why AutoML finally feels real in the LLM era, and where auto-research still falls short today* Why Tangle, Tangent, and SimGym become much more powerful when combined into one system* What SimGym is, why simulated customers only work if you have real historical behavior, and why Shopify's data gives it a moat* How SimGym evolved from comparing A/B variants to telling merchants what to change on a single live storefront to raise conversions* Why customer simulation is so expensive, from multimodal models to browser farms to serving and distillation costs* How Shopify models merchant and buyer trajectories, runs counterfactuals, and thinks about interventions like discounts, campaigns, and notifications* Why category-level behavior is so different across commerce, and why ideas like Chinese Restaurant Processes are showing up again in practice* Shopify's new UCP and catalog work, including runtime product search, bulk lookups, and identity linking* Why Shopify is using Liquid AI, and why Mikhail sees it as the first genuinely competitive non-transformer architecture he has used in practice* Where Liquid already works inside Shopify today, from low-latency query understanding to large-scale catalog and Sidekick Pulse workloads* Whether Liquid could become frontier-scale with enough compute, and why Shopify remains pragmatic and merit-based about model choice* Who Shopify is hiring right now across ML, data science, and distributed databases* The Sydney story at Bing, why its personality was not an accident, and what Mikhail learned from deliberately shaping AI character early onMikhail Parakhin* LinkedIn: https://www.linkedin.com/in/mikhail-parakhin/* X: https://x.com/MParakhinTimestamps00:00:00 Introduction: Mikhail Parakhin, Microsoft, and Shopify00:01:16 Why Shopify Is Talking More About AI00:02:29 Internal AI Adoption at Shopify and the December Inflection00:06:54 Token Budgets, Jensen Huang, and Why Usage Metrics Can Mislead00:10:55 Why Shopify Built Its Own AI PR Review System00:12:38 AI Coding, More Bugs, and the Real Deployment Bottleneck00:14:11 Why Git, PRs, and CI/CD May Need to Change for Agents00:18:24 Tangle: Shopify's Reproducible ML and Data Workflow Engine00:21:19 Why Tangle Is Different from Airflow00:26:14 Tangent: Auto Research for Optimization and Experimentation00:30:07 How Tangent Democratizes Experimentation Beyond ML Engineers00:33:06 The Limits of Auto Research00:36:36 Why Tangle, Tangent, and SimGym Compound Together00:37:20 SimGym: Simulating Customers with Shopify's Historical Data00:42:47 The Infra Behind SimGym00:46:00 Why SimGym Gets Better with Real Customer History00:47:30 Counterfactuals, HSTU, and Modeling Merchant Trajectories00:51:55 CRPs, Clustering, and Category-Level Customer Behavior00:53:30 UCP, Shopify Catalog, and Identity Linking00:55:07 Liquid AI: Why Shopify Uses Non-Transformer Models00:59:13 Real Shopify Use Cases for Liquid01:03:00 Can Liquid Scale into a Frontier Model?01:09:49 Hiring at Shopify: ML, Data Science, and Databases01:10:43 Sydney at Bing: Personality Shaping and AI Character01:13:32 Closing ThoughtsTranscript[00:00:00] swyx: Okay. We're here in the studio, a remote studio, with Mikhail Parakhin, CTO of Shopify. Welcome.[00:00:08] Mikhail Parakhin: Thank you. Welcome.[00:00:10] swyx: I don't even know if I should introduce you as CTO of Shopify. I feel like you have many identities. Uh, you led sort of the, the Bing ML team, I guess, uh, uh, or ads team. I, I don't know, I don't know, uh, you know, it's, uh, people va-variously refer you as like CEO or, or, uh, I don't know what that, that, that said previous role at Microsoft was.[00:00:29] Mikhail Parakhin: Uh, that was... Yeah, my previous role w- at Microsoft was the-- I actually was the CEO of one of Microsoft's business units, which included, as I, you know, as we discussed, all the things that people like to laugh about, uh, including Windows and Edge and Bing and ads and everything.[00:00:47] swyx: Yeah, yeah. What a, what a, what a wild time.You've obviously, uh, done a lot since you landed at Shopify. Uh, one of the reasons I reached out was because you started promoting more sort of internal tooling, uh, primarily Tangle, but also a lot of people have seen and adopted Tobi's QMD, uh, and obviously, I think, uh, Shopify has always been sort of leading in terms of, uh, engineering.I think more-- it's just more recent that you guys have been more vocal about your sort of AI adoption. Is that, is that true?[00:01:16] Mikhail Parakhin: Well, I think AI tools in general are fairly recent development, uh, and we've-- Shopify, you know, at this stage of its development, we're developing AI in-in-house and other, uh, building tools that use AI and, you know, interfacing with the wider AI community, uh, you know, are on the sort of the, uh, runaway trajectory.So it just did by sort of natural byproduct. We, we talk about it more also. We just, uh, just even yesterday, Andrej Karpathy was famous in tweeting about, oh, are there some, uh, ways, uh, that, that you can organize your agents to store the data and then, uh, look up the data so that you don't have to research or, or lose context every- Yestime. And a little bit tongue in cheek, I tweeted that, “Hey, we've, we've done it much earlier, and we even have different approaches, Tobi and I.” Tobi, of course, is a big fan of QMD, and I'm more of a SQL, SQLite fan. But, uh, yeah, very similar things that we've already done here. The point is, yeah, we're very dynamic, you know, explosively growing company, and we have to be at the forefront of AI adoption, obviously.[00:02:29] swyx: Yeah. Yeah. Um, you, your team kindly prepared some slides actually that we were gonna bring up on to, uh, the screen. I think I can, I can screen share, and then we can kind of go through some of the shocking stats that maybe, maybe put some numbers to what exactly is going on. So here we have, uh- An internal AI tool adoption chart.What are we looking at here? What ?[00:02:54] Mikhail Parakhin: Yeah, this is very interesting statistics. Uh, this is number of daily active workers, you know, think of, uh, DAO, basically the active users of-[00:03:05] swyx: Yeah ...[00:03:05] Mikhail Parakhin: AI tool as a percentage of all the people in the company, right? And then- Yeah ... different AI tools. And, uh, you could see two things here is that one is the green is total.Uh, green is just total. So you could see that it approaches really % by now. It's hard not to do your job now without interacting deeply, at least with one tool. You could see another interesting thing is just as many people commented in December was the phase transition when suddenly models gotten good enough that, that everything took off and started growing.Uh, it, it was many people noticed that the thing is that small improvements accumulated into this big change in Sep- December roughly timeframe.[00:03:52] swyx: Yeah.[00:03:52] Mikhail Parakhin: The other thing I would claim you could see is that, uh, CLI-based tools and tools that don't require you to look at the code becoming more popular, and you could see, yeah, various versions of, uh, Cloud Code and Codex and Pi and internal development tools taking off.Uh, exactly, yeah, uh, and blue is our River, just internal agent for coding, where tools, uh, that require IDEs such as, uh, GitHub, Copilot or Cursor, they're not exactly shrinking, but they're not growing as fast. Like, uh, red, red line is, is the IDE kind of tools. So you could see that they're, they're not experiencing as, as fast of a growth.[00:04:37] swyx: As I understand it, basically, every employee has their choice, right? Of choose whatever tool you use, and then you're just kind of doing a, a daily sur-survey or something.[00:04:47] Mikhail Parakhin: Exactly. And, uh, we- Yeah ... the, the push is to get your job done, you can use any tool, and we effectively fund unlimited tokens for everybody.Uh, we, we do, we do try to control the models that, uh, people use, but from the bottom, not from top. Like we basically say, “Hey, please don't use anything less than Opus four point six.”[00:05:09] swyx: Oh .[00:05:10] Mikhail Parakhin: Some people, some people end up using GPT five point four extra high. Some people use Opus four point six. Um, uh, you know, uh, there are some, uh, there are plus and minuses in going for full one million context window versus not.But, uh, we try to discourage people from using anything less than that.[00:05:28] swyx: Yeah, yeah. Got it, got it. Uh, I mean, uh, that's, you know... The, the next chart here, it really kind of shows the expansion and the sort of December twenty twenty-five inflection, right? That, uh, people are using a lot of tokens. I think it's also really interesting that no one was kind of abusing it in twenty twenty-five.Like it was- Had comparatively, uh, to this year, there was almost no growth. I mean, it's still like, you know, probably, probably gave fifty percent.[00:05:56] Mikhail Parakhin: Yeah. This is just a different scale. It's still exponential- Yeah, yeah ...growth at just a different- ...rate of expansion. Uh, there was inflection point, and Sean, I would claim the, the super interesting part here is that you could see that the distribution becoming more and more skewed.Yes. The top percentiles grow faster. So that means- Yeah ...the people in the top ten percentile, they, their consumption grows faster than seventy-five and so forth. So, uh, the distribution skews more and more towards the highest users, which is... I don't know what it tells me. It's like it feels not ideal, to be honest.Or maybe it's okay. We'll see.[00:06:36] swyx: Why does it feel not ideal? Is, is it because of, um, quantity over quality, or what's the concern?[00:06:42] Mikhail Parakhin: Because take it to the limit. That means, you know, if, if this rate of separation continued- Ah, yes ...a year, there will be one person consuming all the tokens. So it's just, it's kinda strange.[00:06:54] swyx: Yeah, I mean, um, uh, I, I think internal like teaching and all that, uh, will, will help sort of distribute things more widely. But in, in the early days, of course, the people who are sort of more AI-pilled will obviously find more ways to use it than the people who are less AI-pilled. Maybe let's, let's call it that.I'll just, I'll just kinda quickly, uh, pause from the, the... You know, we will go back to the rest of the slides, but I just wanna, um, review, you know, there are a lot of CTOs of, of large companies like yourself where they're all considering some kind of token budget, right? Like I think it's something, something that Jensen Huang has been talking about, where like if your 200K engineer is not using 100K of tokens every year, like they're, they're underutilizing coding agents.Of course, Jensen Huang would say that, but like it seems a very quantity over quality approach and like some, some people are basically saying like, well, is this comparable to judging engineer quality by lines of code, right? Which we also know is like kind of flawed, but better than nothing. So I, I don't know if you have like a sort of management take here on, on how to view this kind of, uh, metrics.[00:08:02] Mikhail Parakhin: Well, I mean, you're, you're baiting me. I, I like... This is my favorite topic. Uh, if you let me, I'll probably talk for two hours on just this. I have a lot of things to say. Like I do think Jensen gotten a lot of bad press saying, “Oh, of course you're, you know, this, uh, the- ...the cake seller says you don't need enough cakes.”You know? Like, of course. Uh, but, uh, I actually, uh, think that's undeserved. I think he, he's actually right. Uh, I do think- He,[00:08:33] swyx: he's directionally correct.[00:08:35] Mikhail Parakhin: Yeah. Yeah. He's directionally correct for sure. Uh-[00:08:37] swyx: Who knows what the right number is? Yeah.[00:08:39] Mikhail Parakhin: The thing that I do Uh, want to say, and this is something that we learned through trial and error and very important is like two things.One is that it's not about just consuming tokens. Uh, you can consume tokens and, and in fact, the anti-pattern is running multiple agents, too many agents in parallel that don't communicate with each other. That's almost useless, uh, compared to just fewer agents and burns tokens very efficiently. Uh, setting up the right critique loop, especially with the high quality models, where one agent does something, the other one, ideally with a different model, critiques it, uh, suggests ways to improve it, the agent redoes it with this critique and, and so it takes much longer.So people don't like it because latency goes up. You know, they, they have to wait until this debate is happening. But, uh, the quality of the code is much higher. And another thing, just since you mentioned like, look, uh, uh, yeah, the overall budget is just like, uh, lines of codes. Lines of codes are exploding for everybody right now, or partially because AI is really mover balls, but partially just because AI can write a lot more code, you know, doesn't get tired.And so you have to have to have a very strong narrow waist during PR review. Otherwise, just the number of bugs will go through the roof. It's, uh, it's this unexpected consequence of the just volume trumping everything. I would claim by now good model writes code on average with fewer bugs than, than the average human.But since they write so much more of it, like more of it will make it into production. So you have to- You still[00:10:26] swyx: have[00:10:26] Mikhail Parakhin: more bugs. Yeah. Have to have a very rigorous PR reviews, also automated of course. But, uh, yeah, that to spend a lot budget there. Like this, this for me, for me, actually, the important metric is the ratio of budget spent during code generation versus, uh, spent, uh, expensive tokens like GPT, uh, five point four Pro or, uh, uh, Deep Think from Gemini, you know, checking on PR reviews.[00:10:55] swyx: Yeah, totally. Uh, I noticed in your chart you didn't have any review tools. Do you just use like, like let's say a Claude code to review tools? Or do you have another set of review tools like the Greptiles, the Code Rabbits, uh, Devin Reviews has a review tool. I don't know if you've had those specialist review tools.[00:11:13] Mikhail Parakhin: You are a little bit jumping on my store tool right now because the graphs I was only showing public tools. Uh, uh, the-- I haven't found a good PR review tool that, that does what I think should be done. And, uh, partially my, my thinking is because it's so... It just goes against both what people feel like emotionally they prefer and, uh, some of the, uh, you know, frankly Even business models that, that the companies run.At peer review tool, uh, time, you want to run the largest models. That means, I don't know, Codex or, or, uh, Cloud Code is not gonna cut it. You need to have pro-level models if you really want to, uh, stand the tide of bots from going into production. And you need us to spend a lot of time, the models taking turns, but you don't want, like, a big swarm of, uh, of, uh, agents.So in fact, you end up in a different dual-dualistic world where you generate not that many tokens. You, in fact, generate few tokens, but it takes f-a long time because these are expensive models taking turns rather than many, many agents trying to do many things in parallel. So that's, that's why I feel like I haven't found good tools, so we are using our own for peer review for now.[00:12:33] swyx: Yeah. Yeah. I mean, uh, I think a lot of companies are building their own, uh, especially to their needs, right?[00:12:38] Mikhail Parakhin: Mm-hmm.[00:12:38] swyx: Um, I, uh, you also have a chart here going back to the slides on, uh, PR merge growth, where we're now at thirty percent, uh, month on month rather than ten percent. Uh, and also the, the estimated complexity is going up.You know, this is productivity, right? ‘Cause y- presumably there's more stuff going into the code base and more, more features getting worked on. I'm curious about the backlog, right? Like the, the, the-- I actually don't mind a pro-level model taking an hour or two hours to review my PR, because I've dealt with humans who take a week to review my PR, right?And I keep pinging them on Slack, “Hey, hey, review my PR.” So, you know, I think there's some trade-off here where, like, it still doesn't make sense.[00:13:18] Mikhail Parakhin: Exactly. That, that's exactly m-my point. Uh, that on one hand, you can tolerate longer latencies at, uh, PR. On the other hand, like right now, the real problem is not in spending time waiting for PR.It's real problem is since there's so much more code than- Yeah ... uh, probability of at least some tests failing going up, and then you, like, keep de-failing, then you have to find the offending PR, evict it, retest it without that PR, and so deployment cycle becomes much longer. Uh, so it actually, in terms of the overall time to deploy, it's total time savings if you spend more time on a longer model, like thinking for an hour, because then, then you, you don't have to spend all that time during testing and rolling, you know, rolling back the deployment.[00:14:03] swyx: Yeah, totally. That's still worth it. You know, you don't look at the individual, look at the aggregate, and look at the, the, the change in the aggregate system.[00:14:11] Mikhail Parakhin: Exactly.[00:14:11] swyx: I'm kind of curious if, like, there's this PR mentality and, like, c-- the, the, the CICD paradigm will be changed eventually. Some people are like, obviously a lot of people want new GitHub, but I even wonder if, like, Git is the problem, right?Like, is that the bottleneck? Is the concept of a PR a bottleneck? Do you guys use stack diffs? I don't know if, uh, that's a, like, a merge queue stack diff type of thing.[00:14:34] Mikhail Parakhin: We, we use, we use Stacks, we u- we use Graphite. We worked with, uh, Graphite a lot. Uh, so we use Stack, uh, PRs. I think, uh, like that's clearly the overall CICD in general, and the interaction with the code repository right now is the, clearly the sort of the, the main issue and the bottleneck for us, uh, and highest top of mind.I would say we probably need a different metaphor or different whole design of how to process it in new agentic world. I haven't seen anything dramatically better yet. I, I think everybody right now is just trying to keep their head above the water ‘cause, ‘cause there, there's so many PRs and then everybody's CICD pipelines start creaking, the, the times are increasing, the number of bugs slipping by increasing, and you have to, have to clap on down.And so we are a little bit in this situation when we need to first stabilize that story and then start thinking, hey, what, what it could be a completely different and new world, which I haven't... I know some people working on it. I haven't seen something, like anything super compelling yet, but clearly the old thing were designed for humans will need to be morphed into something new.[00:15:53] swyx: One of the thing that I, I think about is kind of like the merge conflict is basically a global mutex on the whole system, right? And in, in hu- in human organizations, we do have something like that. It's the company standup. But like, other than that, it's like it's actually fitting for us to be somewhat decentralized, somewhat plugged into one stream of information source, but somewhat lossy.Like it's okay, you know, that, that not every delivery is like atomic consistency. Like we're not dealing with a database sometimes.[00:16:27] Mikhail Parakhin: This is a very good point, uh, because since humans don't write code too fast, you know that global mutex is not too bad. Once you-[00:16:36] swyx: Yes ...[00:16:37] Mikhail Parakhin: start writing code at the speed of machine, it becomes the, you know, the bottleneck.Then what do you do? Maybe, and I can't believe I'm saying this because I, I'm long-- lifelong opponent of, uh, microservices, and I always thought that was, like, a really bad idea. And now that you're saying it, like, maybe in new guys like microservices will make a comeback, you know, because then you, you can ship things independently in tiny things and, and the managing all that complexity automatically will be much easier.I don't know. Like, we'll s-- we'll have to see.[00:17:10] swyx: Yeah. I mean, I don't know what the Microsoft or, or Shopify thing is, but I, I read this paper from Google where they have a monorepo that deploys into microservices, right? And then, uh, the other concept that I think about a lot is the Chaos Monkey concept from, from Netflix.Being able to create, like, this robust system where, um, uh, you know, you, you have the service discovery, you have the, uh, the independent, independent microservices discovery and, and, uh, you know, probably going to be a fair amount of duplication. That's how an organic system sort of scales, uh, that, that you have that...I don't know how you call it. Slack? Robustness? Depend-- uh, d-duplication. I, I, I forget the-- I, I'm-- And this-- those-- these are not exactly the terms- Hmm ... I'm looking for, but I c-can't really think of the words. Okay. I was gonna go into Tangent and Tangle. Uh, so, uh, we, we sort of discussed the overall stats that, uh, Shopify has.Uh, but, you know, I, I think some, some pretty cool stuff that you guys are working on is your ML experimentation, uh, and your, your sort of auto tr-research training pipeline. Presumably you're much closer to this one because it's, it's a sort of personal hobby of yours. How, how would you explain them in, together?I thought we have a slide that, like, uh, has the s- the system diagram.[00:18:24] Mikhail Parakhin: Yeah. Tangle first and then Tangent as a-[00:18:27] swyx: Yeah ...[00:18:28] Mikhail Parakhin: as a thing on top of Tangle. And, uh, Tangle is the third generation, I claim, of, uh, systems of, uh, running any data processing, but a bit with a skew for ML experiments, but not necessarily. Any sort of data processing tasks where you need to iterate, share, and you have scale so that you want maximum efficiency.You know how, like, normally you would work, you would-- Imagine you're a data scientist or an ML practitioner, you would get Jupiter notebooks or, or maybe you would get, uh, you know, Pyth- your Python scripts, and you would manage the data, and you produce those TSV files, and you put them in some JFS or something.Then you would notice that, oh, it has this, uh, weird missing values. You go and write another script that, uh, goes and replaces them with, uh-[00:19:20] swyx: Ah ...[00:19:21] Mikhail Parakhin: dash S. And then, then you, then you run some, some, uh, “Oh, I need to filter bots.” And so you run some light GBM model that, uh, removes the bots. And then, then you like-- And then you, you kind of like get into shape, and then you start experimenting, and you run multiple experiments, and then you're like, “Oh my God,” like, “this experiment is worse.”You undo, and you cannot get to previous result. And like, “Ah, what did I do?” Like that. Again, then, then you finally like get everything working. Then you like start throwing it over the fence to production. You, you replicate it, those things don't work, and then sometimes you like don't notice that you forgot some feature naming and the, the features don't match.But then, like imagine you, you did everything, and then six months later you're like, have to repeat it because now there's more data, or you wanted to do another pass, and you're like, “What, what did I do?” Or like, or like, “This script crashes now,” or the, “the path has changed.” And then, then you're trying to, like you spend another month just doing ar- digital archeology on your own, you know, history, right?Now multiply that by many, many teams. Now imagine you got an intern that you wanna ramp up. Now you have to show that intern, “Oh, you know, look, here's the folder, there's the scripts, you know, ask your cloud agent to do, and then, uh, to, to figure it out.” And then cloud agent does something, and then you're, “Ah, yeah, right, right, it was the wrong folder.I forgot to tell you, I actually have this other thing I forgot myself.” And, and that's, that's the, like, the daily life we all, uh, all know it, uh, if, if you're a data scientist, machine practitioner, ma- machine learning practitioner or, uh, or even like any data managing, uh, person.[00:21:00] swyx: Yeah. So I, I used to do this, uh, f- uh, on the quant finance side, uh, in, in my hedge fund.So we did this before Airflow, and then, uh, obviously Airflow came along and, uh, then more recently Dagster, uh, I would say is like, in my mind, what I would use for that shape of problem, uh, where you had to materialize assets and create a pipeline.[00:21:19] Mikhail Parakhin: And that's, that's very good segue because... So Airflow is great, but Airflow is more about you, you have something and you wanna repeatedly run it in production on schedule.It's less about you as a team developing things and being able to share, and you grabbing the standard pipeline and saying, “Hey, I wanna change this tiny little component in the huge sea of data processing, and I don't wanna-- I wanna run ten experiments on this, and I wanna do hyperparameter optimization.”All that is very hard to do with Airflow. It's very easy to do with Tango. Tango is m- more about, it's everything about group of people Running experiments, it might be agents too nowadays. Uh, running experiments cheaply, collaborating, sharing results. Uh, you don't need to understand fully. You, you grab-- you clone somebody else's experiment or somebody else's pipeline, uh, run, uh, change small piece, run it, be, like, get it to production state, and then ship in one click.So then the... You don't have to port it into any other system to, to run in production. You can just run the same experiment. It's, it's fully production ready. And, and it's, uh, it has lots of... Again, as I said, it's third generation system. The original one was, I would claim there was Ether and then, uh, at least in my career, Ether was the first, first, uh, that pioneered this type of approach.And then there was, uh, Nirvana, which, uh, uh, at Yandex, which did kind of sec-second take on this. And now this one aggregates the, the learnings from all of those and, and Airflow as well to, to get to the state where you try it, it, it feels kind of magical. Uh, ‘cause now everything is based on content, uh, hashes.So even if the version changed, but if the output didn't change, nothing is being rerun. It's very efficient. If you... Multiple people start experiment that needs the same sort of data preprocessing, it's not repeated multiple times. It's automatically done only once. If you start ten experiments that all require, you know, some, some data preparation first as the first step, and you don't have to coordinate for that.Like, you don't have to know that other people are starting it. You now, it's very easy compos-, uh, composability, any language you can u- uh, you wanna use, and it's very visual. So you can see immediately, you can edit it easily, you can assemble small things with just even mouse clicks if you want to, and, uh, share, clone.And everybody knows also it's fully kind of static in the sense that we rerun it second time, it will exactly have the same results. Like, you will never have to do digital archeology. So full versioning and everything is also there.[00:24:06] swyx: Uh, so, so people can, uh... It's open source. Go to the GitHub repo and, and, uh, check it out.Uh, and it is also a really good, uh, blog post about it. I think all these is, like, really appealing. The, the, the, the thing that I think sells me the most about it is that, um, sort of development to production transition, right? Which I think, um, a lot of people haven't really solved that, uh, strictly, right?Like, we develop really, really well in, in Python notebooks, but then, you know, that's obviously not a sort of production ready process. I think that, like, any way in which that is solved, I think is, is very appealing. Then the other thing that you mentioned, which also raised my eyebrows, was content-based caching, which you mentioned is, is, um, you know, is ve-very much, uh, um, a sort of efficiency measure about, uh, you know, just like recalculation only on, on sort of content addressing Which I think makes sense.Uh, it surprised me that the savings could be this much, but maybe I just haven't worked at your scale where there's so much duplication, uh, that people just rerun because they change a single ID upstream.[00:25:10] Mikhail Parakhin: It does, yeah. But it's not only you rerun. The, the main savings are coming from the fact that you ran it, you got your job done, and you moved on.Then- Yeah ... somebody else in some department you don't know existed runs the same task, but on a newer version.[00:25:27] swyx: Yeah.[00:25:27] Mikhail Parakhin: Like right now, you can't, in, in most of the organizations, you can't even find out about it so that you can't even measure that you're spending that time twice, right? Here- Yeah ... if everybody's on Tango, that's detected automatically and detected that the output is the same.And then for that person, all it looks like is like experiment just suddenly moved, jumped forward, right? Uh, uh- Yeah ... so that's because, because the, there's network effect of multiple people helping each other.[00:25:51] swyx: Yeah. This is one of those things where it's designed to be a platform from the beginning rather than an individual developer's tool from the beginning, right?And, and everything's gonna streams down from there. That is the sort of Tango, uh, orchestrator, and it's, it manages jobs. We've seen a few versions of this, and this is obviously, uh, uh, the sort of, uh, unique approaches that you guys have, have, uh, figured out. And then there's Tangent.[00:26:14] Mikhail Parakhin: Yeah. And Tangent is basically an automatic auto research loop that can help and kind of do your work for you.Uh- ... you know, uh, effectively, effectively, Andrej Karpathy recently popularized it with auto research. Yes. Remember he said like he was, uh, speed running this, uh... Yeah, uh, you know the story. The, here we're basically bringing the same capability into Tango so that, uh, the, uh, Tangent can analyze it. It's just an agent that can run multiple experiments, figure out what can be changed, and keep on rerunning it, keep on modifying until, uh, maximizing some goal, some loss function, whatever you need to, to achieve.And in general, I would say if you're not using auto research-like approach in whatever you do, like literally whatever you do, then you're missing out. We saw at Shopify that taking like a wildfire, anything where you can put measurements can be done dramatically better. Our-[00:27:19] swyx: Mm-hmm ...[00:27:20] Mikhail Parakhin: uh, speed of, uh, templatization HTML, uh, completely new UX tem- uh, templatization of, uh, reducing latency for liquid themes.Uh, we-- Our, uh, search, uh, recently we moved from It's hard even, uh, quote from eight hundred QPS to forty-two hundred QPS with the same quality just by pure optimizations and not a research loop that kept running and changing code in our index serve on the same number of machines, just increasing the throughput.We, we managed to improve the quality of gisting and machine learning process. Uh, you know, gisting is the prompt compression technique that[00:27:59] swyx: allows for[00:28:00] Mikhail Parakhin: lower latency and, and lower and, uh, actually higher quality slightly. So like literally whatever different walks of life, and it doesn't have to be AI related.Uh, we, we had a reduction in, uh, storage because the agents would go and find data sets that clearly are derivative, uh, and then you don't need to store things twice. You know, we, we, we found somewhat embarrassingly that it was one of the largest tables was hashing random IDs into another random ID, and we literally- Oofput only one. So it was translating, yeah, two random IDs hashed[00:28:36] swyx: into[00:28:37] Mikhail Parakhin: each. So, so[00:28:37] swyx: it has access to the code as well, so it can, it can check the, like what, what the hell is it doing?[00:28:42] Mikhail Parakhin: So there, there cou- it could be run in two levels. You, uh, you know, at the superficial level, it could just use ex-existing components and, uh, reshuffle them.Uh, you know, like you can grab- Yeah ... uh, XGBoost, and you can grab some, some Py- PyTorch module, and then can grab some, you know, grab another tools and, and combine them. At a deeper level, since Tangle is all sort of CLI based underneath you, every, every component is a wrapped really CLI, uh, call and a YAML file, it can analyze code and create new components and, and, uh, keep on iterating as well.So, so you can, you can both have quick modifications of existing t- uh, pipelines with the, with components that are already there pre-baked, or you can create new components, uh, and-[00:29:29] swyx: Yeah ...[00:29:29] Mikhail Parakhin: keep iterating on those. So auto research is, again, this is probably the, the thing I was excited the most in the last two months happening, and we see it taking like, like totally like a wildfire.Just, uh, everybody, every day, every... well, every day, every minute, I would, uh, have somebody Slack message saying, “Oh, look how much better I made it.” And, uh, it's all throughout the research.[00:29:53] swyx: Is this democratized in some way in, in the sense that like is it your ML, uh, engineers and researchers doing this, or is it your regular PMs and software engineers also have the ability to auto-- to use Tangent?[00:30:07] Mikhail Parakhin: This is an awesome question. Like, Tango in general and Tangent in particular are extremely democratizing. Like they- Yeah ... they are the main tools for- ‘Cause I don't[00:30:15] swyx: need the details.[00:30:16] Mikhail Parakhin: Yeah. Exactly. Initially used by ML and AI engineers, but then literally, as you said, PMs are like the highest user right now is one of PMs on our org, uh, Sartak and he was, he was number one by, by usage of, of this ‘cause they're just, uh, energetic and knowledgeable, and now it, it unlocks a lot of capability where you don't have to co-change code manually.[00:30:39] swyx: I mean, I mean, because it kind of cuts out the ML, ML engineer from the process because the, the, the PMs have the domain knowledge and the ability to think about, uh, from first principles about, okay, what, what results do I want? And they can-- they even have the access to the data that, that needs to go in.So it's like in some ways, like this is the magic black box that we've always wanted for, for training and, and for, uh, I guess, uh, uh, hill climbing, whatever.[00:31:04] Mikhail Parakhin: It's basically cloud code for your AI development- ... uh, situation, right? Like now, now you don't have to know exactly how algorithms work. You can just, uh, bring your domain knowledge and expertise and product knowledge and iterate within Tangent until you've gotten the results that you need.[00:31:21] swyx: In my previous roles, every time that someone has pitched AutoML, you know, I've always been like, “Uh, this is not, this is not gonna work. It's, you know, it's, it's always gonna be a flop.” Somehow it's working now. I mean, presumably the answer is now we have LLMs and it's good enough, right? It's, it's an emergent property that we can do auto research, but like, it doesn't feel that satisfying that how come we didn't do this before, right?Like we just did like parameter search and like, I don't know. That's maybe that's it.[00:31:48] Mikhail Parakhin: Yeah. Bayesian optimization and hyperparameter optimization was, was the one that, or facet of AutoML that was used very actively, which incidentally also built into, uh, Tango. But, you know, I know Patrice Simard very well, and, uh, he was such a, uh, such a proponent of AutoML, and he put, like literally spent careers trying to democratize it.Without LLMs, it just turned out to be very hard. Like it, you, you would have flexibility within certain narrow domain, but it was hard to wider scale, and now with LLMs suddenly it's like magic wand, and so suddenly everybody- ... is an AutoML expert.[00:32:28] swyx: Yeah, I, I think it's multiple things, right? Like I'm, I'm just gonna bring up the, the, the chart again, right?Like LLMs can do the monitoring very well. That is the very potentially unbounded, super unstructured. It can do the analysis very well, it can do the... Uh, and basically it is much more intelligence poured into every single step. Uh, there's maybe nothing structurally changed about AutoML, but this is just m-more intelligent and more unstructured.[00:32:53] Mikhail Parakhin: Exactly.[00:32:54] swyx: Any flaws that you've run into? Like everyone is like drinking the Kool-Aid, oh my God, time savings, uh, you know, performance improvements. Like what, what, uh, issues have you have, uh, come up?[00:33:06] Mikhail Parakhin: This is really cool. It's not a solution to all the world's problems for sure. The limitations are usually the ones I-- And this is where we get into a bit of a subjective territory.Uh, I can only share what I've, I've seen so far, and I'm sure the situation, uh, is changing, and, you know, maybe after I say it, like many people will reach out and say, “Hey, what about this?” And you don't know that, and then, then we'll be probably right. But what I've seen is auto research is very good at doing kind of obvious things that you don't have bandwidth to do or you didn't notice or maybe you're not aware of like the-- some standard practices.It is not good at doing something completely out of distribution, something that, you know, you have to think for, for multiple days, uh, and, and do something like none of this. So, so it's, uh, I, uh, set an experiment once, uh, on, on my sort of, uh, hobby thing, and I let it run for, uh, ended up, uh, several weeks run, uh, you know, it's like full production kind of scale, so it, you know, slow runs and, and it ex-- it performed in the end, uh, over four hundred experiments, and only one was successful.I'm like, “Okay, that's, that's good.” But-[00:34:18] swyx: But it saved time.[00:34:19] Mikhail Parakhin: Yeah, I saved time. Like it, it was the, that thing. Yeah, if I, if I were doing four hundred experiments myself, my betting average, as I said, would have been much higher, I'm sure. But also, first of all, it would take me like three years to do four hundred experiments.And, uh, I didn't have to do them. Like the machines were just, uh, the price of electricity did that. So, and I got one improvement, uh, that in, uh, my, my-- Honestly, when I was starting that experiment, my thinking was to go and show that, “Hey, Andre, maybe you just don't know how to optimize.” And I was super smart because in, in my pro-problem, it was optimized for many years, and it was like fully improved.Uh, and I didn't expect it, you know, auto research to find anything at all. Yet it did. So instead of making fun of Andre, I ended up, uh, a big, big supporter. Yeah, that's exactly the tweet. Yes.[00:35:10] swyx: You and Toby really, really go back and forth on-online a lot, which is really funny. Uh, think of it as, as an eval for the optimalness of the code it's running on.Uh, it's almost like it reminds me of like a Kolmogorov complexity thing, but, uh, I guess it's-- there's some optimal thing that you're trying to sort of reduce down to, I guess. Um, and so, so you, you, you know, you should congratulate yourself that you had, uh, you know, uh, ninety-nine percent, uh, optimality.[00:35:36] Mikhail Parakhin: Exactly, yeah. I think Andre really deserves a lot of credit for popularizing this approach. This is, uh, this is incredibly, I think, powerful and cool and You know, the, uh, even him, him just mentioning it led to a lot of gains in a lot of places in the industry, so we should be thankful.[00:35:56] swyx: Yeah. I think he also has a just...I don't know what it is. Like, um, you know, it, it is a simple self-contained project that people can take and apply to other things, which is, is, is one thing, but also just the name. Just like somehow no one, no one managed to call their thing auto research. It's just naming things is very important. I think that that is mostly, uh, our coverage of Tango and, and, uh, Tangents.I think obviously, you know, there's a lot of, uh, ML infra at, at Shopify that people can, uh, dive into. We're about to go into SimGym, but before I do that, any, any other sort of broader comments around this whole effort? Like where is it, where is it leading to?[00:36:36] Mikhail Parakhin: As a segue to SimGym, like all those things start composing strongly.And, uh, you could see a huge unlock when you can look at each one of the tools and, and you see, oh, they're extremely useful. Uh, Tango is useful by itself. Auto Research is useful by itself. SimGym is useful by itself. If you combine all three, you create like synergetic effect. I think that's why we wanted to even, uh, cover them today is because this is something that if you go back even, you know, five years ago, would've been unthinkable.Uh, replicating that, uh, would, would be either incredibly costly or impossible, right? With probably thousands of people are required.[00:37:20] swyx: Well, we have serverless human, uh, serverless intelligence, right? Like, uh, so yes, you do have thousands of hu-- of, of intelligences, not just, not humans. And that's, that's close enough, right?Even if they're not AGI, they're, they're close enough to do the, the task that you need them to do. And, and, you know, that's, there's plenty for, for a lot of routine work, knowledge work. Okay, let's get into SimGym. Um, this is one of those things I, I was surprised to see actually it's apparently your, uh, one of your most popular launches, and I think something that, uh, I think Sim AI, I think Yunjun Park, who did the Smallville thing, there's a very small cottage industry of people trying to do like the simulate customer thing.I think a lot of people maybe don't super trust this yet because they're like, well, obviously they would just do what you prompt them to do, right? But maybe just think, uh, tell us about the sort of inspiration or origin story.[00:38:10] Mikhail Parakhin: That's exactly actually the thing I wanted to cover, because if you don't have the historical data, all you can do is prompt a-agents in a vacuum, and they will do exactly what you prompt them to do.In fact, when I first proposed it, and this is a bit of, um, my brainchild initially, if I, I can boast, even Toby said like, “But wouldn't they, they just repeat what, what you tell them?” And, uh, but I'm like, “Yes, except Shopify has decades of history of how people made changes and what there is, uh, there, what it resulted in terms of sales.”So now what we can do is we can-- we have this... It's not, it's a noisy data. There's a small, usually websites, uh, you know, like things, things are never in isolation. It's almost never AB experiment. It's always AA experiment when there's has two meanings, but basically, you know, in different time you run two different things.But if you aggregate in general, uh, like everything together, and you apply, uh, denoising and collaborative filtering like approach, you can extract a very clear signal. And then you can optimize your agents. And that's why it took so long. It took almost a year of that optimization of just us sitting and fiddling, and, and we had this internal goals of correlation of hitting-- internal goal was to hit zero point seven correlation with, uh, add to cart events, for example.Like that, that if we run real AB test experiment, that it should, it should go and, and rep-uh, replicate, uh, same sort of success that, that humans had or lack thereof. And it, it took forever, and I don't think that's easily replicatable because, uh, like who else would have that data? You have to have this historic, you know, decades, uh, worth of data.And now, now the, like the other thing you need is in-infrastructure and the scale, right? Because, uh, w- again, what we found, uh, stat sig results, you need to run a lot of simulations, a lot of agents, and, and it's-- Those are expensive things. Like you're, you're making actions in the browser because you want a real friction.You want to, to be able to get the image like of what humans will see because you wanna, uh, detect effects like, “Hey, if I make my images larger, will I have more sales or l- uh, fewer sales?” And like usually people's intuition here, by the way, is that I increase my images, I will have more because they look nicer.You know, designers all look sparse and big images. Like usually your sales tank, right? But, but, uh, you know, from HTML, all the characters look the same only the, the size tag looks different, right? So it's very hard. So you have to take visual information, you have to run this in simulated browser environment on the big farm and, and of course, you have to have, uh, like very, very expensive model, good model with multi-model model.So all this it's-- is what's taken so long and, uh, to share my personal fail a little bit there, Sean, is like, you know, we always had this bias to-- for like large company bias. You know, we always, uh, whenever you-- we do, we're like, “Hey, we'll run an experiment,” right? We make, make a change, and we will run an experiment and then, uh, see, uh, see which one's better or like, “No, this is worse,” and most of them are worse, so you discard it and keep iterating, hill climbing.And we're like, “Oh, like smaller merchants, they cannot get stat sig results. They cannot really run experiments simply because, you know, in a week there would be not enough data for them.” So we thought from this perspective. What we didn't realize is that most people don't have A and B, they just have one thing, and they need suggestions of What A and B should be.So, uh, we first build this, hey, we run simulation on two separate teams and, and, uh, say, “Hey, which one is better?” We then morphed it into, and very recently just released it, when you have just your site, your theme, we run over it and we say, “Hey, here's what predicted values of, of, uh, uh, conversions are, and here's how we think you should modify it to increase your conversions.”And then circling back to what you started with, the proof is in the pudding. Like, if we are not correlating with reality, like, people will not be using it. And, uh, thankfully, we see literally every day more users than the previous day. So, so right now, uh, right now- It's working. Yeah. I'm-- Right now my problem is how to pay for it all because the so our major thing is how to optimize the LLMs, do distillation, how to run the headless browsers, uh, and handful browsers, uh, uh, cheaper so that we can accommodate the increase in traffic.[00:42:47] swyx: Yeah. I, I understand that you, uh, you published a lot of technical detail at GTC, so I was just gonna bring it up a little bit. I think s- was this in, in con-conjunction with some kind of GTC presentation? Or something like that, right?[00:42:59] Mikhail Parakhin: Well, we, yeah, we, we did it in several place, but yeah, we had the engineering- Yeahblog, uh, as well. Yeah.[00:43:05] swyx: Yeah. So you're running, uh, GPT OSS. Uh,[00:43:08] Mikhail Parakhin: the, this is an older version. You know, now we run multimodal model. But yeah- Yeah ... GPT OSS, we still run GPT OSS as well for[00:43:15] swyx: And then you have the VMs, and you also have browser-based. I really like this one where it you said, “It violates almost every assumption that standard LLM serving is designed for.”And then you had like, basically orders of magnitude differences between everything.[00:43:29] Mikhail Parakhin: Exactly. Which is, which, uh, which was, you know, a bit of a challenge to implement, like when, like even simple things. Uh, be- since it violates all the assumptions, for example, multi-instance GPUs, like MIGs don't work as well.But we needed, uh, to get MIG to work because, ‘cause otherwise it's way too expensive. And so we had to deal with the, yeah, with, uh, lots of infrastructure and, and, uh, work with, uh, uh, Fireworks and CentML, uh, you know, to help with optimizations and browser-based, as you mentioned. Yeah, like, takes a village.[00:44:04] swyx: Okay. So there's a lot of like, I guess, experimentation in the infrastructure so far, and you've published more or less what you have here. I guess I'm, I'm less familiar with CentML. I, I don't do, uh, that much work in this, this part of the stack. But why was it the sort of preferred instance platform?[00:44:22] Mikhail Parakhin: There are really three probably top companies. There used to be, uh, uh- Three top companies, uh, at least I was aware of that did, uh, LM optimization. You know, together Fireworks and Santa ML, not necessarily in that order. Santa ML recently got acquired by NVIDIA. Uh, what they did is if you have a model and you want to optimize it to a specific prof-- uh, profile of usage, uh, they would go and do it.And, uh, we work with, with those companies, uh, this was work particularly in with Santa ML and NVIDIA to get them the best possible results out of it. And, and sometimes you, you have to retune depending on, like sometimes you want the maximum throughput, sometimes you want minimal latency, sometimes you want like the cheapest, right?And, yeah, or some combination. And so yeah, these are people who would come and help you.[00:45:14] swyx: I see. I see. Yeah, yeah. I'm familiar with these people for the LLM, you know, autoregressive stack. But the other interesting category of these optimizers is also the diffusion people, whereas like Fel and, you know, uh, Pruna recently has come up a lot as well, which I think is like really underappreciated, uh, at least by myself, because I, I thought, oh, all the workload would be LLMs, but actually there's a lot of diffusion as well.[00:45:38] Mikhail Parakhin: Exactly.[00:45:38] swyx: There's a lot here, so I, I, I... it's, it's, uh, it's, it's, it's hard to cover. But I, I do think like people underappreciate the importance of customer simulation, basically. I think this is something that I'm candidly still getting to terms with. Uh, you know, uh, you also-- your team also like prepared this, like, really nice diagram.Uh, I, I assume this is AI generated.[00:46:00] Mikhail Parakhin: Yeah, it looks-[00:46:01] swyx: Maybe it's not.[00:46:01] Mikhail Parakhin: Yeah, it looks, uh, Gemini-ish. Yeah, but, uh, uh, honestly, I, I don't know where, where the hell they generated. It looks, look, uh, looks like it's, uh, Google. But the interesting part, John, that, that, uh, we haven't covered, but I, I wanted to mention is if your store had previous customers, rather than it's a new store, you're like new merchant just launching things, it helps tremendously in just correlation and forecast.Yeah, we take your previous, uh, customer's behavior, and we create agents that replicate those specific distribution of, of customers that you get, and then we a- we apply those to your changes, and then that, that raised raw, you know, the re-- uh, just correlation with the add to cart events or to-- with conversion or whatever it, it, it may be, uh, quite dramatically.So, uh, replicating humans in general seems like an interesting, cool challenge.[00:46:58] swyx: As a shareholder, I think this is the-- like if people are Shopify shareholders, they should really deeply understand this because this is basically the moat. The, the more you use Shopify, the more it will just automatically improve, right?Like you're, you're doing the job for them.[00:47:13] Mikhail Parakhin: Yeah, that's what we started with. Like, uh- ... uh, otherwise, if you're just a startup, I wouldn't do it if, uh, you know, if it was my startup because Without the data, it, yeah, as, as you said, it's, it's exactly the case that, uh, whatever you say in prompt, that's, that's what the agents will be doing.[00:47:30] swyx: The statistician in me wants to like really satisfy the sort of, um, statistical intuition, I guess. Um, to me it's kind of, uh, the, the word that comes to mind is, um, ergodicity. Uh, so let's say a, a customer takes this path, customer takes this path, customer takes this path, right? Um, the... In my mind, the way I explain it is like, okay, here, here's the ninety-five percentile, here's the five percentile, and here's the median, right?Um, but to me, what SimGym is potentially doing is that it can, uh, modify... It can sort of model the sort of in-between sort of journeys as well, that, that maybe are dependent on the previous states. This may be like a very RL-type conclusion where like basically the summary statistics, if you only did naive AB testing, you only have the, the statistics at, at, at a certain point, and you only judge based on the sort of overall summary statistics.But here you can actually model trajectories. Does that make sense? Or-[00:48:31] Mikhail Parakhin: That makes total sense because like, well, that, that makes even more sense that maybe even you realize bec- because-[00:48:38] swyx: Okay. Please,[00:48:38] Mikhail Parakhin: please. Yes ... we do-- Yeah. The, so internally, uh, we have this system, we talked about it briefly once at NeurIPS.We have a huge HSTU-based system that models the whole companies, uh, and their possible paths. And like- Yeah ... what you are, what you are showing, like actually at any point of time, you can either model the user's behavior or you mo- can also think about, uh, the whole merchant as a company, as the entity that acts in the world.You can model that as well. And then you can do, can do counterfactuals. In your graph, like in your blue graph, uh, if you're... Imagine in the center there, uh, somewhere in the middle, you would have an intervention. I give that person a coupon, or I don't know, I send a personal thank you card, or give a discount in some- somewhere.And then you can, uh, then you can do forward rollouts from that counterfactual. So what would have happened with that intervention or without the intervention? And you can even ch- change where that intervention, uh, in time can happen, right? Like some- where, where in this journey. So we, we do this at the Shopify scale for our merchants, and then if we notice that something that they can be fixing, like there's a strong counterfactual, like we have Shopify policy, they basically get a notification like, “Hey, we think your...something is wrong with your-” I don't know, Canadian sales. Like, uh, it looks like it's misconfigured. Here's what you need to do. Or do you think like, uh, you have to set up this campaign with these parameters? And we do that at the buyer level to literally offer discounts or cashback or, or things to buyers.So this is-- I'm getting very excited. Like this is my sort of area of, uh, interest, I guess, and, and hobby. But being able to m-model something complex as human beings or companies and model counterfactuals on it, where you can have interventions in the future and optimize when to make intervention, what kind inter-- uh, what kind of intervention to make.It's such an unlock that previously was completely impossible. Like the-- it was, it was always dreamed of, but never... Like how would you even simulate it without LLMs or HTUs? I think very, very exciting times.[00:50:59] swyx: I just wanted to, uh, to maybe illustrate this. I, I'm not the best illustrator, but I, I am a conceptual statistics guy.And y-you know, you cannot just do this. Like this is a dimensionality AB test doesn't do, right? Like, uh, because it doesn't have the, the, the change over time, uh, stochastic nature, uh, and it doesn't have the sort of contextual like... Here's all the context to this point. Um, okay, cool. Um, that's SimGym.You're, you're gonna burn a lot of tokens on this thing. But you're, you're one of the, the only scale platforms in the world that can, uh, that can do this across a huge variety of workloads, right? I'm even curious on a sort of human, uh, research level of like, well, do, does retail behave d-differently from like clothing sales?D-does that behave differently from electronic sales? I, I don't know. I don't know what else you guys... The Kardashian shoppers, do they differ from like people who buy, uh, I don't know, cars and, uh, whatever.[00:51:55] Mikhail Parakhin: Well, very different, and different sensitivities and different modes of, uh, shopping and, and different levels of what's important.Now, to-totally, you can do aggregations at, uh, at a store level. You can do aggregations at a different, uh, category level. I don't know if, uh, you know, for our statisticians among us, I couldn't believe, but we-- recently we're looking at it, and we had to bring back, uh, CRPs, you know, Chinese restaurant process.It's a, like, way of aggregating and, like, naturally grow clustering. So across... Specifically to answer questions that, uh, like you were just posing on how, how if, if buyers behave different categories. And I'm like, “I haven't seen CRP since two thousand and one.” It's[00:52:37] swyx: so What? It's so- What is... No, I haven't, I haven't seen this.No. This is not in my training. Uh,[00:52:44] Mikhail Parakhin: but, but yeah, it, uh, uh, it actually, like the, the-- there was a very popular kind of theory, popular neurips HTML circles in early two thousands, uh, kind of nice. And now, now it has practical applications, uh- Yeah ... that we were resurrecting.[00:53:03] swyx: Yeah, amazing. Uh, I, I can see, I can see how this is like a, uh, a fun job for you where you get to apply all these things.Um, yeah, yeah, so super cool. Super cool. So, okay, so, so anyone who, who knows what CRPs are and has always wanted to use them at work, uh, they should, they should definitely join Shopify. Okay, so w-we have a lot and but I, I'm, I'm being mindful of the time. I, I do wanted to, to sort of cover some other things.Um, I-I'll give you a choice, UCP or Liquid?[00:53:30] Mikhail Parakhin: Liquid. I think, I think on UCP, you know, like UCP is very important for us and, and it just we are-- UCP, we have a structured, uh, discussions, and you can read about them, and we have, uh, blog posts, and we have a big release this week, in fact, like with our catalog.Oh,[00:53:46] swyx: okay.[00:53:46] Mikhail Parakhin: Uh, yeah,[00:53:46] swyx: but- Le-I mean, we, we can, we can discuss the, the, the release briefly because we'll release this after the-- after it's already announced so whatever. There's a catalog that you guys are doing?[00:53:55] Mikhail Parakhin: Yeah. So we are, we are- Okay ... we are bringing in capabilities of a whole, uh, Shopify catalog.Basically, you now you can search for products, you can do lookups by specific ID, you can do bulk lookups when you need to bring m-multiple products. You don't need to know in ad-in advance what you're trying to show or to sell or check out. Like, you can now, you can now have this decided at, at runtime, and this big area for investment for us for both non-personalized and personalized searches, trying to provide basically a win-window into whole universe of products that are being sold everywhere in the world.And Shopify is really not exactly, but almost like a super set of any-anything being sold. Now we are bringing it into UCP and, uh, and, uh, identity linking is another big thing for us, uh, so that you, you can use, uh, like Google or whatever, whatever identity you have, uh, they're minimizing friction.[00:54:56] swyx: Yeah. So[00:54:57] Mikhail Parakhin: yeah, big release for us.But Liquid AI of course we never talk about, and the problem might be more, more aligned with what we d-discussed previously on this chat.[00:55:07] swyx: Sure. The main thing that everyone understands about Liquid is that it is inspired by Worm, and I still don't know why. I'm curious on your explanation. I think you, you, uh, you can make things very approachable.And also I think like what is the potential of like the, the level of efficiency that you get out of Liquid?[00:55:23] Mikhail Parakhin: You- we all familiar with transformer architectures. And, uh, for the longest time, there was a competing architecture, it's called the state space models. So, so Sams, uh, you know, Chris, Chris Reyes, one of the pioneers and, and lots of startups, uh, trying to make those realities.They have, uh, significant benefits being main being, uh, being much faster and, uh, lower footprint and not quadratic in length, you know, sort of, uh, linear in, in, uh, in your context length. But with state space models- They never quite made it. Like they're used-- They have, uh, certain niches when they thrive, their hybrid architectures are useful, but they never quite made it.And liquid neural networks are, you can think of them as a next step, like, uh, sort of, uh, state-space model square. It's non-transformer architecture that's more complicated than sta-state space and really difficult to code if you-- if I'm being honest. But it's, um, very efficient. It's, uh, subline-- sub, uh, quadratic in, in length of your context.Uh, it's very compact way to represent things, and that's a liquid AI company. They... Their goal is to productize it, and very often you have this need, uh, when you need to have long context and small model, and you want to have low latency. Like in general, it's basically on par with transformers, and if you do hybrids with transformers, it's, it's even better.That's why we at Shopify, when we tried multiple and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and/or if you need longer context lengths, liquid was the best. And so we still use the whole zoo and always like obviously test and use everything, uh, every open source model and, you know, it feels l

Eventyret er tilbage i dansk litteratur - men ikke det eventyr, du kender. Hos Rasmus Daugbjerg har havfruen f.eks. ben, mens den fattige bonde er ligeglad med prinsessen og det halve kongerige. Han vil meget hellere have en rigtig ven. I dagens program taler vært Nanna Mogensen med forfatteren om den moderne eventyrlig roman 'Rasmus Nielsøn', der foregår i 1500tallets Danmark. Hvorfor skal vi huske at være gode ved monstrene? Hvem kan høre naturen tale? Og hvorfor kommer havfruen til Samsø? Redaktør: Hanne Barslund

Chris Turner: The Case for Climate Optimism “We're not just trying to eliminate something bad—we're building something better.” In this episode, Craig speaks with journalist and author Chris Turner about his book How to Be a Climate Optimist and the shifting narrative around climate change from one rooted in fear and sacrifice to one driven by possibility, innovation, and better ways of living. Chris traces his journey from early reporting on climate catastrophe to a pivotal realization: instead of documenting the worst impacts, he could seek out places already solving the problem. That shift led him across the world to communities, technologies, and systems that are quietly building a low-carbon future. “What would it look like to go to the places already beating climate change?” A defining moment came when Chris visited the Danish island of Samsø, where a community had effectively achieved net-zero emissions. It wasn't theoretical, it was real, functional, and deeply ordinary. People were living their lives comfortably, powered by clean energy. That experience reframed the problem: the solutions weren't distant or speculative - they already existed. From “Less Bad” to “Much Better” “You would choose it even if we didn't have an emissions problem.” A central theme of the conversation is that climate solutions must be better, not just less harmful. Chris points to high-speed rail in Spain as an example, not just low-carbon, but more comfortable, efficient, and enjoyable than alternatives. This idea challenges the traditional narrative of sacrifice: Less consumption Lower expectations Reduced quality of life Instead, the energy transition can deliver: Better mobility More resilient communities Higher quality daily experiences The Quiet Revolution: Cheap, Scalable Solar “The most important thing that's happened is that solar became the workhorse.” While breakthrough technologies often capture attention, Chris argues that the most transformative shift has been the dramatic drop in the cost of solar energy. Solar is now: Cheaper than fossil fuels in many cases Scaling faster than expected Driving the global energy transition This changes the entire conversation from if we can transition, to how fast we can build it. What Governments Should Do “Treat energy as a shared national resource.” If advising policymakers, Chris identifies three priorities: Connect and coordinate energy systems Break down provincial silos and treat electricity as a national asset. Apply a clean energy lens to all public spending Every investment should align with long-term decarbonization goals. Rethink transportation Move beyond private vehicle dependency toward electrified mass transit and better urban systems. Adaptation: The Hard Reality “We're going to have to make difficult decisions about where we live.” Even with progress on emissions, climate impacts are accelerating. Chris emphasizes that adaptation will require: Localized responses Infrastructure resilience Difficult conversations about risk and relocation Financial systems such as insurance, lending, and investment may ultimately shape these decisions. Grounds for Optimism “The solutions keep getting better, faster, cheaper.” Despite geopolitical instability and ongoing crises, Chris remains optimistic. Why? Clean technologies are improving rapidly Adoption is accelerating globally Even conflicts are reinforcing the need for energy independence The transition is no longer hypothetical - it's underway. Book Recommendations from Chris Turner Hope in the Dark - Rebecca Solnit The Ministry for the Future - Kim Stanley Robinson He also recommends following the work of Ember Energy and their “Electrotech Revolution” writing for deeper insights into the energy transition.

Retired Col. Thomas Kirk shares his story of surviving solitary confinement for two years as a prisoner of war at the “Hanoi Hilton." In this episode, Host Rick Crandall talks with Tom, a fighter pilot, squadron commander, and Vietnam War POW. From one of the most intense air-combat battlefields in history to the harrowing story that followed, Kirk explores what it took to persevere through the unthinkable. There is a lot to learn!

Scripture: Exodus 14:10-31Resources for a life of following Jesus, every day, everywhere, with everyone.

On this edition of the Sams report, Surface hardware on the horizon, Xbox Game Pass overhaul, and a donkey on a rock...seriously. Check out Connection Explorer! - https://www.stardock.com/products/connectionexplorer/ Chapters: Intro: 00:00-00:36 Tech News: 00:36-5:44 Gaming News: 5:44-12:24 Questions: 12:24-26:33 Outro: 26:33-27:00

In this episode of The Operational Arch, hosts Spencer Bates and DJ Taylor sit down with Steven Box, founder and CEO of Vanguard Tactics and a top-ranked competitive Warhammer coach, alongside SAMS professor Matt Yandura. The conversation explores how Box's "IDEA" framework — developed over years of competitive tabletop wargaming and coaching — maps surprisingly well to military decision-making: gathering the right information under time pressure, making rapid decisions, executing with composure, and assessing outcomes to adapt. The episode also digs into why SAMS invited an outside expert from a non-traditional field and what operational planners can learn from embracing failure, coaching others, and considering future operating environments.Content:0:00 — Intro & episode overview2:31 — Steven Box's background: from Royal Navy aptitude tests to volleyball, bodybuilding, and Warhammer7:42 — What is Warhammer? A layman's explanation9:03 — Why SAMS brings in outside experts: Professor Yandura on SAMS' mandate to experiment11:31 — Introducing the IDEA framework (the "Box Method")12:10 — I — Information: Gathering the right info under time pressure; SWOT analysis on the battlefield15:05 — D — Decision: Translating information into decisive action; identifying priorities and trade-offs18:47 — E — Execution: Resource allocation, composure under pressure, managing variance21:14 — A — Assessment: What went well, what didn't, and when to pivot23:57 — Coaching philosophy: meeting people where they are and building from there25:31 — The value of purposeful failure and learning from losses32:41 — SAMS' Future Operating Environment course: forecasting, futures thinking, and speculative scenarios45:32 — Closing takeaways & acknowledgments (CGSC Foundation shoutout)

The Sams open with the matchday madness: Arsenal's shocking home loss tightens the title race to six points, City goes full steamroller on Chelsea, and both halves of North London are in freefall — Spurs drop at Sunderland while West Ham celebrates a massive home result. Everton and Brentford split the points in a draw that keeps both clubs' European hopes breathing, and Leeds pulls off a historic Old Trafford result against a ten-man United. In the glass: New Riff's Balboa Rye, Whisky Advocate's #4 of 2025, and it does not disappoint. Bournemouth 2 - Arsenal 1 Man City 3 - Chelsea 0 West Ham 4 - Wolverhampton 0 Sunderland 1 - Tottenham 0 Forest 1 - Villa 1 Everton 2 - Brentford 2 Brighton 2 - Burnley 0 Liverpool 2 - Fulham 0 Palace 2 - Newcastle 1 Leeds 2 - Man United 1 www.Dufootballshow.com Facebook @DUfootballshow Instagram @DUfootballshow TikTok @DUfootballshow YouTube @DUfootballshow

Mark and Sal interview Mark Skousen, the Founder of Freedom Fest about his new book about Ben Franklin. How to prepare for Flock Cameras and More.

Scripture: 1 Corinthians 12:27Happy Easter Bel Air Church family!Resources for a life of following Jesus, every day, everywhere, with everyone.

Statins are one of the most commonly prescribed medications for managing high cholesterol, but many people experience muscle pain, fatigue, and weakness while taking them. In this episode of the Pain and Performance Podcast, Dr. Derrick Hines explains the science behind statin-associated muscle symptoms (SAMS) and why these issues occur in some patients. He breaks down how statins can affect CoQ10 levels and mitochondrial energy production, leading to muscle dysfunction. The episode also explores evidence-based lifestyle strategies, metabolic interventions, and supplement options that may help support cardiovascular health for those who struggle to tolerate statins.In This Episode- What statin-associated muscle symptoms (SAMS) are and why they occur- How statins can affect CoQ10, mitochondrial energy production, and muscle function- Important tests that give a clearer picture of cardiovascular risk beyond a standard lipid panel- Lifestyle strategies including nutrition, fasting protocols, and exercise to improve metabolic health- Supplements and therapies that may support cholesterol balance and vascular healthTikTok:https://www.tiktok.com/@drderrickInstagram:https://www.instagram.com/derrickbhines/Youtube:https://www.youtube.com/@DrDerrick

In this episode of The Operational Arch, host Major DJ Taylor hands the mic over to three key SAMS leaders: Mr. Kirk Dorr (Deputy Director of the School of Advanced Military Studies (SAMS)), Dr. Bruce Stanley (Director of the Advanced Military Studies Program (AMSP)), and Colonel Paul Godson (Deputy Director of AMSP). Together, they pull back the curtain on the Advanced Military Studies Program, covering what it is, what it produces, and exactly how officers can go from being curious about the program to becoming a competitive applicant. They dispel myths about AMSP as merely a "planners' school" and show how the program develops leaders to meet the Army's most demanding missions as experts in Army and joint doctrine, leading diverse operational planning teams, and enabling senior leaders. The ten-month program focuses on warfighting, operational art, and the critical and creative thinking required to anticipate and adapt to rapid change on the modern battlefield.0:00 Intro and Guest Introductions4:20 How Has AMSP Changed Over the Years?8:48 What Does the Operational Force Expect from Graduates?10:16 What Attributes Define a Strong AMSP Student?14:25 The Flow of the Academic Year (June–May)19:52 How to Get Competitive / The Application Process23:36 The AMSP Exam and Interview27:19 Selection , the HRC Marketplace, & Recent Process Changes35:22 How to Prepare / Tips for Applicants44:40 "What If I Don't Want to Be a Planner?"46:10 Family Considerations49:57 What Should Every Graduate Take With Them? 52:51 Closing Remarks

Seid dabei! Von und mit Paul Maar, dem Autor vom Sams, der nach dem gemeinsamen Hören live über die Entstehung seiner Geschichten erzählen wird.

Seid dabei und trefft Paul Maar, den Autor vom Sams! Er wird euch nach dem gemeinsamen Hören über die Entstehung seiner Geschichten erzählen. https://www.ardsoundsfestival.de/#/

With a special appearance by Willie BrownSee omnystudio.com/listener for privacy information.

The US-Israeli war against Iran has once again underlined the vulnerability of the world's oil and gas supplies. Iran's chokehold on the Strait of Hormuz has pushed up oil prices and threatened global economic stability. This comes at a time when wind farms and solar panels are providing real value. The wind-powered Danish island of Samsø is showing the way forward. FRANCE 24's Malcolm Brabant reports.   

Angel City's Emily Sams joins the show to fill Sam in on how finding her own path put her on a leadership journey, what she learned on the Orlando Pride, her surprise trip to the Paris Olympics, and of course her hopes for LA's 2026 season.Join the Bracket here: https://fantasy.espn.com/games/tournament-challenge-bracket-women-2026/group?id=73442c55-bf71-4500-bbe6-b86262b197cb&joining=true SUBSCRIBE TO THE WOMEN'S GAME NEWSLETTER: https://mibcourage.co/42X5HpBSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On this episode of Coffee, Country & Cody, we welcome Dean Sams and Sophia Scott   0:00 - Welcome / What’s Coming Up 4:03 - Entertainment with Kelly Sutton  11:41 - Interview with Dean Sams 27:23 - Interview with Sophia Scott 43:44 - Entertainment with Kelly Sutton     Connect with WSM Radio: Visit the WSM Radio WEBSITE: http://bit.ly/650AMWSM Follow WSM Radio on TikTok:  https://www.tiktok.com/@wsmradio Like WSM Radio on FACEBOOK: http://bit.ly/WSMRadioFB Check out WSM Radio on INSTAGRAM: http://bit.ly/WSMRadioInsta Follow WSM Radio on X: http://bit.ly/WSMRadioTweets Listen to WSM Radio LIVE: http://bit.ly/WSMListenLive Listen to WSM on iHeartRadio: https://www.iheart.com/live/wsm-radio...  

There are ‘2 Sams' in the Bible – Samuel the prophet, the first Judge of Israel; and Samson the mighty man of God, the last judge of Israel. They share amazing similarities. Both sons had an amazing start; one ended well, the other not so. The question is – which Sam do you want to be?

Arsenal are nine points clear and starting to look untouchable. Manchester City dropped points to West Ham and are running out of time to make excuses. While everyone else is playing catch-up, Bruno Fernandes is out here personally dragging Manchester United toward relevance through sheer force of will — and the Sams have opinions about it. Aston Villa? The wheels are wobbling. Newcastle? Quietly, dangerously back in the Champions League conversation. And Oh So this week was a crime against football — a pile of 0-0 draws that somehow still need to be talked about. Then the crew cracks open something worth the wait: Sagamore Rye 10 Year Old, Whisky Advocate's #7 Whisky of 2025. Maryland-made, decade-aged, and exactly what surviving a week of Premier League chaos deserves. Arsenal 2 - Everton 0 Man City 1 - West Ham 1 Newcastle 1 - Chelsea 0 Man United 3 - Villa 1 Liverpool 1 - Spurs 1 Burnley 0 - Bournemouth 0 Leeds 0 - Palace 0 Forest 0 - Fulham 0 Brentford 2 - Wolverhampton 2 Brighton 1 - Sunderland 0 www.Dufootballshow.com Facebook @DUfootballshow Instagram @DUfootballshow TikTok @DUfootballshow YouTube @DUfootballshow   Support the bar tab and get extra content: https://www.patreon.com/dufootballshow www.DUdripshack.com 

Scripture: John 10:27-30, 20:20, 20:27; Luke 4:50, 24:50Resources for a life of following Jesus, every day, everywhere, with everyone.

The Actors Room gives a behind the scenes look into its first interview.  It was a wonderful experience.  Thank you once again, Russell.  You made my year!

The MRN broadcast of the 1999 Sams Town 300 from Las Vegas Motor Speedway. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Beth Golay speaks with Saba Sams about her new novel, 'Gunk.' And Suzanne Perez brings us a review of Jeannette McCurdy's 'Half His Age.'

Use code ATCornerStudent to get 10% off your monthly subscription price of ATStudyBuddy, an online BOC exam prep platform built by athletic trainers for athletic training students. ATStudy Buddy gives you realistic practice tests, instant feedback, and over 250 study guides and games—online, affordable, and proven to help you pass. Start your journey today at ⁠⁠ATStudyBuddy.com⁠⁠.--Studying for the BOC or just wanting a review of athletic training topics? Tune in as we compare what the book says to real life stories from athletic trainers.--Whether it's roller coaster POV to challenge balance or Activating transverse abdominus for all exercises under the sun, there are many elements of rehabilitation that an athletic trainer can play around with. In this BOC IRL episode, ATs share their stories about rehabilitation principles in their practice and how it relates to the textbook.Featuring stories from Bre B, Kelsey B, Sarah Y, Gabby L, Evan J, Lucas M, Sam S, Jayde C, Christina S, Annaliese L, Kaetlyn H, Emily M, & many more!--AT CORNER FACEBOOK GROUP: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/groups/atcornerpodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram, Website, YouTube, and other links: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠atcornerds.wixsite.com/home/links⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠EMAIL US: atcornerds@gmail.comSAVE on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Medbridge⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: Use code ATCORNER to get $101 off your subscriptionWant to host a podcast like ours? Use our link to sign up for Zencastr, the service we use to record our interviews: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://zencastr.com/?via=atcorner⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Music: Jahzzar (betterwithmusic.com) CC BY-SA---Sandy & Randy

The Russell Sams Interview continues with details about his audition and performance in the cult classic film "Rules of Attraction." Russell also gives insight on his love for acting and his workshop called "The Actors Way In." This episode is filled with amazing content you can only find in The Actors Room. Below are links to all of Russell Sams sites: https://russellsams.com/ 8 Steps to Your Best Self-Tape IMDB Russell Sams Facebook Russell Sams Instagram Russell Sams Actors Way In

In this episode, Dr. Aref Rifai, ophthalmologist and President of the Syrian American Medical Society, shares how the organization has expanded care across all 13 Syrian provinces following liberation. He discusses scaling hospitals and centers of excellence, launching a new radiation oncology center, strengthening medical education, and partnering with U.S. health systems to rebuild infrastructure and elevate quality of care.

Saturday's midday card was an absolute circus — 19 goals, three matches, zero chill. The Sams break it all down: Brentford's inexplicable collapse against Burnley, Everton doing what Everton does (shockingly), and Liverpool finally remembering they can score goals. Good news for Wolves fans — the history books are safe. Bad news for Villa — the title race has moved on without them. City and Arsenal? Still cooking. Spurs? Still waiting. The crew also pours up the Bardstown Collaboration Series in Ferrand barrels, Whiskey Advocate's #11 pick on their Top 20 of 2025. Football chaos and top-shelf whiskey — just another week on the show.   Brentford 4 - Burnley 3 Everton 3 - Newcastle 2 Liverpool 5 - West Ham 2 Wolverhampton 2 - Villa 0 Man City 1 - Leeds 0 Arsenal 2 - Chelsea 1 Man United 2 - Palace 1 Fulham 2 - Spurs 1 Bournemouth 1 - Sunderland 1 Brighton 2 - Forest 1   www.Dufootballshow.com Facebook @DUfootballshow Instagram @DUfootballshow TikTok @DUfootballshow YouTube @DUfootballshow   Support the bar tab and get extra content: https://www.patreon.com/dufootballshow www.DUdripshack.com 

See omnystudio.com/listener for privacy information.

When Chuck Sams was sworn in as director of the National Park Service in 2021, he became the first Native American to lead the agency. Sams previously served as a member of the Northwest Power and Conservation Council and as executive director of the Confederated Tribes of the Umatilla Indian Reservation.   Not long after Sams returned to Oregon after leaving the agency last year, the Trump administration fired nearly 1,000 park service employees without warning. The agency lost nearly a quarter of its permanent staff in the following months.  Sams has denounced the loss of institutional knowledge within the National Park Service. He joins us to share his thoughts.

I sit down with Colonel Edward Arntson III, who has 24 years of military service, to unpack what leadership really demands when it counts. We start with self-leadership: how you show up, how you carry yourself, and why humility isn't optional. We get into competence, intellectual curiosity, and the overlooked power of energy and tone. What you tolerate becomes the standard. Colonel Arntson shares hard-earned lessons from leading in garrison and in combat, including moments that tested his decision-making, confidence, and character. If you're building a team, leading a family, or trying to lead yourself better, this episode delivers practical takeaways you can apply immediately.More about Colonel Ed Arntson:Ed Arntson, from Buffalo Grove, Illinois, graduated from Concordia College (2002) and commissioned as a Distinguished Military Graduate infantry officer through NDSU. He led rifle and company units in Alaska, Afghanistan, and Iraq, was wounded in combat, and later served with The Old Guard, including a landmark deployment to Taji, Iraq. After CGSC and SAMS, he held planning and operations roles with 1st Cavalry Division, deployed to Korea and Baghdad, and served on the Joint Staff. He commanded 3-187 Infantry and 3rd Brigade, 1st Cavalry Division. His education includes Ranger and airborne schools. He's married with two children.CHAPTERS:00:00 Introduction05:07 The Importance of Leadership Development15:31 The Role of Feedback in Leadership27:22 The Power of Tone in Leadership41:59 The Power of Tone in Communication48:25 Building Effective Teams Through Peer Leadership52:37 The Importance of Humility and Tone in Leadership01:06:04 Early Military Career and Deployment Experiences01:21:01 Dedication and Commitment in the Military01:39:26 The Impact of 9/11 on ROTC and Military Careers01:45:54 The Importance of Physical and Mental Readiness01:52:27 A Formula for Effective Leadership02:00:06 Final Thoughts on Leadership and InspirationBecome a BPN member FOR FREE - Unlock 25% off FOR LIFE ⁠https://www.bareperformancenutrition.com/collections/performance-nutritionFOLLOW:IG: instagram.com/nickbarefitness/YT: youtube.com/@nickbarefitness

In this episode of the Deep in the Hunt of Texas podcast, host Connor Little sits down with his friend Curtis Sams, also known as the Brazoria Bandit. They dive into the diverse hunting culture of Texas, discussing the differences in hunting experiences across the state, from the coastal marshes to the plains. Curtis shares his journey into duck hunting, recounting memorable experiences and the evolution of hunting regulations post-Hurricane Harvey. The conversation also touches on the camaraderie that comes with hunting, emphasizing that the social aspect often outweighs the actual hunting itself. As they reminisce about past hunts, they reflect on how the hunting landscape has changed over the years, particularly in terms of bird populations and hunting practices. Learn more about your ad choices. Visit megaphone.fm/adchoices

Or if you're Shane, things in the air.Guests:Laura Sams (@lauramsams)Shane Torres (@shanetorres)Support the show!Join the AFE Patreon at patreon.com/allfantasy for ad-free episodes, mailbags, auction drafts, and other exclusive content.Watch the video podcast at youtube.com/@AllFantasyEverything.Advertise on AFE!Advertise on All Fantasy Everything via Gumball.fm.Follow the Good Vibes Gang on social media:Ian KarmelSean JordanDavid GborieIsaac K. LeeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.