Podcasts about Smallville

Its time to return to Earth-167! Which you would obviously know as the universe of Smallville, the smash hit ten season long Superman original series that managed to introduce EVERY. SINGLE. DC. CHARACTER. before Clark Kent decides to put on the suit. This time we're going to be covering the storyline of Kara Zor-El aka Supergirl as played by Laura Vandervoort introduced in Season 7. And we've discovered so much more about this show including how many character get clones or are clones (it's a lot which is exciting). Thanks for watching our Caravan Of Garbage reviewSUBSCRIBE HERE ►► http://goo.gl/pQ39jNHelp support the show and get early episodes ► https://bigsandwich.co/Patreon ► https://patreon.com/mrsundaymoviesJames' Twitter ► http://twitter.com/mrsundaymoviesMaso's Twitter ► http://twitter.com/wikipediabrownPatreon ► https://patreon.com/mrsundaymoviesT-Shirts/Merch ► https://www.teepublic.com/stores/mr-sunday-movies The Weekly Planet iTunes ► https://itunes.apple.com/us/podcast/the-weekly-planet/id718158767?mt=2&ign-mpt=uo%3D4 The Weekly Planet Direct Download ► https://play.acast.com/s/theweeklyplanetAmazon Affiliate Link ► https://amzn.to/2nc12P4 Hosted on Acast. See acast.com/privacy for more information.

How does this teenage retelling of Superman's origins hold up? Is Lex Luthor likeable? And who would Mitu befriend in the Smallville universe? Tune in to find out! Edited with thanks to Playlyst Studios Connect with us:   Buy us a coffee at buymeacoffee.com/thepilotpodcast | Visit us at thepilotpodcast.com | Email us at askthepilotpodcast@gmail.com | Follow us @ThePilotPod on Twitter, Instagram, and TikTok | Please leave a rating and review on Apple Podcasts

Host Anthony Desiato and guest Zach Moore (Always Hold On To Smallville) dig into Kara Kent on SMALLVILLE as portrayed by Laura Vandervoort across Season 7 and return appearances in Seasons 8 & 10.They discuss her arrival, characterization, & backstory; dynamics with Clark, Lara, Zor-El, Lex, & Jimmy; mid-season amnesia & powerlessness; banishment to the Phantom Zone & subsequent returns; and ultimate departure to the 31st Century.Support the show and receive exclusive podcast content at Patreon.com/AnthonyDesiato, including the spinoff podcasts BEYOND METROPOLIS and DIGGING FOR JUSTICE!Visit BCW Supplies and use promo code FSP to save 10% on your next order of comics supplies. Get your DFK merch at the podcast's TeePublic storefront!FACEBOOK GROUP: Digging for Kryptonite: A Superman Fan GroupFACEBOOK PAGE: @diggingforkryptonitepodINSTAGRAM: @diggingforkryptonitepodTWITTER: @diggingforkrpodBLUESKY: @diggingforkrpod.bsky.socialEMAIL: flatsquirrelproductions@gmail.comWEBSITE: FlatSquirrelProductions.com Digging for Kryptonite is a Flat Squirrel Production. Theme music by Dan Pritchard. Key art by Isaiah Simmons. Mentioned in this episode:Drunken AvengerSingle Bound PodcastThis Podcast Will Never DieAw Yeah ComicsFat Moose Comics

It's Smallville: The Ultimate Season! This time we're going through the fifteenth episodes of every season and by process of elimination determining the ultimate episode 15 of Smallville.Zach is joined by Leah Vogel, Ronit Troner, and Eddie Bissell.Check out Eddie's animal rescue Valley Cats & Friends!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsInsaiyanIsaiah GoodridgeAtif SheikhJohn CurcioThomas NavenMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullMatt B.Amy J.Mike FranzNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayRajJoey DienbergDJ DoenaDaryn KirschtNicholas CosoJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.com

This might be it. The worst comic we've ever read. Marville, a miniseries from 2002 written by Marvel's Editor in Chief at the time, Bill Jemas. It's a fever dream of a comic that starts as a Smallville parody, but it becomes more. Oh it becomes so much more! Please join us for this bizarre piece of garbage. Then we keep going with Ultimates in issue #9, where there's a fairly inconsequential fight! Next Time: Archie Meets Batman '66!

Director extraordinaire and friend of the pod, Glen Winter joins us to break down the technical side of things as we dive into season 8 episode 15, Infamous. With his sparkling attitude and wealth of knowledge, Glen sheds light on the cinematic choices and production hazards that go into creating the Smallville spectacle. In the episode, Clark decides to reveal his secret to the world before he can be outed by disgraced and damp reporter Linda Lake (guest star Tori Spelling). How will the citizens respond? What will Lois think? And how much does it cost to soak all the actors with rain towers? Find out all that and more in this week's episode! ... ❤️ Sign up for therapy and get 10% off at https://betterhelp.com/talkville

It's Smallville: The Ultimate Season! This time we're going through the fourteenth episodes of every season and by process of elimination determining the ultimate episode 14 of Smallville.Zach is joined by Lance Laster from Always Hold On To Arrow, Matt Truex from Lois & Clark'd: The New Podcasts of Superman and Victoria Male.Check out Lance on Always Hold On To Arrow!Check out Matt's work including Lois & Clark'd: The New Podcasts of Superman at The Daily Knockoff!Check out Victoria's work on her website!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsKevonte ChilousInsaiyanIsaiah GoodridgeAtif SheikhJohn CurcioThomas NavenMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullMatt B.Amy J.Mike FranzNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayMichael H.RajJoey DienbergDJ DoenaNicholas CosoJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.comMatt Truex is a Warner Bros. Discovery employee. The views and opinions expressed in this podcast are his own and do not necessarily reflect the views or positions of Warner Bros. Discovery.PATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.com

A gang of interstellar elites make it their business to elaborately prank the Boy of Steel, but nothing they do can top Superboy's own elaborate prank of (checks notes) flying 24 hours into the future to fake his entire family's death and also pretend to be blind. It's a laff riot, all right here in Superboy #137! Chapters (00:00:00) - Superboy 137(00:02:52) - Spring cleaning in the house(00:04:57) - In the world of managerial accounting(00:06:46) - Boring job costing class(00:09:59) - Flute Concert and Guitar(00:11:17) - Oh, My Love Boat!(00:12:06) - Ashes in the Carpet(00:14:18) - How to Create Go Go Check Comics Covers(00:16:21) - milo on The Comeback and For All Mankind(00:19:31) - The Montreal Canadiens(00:19:43) - Superboy in the New Home...(00:22:49) - Martin Gray on Biscuits Flavored Tea(00:24:04) - How Smallville Stands Up to Earthquakes(00:26:54) - When Superboy Needs To Travel Back In Time(00:30:25) - Superman and Clark Kent: The Alternate Lives(00:34:50) - The Boy Who Says He's Blind(00:35:23) - How To Pass as Superboy in Smallville(00:37:22) - How My Great Grandmother Met Her husband(00:38:34) - Superboy(00:41:14) - Chuck Kendall switches to Superboy after witnessing bank robbery(00:45:20) - Super baby from Krypton is featured in this week's podcast(00:47:02) - Baby Found in the Basement of Smallville(00:50:57) - Strawberry Scratches on a Television Set(00:53:52) - Superboy: Build a Children's Zoo(00:58:27) - Tarzan: A Super Genius(01:00:06) - Clark Kent: On Becoming Superboy(01:04:20) - Superboy meets Old Man on Geryon(01:07:01) - Superboy(01:11:06) - Superboy And The Kryptonian Fabric

Mikey & Jeremy watch S7E19 of Smallville, "Quest". They discuss The Clock King. Robert Picardo, and the hierachy of Lex Luthors. 

Send us a text or a voicemailLong-buried wounds rise to the surface when has-been podcasters reunite with their estranged best friend and former producer, on the eve of their comeback episode. On Episode 722 of Trick or Treat Radio our featured film discussion is Mother Mary from director David Lowery! We also talk about dumb Transformer names, scream kings, and we react to trailers for the films; Hope and Blowie! So grab the outfit you wish to make your triumphant return in, steer clear of any unwanted imprints, and strap on for the world's most dangerous podcast!Stuff we talk about: Cannibal Holocaust, Grindhouse Releasing, Ruggero Deodato, 16mm aspect ratio, Sage Stallone, The Truth Commission, Sheiky Baby, Mounties, Force Insensitive, Mandalorian and Grogu, Skids, Transformers, dumb Transformer names, Beachcombers, The H Man, The Changeling, Visiting Hours, Seed People, Urban Legend, Psycho Beach Party, An Erotic Vampire in Paris, Cherry Falls, Night of the Demons, Hellraiser, Ashley Lawrence, Stepfather 3, The Andromeda Strain, The Drew Carey Show, Willard, Reflection of Fear, Zelda Rubenstein, I Hate My Body, Mummy's Revenge, Night of the Seagulls, The Bat, Karen Carpenter's The Thing, Morris Day and the Time, Young MC, The Circle Jerks, Todd Browning's Freaks, Freaked, Psychos in Love, Na Hong-Jin, Hope, Miami Connection, Drew Struzan, Blowie, Deadstream, Nightmare on Elm St Pt. 2, Scream Kings, Mark Patton, Curse of the Queer Wolf, Dice Gottfried, Smallville, The Green Knight, Dev Patel, Anne Hathaway, David Lowery, Stephen King, The Shining, Pain Cave, Hawk the Slayer, imprinting, Free Britney, Dark Knight Rises, Abigail, Elijah Wood, Ready or Not 2: Here I Come, Samara Weaving, Kathryn Newton, David Cronenberg, Anthony Michael Hall, a humble egomaniac, hope nope and rope, a beautiful nothing, and more money than Zod.Support us on Patreon: https://www.patreon.com/trickortreatradioJoin our Discord Community: discord.trickortreatradio.comSend Email/Voicemail: mailto:podcast@trickortreatradio.comVisit our website: http://trickortreatradio.comStart your own podcast: https://www.buzzsprout.com/?referrer_id=386Use our Amazon link: http://amzn.to/2CTdZzKFB Group: http://www.facebook.com/groups/trickortreatradioTwitter: http://twitter.com/TrickTreatRadioFacebook: http://facebook.com/TrickOrTreatRadioYouTube: http://youtube.com/TrickOrTreatRadioInstagram: http://instagram.com/TrickorTreatRadioSupport the show

Kristin Kreuk joins us one more time to discuss Lana Lang's final episode of Smallville. We talk about the heartbreak, deeper meanings, and contractual obligations behind her return and exit from the show. In the episode, Clark and Lana are super-powered lovers at last, until Lex reenters the fold to tear them apart with the help of demolitions expert, the Toyman. Will love outlast an obscene level of kryptonite? Tune in for what could be Kristin's last appearance on Talk Ville. And yes, Michael does get righteously upset about this portrayal of Lex. ...

It's Smallville: The Ultimate Season! This time we're going through the thirteenth episodes of every season and by process of elimination determining the ultimate episode 13 of Smallville.Zach is joined by Leah Vogel, Ronit Troner, Billy Pollihan from See You Next Summer.Check out Billy's podcast See You Next Summer!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsKevonte ChilousInsaiyanIsaiah GoodridgeAtif SheikhJohn CurcioThomas NavenMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullMatt B.Amy J.Mike FranzNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayMichael H.RajJoey DienbergDJ DoenaNicholas CosoJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.com

Mikey & Jeremy watch S7E18 of Smallville, "Apocalypse". They discuss time travel, alternate earths, and fabricated realities.

Lana's back and now we know why. In this controversial episode, we get into the Foo Fighters, 80s montages, and HBO's the Pitt. We do also analyze the episode and actually come to our own revelations about character motivations and Smallville's overarching themes. As Tom says, "it's just good drama" and boy do we have it. Join us for season 8, episode 13 Power. ... ☄️ Mars Men: For a limited time, our listeners get 50% off FOR LIFE, Free Shipping, AND 3 Free Gifts at Mars Men at https://Mengotomars.com ❤️ Better Help: Sign up and get 10% off at https://betterhelp.com/TALKVILLE __________________________________________________

It's Smallville: The Ultimate Season! This time we're going through the twelfth episodes of every season and by process of elimination determining the ultimate episode 12 of Smallville.Zach is joined by Anthony Desiato from Digging for Kryptonite, Craig McKenzie from Kneel Before Blog, Eric Folk from Smallville Papers, and Leah Vogel.Check out Anthony and his podcasts including Digging for Kryptonite at Flat Squirrel Productions!Check out Craig and his work including the Kneel Before Pod podcast at Kneel Before Blog!Check out Eric's work on Smallville Papers!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsKevonte ChilousInsaiyanIsaiah GoodridgeAtif SheikhJohn CurcioThomas NavenMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullMatt B.Amy J.Mike FranzNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayMichael H.RajJoey DienbergDJ DoenaNicholas CosoJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.com

Mikey & Jeremy watch S7E17 of Smallville, "Sleeper". They discuss Jimmy Olsen, Jimmy Olsen, and Jimmy Olsen. 

Host Anthony Desiato celebrates the milestone 250th episode of the podcast with guest Zach Moore (Always Hold On To Smallville) as they build the "perfect" Superman origin through a series of 20 questions covering Krypton, Smallville, and Metropolis.What should be the cause of Krypton's destruction? Should Clark be Superboy? Who should make the Superman costume? Anthony & Zach tackle these and many more questions!PLUS! Anthony reads & responds to YOUR questions & testimonials about the podcast in a special "mailbag" epilogue."A Supergirl Fan Journey" resumes next week with coverage of the 90s SUPERGIRL series written by Peter David!Support the show and receive exclusive podcast content at Patreon.com/AnthonyDesiato, including the spinoff podcasts BEYOND METROPOLIS and DIGGING FOR JUSTICE!Visit BCW Supplies and use promo code FSP to save 10% on your next order of comics supplies. Get your DFK merch at the podcast's TeePublic storefront!FACEBOOK GROUP: Digging for Kryptonite: A Superman Fan GroupFACEBOOK PAGE: @diggingforkryptonitepodINSTAGRAM: @diggingforkryptonitepodTWITTER: @diggingforkrpodBLUESKY: @diggingforkrpod.bsky.socialEMAIL: flatsquirrelproductions@gmail.comWEBSITE: FlatSquirrelProductions.com Digging for Kryptonite is a Flat Squirrel Production. Theme music by Dan Pritchard. Key art by Isaiah Simmons. Mentioned in this episode:This Podcast Will Never DieAw Yeah ComicsFat Moose ComicsSingle Bound PodcastDrunken Avenger

Chloe really looked Clark in the eyes and went, “Nope… I'm choosing Davis.”

It's Smallville: The Ultimate Season! This time we're going through the eleventh episodes of every season and by process of elimination determining the ultimate episode 11 of Smallville.Zach is joined by Lance Laster from Always Hold On To Arrow, Matt Truex from Lois & Clark'd: The New Podcasts of Superman and Victoria Male.Check out Lance on Always Hold On To Arrow!Check out Matt's work including Lois & Clark'd: The New Podcasts of Superman at The Daily Knockoff!Check out Victoria's work on her website!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsKevonte ChilousInsaiyanIsaiah GoodridgeAtif SheikhJohn CurcioThomas NavenMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullMatt B.Amy J.Mike FranzNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayMichael H.RajJoey DienbergDJ DoenaNicholas CosoJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.comMatt Truex is a Warner Bros. Discovery employee. The views and opinions expressed in this podcast are his own and do not necessarily reflect the views or positions of Warner Bros. Discovery.

Mikey & Jeremy watch S7E16 of Smallville, "Descent". They discuss the fall of Lionel Luthor, Lex's last bastion of humanity and Clark's mastery of heat vision. 

Send us Fan MailHannah and Laura are on indie intermission, covering the first half of The Heavenly Sword by Alice Poon!! They also chat about a complicated TV show, a beautiful literary fiction, and Laura gives Hannah some career advice.**This episode contains SPOILERS for The Heavenly Sword by Alice Poon. Spoiler section begins at: 41 min 40 secs. ***CW for the episode: discussions of sex, sexual assault, religion, violence, politics, family trauma, death *Media Mentions:The Heavenly Sword by Alice PoonThe Boys---Prime VideoDark AngelSupernatural---PeacockSmallville---HuluTaskmaster---YouTubeMonster by Naoki Urasawa Pluto by Naoki Urasawa River Valley Glassworks board game Eager: The Surprising Secret Life of Beavers and Why They Matter by Ben Goldfarb Real Americans by Rachel Khong Veronica Mars---Hulu Support the showBe sure to follow OWWR Pod!www.owwrpod.com YouTube: @owwrpodBlueSky: @OwwrPodTikTok: @OwwrPodInstagram: @owwrpodThreads: @OwwrPodSend us an email at: owwrpod@gmail.comCheck out OWWR Patreon: patreon.com/owwrpodOr join OWWR Discord! We'd love to chat with you!You can follow Hannah at:Instagram: @brews.and.booksThreads: @brews.and.booksTikTok: @brews.and.booksYou can follow Laura at:Instagram: @goodbooksgreatgoatsBlueSky: @myyypod

In this episode of Going Back to Smallville, we're breaking down Season 8 Episode 19, “Stiletto,” This one is such a wild mix of chaos, heart, and character growth. We spend a lot of time digging into Lois creating Stiletto. Not just the costume (which we have thoughts on), but what it says about where she's at as a reporter who's tired of being overlooked and just wants her shot. It's messy, it's a little unhinged, and honestly… kind of relatable. We also get into how that whole plan accidentally leads to one of the most important moments of the episode: Lois finally connecting with the Red-Blue Blur in a way that's not about chasing a headline, but understanding him as a person.It's funny, it's awkward, it's surprisingly emotional… and it's a big step forward for Lois Lane becoming Lois Lane.Support the show: https://patreon.com/hopefullyawesomeBecome a Member on Youtube: https://www.youtube.com/channel/UCHRvjz_pKP1Th5Y8ZIwFMtQ/joinCheck out our Merch! - https://hopefullyawesome.creator-spring.com/This video is NOT sponsored. Some product links are affiliate links which means if you buy something we'll receive a small commission.Mail to:Matt & Maggie - PO Box 3924, Kingsport TN, 37664, United StatesMatt & Maggie - 1001 N Eastman Road # 3924, Kingsport TN, 37664, United States

It's Smallville: The Ultimate Season! This time we're going through the tenth episodes of every season and by process of elimination determining the ultimate episode 10 of Smallville.Zach is joined by Matthew Rocca, JJ Hodges from For Comic Junkies, Eric Folk from Smallville Papers, and Isaiah Goodridge.Check out and support Rocca's latest project, Morte!Check out JJ on For Comic Junkies!Check out Eric's work on Smallville Papers!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsKevonte ChilousInsaiyanIsaiah GoodridgeAtif SheikhJohn CurcioThomas NavenMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullMatt B.Amy J.Mike FranzNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayMichael H.RajJoey DienbergDJ DoenaNicholas CosoJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.com

Steph and Kara review this iconic millennial take on Superman's origin story.   First, a meteor shower in Smallville, Kansas, alters the lives of this small town's residents forever. Lana Lang loses her parents. Lex Luthor loses his hair. And Jonathan and Martha Kent adopt a boy who really isn't from around here. Years later, that boy has grown up to become Tom Welling, and it's time for the world to meet his abs.   Then, Lana Lang is That Girl. She has a drawer full of awards to prove it. Unfortunately, this also means she's prone to being kidnapped, and when bug guy Greg sets his sights on her, she needs somebody to saaaaaaave her, and Clark thinks he might be The One.   (Minor spoilers for the series.)   Hear us discuss… What this show owes to Buffy How this show made the CW Lex Luthor and Clark Kent as frenemies Kristin Kreuk as a biracial icon The hottest men in Smallville, ranked by Steph   Trigger warnings Car crashes, electrocution, insects  

#CalgaryExpo #CalgaryExpo2026This week on Toon'd In!, Jim Cummings welcomes acclaimed actress Erica Durance for a special episode recorded live at Calgary Expo! Known for her commanding presence and emotionally grounded performances across television, Durance has built a standout career in fan-favorite series and character-driven dramas. She is widely recognized for her iconic portrayal of Lois Lane on Smallville, as well as her leading role as Dr. Alex Reid on Saving Hope, along with a range of work across television movies and genre storytelling.In this engaging and wide-ranging episode—captured in front of a live convention audience—Erica shares stories from her journey in the entertainment industry, reflecting on stepping into one of pop culture's most enduring characters, developing on-screen chemistry, and navigating the evolving landscape of television. She discusses the nuances of portraying a modern Lois Lane, the balance between strength and vulnerability in her performances, and the experience of working on long-running, fan-driven series.Jim and Erica also dive into the world of fandom and conventions, exploring the lasting impact of Smallville, the connection between actors and audiences, and how beloved roles continue to resonate years after they first air. Along the way, Erica offers insights into her acting process, behind-the-scenes moments from set, and the challenges and rewards of sustaining a dynamic career in television.

It's Friday and the Big Shot Bob crew is back with Episode 131 of Shoot Around — the weekly bonus show where Robert Horry, Rob Jenners, and B-Dog Brandon Harper dig into the questions YOU send in and whatever's on their minds.   This week kicks off with a debate born from the new Michael Jackson biopic hitting theaters: Start, Bench, Cut — MJ era edition. The Thriller era (1982–85), the Bad era (1987–89), and the Dangerous era (1991–94) are all on the table. Which era do you start? Which do you bench? Which do you cut? The guys break down their picks, argue over the greatest MJ music videos of all time (Smooth Criminal vs. Remember the Time), debate whether Chris Tucker was actually IN Smooth Criminal (he wasn't), and Harper goes deep on the Ghost mini-movie that almost nobody's seen. The verdict? Unanimous love for Thriller, but serious disagreement on whether Bad or Dangerous gets the bench.   Then the guys jump into the all-time greatest NBA coaches debate, sparked by Pat Riley making it clear at 81 years old that he has zero plans to retire or step aside. Phil Jackson, Pat Riley, Gregg Popovich, Don Nelson, Lenny Wilkins, Red Auerbach — who makes the cut? Robert Horry gives a unique firsthand perspective on Phil Jackson, including the detail that Phil literally never spoke to him during their championship runs together. There's also the Del Harris comparison — same roster, completely different results — and what that says about coaching being as much about managing personalities as drawing up plays.   A Reddit user's anti-tanking beer pricing proposal gets a quick airing, and then the crew weighs in on Diego Pavia going undrafted after representing himself at the NFL Draft — no agent, no deal. Robert Horry reflects on firing his own agent in 2001 and what he missed out on off the court despite winning six championships, and the guys debate whether being your own representative ever actually works when you're already fighting an uphill battle.   To close things out, a fan question about the best superhero shows ever made opens up a great conversation about Invincible (currently must-watch TV), The Boys, Smallville, The Flash, Dragon Ball Z, the upcoming Mortal Kombat film, and a He-Man movie trip that Robert Horry is already planning with his son. Plus — the case for a live-action Thundercats reboot that the world is clearly not ready for.  

Mikey & Jeremy watch S7E15 of Smallville, "Veritas". They discuss Lionel's warnings, convenient flashbacks, and secret keys to unknown locks. 

The Davis in our dreams, the Doomsday in our nightmares, Sam Witwer joins us today to talk through the heartbreaking halfway point of season 8. In his dulcet tones, he shows reverence for the cast and crew and opens up about about how he wishes his younger self just shut up and enjoyed the moment. We've all been there, Sam. In this landmark episode, Chloe and Jimmy's wedding gets crashed by Lana Lang and Doomsday but with very different agendas. People are torn apart just as they come together. Smallville gets turned upside down. Don't miss this one! ...

Host Anthony Desiato and guest Tyler Patrick (The Krypton Report) assemble the ultimate all-media Luthor family tree from across time & media!Branches of the family tree include Lex's parents (Jules & Arlene, then Lionel & Lillian/Leticia), siblings (Lena, Lucas, & Julian), wives (Contessa, Lana, & many more), children (Lex Luthor Jr., Jerry White, Lena, & Conner), nieces (Nasthalthia & Lori), nephews (Val Colby & Lenny), and distant relatives (the Olsens).Even more deep cuts abound in this special episode. In the immortal words of SMALLVILLE's Lionel: "We're Luthors . . . We're Luthors!"Support the show and receive exclusive podcast content at Patreon.com/AnthonyDesiato, including the spinoff podcasts BEYOND METROPOLIS and DIGGING FOR JUSTICE!Visit BCW Supplies and use promo code FSP to save 10% on your next order of comics supplies. Get your DFK merch at the podcast's TeePublic storefront!FACEBOOK GROUP: Digging for Kryptonite: A Superman Fan GroupFACEBOOK PAGE: @diggingforkryptonitepodINSTAGRAM: @diggingforkryptonitepodTWITTER: @diggingforkrpodBLUESKY: @diggingforkrpod.bsky.socialEMAIL: flatsquirrelproductions@gmail.comWEBSITE: FlatSquirrelProductions.com Digging for Kryptonite is a Flat Squirrel Production. Theme music by Dan Pritchard. Key art by Isaiah Simmons. Mentioned in this episode:Always Hold On To SmallvilleDrunken AvengerAw Yeah ComicsSingle Bound PodcastThis Podcast Will Never DieFat Moose Comics

Our Smallville Season 8 rewatch covers S8xE18: Eternal, where Davis Bloome's origin finally comes into focus and it completely reframes him as both tragic and terrifying. We're looking at how he arrived the same night as Clark, what his childhood with Lionel and Lex did to him, and how that lack of love shaped the monster he's becoming. At the same time, the episode doesn't let him off the hook. His killing spree, his connection to Chloe, and that brutal lab scene force everyone into impossible choices. It's one of those episodes that makes you sit with the idea that Clark got a chance Davis never did… and still asks if things could've gone differently.Support the show: https://patreon.com/hopefullyawesomeBecome a Member on Youtube: https://www.youtube.com/channel/UCHRvjz_pKP1Th5Y8ZIwFMtQ/joinCheck out our Merch! - https://hopefullyawesome.creator-spring.com/This video is NOT sponsored. Some product links are affiliate links which means if you buy something we'll receive a small commission.Mail to: Matt & Maggie - PO Box 3924, Kingsport TN, 37664, United StatesMatt & Maggie - 1001 N Eastman Road # 3924, Kingsport TN, 37664, United States

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

In the one-hundred-and-ninety-first episode, we take another look at the Galileo Fallacy, starting with Trump being compared to Galileo and Einstein, Karoline Leavitt defending RFK Jr, and Rick Perry denying climate change.In Mark's British Politics Corner, we look at Farage misquoting Gandhi, Jonathan Dimbleby defending the BBC, and Zia Yusuf attacking Zack Polanski.In the Fallacy in the Wild section, we check out examples from The Newsroom, Smallville, and Monty Python's Flying Circus.Jim and Mark go head to head in Fake News, the game in which Mark has to guess which of three Trump quotes was made up by Jim.Then we talk about FEMA official Gregg Phillips and his claims of teleportation.And finally, we round up some of the other crazy Trump stories from the past week.The full show notes for this episode can be found at https://fallacioustrump.com/ft191 You can contact the guys at pod@fallacioustrump.com, on BlueSky @FallaciousTrump, Discord at fallacioustrump.com/discord or facebook at facebook.com/groups/fallacioustrumpAnd you can buy our T-shirts here: https://fallacioustrump.com/teeSubscribe to Fallacious Trump to make sure you never miss a logical fallacy. Rather than just mindless anti-Trump rhetoric, we apply skepticism and critical thinking to our Donald Trump analysis by exploring his liberal use of logical fallacies and cognitive biases, along with a bit of humor and news about US politics. (But there is also some of that much needed anti-Trump rhetoric.)Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

This week on our Smallville rewatch podcast, we're breaking down Season 8 Episode 17, “Hex,” where Zatanna drops into the story and turns everything upside down. Including giving Chloe the life she thinks she wants by swapping her into Lois' shoes. What starts as a fun Freaky Friday-ish situation quickly hits deeper, as Chloe's jealousy, purpose, and relationships all get put under a microscope, while Zatanna's emotional story about bringing her father back adds real weight to the magic. On top of that, Clark gets flipped into a version of himself without his usual instincts, forcing Chloe to step up in a way that quietly sets up her future. Between the character growth, the chaos, and Zatanna absolutely stealing the episode, this one gave us way more to talk about than we expected.Support the show: https://patreon.com/hopefullyawesomeBecome a Member on Youtube: https://www.youtube.com/channel/UCHRvjz_pKP1Th5Y8ZIwFMtQ/joinCheck out our Merch! - https://hopefullyawesome.creator-spring.com/This video is NOT sponsored. Some product links are affiliate links which means if you buy something we'll receive a small commission.Mail to: Matt & Maggie - PO Box 3924, Kingsport TN, 37664, United StatesMatt & Maggie - 1001 N Eastman Road # 3924, Kingsport TN, 37664, United States

Host Anthony Desiato presents a special Superman Day bonus episode! Anthony and guest Ed Gross (Superman: The Definitive History) survey Supergirl's adaptations across film, television, and animation — including the 1984 Helen Slater film, SMALLVILLE, the CBS/CW series starring Melissa Benoist, and more.They also dig into the original SUPERMAN III treatment by Ilya Salkind, which reimagined Superman & Supergirl as lovers and Brainiac as Supergirl's adoptive father-turned-suitor.MORE talk about Supergirl on STAS, SMALLVILLE, and the CBS/CW series is coming later in this event!Support the show and receive exclusive podcast content at Patreon.com/AnthonyDesiato, including the spinoff podcasts BEYOND METROPOLIS and DIGGING FOR JUSTICE!Visit BCW Supplies and use promo code FSP to save 10% on your next order of comics supplies. Get your DFK merch at the podcast's TeePublic storefront!FACEBOOK GROUP: Digging for Kryptonite: A Superman Fan GroupFACEBOOK PAGE: @diggingforkryptonitepodINSTAGRAM: @diggingforkryptonitepodTWITTER: @diggingforkrpodBLUESKY: @diggingforkrpod.bsky.socialEMAIL: flatsquirrelproductions@gmail.comWEBSITE: FlatSquirrelProductions.com Digging for Kryptonite is a Flat Squirrel Production. Theme music by Dan Pritchard. Key art by Isaiah Simmons.

160 episodes… wow

Welcome to the Infinite Taylorverse!  Here at the Infinite Taylorverse, we talk about all things nerdy and pop culture! Movies, TV, cartoons, comics, books, video games, tabletop games, and so much more! We talk about the latest pop culture news as well as rumors and fan theories.  Be advised that spoilers are imminent! In this, our 269th episode, we talk about Billie's time at Missouri Comic Con!  Listen to his recap of the event, as well as conversations with Grand Master B himself, Mako from Legend of Korra, David Faustino - Disney Legend and the voice of Goofy for 4 decades, Bill Farmer - Ren Stevens and Kim Possible herself, Christy Carlson Romano - and Ryan Evens from High School Musical, and Connor Kent from Smallville, Lucas Grabeel!  As always, thanks for strapping in for a ride through The Infinite Taylorverse!

We've never read a Supergirl comic and it's time to change that! We're taking a look at Tom King & Bilquis Evely's 8-issue miniseries, Supergirl: Woman of Tomorrow, which also serves as the inspiration behind her new movie. But is it secretly a Coen Brothers movie in disguise??? Then we're continuing The Ultimates with issue #8 where we meet some new cast members! Next Time: We celebrate April Fool's Day with Marvel's Smallville parody, Marville (2002)!

Dust off your capes and grab your kryptonite shields! We are officially in full-motion celebration mode for Superman Day, and we've put together an episode that's faster than a speeding bullet! This week we are traveling back in time to visit a version of the Man of Steel that captured the hearts of a generation. We are honored to welcome the legendary GERARD CHRISTOPHER (Superboy, Days of Our Lives, General Hospital) to the show! Gerard famously brought Clark Kent to life during his college years in the hit series Superboy. We're pulling back the curtain on the Smallville-before-Smallville era to discuss, Becoming the Hero, The What Ifs, Pre-Streaming Struggles, The Fans and more! And it wouldn't be a DizRadio without Jonathan from the D-Team! He's flying into the studio to drop some truth bombs about our favorite caped crusaders. He dives into the Dark Knight trend asking the tough questions, mainly has society made our heroes too dark? We're looking at the massive impact these symbols have on kids and why we need that classic Big Blue Boy Scout energy now more than ever. Plus… you aren't going to want to miss his hilarious (and slightly nostalgic) story about a certain pair of Underoos! Whether you grew up watching Superboy or you're a new fan of the Superman legacy, keep reaching for the skies, stay magical, and remember, Truth, Justice, and the DizRadio Way! So enjoy the Nostalgia, the Magic, the Wonder, and the Memories with The DizRadio Show "A Pop Culture Celebrity Guest Show"!

This Smallville Season 8 rewatch dives into “Turbulence,” where Clark starts pushing his luck as the Red-Blue Blur, Tess gets way too close to the truth, and everything around Davis, Chloe, and Jimmy spirals into something messy and honestly heartbreaking. We talk about Clark wanting to be seen, that intense plane rescue, and how Tess might already know more than she's saying, while the Davis storyline goes full dark with Jimmy unraveling and Chloe caught in the middle. It's an episode all about trust falling apart—who's losing it, who never had it, and how it all sets up some serious endgame energy for Season 8.Support the channel

Get ready to soar through the skies of Smallville and Metropolis with our Superman Family Mega Episode celebrating the Reign of the Superboys! In this jam-packed celebration of the Man of Steel and his incredible legacy, we're diving deep into the stories of Superman's extended family, from Kryptonian clones to super-powered sons.Here's what's in store for this week's super-powered lineup:Jon Kent (GHL 356) – Discover the origins of Superman and Lois Lane's son, the newest Superboy, and his journey from comics to live-action stardom on Superman & Lois.Conner Kent (GHL 336) – Half Superman, half Lex Luthor, all heart. We explore Conner's rise to fame during the Reign of the Supermen and his adventures in Smallville and beyond.Superman: The Movie (45th Anniversary) (GHL 484) – Celebrate 45 years of the film that defined the superhero genre, with a deep dive into Christopher Reeve's iconic portrayal and the movie's groundbreaking legacy.Man of Steel 10th Anniversary (GHL 462) – A decade after its release, we revisit the DCEU movie that redefined Superman for a new era and left an undeniable impact on the superhero film landscape.Top 5 Batman v Superman Stories – From allies to adversaries, we rank the best stories that capture the legendary dynamic between the Dark Knight and the Man of Steel.Lana Lang (GHL 206) – Explore the life of Superman's childhood best friend, Lana Lang, and her evolution into the powerful Superwoman.Whether you're a die-hard Superman fan or just discovering the rich history of the Superman Family, this episode is packed with epic moments, fascinating discussions, and nostalgic deep dives into the stories that made us believe a man can fly.Don't miss this super-sized celebration of all things Superman! Listen now and join us next week for even more Geek History Lesson adventures.For more exclusive bonus podcasts like our Justice League Review show, our Teen Titans Podcast, and our GHL Exclusive Discord, join the Geek History Lesson Patreon ► https://www.patreon.com/JawiinGHL RECOMMENDED READING from this episode► https://www.geekhistorylesson.com/recommendedreadingFOLLOW GHL►Instagram: https://www.instagram.com/geekhistorylessonThreads: https://www.threads.net/@geekhistorylessonFacebook: http://www.facebook.com/geekhistorylessonGet Your GHL Pin: https://geekhistorylesson.etsy.comYou can follow Ashley at https://www.threads.net/@ashleyvrobinson or https://www.ashleyvictoriarobinson.com/Follow Jason at https://www.threads.net/@jawiin or https://bsky.app/profile/jasoninman.bsky.socialThanks for showing up to class today. Class is dismissed!

Clark tells Lois EVERYTHING… and somehow that's only the beginning of the chaos!This episode really feels like a “what if?” timeline, and we get into all of it: Clark choosing to reveal himself to the world, Lois being the one he trusts with his biggest secret, and how surprisingly fast everything spirals once the truth is out there. We've got a lot to unpack about identity, trust, and whether the world is actually ready for the truth.Support the channel

In this episode of Going Back to Smallville, we're unpacking the incident at LuthorCorp, Oliver crossing a line he can't come back from, and the twisted Toyman plot that pulls everyone into Lex's final game. But let's be real… this is about Clark and Lana. Their relationship finally feels real, grounded, and hopeful for a second… and then the show rips it away in one of the most emotional exits Smallville ever gave a character.Support the channel

Cole and beloved friend of the show Seqarts discuss the first season of "Smallville", The WB's successful attempt to replace Buffy with a Superman prequel show about hunky teen Clark Kent and his best friends: Lex Luthor and the chick from NXIVM.  Discussions of future seasons will appear exclusively on the Patreon. Tickets and schedule for the Boston Underground Film Festival at Brattle (and Coolidge) Theater An extra special thanks to our $10 Executive Producers: JetChiclete, Isaac, squishward, Walt Lewellyn of The Black Casebook, Tropical Doves, jprestonpoole, Lohik, bernventers, and Owen2. If you can, please lend some support to these organizations: Gaza Funds PCRF (Palestinian Children's Relief Fund) MAP (Medical Aid for Palestinians) National Networks of Abortion Funds Immigrant Law Center of Minnesota If you enjoy the show please consider: Subscribing to our Patreon, where you can enjoy exclusive subscriber only episodes. Joining our Discord. Checking out our Credits page where you can view a complete list of Patrons. Leaving a rating and review on your podcast provider of choice.  Production by Miguel Tahni. Art by Kly, Zoe Woolley, and Jo Hermeer. Follow @MarvelousDeath for updates. 

The Return of Chloe "JUST TRUST ME G.D.I.!" SullivanWelcome to Smallville's version of The Matrix where our heroes have been abducted by the V.R.A. enforcers and put into virtual "real world" simulator. Chloe, basically Neo, is using her virtual avatar in order to get Clark, Lois, Oliver, Dinah and the rest of the team back into reality. But guess what? They're having a hard time trusting her! Also, we find out Chloe has been running The Suicide Squad for a while now...so, yeah, she still sucks.Alaina Huffman and Ted Whittall return for their final appearances as DC Comics' original characters Dinah Lance (a.k.a. "Black Canary") and Colonel Rick Flag, respectively.As always, enjoy the show and leave those FIVE STARS!

Mikey & Jeremy watch S7E14 of Smallville, "Traveler". They discuss Lionel's duplicity, astronomy cults, and Kryptonite lasers. 

The juice is loose in St. Louis. In this episode of Mostly Superheroes, host Logan Janis sits down with local performer Bruce the Juice, a Beetlejuice-inspired cosplay entertainer bringing the chaotic spirit of Beetlejuice to events across the city. With Tim Burton's cult classic experiencing a massive resurgence—thanks to nostalgia, cosplay culture, and the touring stage production of Beetlejuice the Musical—fans are rediscovering the strange, weird, and wonderful world first brought to life by Michael Keaton. Bruce shares his origin story, how he turned cosplay into live performance gigs, and wild behind-the-scenes stories from improv shows, cruise ship acting gigs, murder mystery theater, and unexpected celebrity encounters—including a hilarious casino moment with Tom Welling from Smallville. You'll also hear: • How Beetlejuice became one of the most iconic cult characters in pop culture • The rise of cosplay performers as live event entertainers • Inside stories from improv, murder mysteries, and cruise ship theater • The strange world of fan conventions and fandom communities • Why Beetlejuice fandom is hotter than ever

It's Smallville: The Ultimate Season! This time we're going through the ninth episodes of every season and by process of elimination determining the ultimate episode 9 of Smallville.Zach is joined by Mary Kwiatkowski from The KowSkiCast, Chris Fuchs from Always Hold On To Star Wars, Eddie Bissell and John Curcio.Check out Mary on The KowSkiCast!Check out Chris on Always Hold On To Star Wars!Check out Eddie's animal rescue Valley Cats & Friends!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsKevonte ChilousJoey DienbergInsaiyanNathan RothacherIsaiah GoodridgeAtif SheikhJohn CurcioMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullMatt B.Mike FranzJon ExendineNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeRob O'ConnorTina BJakeJacobJohn BobDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayMichael H.CherrylRajDJ DoenaNicholas CosoJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.com

It's Smallville: The Ultimate Season! This time we're going through the eighth episodes of every season and by process of elimination determining the ultimate episode 8 of Smallville.Zach is joined by Anthony Desiato from Digging for Kryptonite, Chris Fuchs from Always Hold On To Star Wars and Amanda Rose.Check out Anthony and his podcasts including Digging for Kryptonite at Flat Squirrel Productions!Check out Chris on Always Hold On To Star Wars!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsKevonte ChilousJoey DienbergInsaiyanNathan RothacherIsaiah GoodridgeAtif SheikhJohn CurcioMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullMatt B.Mike FranzJon ExendineNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeRob O'ConnorTina BJakeJacobJohn BobDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayMichael H.CherrylRajDJ DoenaNicholas CosoJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.com

Erica Durance (Smallville, Saving Hope) joins us for an open and reflective conversation about life after Smallville and the internal shifts that came with stepping away from constant momentum. She talks about burnout, aging, and learning how to listen to her body instead of pushing through everything. Erica also opens up about redefining success, letting go of hustle culture, and finding peace in slowing down. Thank you to our sponsors: ❤️ This episode is sponsored by BetterHelp. Give online therapy a try at https://betterhelp.com/inside and get on your way to being your best self __________________________________________________