Podcasts about Django

Don your dusters, stare down the sights of your six-shooters, and prepare to drag some coffins across the wastes--because it's time for Django: Sergio Corbucci's iconic 1966 spaghetti western! Michael is a lonesome drifter with steely eyes and a peculiar bundle looped about his shoulders. But when he's blown into a war-torn ghost town, he finds himself caught between a pair of rival bandit clans--one of which is headed by Blake, a man he has a personal grudge with. Can he liberate this mud-spackled town from evil? Or does he have some dark work set in mind himself?

Jeff shows up this week ready to extoll the virtues of a certain aquatic individual while Roman and Django just try to stay afloat in the comic book waters of this last week. We take a moment to revisit and have a lengthier discussion about Daredevil (honor the pact), and catch up on a lot of some of the best books coming out right now. You really should just hit play!0:04:30 - Well Welcome Wellmer!0:11:27 - Daredevil: Cold Day in Hell #1 (Discussion Part 2)0:23:21 - Amazing Spider-Man #10:31:20 - Adventure Time #10:35:43 - Resurrection Man: Quantum Karma #10:42:55 - Absolute Batman #70:48:53 - Phantom Road #110:51:20 - Aquaman #40:57:28 - The Ultimates #111:02:56 - BUCKSHOTS!!!1:18:06 - Announcement!SPOILERS! Tread carefully dear listener, because we're going to talk about what happened in these books. So definitely pause this, read your comics, and come back. We'll still be here!And an enormous thank you, as always, to Andrew Carlson for editing this mess into something listenable.Subscribe to us on Apple, Spotify or wherever you like to get your podcasts.Email us at jeff@thecomicsplace.com! We love hearing from you and there's a good chance we will read it on air!Cover art by Anand RadhakrishnanVisit us at The Comics Place next time you're in Bellingham, Washington!

In this episode, we dive deep into Django Unchained and explore what makes it one of Quentin Tarantino's most iconic films. We break down the characters and their motivations—from Django's revenge-driven arc to Dr. Schultz's moral compass and Calvin Candie's twisted charm. We analyze the brilliant camera work that enhances tension and emotion, and unpack the bold storytelling, unforgettable moments, and everything we love about the movie.Chapters:00:00:00 Intro Convo: 100th episode celebration00:04:29 Movie Breakdown Starts: Tarantino's creative character names00:07:53 How to perfectly hook an audience from the jump00:10:51 Samuel L Jackson's brilliantly written character00:15:07 Calvin Candie's real reason for being in the movie00:18:15 Shultz's misunderstanding of American slavery00:21:49 Dr.Shultz's character arc and death explained00:30:01 The backlash00:33:52 The pure brutality Tarantino puts you through00:38:24 Proof Tarantino has the most fun making a movie00:43:21 The KKK scene 00:48:45 The absurd amount of money they were making00:50:51 Diving deeper into Stephen00:54:56 Leonardo DiCaprio struggled being this evil00:58:46 Christoph Walz didn't want to be in this movie01:00:43 Tarantino spazzing on Jamie Foxx01:03:27 Breaking down some of the best camera work01:21:02 Our ratings: The NEW rating system (do it with us)01:28:36 Final thoughts: A cool metaphor01:35:17 Cue the music

¿Teatro, cine o terapia? “Django, con la soga al cuello” es todo eso y más. En este episodio de La vida es un teatro, te contamos por qué esta obra escrita y protagonizada por Antonio Vega se ha convertido en una de las experiencias más potentes y sanadoras de la cartelera mexicana.Un western existencial, un viaje entre la risa, la ansiedad y la belleza del arte en su máxima expresión.Con funciones en el Teatro Juan Ruiz de Alarcón, esta joya no se ve… se vive.Sube el volumen y acompáñanos.

durée : 02:28:19 - France Musique est à vous du samedi 12 avril 2025 - par : Gabrielle Oliveira-Guyon - Cap sur les années 50' ! Connaissez-vous Django (1954), le standard de jazz de John Lewis ; Moscou, quartier des cerises (1959), l'opéra de Chostakovitch ; et Laura, la chanson composée pour le film éponyme d'Otto Preminger interprétée par Nat King Cole (1952) ? - réalisé par : Emmanuel Benito

We're sitting down with Eric Matthes, the educator, author, and developer behind Django Simple Deploy. If you've ever struggled with taking that final step of getting your Django app onto a live server (without spending days wrestling with DevOps complexities), then give Django Simple Deploy a look. Eric shares how Django Simple Deploy automates away the boilerplate parts of deployment, so you can focus on building features instead of deciphering endless configs. We'll talk about this new project's journey to 1.0, the range of hosting platforms it supports, and why it's not just for beginners. Episode sponsors Worth Search Talk Python Courses Links from the show django-simple-deploy documentation: readthedocs.io django-simple-deploy repository: github.com Python Crash Course book: ehmatthes.github.io Code Red: codered.cloud Docker: docker.com Caddy: caddyserver.com Bunny.net CDN: bunny.net Platform.sh: platform.sh fly.io: fly.io Heroku: heroku.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

DJANGOOOOOOO No hace falta presentación, ¿no? Héctor nos trae el súper clásico #Django a esta cápsula. Sergio Corbucci, Franco Nero y mucha suciedad y violencia en forma de Spaghetti Western. ¿A quién no le atrae eso? Podéis ver Django en #Movistar y #Flixolé Ya sabéis, ¡Escuchad y opinad! También nos podéis encontrar aquí: Spotify: https://open.spotify.com/show/52i1iqZ56ACal18GPkCxiW Apple Podcasts: https://podcasts.apple.com/es/podcast/los-tres-amigos/id1198252523 Youtube: https://www.youtube.com/channel/UC3zK2XsnpHDGRujSTWHpL8Q Amazon Music: https://music.amazon.com/podcasts/e0b56d4f-4537-47e0-a252-9dfe56b5a490/los-tres-amigos Grupo de Telegram: https://t.me/LosTresAmigos https://www.facebook.com/LosTresAmigosPodcast/ Instagram: lostresamigospodcast Bluesky: @los3amigospodcast.bsky.social X / Twitter: @tresamigospod Threads: lostresamigospodcast Letterbox: https://letterboxd.com/LosTresAmigos/ #Western #SpaghettiWestern #Classic #SergioCorbucci

Jeff is coming out of his Maker-Sphere to return to the normal flow of time and grace us with the knowledge he gained while away. Actually, Django and Roman have to catch HIM up, since he forgot comic books existed while expanding his consciousness beyond the realm of our reality. So just hit play!0:05:01 - Absolute Superman #60:16:00 - Terminator #60:21:02 - Daredevil: Cold Day in Hell #10:27:20 - Let This One Be A Devil #20:29:41 - Absolute Green Lantern #10:39:30 - Django Buckshot!SPOILERS! Tread carefully dear listener, because we're going to talk about what happened in these books. So definitely pause this, read your comics, and come back. We'll still be here!And an enormous thank you, as always, to Andrew Carlson for editing this mess into something listenable.Subscribe to us on Apple, Spotify or wherever you like to get your podcasts.Email us at jeff@thecomicsplace.com! We love hearing from you and there's a good chance we will read it on air!Cover art by Rose BeschVisit us at The Comics Place next time you're in Bellingham, Washington!

What a night to endulge in the super sounds of the infinate groove. join us as we rise… Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django and DJ Ben Vera (@BenVeraOfficial), yourREAD MORE

Wow what can really be said about the great John Williams, he has provided the soundtrack for our lives. so much good music!!! Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django andREAD MORE

Music is the food of love, we are always jazzed to bring you these juicy grooves… Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django and DJ Ben Vera (@BenVeraOfficial), your tour guidesREAD MORE

This Week DJ Ben Vera holds down the airwaves on Global Village and delivers a super funky Afrobeat and Amapiano set. Enjoy! Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django and DJREAD MORE

We welcome you the the frequency of fun, join our sonic travels won't you? Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django and DJ Ben Vera (@BenVeraOfficial), your tour guides through thisREAD MORE

Another wonderful night of great music to stimulate the senses and open the mind! Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django and DJ Ben Vera (@BenVeraOfficial), your tour guides through thisREAD MORE

Tonight we give it up for an R & B Legend, the queen of hip hop soul Mary! Let's get it!!! Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django and DJ BenREAD MORE

Episode 2238 – Kid A.G., Don Tang, and Pooty Tang are your guides to a springtime shitshow of beeriods, barf, and ballsy chaos. Let's dive in, degenerates. It's 2013, and Kid's fumbling mics while Don's live, yelling “Let's do this!” Pooty's “Hi” is pure bait—cute, but she's no saint. They're chugging Giant Slayer and 12% Zombie Killer, because Michigan winters demand booze-fueled fuckery. Kid's stuffed on El Mexicano, Don and Pooty confess to fruit and string cheese—drunk toddler vibes, confirmed. Shit gets wild: Kid's dog sniffs his nuts mid-dry hump, Don's pup eats cat shit (“Protein!”), and St. Paddy's leaves ‘em puking black—blood or booze, per Nurse Pooty. Social media's popping— @DonaldPTang's tweeting porn star buttholes, Kid's shilling Wunderlist, and peanut butter Cinnamon Toast Crunch has him raging for chocolate dust. General Mills, you listening? Kid drops Django's N-bombs to piss off snowflakes, nearly punches a chick while Muppet-dancing, and dreams of church pew blowjobs—Californication style. Don pitches fucking on Mecca's box during prayer. Pooty's panty drawer's fair game, but her Mason-Jizm line's “above the head.” Beeriod—runny shits post-bender—debuts, and Don's Alaskan Fire Dragon (syphilis scare, jizz-out-the-nose BJ) steals the show. Final words? Don: “Swallow.” Pooty: “Bye.” Kid plugs porn.tumblr.com and Shoninzo's hospital bed. Call 206-202-DEEP, hit thegds.com for that millionth download (butt-crack undies prize!), and follow @DonaldPTang for filth. Spring's here—get sloppy. Original Release Date: April 5, 2013

durée : 00:08:59 - "Nuages" de Django Reinhardt (1910-1953) - "Nuages est une des mélodies parmi les plus connues, les plus emblématiques et les plus représentatives du style de Django Reinhardt. Elle possède pourtant en elle des caractères contrastées. Beaucoup de douceur mêlée à une certaine étrangeté chromatique." Sébastien Llinarès

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Jeff, Django, and Roman are very excited to talk endlessly about all the crazy good #1s that came out this last week! Godzilla is back to fight once again, Garth Ennis is here to share some weird stories, a flagship creative team has returned to their staple, and maybe a surprise return of some fun times... just hit play already!0:03:25 - Well Welcome Wellmer!0:12:46 - Fantastic Four / Godzilla #10:21:45 - Free For All #10:27:35 - Absolute Martian Manhunter #10:36:16 - We're Taking Everyone Down With Us #10:42:13 - You'll Do Bad Things #10:44:41 - For Down Below #10:48:38 - The Kids #10:52:16 - Batman #1580:59:40 - BUCKSHOTS!!!SPOILERS! Tread carefully dear listener, because we're going to talk about what happened in these books. So definitely pause this, read your comics, and come back. We'll still be here!And an enormous thank you, as always, to Andrew Carlson for editing this mess into something listenable.Subscribe to us on Apple, Spotify or wherever you like to get your podcasts.Email us at jeff@thecomicsplace.com! We love hearing from you and there's a good chance we will read it on air!Cover art by Javier RodriguezVisit us at The Comics Place next time you're in Bellingham, Washington!Bonus! If you live in town, head on down to the shop to pick up a FREE copy of We're Taking Everyone Down With Us #1 courtesy of William Elmer! Even better - stop by the shop on Friday, April 25th to get it signed by the creators!

You can now text us anonymously to leave feedback, suggest future content or simply hurl abuse at us. We'll read out any texts we receive on the show. Click here to try it out!Welcome back to Bad Dads Film Review!This episode, we're diving into the Top 5 Tarantino Moments, revisiting the high-stakes drama of ER, and wrapping up with some animated sci-fi fun with Duck Dodgers.Top 5 Tarantino MomentsQuentin Tarantino has given us some of the most unforgettable moments in modern cinema. His signature mix of sharp dialogue, brutal violence, and unexpected humor makes his films stand out. Here are five of his most iconic moments:The “Royale with Cheese” Conversation – Pulp Fiction (1994) Before anything even happens, Vincent (John Travolta) and Jules (Samuel L. Jackson) discuss European fast food with absurd detail, setting the tone for Tarantino's unique blend of casual conversation and impending violence.The Tavern Standoff – Inglourious Basterds (2009) What starts as a simple undercover mission turns into a slow-burn, tension-filled masterpiece. The suspense, the performances, and that final explosion of violence make this one of Tarantino's greatest scenes.Mr. Blonde's Dance – Reservoir Dogs (1992) Michael Madsen's sadistic yet strangely charming dance to Stuck in the Middle with You before torturing a cop is pure Tarantino—cool, unsettling, and unforgettable.The Bride vs. The Crazy 88 – Kill Bill: Volume 1 (2003) Uma Thurman's battle against a seemingly endless wave of sword-wielding assassins is one of the greatest action sequences in modern cinema, complete with Tarantino's love for stylized violence.Candyland Dinner – Django Unchained (2012) Leonardo DiCaprio's chilling performance as Calvin Candie reaches its peak during this tense dinner scene. The reveal of Django's plan and the real-life glass-smashing moment make this unforgettable.Main Feature: ERBefore Tarantino became a household name, he directed an episode of ER in 1995 titled Motherhood. While he didn't get to add any of his trademark bloodbaths, his influence is evident in the episode's fast-paced, chaotic energy, whip-smart dialogue, and stylish camera work.Beyond Tarantino's contribution, ER was one of the defining medical dramas of the ‘90s and early 2000s. It delivered intense, emotionally charged episodes with a cast led by George Clooney, Julianna Margulies, and Noah Wyle. The series set the gold standard for hospital dramas, balancing medical realism with compelling character arcs.Kids Feature: Duck DodgersShifting gears completely, we end this episode on a lighter note with Duck Dodgers, the hilarious sci-fi spin on Daffy Duck's adventures. Originally appearing in the 1953 short Duck Dodgers in the 24½th Century, the series expanded in the early 2000s, bringing us more of Daffy's over-the-top antics as an incompetent space hero battling the evil Martian Commander.Final ThoughtsFrom Tarantino's most iconic moments to high-stakes hospital drama and Looney Tunes in space, this episode has something for everyone. So grab aWe love to hear from our listeners! By which I mean we tolerate it. If it hasn't been completely destroyed yet you can usually find us on twitter @dads_film, on Facebook Bad Dads Film Review, on email at baddadsjsy@gmail.com or on our website baddadsfilm.com. Until next time, we remain... Bad Dads

In this episode: Ode (@thatsod.e / @thatsod_e) and Mo "Kid" Licorish (@licorishislegit) discuss a wide range of nerdy, pop culture, and political topics. Topics include:RIP George ForemanDepartment of EducationNasaKlarna/DoorDashQuinta BrunsonJonathan MajorDrakeKanye WestAnd Much More!

Hey! Únete a este canal para acceder a sus beneficios:https://www.youtube.com/channel/UC6ovKbWpg3jEsvisKXPbEwQ/joinRedes de Lauhttps://www.instagram.com/lau_alcala_/https://www.youtube.com/channel/UC0KJrg4TZGTVSNzqeV1EnOA00:00 Intro02:36 5 Datos Supremos Django03:26 Django: Una Historia De Amor01:08:38 Lectura y MásCanal de Berny: @bhruyBanda de Polo: @losdiosesmusik¡Disfruten este episodio!Django: Una Historia De Amor | Geeks Supremos EP 214 _________________________________________________________________________Recuerden seguirnos en las redes sociales como Geeks Supremos.  Twitter: https://twitter.com/GeeksSupremos  Instagram: https://www.instagram.com/geekssuprem...Spotify: https://open.spotify.com/show/7FkvYOW...  Anchor: https://anchor.fm/geekssupremosFacebook: https://https://www.facebook.com/GeeksSupremosGrupo de Face: https://www.facebook.com/groups/276372618378578Youtube: ⁠@GeeksSupremosCorreo para mandar historias: historiasgeekssupremos@gmail.com___________________________________________________________________________Redes de Los Dioses MusikSpotify: https://open.spotify.com/track/3PvRzV...Instagram: https://www.instagram.com/losdiosesmu...TikTok: https://www.tiktok.com/@losdiosesmusikYoutube: ⁠@losdiosesmusik  Apple Music: https://music.apple.com/mx/artist/los...X

Brandon Sheffield subjects well known video game characters who just happen to be attending GDC 2025 to the famous Proust Questionnaire. Yes, we know it's pronounced “proost.” Hosted by Brandon Sheffield, with Matthew Burns, Frank Cifaldi, Naomi Clark, Nikki Grayson, David Hellman, merritt k, Shannon Liao, Laura Michet, Alx Preston, Liz Ryerson, Phil Salvador, and Derek Yu. Edited by Esper Quinn, original music by Kurt Feldman. Watch episodes with full video on YouTube Discuss this episode in the Insert Credit Forums SHOW NOTES: Luigi, via merritt k (00:14) Rick Taylor, via Derek Yu (07:12) Astarion Ancunín, via Shannon Liao (11:34) Squall Leonhart, via Phil Salvador (13:55) Headcrab, via Laura Michet (17:50) Samus Aran, via Alx Preston (20:53) Miles “Tails” Prower, via Liz Ryerson (25:44) Alan Wake, via Matthew Burns (31:02) Geralt of Rivia, via Naomi Clark (36:08) Django, via Nikki Grayson (41:30) Tim, via David Hellman (45:15) Princess Peach, via Frank Cifaldi (47:37) This week's Insert Credit Show is brought to you by patrons like you. Thank you. Subscribe: RSS, YouTube, Apple Podcasts, Spotify, and more!

Brandon Sheffield subjects well known video game characters who just happen to be attending GDC 2025 to the famous Proust Questionnaire. Yes, we know it's pronounced “proost.” Hosted by Brandon Sheffield, with Matthew Burns, Frank Cifaldi, Naomi Clark, Nikki Grayson, David Hellman, merritt k, Shannon Liao, Laura Michet, Alx Preston, Liz Ryerson, Phil Salvador, and Derek Yu. Edited by Esper Quinn, original music by Kurt Feldman. Watch episodes with full video on YouTube Discuss this episode in the Insert Credit Forums SHOW NOTES: Luigi, via merritt k (00:14) Rick Taylor, via Derek Yu (07:12) Astarion Ancunín, via Shannon Liao (11:34) Squall Leonhart, via Phil Salvador (13:55) Headcrab, via Laura Michet (17:50) Samus Aran, via Alx Preston (20:53) Miles “Tails” Prower, via Liz Ryerson (25:44) Alan Wake, via Matthew Burns (31:02) Geralt of Rivia, via Naomi Clark (36:08) Django, via Nikki Grayson (41:30) Tim, via David Hellman (45:15) Princess Peach, via Frank Cifaldi (47:37) This week's Insert Credit Show is brought to you by patrons like you. Thank you. Subscribe: RSS, YouTube, Apple Podcasts, Spotify, and more!

On this episode of Drinks and a Movie, we're diving into Quentin Tarantino's Django Unchained (2012)—a brutal, stylish, and unapologetically bold Western that redefined the genre. From Jamie Foxx's badass performance as Django to Christoph Waltz's unforgettable turn as Dr. King Schultz and Leonardo DiCaprio's menacing Calvin Candie, we're breaking down the action, historical influences, and signature Tarantino dialogue that make this film an instant classic. And what better way to pair this film than with a whiskey that embodies history, resilience, and bold flavor? We're sipping on Uncle Nearest 1856, a whiskey with deep historical roots, named after Nathan “Nearest” Green, the enslaved man who taught Jack Daniel how to distill. 

In this episode: Martin has created smiti18n (pronounced smitten) - A very complete internationalization library for Lua with LÖVE support

In this episode: Martin has created smiti18n (pronounced smitten) - A very complete internationalization library for Lua with LÖVE support

In this episode: Martin has created smiti18n (pronounced smitten) – A very complete internationalization library for Lua with LÖVE support

As with Django, Corbucci wrote the film with his brother Bruno, as well as Vittoriano Petrilli and Mario Amendola. He'd been deeply influenced by the recent assassinations of Che Guevara, Cuban revolutionary who had tried to spark a Communist overthrow of Bolivia, and the US' Malcolm X, a one-time Nation of Islam leader converted to the Muslim faith and killed at a speaking engagement. As the end of the 60s approached, Corbucci felt that the era of progressive political action was dwindling, to be overtaken by fierce reactionary elements. The earlier activism seemed to him to be hurled backward in progress and time. As Alex Cox noted about Corbucci's thoughts, “You could only take on the powerful and the wicked for a short while, it seemed, before they crushed you.” Corbucci set the film's action in 1899 Utah just prior to the Great Blizzard, the winter scenes reflecting his feelings of pessimism, depression, and disgust. Another influence --- famed Italian actor Marcello Mastroianni had secretly wished to play a role in a Spaghetti but felt his poor English would interfere --- he suggested to Corbucci to pen a film of a mute protagonist. Corbucci adapted the idea into the film sans Marcello --- it would become the second Mud and Blood work, 1968's The Great Silence, a word play on the bleakness of the winter setting and the mute anti-hero. Website and blog: www.thosewonderfulpeople.comIG: @thosewonderfulpeopleTwitter: @FilmsInTheDark

SPOILERES! A Tune Up Radio 282. adását hallgathatjátok, melyben a Világ legjobb filmje II. versenysorozat negyedik rájátszását tárgyaljuk. Résztvevők: Madarász Isti filmrendező (Tündérkert, Átjáróház, Hurok stb.), Ákos, Gáspár, Lóri, András Intro (0:00) Rekviem egy álomért - Django elszabadul* (0:19:41) Take Shelter - A függetlenség napja (1:02:10) Kapcsolat - Star Wars: Egy új remény* (1:27:21) A segítség - Életrevalók (2:06:49) Éjféli cowboy* - Száll a kakukk fészkére (2:25:04) Támogass minket Patreonon: https://www.patreon.com/radiotuneup SOUNDCLOUD https://soundcloud.com/radiotuneup SPOTIFY https://open.spotify.com/show/6g69TlNlr7jk0ojcM05aAb APPLE PODCASTS https://podcasts.apple.com/hu/podcast/tune-up-radio-podcast/id1460229855?fbclid=IwAR0kyH86OgonL8mzsAfFnDCQHDIVEvQcyUX8wD8BA-vL5h5Lk9di3Dp12Ko MP3 LINK: https://drive.google.com/file/d/1LAudIkE5siTjiMNgi6xkczjrEGzG2E63/view A Tune Up Radio Soundboardot a Google Play Store-ban találjátok, vagy ezen a linken keresztül tudjátok letölteni: https://play.google.com/store/apps/details?id=com.B13Soft.tuneupradio_soundboard Webes változat: https://bbbotond.github.io/tuneup-soundboard/ Isti Twitter fiókja: @madaraszisti Ákos Twitter (X) fiókja: @lennoxasaki András Twitter (X) fiókja: @AndrasUp Facebook: https://www.facebook.com/radiotuneup/ Twitter (X): @radiotuneup E-mail: tuneup314@gmail.com

Roman, Jeff, and Django are here to talk shop with the debbil before their upcoming trip! We've got some temporal displacement, temporal displacement, and temporal displacement. Wait... something's not right here...0:02:26 - Well Welcome Wellmer!0:06:52 - Assorted Crisis Events #10:13:20 - Batman: Dark Patterns #40:18:43 - Absolute Superman #50:22:34 - Babs #60:25:53 - Voicemail from Ethan! Thank you Ethan!0:27:48 - Aquaman #30:29:49 - Nick Fury & Fin Fang Foom #10:32:49 - Amazing Spider-Man #690:34:25 - Geiger / Redcoat #100:34:45 - All-New Venom #40:35:59 - Transformers #140:37:22 - El FuegoSPOILERS! Tread carefully dear listener, because we're going to talk about what happened in these books. So definitely pause this, read your comics, and come back. We'll still be here!And an enormous thank you, as always, to Andrew Carlson for editing this mess into something listenable.Subscribe to us on Apple, Spotify or wherever you like to get your podcasts.Email us at jeff@thecomicsplace.com! We love hearing from you and there's a good chance we will read it on air!Cover art by Eric ZawadskiVisit us at The Comics Place next time you're in Bellingham, Washington!

The two rival clans of Concessions deep fry some story conventions before hurling them back and forth at each other with stylish displays of violence. PLEASE LIKE/RATE/REVIEW!Find us on Threads: @danconcesssions @jaredconcessions

Trente-deuxième épisode de Vanishing Point, le podcast itinérant qui vous invite à voyager sur les routes imaginaires du Cinéma, sans gps ni direction assistée, avec comme seule boussole : notre passion, nos échanges et nos envies. Piloté par Mad, Ben et Franck. Les films abordés dans cet épisode : 00:01:24 : Skip générique (on juge pas promis) 00:03:36 : Day of the dead, George Romero (1985) 02:09:16 : 28 days later, Danny Boyle (2002) 04:05:31 : BLINDTEST 04:13:11 : La nuit a dévoré le monde, Dominique Rocher (2018) Les réponses au blindtest précédent (Épisode 31 - Six feet under) sur Don't fear the Reaper, The Mutton Bird : Terminator 3, Le bon la brute et le truand, Simetierre, Le roi lion, Sleepy Hollow, Texas chainsaw massacre, L'homme qui aimait les femmes, Frighteners, A monster call, Django, Thriller Rejoignez-nous sur les réseaux sociaux : BLUESKY : https://bsky.app/profile/vpointpodcast.bsky.social DISCORD : https://discord.gg/mDgZZd6Czj FACEBOOK : https://www.facebook.com/podcast.vanishingpoint INSTAGRAM : https://www.instagram.com/vanishingpoint_podcast/ TWITTER : https://twitter.com/vpoint_podcast Et sur votre plateforme de podcasts préférée ! Abonnez-vous

We are working with Amplify on the 2025 State of AI Engineering Survey to be presented at the AIE World's Fair in SF! Join the survey to shape the future of AI Eng!We first met Snipd over a year ago, and were immediately impressed by the design, but were doubtful about the behavior of snipping as the title behavior:Podcast apps are enormously sticky - Spotify spent almost $1b in podcast acquisitions and exclusive content just to get an 8% bump in market share among normies.However, after a disappointing Overcast 2.0 rewrite with no AI features in the last 3 years, I finally bit the bullet and switched to Snipd. It's 2025, your podcast app should be able to let you search transcripts of your podcasts. Snipd is the best implementation of this so far.And yet they keep shipping:What impressed us wasn't just how this tiny team of 4 was able to bootstrap a consumer AI app against massive titans and do so well; but also how seriously they think about learning through podcasts and improving retention of knowledge over time, aka “Duolingo for podcasts”. As an educational AI podcast, that's a mission we can get behind.Full Video PodFind us on YouTube! This was the first pod we've ever shot outdoors!Show Notes* How does Shazam work?* Flutter/FlutterFlow* wav2vec paper* Perplexity Online LLM* Google Search Grounding* Comparing Snipd transcription with our Bee episode* NIPS 2017 Flo Rida* Gustav Söderström - Background AudioTimestamps* [00:00:03] Takeaways from AI Engineer NYC* [00:00:17] Weather in New York.* [00:00:26] Swyx and Snipd.* [00:01:01] Kevin's AI summit experience.* [00:01:31] Zurich and AI.* [00:03:25] SigLIP authors join OpenAI.* [00:03:39] Zurich is very costly.* [00:04:06] The Snipd origin story.* [00:05:24] Introduction to machine learning.* [00:09:28] Snipd and user knowledge extraction.* [00:13:48] App's tech stack, Flutter, Python.* [00:15:11] How speakers are identified.* [00:18:29] The concept of "backgroundable" video.* [00:29:05] Voice cloning technology.* [00:31:03] Using AI agents.* [00:34:32] Snipd's future is multi-modal AI.* [00:36:37] Snipd and existing user behaviour.* [00:42:10] The app, summary, and timestamps.* [00:55:25] The future of AI and podcasting.* [1:14:55] Voice AITranscriptswyx [00:00:03]: Hey, I'm here in New York with Kevin Ben-Smith of Snipd. Welcome.Kevin [00:00:07]: Hi. Hi. Amazing to be here.swyx [00:00:09]: Yeah. This is our first ever, I think, outdoors podcast recording.Kevin [00:00:14]: It's quite a location for the first time, I have to say.swyx [00:00:18]: I was actually unsure because, you know, it's cold. It's like, I checked the temperature. It's like kind of one degree Celsius, but it's not that bad with the sun. No, it's quite nice. Yeah. Especially with our beautiful tea. With the tea. Yeah. Perfect. We're going to talk about Snips. I'm a Snips user. I'm a Snips user. I had to basically, you know, apart from Twitter, it's like the number one use app on my phone. Nice. When I wake up in the morning, I open Snips and I, you know, see what's new. And I think in terms of time spent or usage on my phone, I think it's number one or number two. Nice. Nice. So I really had to talk about it also because I think people interested in AI want to think about like, how can we, we're an AI podcast, we have to talk about the AI podcast app. But before we get there, we just finished. We just finished the AI Engineer Summit and you came for the two days. How was it?Kevin [00:01:07]: It was quite incredible. I mean, for me, the most valuable was just being in the same room with like-minded people who are building the future and who are seeing the future. You know, especially when it comes to AI agents, it's so often I have conversations with friends who are not in the AI world. And it's like so quickly it happens that you, it sounds like you're talking in science fiction. And it's just crazy talk. It was, you know, it's so refreshing to talk with so many other people who already see these things and yeah, be inspired then by them and not always feel like, like, okay, I think I'm just crazy. And like, this will never happen. It really is happening. And for me, it was very valuable. So day two, more relevant, more relevant for you than day one. Yeah. Day two. So day two was the engineering track. Yeah. That was definitely the most valuable for me. Like also as a producer. Practitioner myself, especially there were one or two talks that had to do with voice AI and AI agents with voice. Okay. So that was quite fascinating. Also spoke with the speakers afterwards. Yeah. And yeah, they were also very open and, and, you know, this, this sharing attitudes that's, I think in general, quite prevalent in the AI community. I also learned a lot, like really practical things that I can now take away with me. Yeah.swyx [00:02:25]: I mean, on my side, I, I think I watched only like half of the talks. Cause I was running around and I think people saw me like towards the end, I was kind of collapsing. I was on the floor, like, uh, towards the end because I, I needed to get, to get a rest, but yeah, I'm excited to watch the voice AI talks myself.Kevin [00:02:43]: Yeah. Yeah. Do that. And I mean, from my side, thanks a lot for organizing this conference for bringing everyone together. Do you have anything like this in Switzerland? The short answer is no. Um, I mean, I have to say the AI community in, especially Zurich, where. Yeah. Where we're, where we're based. Yeah. It is quite good. And it's growing, uh, especially driven by ETH, the, the technical university there and all of the big companies, they have AI teams there. Google, like Google has the biggest tech hub outside of the U S in Zurich. Yeah. Facebook is doing a lot in reality labs. Uh, Apple has a secret AI team, open AI and then SwapBit just announced that they're coming to Zurich. Yeah. Um, so there's a lot happening. Yeah.swyx [00:03:23]: So, yeah, uh, I think the most recent notable move, I think the entire vision team from Google. Uh, Lucas buyer, um, and, and all the other authors of Siglip left Google to join open AI, which I thought was like, it's like a big move for a whole team to move all at once at the same time. So I've been to Zurich and it just feels expensive. Like it's a great city. Yeah. It's great university, but I don't see it as like a business hub. Is it a business hub? I guess it is. Right.Kevin [00:03:51]: Like it's kind of, well, historically it's, uh, it's a finance hub, finance hub. Yeah. I mean, there are some, some large banks there, right? Especially UBS, uh, the, the largest wealth manager in the world, but it's really becoming more of a tech hub now with all of the big, uh, tech companies there.swyx [00:04:08]: I guess. Yeah. Yeah. And, but we, and research wise, it's all ETH. Yeah. There's some other things. Yeah. Yeah. Yeah.Kevin [00:04:13]: It's all driven by ETH. And then, uh, it's sister university EPFL, which is in Lausanne. Okay. Um, which they're also doing a lot, but, uh, it's, it's, it's really ETH. Uh, and otherwise, no, I mean, it's a beautiful, really beautiful city. I can recommend. To anyone. To come, uh, visit Zurich, uh, uh, let me know, happy to show you around and of course, you know, you, you have the nature so close, you have the mountains so close, you have so, so beautiful lakes. Yeah. Um, I think that's what makes it such a livable city. Yeah.swyx [00:04:42]: Um, and the cost is not, it's not cheap, but I mean, we're in New York city right now and, uh, I don't know, I paid $8 for a coffee this morning, so, uh, the coffee is cheaper in Zurich than the New York city. Okay. Okay. Let's talk about Snipt. What is Snipt and, you know, then we'll talk about your origin story, but I just, let's, let's get a crisp, what is Snipt? Yeah.Kevin [00:05:03]: I always see two definitions of Snipt, so I'll give you one really simple, straightforward one, and then a second more nuanced, um, which I think will be valuable for the rest of our conversation. So the most simple one is just to say, look, we're an AI powered podcast app. So if you listen to podcasts, we're now providing this AI enhanced experience. But if you look at the more nuanced, uh, podcast. Uh, perspective, it's actually, we, we've have a very big focus on people who like your audience who listened to podcasts to learn something new. Like your audience, you want, they want to learn about AI, what's happening, what's, what's, what's the latest research, what's going on. And we want to provide a, a spoken audio platform where you can do that most effectively. And AI is basically the way that we can achieve that. Yeah.swyx [00:05:53]: Means to an end. Yeah, exactly. When you started. Was it always meant to be AI or is it, was it more about the social sharing?Kevin [00:05:59]: So the first version that we ever released was like three and a half years ago. Okay. Yeah. So this was before ChatGPT. Before Whisper. Yeah. Before Whisper. Yeah. So I think a lot of the features that we now have in the app, they weren't really possible yet back then. But we already from the beginning, we always had the focus on knowledge. That's the reason why, you know, we in our team, why we listen to podcasts, but we did have a bit of a different approach. Like the idea in the very beginning was, so the name is Snips and you can create these, what we call Snips, which is basically a small snippet, like a clip from a, from a podcast. And we did envision sort of like a, like a social TikTok platform where some people would listen to full episodes and they would snip certain, like the best parts of it. And they would post that in a feed and other users would consume this feed of Snips. And use that as a discovery tool or just as a means to an end. And yeah, so you would have both people who create Snips and people who listen to Snips. So our big hypothesis in the beginning was, you know, it will be easy to get people to listen to these Snips, but super difficult to actually get them to create them. So we focused a lot of, a lot of our effort on making it as seamless and easy as possible to create a Snip. Yeah.swyx [00:07:17]: It's similar to TikTok. You need CapCut for there to be videos on TikTok. Exactly.Kevin [00:07:23]: And so for, for Snips, basically whenever you hear an amazing insight, a great moment, you can just triple tap your headphones. And our AI actually then saves the moment that you just listened to and summarizes it to create a note. And this is then basically a Snip. So yeah, we built, we built all of this, launched it. And what we found out was basically the exact opposite. So we saw that people use the Snips to discover podcasts, but they really, you know, they don't. You know, really love listening to long form podcasts, but they were creating Snips like crazy. And this was, this was definitely one of these aha moments when we realized like, hey, we should be really doubling down on the knowledge of learning of, yeah, helping you learn most effectively and helping you capture the knowledge that you listen to and actually do something with it. Because this is in general, you know, we, we live in this world where there's so much content and we consume and consume and consume. And it's so easy to just at the end of the podcast. You just start listening to the next podcast. And five minutes later, you've forgotten everything. 90%, 99% of what you've actually just learned. Yeah.swyx [00:08:31]: You don't know this, but, and most people don't know this, but this is my fourth podcast. My third podcast was a personal mixtape podcast where I Snipped manually sections of podcasts that I liked and added my own commentary on top of them and published them as small episodes. Nice. So those would be maybe five to 10 minute Snips. Yeah. And then I added something that I thought was a good story or like a good insight. And then I added my own commentary and published it as a separate podcast. It's cool. Is that still live? It's still live, but it's not active, but you can go back and find it. If you're, if, if you're curious enough, you'll see it. Nice. Yeah. You have to show me later. It was so manual because basically what my process would be, I hear something interesting. I note down the timestamp and I note down the URL of the podcast. I used to use Overcast. So it would just link to the Overcast page. And then. Put in my note taking app, go home. Whenever I feel like publishing, I will take one of those things and then download the MP3, clip out the MP3 and record my intro, outro and then publish it as a, as a podcast. But now Snips, I mean, I can just kind of double click or triple tap.Kevin [00:09:39]: I mean, those are very similar stories to what we hear from our users. You know, it's, it's normal that you're doing, you're doing something else while you're listening to a podcast. Yeah. A lot of our users, they're driving, they're working out, walking their dog. So in those moments when you hear something amazing, it's difficult to just write them down or, you know, you have to take out your phone. Some people take a screenshot, write down a timestamp, and then later on you have to go back and try to find it again. Of course you can't find it anymore because there's no search. There's no command F. And, um, these, these were all of the issues that, that, that we encountered also ourselves as users. And given that our background was in AI, we realized like, wait, hey, this is. This should not be the case. Like podcast apps today, they're still, they're basically repurposed music players, but we actually look at podcasts as one of the largest sources of knowledge in the world. And once you have that different angle of looking at it together with everything that AI is now enabling, you realize like, hey, this is not the way that we, that podcast apps should be. Yeah.swyx [00:10:41]: Yeah. I agree. You mentioned something that you said your background is in AI. Well, first of all, who's the team and what do you mean your background is in AI?Kevin [00:10:48]: Those are two very different things. I'm going to ask some questions. Yeah. Um, maybe starting with, with my backstory. Yeah. My backstory actually goes back, like, let's say 12 years ago or something like that. I moved to Zurich to study at ETH and actually I studied something completely different. I studied mathematics and economics basically with this specialization for quant finance. Same. Okay. Wow. All right. So yeah. And then as you know, all of these mathematical models for, um, asset pricing, derivative pricing, quantitative trading. And for me, the thing that, that fascinates me the most was the mathematical modeling behind it. Uh, mathematics, uh, statistics, but I was never really that passionate about the finance side of things.swyx [00:11:32]: Oh really? Oh, okay. Yeah. I mean, we're different there.Kevin [00:11:36]: I mean, one just, let's say symptom that I noticed now, like, like looking back during that time. Yeah. I think I never read an academic paper about the subject in my free time. And then it was towards the end of my studies. I was already working for a big bank. One of my best friends, he comes to me and says, Hey, I just took this course. You have to, you have to do this. You have to take this lecture. Okay. And I'm like, what, what, what is it about? It's called machine learning and I'm like, what, what, what kind of stupid name is that? Uh, so you sent me the slides and like over a weekend I went through all of the slides and I just, I just knew like freaking hell. Like this is it. I'm, I'm in love. Wow. Yeah. Okay. And that was then over the course of the next, I think like 12 months, I just really got into it. Started reading all about it, like reading blog posts, starting building my own models.swyx [00:12:26]: Was this course by a famous person, famous university? Was it like the Andrew Wayne Coursera thing? No.Kevin [00:12:31]: So this was a ETH course. So a professor at ETH. Did he teach in English by the way? Yeah. Okay.swyx [00:12:37]: So these slides are somewhere available. Yeah. Definitely. I mean, now they're quite outdated. Yeah. Sure. Well, I think, you know, reflecting on the finance thing for a bit. So I, I was, used to be a trader, uh, sell side and buy side. I was options trader first and then I was more like a quantitative hedge fund analyst. We never really use machine learning. It was more like a little bit of statistical modeling, but really like you, you fit, you know, your regression.Kevin [00:13:03]: No, I mean, that's, that's what it is. And, uh, or you, you solve partial differential equations and have then numerical methods to, to, to solve these. That's, that's for you. That's your degree. And that's, that's not really what you do at work. Right. Unless, well, I don't know what you do at work. In my job. No, no, we weren't solving the partial differential. Yeah.swyx [00:13:18]: You learn all this in school and then you don't use it.Kevin [00:13:20]: I mean, we, we, well, let's put it like that. Um, in some things, yeah, I mean, I did code algorithms that would do it, but it was basically like, it was the most basic algorithms and then you just like slightly improve them a little bit. Like you just tweak them here and there. Yeah. It wasn't like starting from scratch, like, Oh, here's this new partial differential equation. How do we know?swyx [00:13:43]: Yeah. Yeah. I mean, that's, that's real life, right? Most, most of it's kind of boring or you're, you're using established things because they're established because, uh, they tackle the most important topics. Um, yeah. Portfolio management was more interesting for me. Um, and, uh, we, we were sort of the first to combine like social data with, with quantitative trading. And I think, uh, I think now it's very common, but, um, yeah. Anyway, then you, you went, you went deep on machine learning and then what? You quit your job? Yeah. Yeah. Wow.Kevin [00:14:12]: I quit my job because, uh, um, I mean, I started using it at the bank as well. Like try, like, you know, I like desperately tried to find any kind of excuse to like use it here or there, but it just was clear to me, like, no, if I want to do this, um, like I just have to like make a real cut. So I quit my job and joined an early stage, uh, tech startup in Zurich where then built up the AI team over five years. Wow. Yeah. So yeah, we built various machine learning, uh, things for, for banks from like models for, for sales teams to identify which clients like which product to sell to them and with what reasons all the way to, we did a lot, a lot with bank transactions. One of the actually most fun projects for me was we had an, an NLP model that would take the booking text of a transaction, like a credit card transaction and pretty fired. Yeah. Because it had all of these, you know, like numbers in there and abbreviations and whatnot. And sometimes you look at it like, what, what is this? And it was just, you know, it would just change it to, I don't know, CVS. Yeah.swyx [00:15:15]: Yeah. But I mean, would you have hallucinations?Kevin [00:15:17]: No, no, no. The way that everything was set up, it wasn't like, it wasn't yet fully end to end generative, uh, neural network as what you would use today. Okay.swyx [00:15:30]: Awesome. And then when did you go like full time on Snips? Yeah.Kevin [00:15:33]: So basically that was, that was afterwards. I mean, how that started was the friend of mine who got me into machine learning, uh, him and I, uh, like he also got me interested into startups. He's had a big impact on my life. And the two of us were just a jam on, on like ideas for startups every now and then. And his background was also in AI data science. And we had a couple of ideas, but given that we were working full times, we were thinking about, uh, so we participated in Hack Zurich. That's, uh, Europe's biggest hackathon, um, or at least was at the time. And we said, Hey, this is just a weekend. Let's just try out an idea, like hack something together and see how it works. And the idea was that we'd be able to search through podcast episodes, like within a podcast. Yeah. So we did that. Long story short, uh, we managed to do it like to build something that we realized, Hey, this actually works. You can, you can find things again in podcasts. We had like a natural language search and we pitched it on stage. And we actually won the hackathon, which was cool. I mean, we, we also, I think we had a good, um, like a good, good pitch or a good example. So we, we used the famous Joe Rogan episode with Elon Musk where Elon Musk smokes a joint. Okay. Um, it's like a two and a half hour episode. So we were on stage and then we just searched for like smoking weed and it would find that exact moment. It will play it. And it just like, come on with Elon Musk, just like smoking. Oh, so it was video as well? No, it was actually completely based on audio. But we did have the video for the presentation. Yeah. Which had a, had of course an amazing effect. Yeah. Like this gave us a lot of activation energy, but it wasn't actually about winning the hackathon. Yeah. But the interesting thing that happened was after we pitched on stage, several of the other participants, like a lot of them came up to us and started saying like, Hey, can I use this? Like I have this issue. And like some also came up and told us about other problems that they have, like very adjacent to this with a podcast. Where's like, like this. Like, could, could I use this for that as well? And that was basically the, the moment where I realized, Hey, it's actually not just us who are having these issues with, with podcasts and getting to the, making the most out of this knowledge. Yeah. The other people. Yeah. That was now, I guess like four years ago or something like that. And then, yeah, we decided to quit our jobs and start, start this whole snip thing. Yeah. How big is the team now? We're just four people. Yeah. Just four people. Yeah. Like four. We're all technical. Yeah. Basically two on the, the backend side. So one of my co-founders is this person who got me into machine learning and startups. And we won the hackathon together. So we have two people for the backend side with the AI and all of the other backend things. And two for the front end side, building the app.swyx [00:18:18]: Which is mostly Android and iOS. Yeah.Kevin [00:18:21]: It's iOS and Android. We also have a watch app for, for Apple, but yeah, it's mostly iOS. Yeah.swyx [00:18:27]: The watch thing, it was very funny because in the, in the Latent Space discord, you know, most of us have been slowly adopting snips. You came to me like a year ago and you introduced snip to me. I was like, I don't know. I'm, you know, I'm very sticky to overcast and then slowly we switch. Why watch?Kevin [00:18:43]: So it goes back to a lot of our users, they do something else while, while listening to a podcast, right? Yeah. And one of the, us giving them the ability to then capture this knowledge, even though they're doing something else at the same time is one of the killer features. Yeah. Maybe I can actually, maybe at some point I should maybe give a bit more of an overview of what the, all of the features that we have. Sure. So this is one of the killer features and for one big use case that people use this for is for running. Yeah. So if you're a big runner, a big jogger or cycling, like really, really cycling competitively and a lot of the people, they don't want to take their phone with them when they go running. So you load everything onto the watch. So you can download episodes. I mean, if you, if you have an Apple watch that has internet access, like with a SIM card, you can also directly stream. That's also possible. Yeah. So of course it's a, it's basically very limited to just listening and snipping. And then you can see all of your snips later on your phone. Let me tell you this error I just got.swyx [00:19:47]: Error playing episode. Substack, the host of this podcast, does not allow this podcast to be played on an Apple watch. Yeah.Kevin [00:19:52]: That's a very beautiful thing. So we found out that all of the podcasts hosted on Substack, you cannot play them on an Apple watch. Why is this restriction? What? Like, don't ask me. We try to reach out to Substack. We try to reach out to some of the bigger podcasters who are hosting the podcast on Substack to also let them know. Substack doesn't seem to care. This is not specific to our app. You can also check out the Apple podcast app. Yeah. It's the same problem. It's just that we actually have identified it. And we tell the user what's going on.swyx [00:20:25]: I would say we host our podcast on Substack, but they're not very serious about their podcasting tools. I've told them before, I've been very upfront with them. So I don't feel like I'm shitting on them in any way. And it's kind of sad because otherwise it's a perfect creative platform. But the way that they treat podcasting as an afterthought, I think it's really disappointing.Kevin [00:20:45]: Maybe given that you mentioned all these features, maybe I can give a bit of a better overview of the features that we have. Let's do that. Let's do that. So I think we're mostly in our minds. Maybe for some of the listeners.swyx [00:20:55]: I mean, I'll tell you my version. Yeah. They can correct me, right? So first of all, I think the main job is for it to be a podcast listening app. It should be basically a complete superset of what you normally get on Overcast or Apple Podcasts or anything like that. You pull your show list from ListenNotes. How do you find shows? You've got to type in anything and you find them, right?Kevin [00:21:18]: Yeah. We have a search engine that is powered by ListenNotes. Yeah. But I mean, in the meantime, we have a huge database of like 99% of all podcasts out there ourselves. Yeah.swyx [00:21:27]: What I noticed, the default experience is you do not auto-download shows. And that's one very big difference for you guys versus other apps, where like, you know, if I'm subscribed to a thing, it auto-downloads and I already have the MP3 downloaded overnight. For me, I have to actively put it onto my queue, then it auto-downloads. And actually, I initially didn't like that. I think I maybe told you that I was like, oh, it's like a feature that I don't like. Like, because it means that I have to choose to listen to it in order to download and not to... It's like opt-in. There's a difference between opt-in and opt-out. So I opt-in to every episode that I listen to. And then, like, you know, you open it and depends on whether or not you have the AI stuff enabled. But the default experience is no AI stuff enabled. You can listen to it. You can see the snips, the number of snips and where people snip during the episode, which roughly correlates to interest level. And obviously, you can snip there. I think that's the default experience. I think snipping is really cool. Like, I use it to share a lot on Discord. I think we have tons and tons of just people sharing snips and stuff. Tweeting stuff is also like a nice, pleasant experience. But like the real features come when you actually turn on the AI stuff. And so the reason I got snipped, because I got fed up with Overcast not implementing any AI features at all. Instead, they spent two years rewriting their app to be a little bit faster. And I'm like, like, it's 2025. I should have a podcast that has transcripts that I can search. Very, very basic thing. Overcast will basically never have it.Kevin [00:22:49]: Yeah, I think that was a good, like, basic overview. Maybe I can add a bit to it with the AI features that we have. So one thing that we do every time a new podcast comes out, we transcribe the episode. We do speaker diarization. We identify the speaker names. Each guest, we extract a mini bio of the guest, try to find a picture of the guest online, add it. We break the podcast down into chapters, as in AI generated chapters. That one. That one's very handy. With a quick description per title and quick description per each chapter. We identify all books that get mentioned on a podcast. You can tell I don't use that one. It depends on the podcast. There are some podcasts where the guests often recommend like an amazing book. So later on, you can you can find that again.swyx [00:23:42]: So you literally search for the word book or I just read blah, blah, blah.Kevin [00:23:46]: No, I mean, it's all LLM based. Yeah. So basically, we have we have an LLM that goes through the entire transcript and identifies if a user mentions a book, then we use perplexity API together with various other LLM orchestration to go out there on the Internet, find everything that there is to know about the book, find the cover, find who or what the author is, get a quick description of it for the author. We then check on which other episodes the author appeared on.swyx [00:24:15]: Yeah, that is killer.Kevin [00:24:17]: Because that for me, if. If there's an interesting book, the first thing I do is I actually listen to a podcast episode with a with a writer because he usually gives a really great overview already on a podcast.swyx [00:24:28]: Sometimes the podcast is with the person as a guest. Sometimes his podcast is about the person without him there. Do you pick up both?Kevin [00:24:37]: So, yes, we pick up both in like our latest models. But actually what we show you in the app, the goal is to currently only show you the guest to separate that. In the future, we want to show the other things more.swyx [00:24:47]: For what it's worth, I don't mind. Yeah, I don't think like if I like if I like somebody, I'll just learn about them regardless of whether they're there or not.Kevin [00:24:55]: Yeah, I mean, yes and no. We we we have seen there are some personalities where this can break down. So, for example, the first version that we released with this feature, it picked up much more often a person, even if it was not a guest. Yeah. For example, the best examples for me is Sam Altman and Elon Musk. Like they're just mentioned on every second podcast and it has like they're not on there. And if you're interested in it, you can go to Elon Musk. And actually like learning from them. Yeah, I see. And yeah, we updated our our algorithms, improved that a lot. And now it's gotten much better to only pick it up if they're a guest. And yeah, so this this is maybe to come back to the features, two more important features like we have the ability to chat with an episode. Yes. Of course, you can do the old style of searching through a transcript with a keyword search. But I think for me, this is this is how you used to do search and extracting knowledge in the in the past. Old school. And the A.I. Web. Way is is basically an LLM. So you can ask the LLM, hey, when do they talk about topic X? If you're interested in only a certain part of the episode, you can ask them for four to give a quick overview of the episode. Key takeaways afterwards also to create a note for you. So this is really like very open, open ended. And yeah. And then finally, the snipping feature that we mentioned just to reiterate. Yeah. I mean, here the the feature is that whenever you hear an amazing idea, you can trip. It's up your headphones or click a button in the app and the A.I. summarizes the insight you just heard and saves that together with the original transcript and audio in your knowledge library. I also noticed that you you skip dynamic content. So dynamic content, we do not skip it automatically. Oh, sorry. You detect. But we detect it. Yeah. I mean, that's one of the thing that most people don't don't actually know that like the way that ads get inserted into podcasts or into most podcasts is actually that every time you listen. To a podcast, you actually get access to a different audio file and on the server, a different ad is inserted into the MP3 file automatically. Yeah. Based on IP. Exactly. And that's what that means is if we transcribe an episode and have a transcript with timestamps like words, word specific timestamps, if you suddenly get a different audio file, like the whole time says I messed up and that's like a huge issue. And for that, we actually had to build another algorithm that would dynamically on the floor. I re sync the audio that you're listening to the transcript that we have. Yeah. Which is a fascinating problem in and of itself.swyx [00:27:24]: You sync by matching up the sound waves? Or like, or do you sync by matching up words like you basically do partial transcription?Kevin [00:27:33]: We are not matching up words. It's happening on the basically a bytes level matching. Yeah. Okay.swyx [00:27:40]: It relies on this. It relies on the exact match at some point.Kevin [00:27:46]: So it's actually. We're actually not doing exact matches, but we're doing fuzzy matches to identify the moment. It's basically, we basically built Shazam for podcasts. Just as a little side project to solve this issue.swyx [00:28:02]: Actually, fun fact, apparently the Shazam algorithm is open. They published the paper, it's talked about it. I haven't really dived into the paper. I thought it was kind of interesting that basically no one else has built Shazam.Kevin [00:28:16]: Yeah, I mean, well, the one thing is the algorithm. If you now talk about Shazam, the other thing is also having the database behind it and having the user mindset that if they have this problem, they come to you, right?swyx [00:28:29]: Yeah, I'm very interested in the tech stack. There's a big data pipeline. Could you share what is the tech stack?Kevin [00:28:35]: What are the most interesting or challenging pieces of it? So the general tech stack is our entire backend is, or 90% of our backend is written in Python. Okay. Hosting everything on Google Cloud Platform. And our front end is written with, well, we're using the Flutter framework. So it's written in Dart and then compiled natively. So we have one code base that handles both Android and iOS. You think that was a good decision? It's something that a lot of people are exploring. So up until now, yes. Okay. Look, it has its pros and cons. Some of the, you know, for example, earlier, I mentioned we have a Apple Watch app. Yeah. I mean, there's no Flutter for that, right? So that you build native. And then of course you have to sort of like sync these things together. I mean, I'm not the front end engineer, so I'm not just relaying this information, but our front end engineers are very happy with it. It's enabled us to be quite fast and be on both platforms from the very beginning. And when I talk with people and they hear that we are using Flutter, usually they think like, ah, it's not performant. It's super junk, janky and everything. And then they use it. They use our app and they're always super surprised. Or if they've already used our app, I couldn't tell them. They're like, what? Yeah. Um, so there is actually a lot that you can do with it.swyx [00:29:51]: The danger, the concern, there's a few concerns, right? One, it's Google. So when were they, when are they going to abandon it? Two, you know, they're optimized for Android first. So iOS is like a second, second thought, or like you can feel that it is not a native iOS app. Uh, but you guys put a lot of care into it. And then maybe three, from my point of view, JavaScript, as a JavaScript guy, React Native was supposed to be there. And I think that it hasn't really fulfilled that dream. Um, maybe Expo is trying to do that, but, um, again, it is not, does not feel as productive as Flutter. And I've, I spent a week on Flutter and dot, and I'm an investor in Flutter flow, which is the local, uh, Flutter, Flutter startup. That's doing very, very well. I think a lot of people are still Flutter skeptics. Yeah. Wait. So are you moving away from Flutter?Kevin [00:30:41]: I don't know. We don't have plans to do that. Yeah.swyx [00:30:43]: You're just saying about that. What? Yeah. Watch out. Okay. Let's go back to the stack.Kevin [00:30:47]: You know, that was just to give you a bit of an overview. I think the more interesting things are, of course, on the AI side. So we, like, as I mentioned earlier, when we started out, it was before chat GPT for the chat GPT moment before there was the GPT 3.5 turbo, uh, API. So in the beginning, we actually were running everything ourselves, open source models, try to fine tune them. They worked. There was us, but let's, let's be honest. They weren't. What was the sort of? Before Whisper, the transcription. Yeah, we were using wave to work like, um, there was a Google one, right? No, it was a Facebook, Facebook one. That was actually one of the papers. Like when that came out for me, that was one of the reasons why I said we, we should try something to start a startup in the audio space. For me, it was a bit like before that I had been following the NLP space, uh, quite closely. And as, as I mentioned earlier, we, we did some stuff at the startup as well, that I was working up. But before, and wave to work was the first paper that I had at least seen where the whole transformer architecture moved over to audio and bit more general way of saying it is like, it was the first time that I saw the transformer architecture being applied to continuous data instead of discrete tokens. Okay. And it worked amazingly. Ah, and like the transformer architecture plus self-supervised learning, like these two things moved over. And then for me, it was like, Hey, this is now going to take off similarly. It's the text space has taken off. And with these two things in place, even if some features that we want to build are not possible yet, they will be possible in the near term, uh, with this, uh, trajectory. So that was a little side, side note. No, it's in the meantime. Yeah. We're using whisper. We're still hosting some of the models ourselves. So for example, the whole transcription speaker diarization pipeline, uh,swyx [00:32:38]: You need it to be as cheap as possible.Kevin [00:32:40]: Yeah, exactly. I mean, we're doing this at scale where we have a lot of audio.swyx [00:32:44]: We're what numbers can you disclose? Like what, what are just to give people an idea because it's a lot. So we have more than a million podcasts that we've already processed when you say a million. So processing is basically, you have some kind of list of podcasts that you will auto process and others where a paying pay member can choose to press the button and transcribe it. Right. Is that the rough idea? Yeah, exactly.Kevin [00:33:08]: Yeah. And if, when you press that button or we also transcribe it. Yeah. So first we do the, we do the transcription. We do the. The, the speaker diarization. So basically you identify speech blocks that belong to the same speaker. This is then all orchestrated within, within LLM to identify which speech speech block belongs to which speaker together with, you know, we identify, as I mentioned earlier, we identify the guest name and the bio. So all of that comes together with an LLM to actually then assign assigned speaker names to, to each block. Yeah. And then most of the rest of the, the pipeline we've now used, we've now migrated to LLM. So we use mainly open AI, Google models, so the Gemini models and the open AI models, and we use some perplexity basically for those things where we need, where we need web search. Yeah. That's something I'm still hoping, especially open AI will also provide us an API. Oh, why? Well, basically for us as a consumer, the more providers there are.swyx [00:34:07]: The more downtime.Kevin [00:34:08]: The more competition and it will lead to better, better results. And, um, lower costs over time. I don't, I don't see perplexity as expensive. If you use the web search, the price is like $5 per a thousand queries. Okay. Which is affordable. But, uh, if you compare that to just a normal LLM call, um, it's, it's, uh, much more expensive. Have you tried Exa? We've, uh, looked into it, but we haven't really tried it. Um, I mean, we, we started with perplexity and, uh, it works, it works well. And if I remember. Correctly, Exa is also a bit more expensive.swyx [00:34:45]: I don't know. I don't know. They seem to focus on the search thing as a search API, whereas perplexity, maybe more consumer-y business that is higher, higher margin. Like I'll put it like perplexity is trying to be a product, Exa is trying to be infrastructure. Yeah. So that, that'll be my distinction there. And then the other thing I will mention is Google has a search grounding feature. Yeah. Which you, which you might want. Yeah.Kevin [00:35:07]: Yeah. We've, uh, we've also tried that out. Um, not as good. So we, we didn't, we didn't go into. Too much detail in like really comparing it, like quality wise, because we actually already had the perplexity one and it, and it's, and it's working. Yeah. Um, I think also there, the price is actually higher than perplexity. Yeah. Really? Yeah.swyx [00:35:26]: Google should cut their prices.Kevin [00:35:29]: Maybe it was the same price. I don't want to say something incorrect, but it wasn't cheaper. It wasn't like compelling. And then, then there was no reason to switch. So, I mean, maybe like in general, like for us, given that we do work with a lot of content, price is actually something that we do look at. Like for us, it's not just about taking the best model for every task, but it's really getting the best, like identifying what kind of intelligence level you need and then getting the best price for that to be able to really scale this and, and provide us, um, yeah, let our users use these features with as many podcasts as possible. Yeah.swyx [00:36:03]: I wanted to double, double click on diarization. Yeah. Uh, it's something that I don't think people do very well. So you know, I'm, I'm a, I'm a B user. I don't have it right now. And, and they were supposed to speak, but they dropped out last minute. Um, but, uh, we've had them on the podcast before and it's not great yet. Do you use just PI Anode, the default stuff, or do you find any tricks for diarization?Kevin [00:36:27]: So we do use the, the open source packages, but we have tweaked it a bit here and there. For example, if you mentioned the BAI guys, I actually listened to the podcast episode was super nice. Thank you. And when you started talking about speaker diarization, and I just have to think about, uh, I don't know.Kevin [00:36:49]: Is it possible? I don't know. I don't know. F**k this. Yeah, no, I don't know.Kevin [00:36:55]: Yeah. We are the best. This is a.swyx [00:37:07]: I don't know. This is the best. I don't know. This is the best. Yeah. Yeah. Yeah. You're doing good.Kevin [00:37:12]: So, so yeah. This is great. This is good. Yeah. No, so that of course helps us. Another thing that helps us is that we know certain structural aspects of the podcast. For example, how often does someone speak? Like if someone, like let's say there's a one hour episode and someone speaks for 30 seconds, that person is most probably not the guest and not the host. It's probably some ad, like some speaker from an ad. So we have like certain of these heuristics that we can use and we leverage to improve things. And in the past, we've also changed the clustering algorithm. So basically how a lot of the speaker diarization works is you basically create an embedding for the speech that's happening. And then you try to somehow cluster these embeddings and then find out this is all one speaker. This is all another speaker. And there we've also tweaked a couple of things where we again used heuristics that we could apply from knowing how podcasts function. And that's also actually why I was feeling so much with the BAI guys, because like all of these heuristics, like for them, it's probably almost impossible to use any heuristics because it can just be any situation, anything.Kevin [00:38:34]: So that's one thing that we do. Yeah, another thing is that we actually combine it with LLM. So the transcript, LLMs and the speaker diarization, like bringing all of these together to recalibrate some of the switching points. Like when does the speaker stop? When does the next one start?swyx [00:38:51]: The LLMs can add errors as well. You know, I wouldn't feel safe using them to be so precise.Kevin [00:38:58]: I mean, at the end of the day, like also just to not give a wrong impression, like the speaker diarization is also not perfect that we're doing, right? I basically don't really notice it.swyx [00:39:08]: Like I use it for search.Kevin [00:39:09]: Yeah, it's not perfect yet, but it's gotten quite good. Like, especially if you compare, if you look at some of the, like if you take a latest episode and you compare it to an episode that came out a year ago, we've improved it quite a bit.swyx [00:39:23]: Well, it's beautifully presented. Oh, I love that I can click on the transcript and it goes to the timestamp. So simple, but you know, it should exist. Yeah, I agree. I agree. So this, I'm loading a two hour episode of Detect Me Right Home, where there's a lot of different guests calling in and you've identified the guest name. And yeah, so these are all LLM based. Yeah, it's really nice.Kevin [00:39:49]: Yeah, like the speaker names.swyx [00:39:50]: I would say that, you know, obviously I'm a power user of all these tools. You have done a better job than Descript. Okay, wow. Descript is so much funding. They had their open AI invested in them and they still suck. So I don't know, like, you know, keep going. You're doing great. Yeah, thanks. Thanks.Kevin [00:40:12]: I mean, I would, I would say that, especially for anyone listening who's interested in building a consumer app with AI, I think the, like, especially if your background is in AI and you love working with AI and doing all of that, I think the most important thing is just to keep reminding yourself of what's actually the job to be done here. Like, what does actually the consumer want? Like, for example, you now were just delighted by the ability to click on this word and it jumps there. Yeah. Like, this is not, this is not rocket science. This is, like, you don't have to be, like, I don't know, Android Kapathi to come up with that and build that, right? And I think that's, that's something that's super important to keep in mind.swyx [00:40:52]: Yeah, yeah. Amazing. I mean, there's so many features, right? It's, it's so packed. There's quotes that you pick up. There's summarization. Oh, by the way, I'm going to use this as my official feature request. I want to customize what, how it's summarized. I want to, I want to have a custom prompt. Yeah. Because your summarization is good, but, you know, I have different preferences, right? Like, you know.Kevin [00:41:14]: So one thing that you can already do today, I completely get your feature request. And I think it just.swyx [00:41:18]: I'm sure people have asked it.Kevin [00:41:19]: I mean, maybe just in general as a, as a, how I see the future, you know, like in the future, I think all, everything will be personalized. Yeah, yeah. Like, not, this is not specific to us. Yeah. And today we're still in a, in a phase where the cost of LLMs, at least if you're working with, like, such long context windows. As us, I mean, there's a lot of tokens in, if you take an entire podcast, so you still have to take that cost into consideration. So if for every single user, we regenerate it entirely, it gets expensive. But in the future, this, you know, cost will continue to go down and then it will just be personalized. So that being said, you can already today, if you go to the player screen. Okay. And open up the chat. Yeah. You can go to the, to the chat. Yes. And just ask for a summary in your style.swyx [00:42:13]: Yeah. Okay. I mean, I, I listen to consume, you know? Yeah. Yeah. I, I've never really used this feature. I don't know. I think that's, that's me being a slow adopter. No, no. I mean, that's. It has, when does the conversation start? Okay.Kevin [00:42:26]: I mean, you can just type anything. I think what you're, what you're describing, I mean, maybe that is also an interesting topic to talk about. Yes. Where, like, basically I told you, like, look, we have this chat. You can just ask for it. Yeah. And this is, this is how ChatGPT works today. But if you're building a consumer app, you have to move beyond the chat box. People do not want to always type out what they want. So your feature request was, even though theoretically it's already possible, what you are actually asking for is, hey, I just want to open up the app and it should just be there in a nicely formatted way. Beautiful way such that I can read it or consume it without any issues. Interesting. And I think that's in general where a lot of the, the. Opportunities lie currently in the market. If you want to build a consumer app, taking the capability and the intelligence, but finding out what the actual user interface is the best way how a user can engage with this intelligence in a natural way.swyx [00:43:24]: Is this something I've been thinking about as kind of like AI that's not in your face? Because right now, you know, we like to say like, oh, use Notion has Notion AI. And we have the little thing there. And there's, or like some other. Any other platform has like the sparkle magic wand emoji, like that's our AI feature. Use this. And it's like really in your face. A lot of people don't like it. You know, it should just kind of become invisible, kind of like an invisible AI.Kevin [00:43:49]: 100%. I mean, the, the way I see it as AI is, is the electricity of, of the future. And like no one, like, like we don't talk about, I don't know, this, this microphone uses electricity, this phone, you don't think about it that way. It's just in there, right? It's not an electricity enabled product. No, it's just a product. Yeah. It will be the same with AI. I mean, now. It's still a, something that you use to market your product. I mean, we do, we do the same, right? Because it's still something that people realize, ah, they're doing something new, but at some point, no, it'll just be a podcast app and it will be normal that it has all of this AI in there.swyx [00:44:24]: I noticed you do something interesting in your chat where you source the timestamps. Yeah. Is that part of this prompt? Is there a separate pipeline that adds source sources?Kevin [00:44:33]: This is, uh, actually part of the prompt. Um, so this is all prompt engine. Engineering, um, uh, you should be able to click on it. Yeah, I clicked on it. Um, this is all prompt engineering with how to provide the, the context, you know, we, because we provide all of the transcript, how to provide the context and then, yeah, I get them all to respond in a correct way with a certain format and then rendering that on the front end. This is one of the examples where I would say it's so easy to create like a quick demo of this. I mean, you can just go to chat to be deep, paste this thing in and say like, yeah, do this. Okay. Like 15 minutes and you're done. Yeah. But getting this to like then production level that it actually works 99% of the time. Okay. This is then where, where the difference lies. Yeah. So, um, for this specific feature, like we actually also have like countless regexes that they're just there to correct certain things that the LLM is doing because it doesn't always adhere to the format correctly. And then it looks super ugly on the front end. So yeah, we have certain regexes that correct that. And maybe you'd ask like, why don't you use an LLM for that? Because that's sort of the, again, the AI native way, like who uses regexes anymore. But with the chat for user experience, it's very important that you have the streaming because otherwise you need to wait so long until your message has arrived. So we're streaming live the, like, just like ChatGPT, right? You get the answer and it's streaming the text. So if you're streaming the text and something is like incorrect. It's currently not easy to just like pipe, like stream this into another stream, stream this into another stream and get the stream back, which corrects it, that would be amazing. I don't know, maybe you can answer that. Do you know of any?swyx [00:46:19]: There's no API that does this. Yeah. Like you cannot stream in. If you own the models, you can, uh, you know, whatever token sequence has, has been emitted, start loading that into the next one. If you fully own the models, uh, I don't, it's probably not worth it. That's what you do. It's better. Yeah. I think. Yeah. Most engineers who are new to AI research and benchmarking actually don't know how much regexing there is that goes on in normal benchmarks. It's just like this ugly list of like a hundred different, you know, matches for some criteria that you're looking for. No, it's very cool. I think it's, it's, it's an example of like real world engineering. Yeah. Do you have a tooling that you're proud of that you've developed for yourself?Kevin [00:47:02]: Is it just a test script or is it, you know? I think it's a bit more, I guess the term that has come up is, uh, vibe coding, uh, vibe coding, some, no, sorry, that's actually something else in this case, but, uh, no, no, yes, um, vibe evals was a term that in one of the talks actually on, on, um, I think it might've been the first, the first or the first day at the conference, someone brought that up. Yeah. Uh, because yeah, a lot of the talks were about evals, right. Which is so important. And yeah, I think for us, it's a bit more vibe. Evals, you know, that's also part of, you know, being a startup, we can take risks, like we can take the cost of maybe sometimes it failing a little bit or being a little bit off and our users know that and they appreciate that in return, like we're moving fast and iterating and building, building amazing things, but you know, a Spotify or something like that, half of our features will probably be in a six month review through legal or I don't know what, uh, before they could sell them out.swyx [00:48:04]: Let's just say Spotify is not very good at podcasting. Um, I have a documented, uh, dislike for, for their podcast features, just overall, really, really well integrated any other like sort of LLM focused engineering challenges or problems that you, that you want to highlight.Kevin [00:48:20]: I think it's not unique to us, but it goes again in the direction of handling the uncertainty of LLMs. So for example, with last year, at the end of the year, we did sort of a snipped wrapped. And one of the things we thought it would be fun to, just to do something with, uh, with an LLM and something with the snips that, that a user has. And, uh, three, let's say unique LLM features were that we assigned a personality to you based on the, the snips that, that you have. It was, I mean, it was just all, I guess, a bit of a fun, playful way. I'm going to look up mine. I forgot mine already.swyx [00:48:57]: Um, yeah, I don't know whether it's actually still in the, in the, we all took screenshots of it.Kevin [00:49:01]: Ah, we posted it in the, in the discord. And the, the second one, it was, uh, we had a learning scorecard where we identified the topics that you snipped on the most, and you got like a little score for that. And the third one was a, a quote that stood out. And the quote is actually a very good example of where we would run that for user. And most of the time it was an interesting quote, but every now and then it was like a super boring quotes that you think like, like how, like, why did you select that? Like, come on for there. The solution was actually just to say, Hey, give me five. So it extracted five quotes as a candidate, and then we piped it into a different model as a judge, LLM as a judge, and there we use a, um, a much better model because with the, the initial model, again, as, as I mentioned also earlier, we do have to look at the, like the, the costs because it's like, we have so much text that goes into it. So we, there we use a bit more cheaper model, but then the judge can be like a really good model to then just choose one out of five. This is a practical example.swyx [00:50:03]: I can't find it. Bad search in discord. Yeah. Um, so, so you do recommend having a much smarter model as a judge, uh, and that works for you. Yeah. Yeah. Interesting. I think this year I'm very interested in LM as a judge being more developed as a concept, I think for things like, you know, snips, raps, like it's, it's fine. Like, you know, it's, it's, it's, it's entertaining. There's no right answer.Kevin [00:50:29]: I mean, we also have it. Um, we also use the same concept for our books feature where we identify the, the mention. Books. Yeah. Because there it's the same thing, like 90% of the time it, it works perfectly out of the box one shot and every now and then it just, uh, starts identifying books that were not really mentioned or that are not books or made, yeah, starting to make up books. And, uh, they are basically, we have the same thing of like another LLM challenging it. Um, yeah. And actually with the speakers, we do the same now that I think about it. Yeah. Um, so I'm, I think it's a, it's a great technique. Interesting.swyx [00:51:05]: You run a lot of calls.Kevin [00:51:07]: Yeah.swyx [00:51:08]: Okay. You know, you mentioned costs. You move from self hosting a lot of models to the, to the, you know, big lab models, open AI, uh, and Google, uh, non-topic.Kevin [00:51:18]: Um, no, we love Claude. Like in my opinion, Claude is the, the best one when it comes to the way it formulates things. The personality. Yeah. The personality. Okay. I actually really love it. But yeah, the cost is. It's still high.swyx [00:51:36]: So you cannot, you tried Haiku, but you're, you're like, you have to have Sonnet.Kevin [00:51:40]: Uh, like basically we like with Haiku, we haven't experimented too much. We obviously work a lot with 3.5 Sonnet. Uh, also, you know, coding. Yeah. For coding, like in cursor, just in general, also brainstorming. We use it a lot. Um, I think it's a great brainstorm partner, but yeah, with, uh, with, with a lot of things that we've done done, we opted for different models.swyx [00:52:00]: What I'm trying to drive at is how much cheaper can you get if you go from cloud to cloud? Closed models to open models. And maybe it's like 0% cheaper, maybe it's 5% cheaper, or maybe it's like 50% cheaper. Do you have a sense?Kevin [00:52:13]: It's very difficult to, to judge that. I don't really have a sense, but I can, I can give you a couple of thoughts that have gone through our minds over the time, because obviously we do realize like, given that we, we have a couple of tasks where there are just so many tokens going in, um, at some point it will make sense to, to offload some of that. Uh, to an open source model, but going back to like, we're, we're a startup, right? Like we're not an AI lab or whatever, like for us, actually the most important thing is to iterate fast because we need to learn from our users, improve that. And yeah, just this velocity of this, these iterations. And for that, the closed models hosted by open AI, Google is, uh, and swapping, they're just unbeatable because you just, it's just an API call. Yeah. Um, so you don't need to worry about. Yeah. So much complexity behind that. So this is, I would say the biggest reason why we're not doing more in this space, but there are other thoughts, uh, also for the future. Like I see two different, like we basically have two different usage patterns of LLMs where one is this, this pre-processing of a podcast episode, like this initial processing, like the transcription, speaker diarization, chapterization. We do that once. And this, this usage pattern it's, it's quite predictable. Because we know how many podcasts get released when, um, so we can sort of have a certain capacity and we can, we, we're running that 24 seven, it's one big queue running 24 seven.swyx [00:53:44]: What's the queue job runner? Uh, is it a Django, just like the Python one?Kevin [00:53:49]: No, that, that's just our own, like our database and the backend talking to the database, picking up jobs, finding it back. I'm just curious in orchestration and queues. I mean, we, we of course have like, uh, a lot of other orchestration where we're, we're, where we use, uh, the Google pub sub, uh, thing, but okay. So we have this, this, this usage pattern of like very predictable, uh, usage, and we can max out the, the usage. And then there's this other pattern where it's, for example, the snippet where it's like a user, it's a user action that triggers an LLM call and it has to be real time. And there can be moments where it's by usage and there can be moments when there's very little usage for that. There. So that's, that's basically where these LLM API calls are just perfect because you don't need to worry about scaling this up, scaling this down, um, handling, handling these issues. Serverless versus serverful.swyx [00:54:44]: Yeah, exactly. Okay.Kevin [00:54:45]: Like I see them a bit, like I see open AI and all of these other providers, I see them a bit as the, like as the Amazon, sorry, AWS of, of AI. So it's a bit similar how like back before AWS, you would have to have your, your servers and buy new servers or get rid of servers. And then with AWS, it just became so much easier to just ramp stuff up and down. Yeah. And this is like the taking it even, even, uh, to the next level for AI. Yeah.swyx [00:55:18]: I am a big believer in this. Basically it's, you know, intelligence on demand. Yeah. We're probably not using it enough in our daily lives to do things. I should, we should be able to spin up a hundred things at once and go through things and then, you know, stop. And I feel like we're still trying to figure out how to use LLMs in our lives effectively. Yeah. Yeah.Kevin [00:55:38]: 100%. I think that goes back to the whole, like that, that's for me where the big opportunity is for, if you want to do a startup, um, it's not about, but you can let the big labs handleswyx [00:55:48]: the challenge of more intelligence, but, um, it's the... Existing intelligence. How do you integrate? How do you actually incorporate it into your life? AI engineering. Okay, cool. Cool. Cool. Cool. Um, the one, one other thing I wanted to touch on was multimodality in frontier models. Dwarcash had a interesting application of Gemini recently where he just fed raw audio in and got diarized transcription out or timestamps out. And I think that will come. So basically what we're saying here is another wave of transformers eating things because right now models are pretty much single modality things. You know, you have whisper, you have a pipeline and everything. Yeah. You can't just say, Oh, no, no, no, we only fit like the raw, the raw files. Do you think that will be realistic for you? I 100% agree. Okay.Kevin [00:56:38]: Basically everything that we talked about earlier with like the speaker diarization and heuristics and everything, I completely agree. Like in the, in the future that would just be put everything into a big multimodal LLM. Okay. And it will output, uh, everything that you want. Yeah. So I've also experimented with that. Like just... With, with Gemini 2? With Gemini 2.0 Flash. Yeah. Just for fun. Yeah. Yeah. Because the big difference right now is still like the cost difference of doing speaker diarization this way or doing transcription this way is a huge difference to the pipeline that we've built up. Huh. Okay.swyx [00:57:15]: I need to figure out what, what that cost is because in my mind 2.0 Flash is so cheap. Yeah. But maybe not cheap enough for you.Kevin [00:57:23]: Uh, no, I mean, if you compare it to, yeah, whisper and speaker diarization and especially self-hosting it and... Yeah. Yeah. Yeah.swyx [00:57:30]: Yeah.Kevin [00:57:30]: Okay. But we will get there, right? Like this is just a question of time.swyx [00:57:33]: And, um, at some point, as soon as that happens, we'll be the first ones to switch. Yeah. Awesome. Anything else that you're like sort of eyeing on the horizon as like, we are thinking about this feature, we're thinking about incorporating this new functionality of AI into our, into our app? Yeah.Kevin [00:57:50]: I mean, we, there's so many areas that we're thinking about, like our challenge is a bit more... Choosing. Yeah. Choosing. Yeah. So, I mean, I think for me, like looking into like the next couple of years, like the big areas that interest us a lot, basically four areas, like one is content. Um, right now it's, it's podcasts. I mean, you did mention, I think you mentioned like you can also upload audio books and YouTube videos. YouTube. I actually use the YouTube one a fair amount. But in the future, we, we want to also have audio books natively in the app. And, uh, we want to enable AI generated content. Like just think of, take deep research and notebook analysis. Like put these together. That should be, that should be in our app. The second area is discovery. I think in general. Yeah.swyx [00:58:38]: I noticed that you don't have, so you

When I found out that Django developer and Python Software Foundation chair Dawn Wages has a chapter in her upcoming Domain-Driven Django book called “Just Use Postgres”, I knew we had to get her on the show. In this episode of Talking Postgres with Claire Giordano, Dawn breaks down why so many Python and Django developers have such an affinity for Postgres. And we dive into the Djangonaut Space mentoring program (where contributors launch), learn why “free as in puppies” beats “free as in cake” for open source vibes, and dig into why Python is the second-best language for everything.Links mentioned in this episode:Project page: psycopgDocumentation: Psycopg 3 – PostgreSQL database adapter for PythonProject page: PostgreSQL open source projectGit repo: code for PostgreSQL.org websiteConference: PyCon US 2025, happening May 14-22 in PittsburghConference: PGConf.dev 2025 Schedule, happening May 13-16 in Montreal CanadaConference: Prague PostgreSQL Developer Day 2025 (P2D2) Schedule, which took place Jan 28-29Wikipedia page: Model-view-controller software design patternBook: Professional ASP.NET MVC 1.0, affectionately called “the four heads book”Podcast episode: Working in Public with Simon Willison & Marco SlotBlog: Simon Willison's TILs, aka Things I've learnedSimon Willison's Weblog: Here's how I use LLMs to help me write codeSimon Willison's Weblog: How I make annotated presentationsSurvey: Python Developers Survey 2023 ResultsPython Docs: What's new in Python 3.14Mentorship program: Djangonaut SpaceMentorship program: Media & Talks about Djangonaut SpacePodcast episode: Why mentor Postgres developers with Robert HaasSlides: PGConf EU 2024 talk by Claire Giordano about Contributions to Postgres, including maps showing how global the Postgres project isVideo of POSETTE 2024 talk by Paolo Melchiorre: Semantic search with Django, PostgreSQL, & pgvectorVideo of Citus Con 2023 talk: Maps with Django (and PostGIS), by Paolo MelchiorreVideo of Citus Con 2022 talk: Django with PostgreSQL superpowers, by Paolo MelchiorreConference: DjangoCon Africa 2025, happening August 11-15 in Tanzania Calendar invite: LIVE recording of Ep26 of Talking Postgres to happen on Wed Apr 02, 2025 with guest Bruce Momjian, to talk about Open Source Leadership 

Django, Jeff, and Roman spend some time at the beginning of this episode covering themselves in the bodyparts of comic creators they met at ECCC so that they can wade through the sea of freshly-turned comic books that just came out last week. Wait, did that Walking Dead reference get through alright? Eh, you'll survive. All you have to do is hit play!0:02:19 - Email from the Editor!0:09:20 - Well Welcome Wellmer!0:14:41 - The Ultimates #100:23:08 - Secret Six #10:26:40 - The Terminator #50:30:07 - Mothra: Queen of the Monsters #10:33:29 - Teen Titans GO! #10:36:11 - Doctor Strange of Asgard #10:40:27 - Monkey Meat: Summer Batch #10:41:48 - The Missionary #20:43:26 - Hyde Street #40:45:03 - Uncanny Valley #80:45:43 - Uncanny X-Men #110:49:42 - Fat Cop0:52:37 - Hello Darkness #8SPOILERS! Tread carefully dear listener, because we're going to talk about what happened in these books. So definitely pause this, read your comics, and come back. We'll still be here!And an enormous thank you, as always, to Andrew Carlson for editing this mess into something listenable.Subscribe to us on Apple, Spotify or wherever you like to get your podcasts.Email us at jeff@thecomicsplace.com! We love hearing from you and there's a good chance we will read it on air!Cover art by Mateus ManhaniniVisit us at The Comics Place next time you're in Bellingham, Washington!

In this thought-provoking episode, we sit down with Django De Gree II, CEO and co-founder of Wrote By Me, to explore the profound connection between storytelling, mental health, and personal transformation. Django shares how books and narratives shape our psychology, helping us reframe our realities, ask deeper questions, and build stronger communities. From overcoming personal struggles to integrating technology in the publishing world, Django explains how independent platforms can empower authors and readers alike. We also discuss how Eastern philosophy, technology, and self-reflection can contribute to a healthier mind and a more connected life. If you're interested in self-growth, mental well-being, and the power of storytelling, this episode is for you!   About Django De Gree:   Django De Gree II is a visionary entrepreneur revolutionizing the publishing industry with Wrote By Me, an independent platform that fosters authentic storytelling and deeper connections between authors and readers. His book, I Hope You Wake Up, explores personal transformation through a unique lens of psychology and philosophy.   Key Takeaways:  

Show Notes: Unveiling the Mind of Django Degree – A Journey of Innovation and Impact   Episode Highlights: Join Amb. Elisha as he welcomes Django Degree, a tech innovator, CEO, and author, to discuss his journey from technology sales to leading Wrote by Me—a platform revolutionizing the way authors connect with readers.   In this episode, Django shares powerful insights into AI, self-discovery, and the art of marketing books successfully.   Guest Spotlight: Django Degree   CEO & Co-founder of Rope by Me – Helping authors sell books, increase revenue, and get paid faster.   Author of I Hope You Wake Up – A transformative book exploring self-awareness and personal growth through AI-driven reflections.   Product Expert – Experience with Fortune 500 companies and innovative startups.   Key Takeaways from the Conversation:   1. The Power of Self-Investment: Django's journey reflects the importance of growth, learning, and adaptability. His work is the “fruit” that naturally attracts opportunities.   2. AI's Role in Writing & Creativity:   AI isn't replacing authors—it's a tool that enhances creativity.   Django shares how AI helped him explore new perspectives while writing his book.   3. Breaking the Myths of Book Marketing:   Writing a great book isn't enough—authors must actively market it.   Discover the marketing mistakes authors make and how to avoid them.   A fresh take on using social media without burnout.   4. The Story Behind I Hope You Wake Up   Inspired by Anthony de Mello, the book challenges readers to rethink their perceptions of reality.   The meaning of “waking up” and how it leads to transformation.   5. The Future of Publishing & Reader Connection:   How Wrote by Me is changing the game by giving authors direct access to their readers.   Why traditional platforms like Amazon limit an author's potential for engagement.   Final Thoughts:   Django's insights offer a roadmap for anyone looking to leverage technology, storytelling, and strategic marketing for success.   Whether you're an author, entrepreneur, or simply seeking a shift in perspective, this episode will leave you inspired.   Connect with Django Degree:   Website: https://www.wroteby.me/django   Book: I Hope You Wake Up – Available now!  

3/4/25: Sen. Paul Mark: federal cuts & the future of West-East Rail. Rich Michelson w/ UMass prof, translator & poet John Hennessy: poetry from Ukraine & “Exit Garden State.” Atty John Pucci: DOJ's new mission: vengeance & retribution. Paul Newlin: "Watermelon Wednesdays" w/ violinist Tim Kliphuis (Django Jazz) & Petri Prauda of the Finnish band Frigg.

Django and Jeff have returned from ComicsPro to give Roman a shakedown on everything that went down. At the same time, Roman is keeping them in the loop of all the stuff they missed while they were gone. We've got crazy one-shots, amazing continuations, and fantastic conclusions. Also, shout-out to all the sweet folks who talked to Jeff and Django at ComicsPro and mentioned their love of the podcast!0:03:41 - ComicsPro!0:06:39 - Well Welcome Wellmer!0:14:41 - Godzilla vs America: Chicago #10:20:19 - Batman: The Long Halloween - The Last Halloween #50:27:43 - Fantastic Four #290:33:19 - Lil Batman #40:35:53 - Absolute Wonder Woman #50:40:27 - The Seasons #20:43:14 - Ultimate Spider-Man #140:46:58 - Metamorpho: The Element Man #30:50:28 - Superman #230:51:02 - Detective Comics #10940:52:15 - Captain America & Volstagg #10:55:02 - Email from the Gar-Side!SPOILERS! Tread carefully dear listener, because we're going to talk about what happened in these books. So definitely pause this, read your comics, and come back. We'll still be here!And an enormous thank you, as always, to Andrew Carlson for editing this mess into something listenable.Subscribe to us on Apple, Spotify or wherever you like to get your podcasts.Email us at jeff@thecomicsplace.com! We love hearing from you and there's a good chance we will read it on air!Cover art by Tim SaleVisit us at The Comics Place next time you're in Bellingham, Washington!

As we come to a conclusion of our Black History Month celebration, Jason hates on the famous “Dru Hill” step and more, smh. For episode 142, and if you've been under a rock, we are reviewing “Django Unchained” from the mind of Quentin Tarantino. Jazz tells the crew about her movie “The Monster Balls” featuring her Trevnate Rhodes and Mufasa. The crew , which is stacked featuring “Talkin Ish: A Podcast Amongst Friends, “ “Recap Kickback” , and newcomer Rico who defines the film a “Tasty Slave Film,” discussed if Django is in Jamie Foxx's top 5 movies. Better movie? Booty Call or Django? The crew gives their theater experience back in 2012. Could Will Smith have portrayed Django? Did the Oscar's get it right for all of Leonardo DiCaprio losses ? We give our top 3 Quentin Tarantino films . Of course we discuss our favorite moments of the movie . Samuel L Jackason as Steven was a very devilish, the crew discussed Sam running Candy Land. We discuss if Django would get greenlit with a black Writer/director. Did King Schultz fall under the white savior trope ? We get into this and much much more in this chaotic episode but we also answer the question you've been dying 13 years to know, WAS IT GOOD THOUGH!??

The Voltron theory is in full effect!! We form to funk and become one on the one for fun ya dig??!!!! Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django and DJ BenREAD MORE

Let's have some fun til the sun comes up and beyond… Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django and DJ Ben Vera (@BenVeraOfficial), your tour guides through this cloud drift inREAD MORE

The most wonderful time of the year, when Lamb Sandwich comes ut of hibernation to bless us with some holiday cheer!! Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django and DJ BenREAD MORE

We wanna make you move and let you set your spirit free… time to travel… Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django and DJ Ben Vera (@BenVeraOfficial), your tour guides throughREAD MORE

Float in the clouds with us while we treat your ears to some good tunes! Thank you for tuning in & be sure to GIVE US A ‘LIKE' ON FaceBook—>  www.facebook.com/AOTARadio You can tune in LIVE every Sunday from 12AM – 4AM PST with your host Django and DJ Ben Vera (@BenVeraOfficial), your tour guides throughREAD MORE

On tonight's show: Bing Crosby, Sweet Georgia Brown, 1932 Chu Berry, Body and Soul, 1938 The Spencer Trio, Baby, Won't You Please Come Home? 1938 Winifred Atwell, Choo Choo Samba, 1950s Claude Williamson Trio, Stella by Starlight, 1956 Illinois Jacquet, Have You Met Miss Jones? 1957 Doris Day, There Will Never Be Another You, 1957 Clark Terry, Take the A Train, 1957 Duke Ellington, Night and Day, 1958 Howard Roberts, When the Sun Comes Out, 1959 Ella Fitzgerald, In the Wee Small Hours of the Morning (Live at the Crescendo), 1961 Oscar Peterson Trio, Moten Swing, 1963 Paul Desmond, The Theme from Black Orpheus, 1963 Zoot Sims, The Girl from Ipanema, 1977 The Rosenberg Trio, Django, 1995 The Spencer Wyatt Big Band, Blues in The Bucket, 2001

Topics covered in this episode: PEP 772 – Packaging governance process Official Django MongoDB Backend Now Available in Public Preview Developer Philosophy Python 3.13.2 released Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: PEP 772 – Packaging governance process draft, created 21-Jan, by Barry Warsaw, Deb Nicholson, Pradyun Gedam “As Python packaging has matured, several interrelated problems with the current way of managing the technical development, decision making and processes have become apparent.” “This PEP proposes a Python Packaging Council with broad authority over packaging standards, tools, and implementations. Like the Python Steering Council, the Packaging Council seeks to exercise this authority as rarely as possible; instead, they use this power to establish standard processes.” PEP discusses PyPA, Packaging-WG, Interoperability Standards, Python Steering Council, and Expectations of an elected Packaging Council A specification with Composition: 5 people Mandate, Responsibilities, Delegations, Process, Terms, etc. Michael #2: Official Django MongoDB Backend Now Available in Public Preview Over the last few years, Django developers have increasingly used MongoDB, presenting an opportunity for an official MongoDB-built Python package to make integrating both technologies as painless as possible. Features The ability to use Django models with confidence. Developers can use Django models to represent MongoDB documents, with support for Django forms, validations, and authentication. Django admin support. The package allows users to fire up the Django admin page as they normally would, with full support for migrations and database schema history. Native connecting from settings.py. Just as with any other database provider, developers can customize the database engine in settings.py to get MongoDB up and running. MongoDB-specific querying optimizations. Field lookups have been replaced with aggregation calls (aggregation stages and aggregate operators), JOIN operations are represented through $lookup, and it's possible to build indexes right from Python. Limited advanced functionality. While still in development, the package already has support for time series, projections, and XOR operations. Aggregation pipeline support. Raw querying allows aggregation pipeline operators. Since aggregation is a superset of what traditional MongoDB Query API methods provide, it gives developers more functionality. Brian #3: Developer Philosophy by qntm Intended as “advice for junior developers about personal dev philosophy”, I think these are just great tips to keep in mind. The items Avoid, at all costs, arriving at a scenario where the ground-up rewrite starts to look attractive This is less about “don't do rewrites”, but about noticing the warning signs ahead of time. Aim to be 90% done in 50% of the available time Great quote: “The first 90% of the job takes 90% of the time. The last 10% of the job takes the other 90% of the time.” Automate good practices Think about pathological data “Nobody cares about the golden path. Edge cases are our entire job.” Brian's note: But also think about the happy path. Documenting and testing what you think of as the happy path is a testing start and helps others understand your idea of how things are supposed to work. There's usually a simpler way to write it Write code to be testable It is insufficient for code to be provably correct; it should be obviously, visibly, trivially correct Brian's note: Even if it's obviously, visibly, trivially correct, it will still break. So test it anyway. Michael #4: Python 3.13.2 released Python 3.13's second maintenance release. About 250 changes went into this update Also Python 3.12.9, Python 3.12's ninth maintenance release already. Just 180 changes for 3.12, but it's still worth upgrading. For us, it's simply rebuilding our Docker base (i.e. —no-cache) with these lines: RUN curl -LsSf https://astral.sh/uv/install.sh | sh RUN --mount=type=cache,target=/root/.cache uv venv --python 3.13 /venv Extras Brian: Still thinking about pytest plugins a lot. The top pytest plugin list Has been updated for Feb Is starting to include things without “pytest” in the name, like Hypothesis and Syrupy. Eventually I'll have to add “looking at trove classifiers” as part of the search, but for now, let me know if you're favorite is missing. Includes T&C podcast episode links if I've covered it on the show. There's 2 so far Michael: There's a new release of PyScript out. All the details are here: Highlight is new PyGame-CE support. Go play! PEP 2026 – Calendar versioning for Python rejected. :( PEP 759 – External Wheel Hosting withdrawn Joke: Pride Versioning

Django, I've Shrunken the Podcast! Wait... no, Jeff and Roman are still here and normal-sized. So prepare yourselves, dear listeners, for green men, finger-eyes, a surprise returning character, and some bloody good times. Hit play already!0:02:30 - Well Welcome Wellmer!0:08:19 - Bug Wars #10:15:51 - Into the Unbeing: Part Two #10:19:23 - One World Under Doom #10:25:55 - Absolute Batman #50:31:28 - Batman: Dark Patterns #30:38:21 - Eddie Brock: Carnage #10:41:21 - Mine Is a Long, Lonesome Grave #10:43:23 - The Hive #10:45:12 - Aquaman #20:47:18 - Green Lantern Corps #10:50:04 - The Lucky Devils #20:50:55 - Those Not Afraid #2SPOILERS! Tread carefully dear listener, because we're going to talk about what happened in these books. So definitely pause this, read your comics, and come back. We'll still be here!And an enormous thank you, as always, to Andrew Carlson for editing this mess into something listenable.Bonus: I've been including Django's failed attempts to end the recording on the last few - if you didn't catch it, go back and check it out!Subscribe to us on Apple, Spotify or wherever you like to get your podcasts.Email us at jeff@thecomicsplace.com! We love hearing from you and there's a good chance we will read it on air!Cover art by Hayden ShermanVisit us at The Comics Place next time you're in Bellingham, Washington!

Django is a singer/songwriter/guitarist who released his first solo album in 1996 and has released seven albums with The Old Ceremony since 2004 plus several more solo releases. We discuss The Old Ceremony songs "Too Big to Fail" (and listen to "Hangman's Party at the end) from Earthbound (2024), "The Disappear" from Walk On Thin Air (2009), and "Reservations" from Our One Mistake (2006). Intro: "Beautiful" from Folding Stars (1996). More at theoldceremony.com. Hear more Nakedly Examined Music. Support us on Patreon.