Podcasts about animations

  From Wikipedia, the free encyclopedia 314 – "Lux"  episode Promotional title-card Cast  –   –  Others  – Newsreader Cassius Hackforth – Tommy Lee Ryan Speakman – Husband  – Reginald Pye  – Mr Ring-a-Ding  – Sunshine Sally Lewis Cornay – Logan Cheever Lucy Thackeray – Renée Lowenstein Jane Hancock – Helen Pye William Meredith – Policeman Samir Arrian – Hassan Chowdry Bronté Barbé – Lizzie Abel Steph Lacey – Robyn Gossage  – Mrs Flood Production Directed by Written by Script editor David Cheung Produced by Chris May Executive producer(s) Russell T Davies Joel Collins Music by Series Running time 43 minutes First broadcast 19 April 2025 Chronology ← Preceded by "" Followed by → "" "Lux" is the second episode of the  of the  series . It was written by , the Doctor Who showrunner, from a concept first developed over two decades prior, and directed by . In the episode, the  () and his , , land in 1952 , while trying to make their way back to 2025. After doing so, they discover a mysterious  where fifteen people have disappeared. The pair stay to investigate and ultimately confront an animated God, Lux (voiced by ), who traps them in film. "Lux" is unusual for the programme in that it features a mixed live-action and animated format. Filming for the episode took place in  and at  in January 2024. Some voice-over work took place internationally in  in June. Animation and other  work continued later into the year, with some tasks still being completed as late as September. The episode includes references to ,  and  to its own . It was released on , , and  on 19 April 2025. Reception to the episode was positive, with critics praising its use of animation in a live-action setting. A novelisation written by  is set to be released in July 2025. Plot [] Unable to return to 24 May 2025, the  lands in 1952 , where the Doctor and  find a cinema that is chained shut. At a diner, they speak to the mother of one of fifteen people who disappeared from the cinema, which continues to play movies at night. Although  is still enforced, the waiter allows them to stay. Inside the cinema, the pair discover a living cartoon, Mr Ring-a-Ding, the embodiment of Lux Imperator, God of Light, is responsible. The projectionist, Reginald Pye, plays films for Lux, who uses his power to recreate Pye's dead wife. Lux has trapped the missing people in a film reel. He similarly traps the Doctor and Belinda, and turns them into cartoon characters, until they regain their usual forms. They flee to another false reality, where a racist  cop challenges them. They escape through a television watched by . Though delighted to meet the Doctor they reveal that their world is the fake one, and encourage him to return and defeat Lux, even though they will then cease to exist. Back in the cinema, the Doctor heals his injured hand using residual  energy. Lux steals the energy to create a solid body. Belinda attempts to burn film reels to cause an explosion, but encouraged by his wife, Pye sacrifices himself. The explosion exposes the cinema to sunlight causing Lux to expand infinitely until he becomes one with the universe. The missing people return. As the Doctor and Belinda leave, Mrs Flood encourages the bystanders to watch the TARDIS dematerialise, claiming this "show" is a "limited run" that ends on 24 May. The Doctor's fans critique the episode, realising that they still exist. Production [] Development and production design [] "Lux" was written by . He had wanted to do an episode that included a living cartoon for a long time, but was unable to do one until now for funding reasons. He also revealed that he had considered variants of such an episode, including one two decades prior that would have featured a hologram rather than a legitimate animation as a result of the budgetary constraints. The story also contains mentions of segregation and racism in which Davies said he added to address issues in present-day society. It was also used as a subversion to the Doctor usually situating themselves as the main authority figure. While including such themes, he didn't want it to be the dominant subject. Among these are the NYPD officer saying the cinema is a space "reserved for " and prejudicely assuming that Belinda is . Other motifs present explore grief, hope, friendship, and sadness.  references to  and the  character  were included. Additionally, Davies has continued a recent trend of . Also unusual for the show, the episode featured a . Costume designer Pam Downe created the Doctor's and Belinda's outfits using the  of blue and yellow. Sethu's dress was inspired by a similar one worn by Anita () and designed by  in the  of . Meanwhile, Gatwa's blue suit was influenced by American musical artists of the 1950s. These hues were intended to further contrast with the red interior of the cinema by ultimately using all three . Downe ultimately wanted to successfully convey movement during the episodes action scenes. As such, three different  were considered for Sethu to wear and her dress had multiple . The concept of Mr. Ring-a-Ding was inspired by animations from . Ian Spendloff worked as a creative designer for the episode, and was the designer of Mr. Ring-a-Ding. Davies compared the concept of Mr. Ring-a-Ding to  from the 1930s. Spendloff drafted thirty different sketches that were considered before finally settling on the one used in the episode. Each one featured variations in noses, hair, and other elements. Mr. Ring-a-Ding was ultimately given a pig-like nose and blue skin to reflect characteristics of cartoon characters from the time period, with Davies wanting the character to look vaguely human but not be immediately identifiable as something else.  to  were also present in the episode because Davies recalled his enjoyment of cartoons while writing it, which made him consider people who loved the programme. Within the episode, the fans wore Doctor Who apparel (including a  scarf, another a  "Telos" sweatshirt) and declared "" (2008) as their favourite episode. One of them also mentioned the impending cancellation of the show. They then point out the "obviousness" of the episode's , and made references to . The show's BBC ident appears on the screen of their television. Although they say they are too inconsequential to be given surnames, all three characters—Hassan Chowdry, Lizzie Abel and Robyn Gossage—are fully named in the credits. The concept of Doctor Who existing within  had previously been briefly explored in  (1988) and other expanded media. Following this instance, such an idea was considered quasi-. This scene raised suspicion that Davies had potentially been planting  online himself regarding upcoming episodes as well as rumours that the series would enter into another hiatus. When the fact that the episode was written and filmed well before the leaks began appearing was considered, it was compared to the . Casting [] The episode stars  as the  of  and  as his , .  voices the antagonist, Mr. Ring-a-Ding. It marks Cumming's second appearance in the show after his role as King  in the 2018 episode "". Davies said that he and the production team had considered whether it was too soon to cast Cumming again and that had it been a live action role, he likely would not have been. Mr. Ring-a-Ding is the "God of Light" and part of the "Pantheon of Gods" that Davies has been developing since "" (2023).  stars as Reginald Pye, the theatre's projectionist and Lewis Cornay plays a diner worker who helps the Doctor and Belinda investigate the disappearances.  also makes a brief appearance as recurring character Mrs. Flood. The trio of fans were portrayed by Samir Arrian, Bronte Barbe, and Steph Lacey. Filming and post-production [] Exterior shots for the theater were filmed at  Pavilion. The wooden ramp can be seen at the bottom of the photo. The story was filmed in the series' third production block, along with the following episode, "". It was directed by  and recorded in late-January 2024.  took place in . The surrounding area was made up to look like an American city in the 1950s by adding vehicles and American flags.  pavilion was used for exterior shots of the theatre. The production team painted the building, added a period theatre sign, and removed a modern ramp at the front of the building. Its removal led to the discovery of rotting wood that had to be replaced at the show's expense. The episode was shot during  causing the cast and crew to struggle with unexpected rain and winds, requiring hot water bottles to keep warm during takes. Interior shots for the studio were filmed on  4 at . Pieces of that set were reused from "" (2024). As a result of Mr. Ring-a-Ding being an animated creation, the performers had to interact with a two-foot acrylic cutout of the character or a thin green pole on set.The scenes were then edited during the  process. References for Mr. Ring-a-Ding's movement were filmed by crew, and then drawn directly into the scene by animators from . Cumming voiced his scenes in  on 28 June 2024. Elements of Cumming's facial expressions during this recording for were incorporated into Mr. Ring-a-Ding. The scene where the Doctor and Belinda are turned into cartoons were first recorded on a , in which Gatwa and Sethu had to portray the characters in a rigid and cartoon-like manner. The animators used this as a reference for interaction between the two characters when redrawing them as a cartoon. Animations were done at twenty-five frames a second, requiring twenty-five drawings for each second of screen time, or fewer if characters' movement was limited.  artists recorded  at Bang Post Production in  on 4 September 2024. The episode's soundtrack included two singles: "" by , and the  rendition of "". Despite the episode taking place in 1952, the tracks were not released until 1956 and 1969, respectively. , the show's , also included the song "The Sad Man With A Box", a piece that he originally composed for . Broadcast and reception [] Professional ratings Aggregate scores Source Rating  (Tomatometer) 100%  (Average Score) 7.70/10 Review scores Source Rating B 10/10 9/10 Broadcast [] "Lux" was  released on  at 8 a.m.  (BST) in the United Kingdom and on  in the United States at 12 a.m.  on 12 April 2025. A  broadcast followed at 7:15 p.m. BST.  also handled international distribution of the episode. Ratings [] The episode received overnight viewing figures of 1.58 million, the lowest broadcast ratings in Doctor Who's history. It was the fourth most-watched programme of the day on BBC One, with one show on  also achieving higher numbers. Critical reception [] On the  website , 100% of 10 critics' reviews are positive, with an average rating of 7.70/10. Robert Anderson, writing for , praised the episode, highlighting Mr. Ring-a-Ding, several individual scenes, such as the fourth-wall-breaking scene, and the performances of Gatwa and Sethu. 's Martin Belam responded positively to the episode, praising Cumming's performance and the fourth-wall-breaking scene. Will Salmon, writing for , highlighted Mr. Ring-a-Ding and Gatwa's performance, though felt Murray Gold's musical score "drowned out" some scenes. Adi Tantimedh, writing for , found the episode to be the strongest out of the episodes headed by Davies in his second tenure as showrunner. Andrew Blair, writing for , highlighted the characterization of Mr. Ring-a-Ding, but criticized the episode's similarities to "", which he felt made the episode feel repetitive and weaker than it should have. He also felt the episode's handling of race was not effective, as while he felt the episode's inclusion of the topic was commendable, he felt the Doctor's in-universe way of handling it "can't help but scrape awkwardly against our real-world knowledge." Vicky Jessop, writing for the , criticized the inclusion of the scene featuring Doctor Who fans, stating that while it was initially entertaining, it quickly became "strained".    

My favorite 2024 animes, some suprised me.  Patreon: Tv Movie Mistress Twitter: @BookDreamer01 @TVMovieMistress  Facebook Group: www.facebook.com/groups/tvmoviemistress/ Email Address: tvmoviemistress@gmail.com YouTube Channel: https://www.youtube.com/tvmoviemistress

Dans ce premier épisode consacré à la musique composée pour les séries et épisodes en animation, Frédéric et Aude offrent une liste de thèmes coups de coeur et quelques surprises. Star Wars en Direct est disponible sur les applications  Apple Podcast / Spotify / Amazon Music / Audible / Deezer Venez rejoindre et participer à notre communauté d'auditeurs sur les plateformes Discord / Facebook / X / Instagram Merci à nos partenaires : MintInBox.net, Planete-StarWars.com, Boutique Kaia et Générations SW et SF Comme toujours, si vous avez suggestions ou réactions sur les sujets que nous venons de discuter, écrivez-nous un commentaire ci-dessous ou envoyez-nous un e-mail à studio@starwars.direct.

Qui dit vacances de printemps dit fête de Pâque. A Turckheim, comme depuis de nombreuses année, c'est tout un marché qui est organisé avec de nombreuses animations jusqu'au 27 avril prochain. Expositions, concerts, ateliers culinaires, distributions de chocolats, petits et grands seront comblés.Magali Paillier, chargée de communication à la ville de Turckheim dévoile le programmeInfos pratiques : Du 12 au 27 avril 2025Turckheimhttps://www.turckheim.fr/evenement/paques-a-turckheim/Les interviews sont également à retrouver sur les plateformes Spotify, Deezer, Apple Podcasts, Podcast Addict ou encore Amazon Music.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Avec le retour des beaux jours, c'est aussi le retour de l'animal totem de l'Alsace, la cigogne. En cette période de vacances scolaires, tout un programme de festivités est proposé, même au-delà puisque les animations se poursuivent jusqu'au 11 mai prochain. Océane Besugo, chargée de communication à l'office de tourisme était dans nos studios.Infos pratiques : Jusqu'au 11 mai 2025Vallée de Munsterhttps://www.munster.alsace/wp-inside/uploads/2024/03/depliant-printemps-des-cigognes-2025.pdfLes interviews sont également à retrouver sur les plateformes Spotify, Deezer, Apple Podcasts, Podcast Addict ou encore Amazon Music.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Pendant ces vacances scolaires, les familles auront notamment rendez-vous du côté du château du Haut-Koenigsbourg. Durant toute cette période, l'édifice situé à Orschwiller proposera au public divers ateliers sur les thèmes des vitraux ou encore de Pâques. Un spectacle avec Contesse Luciole, "Que d'eau !", se tiendra également le samedi 12 avril dans le cadre du Festival L'Alsace se (ra)conte. Et pour une sortie au grand air, il sera encore possible de découvrir le massif du château, ainsi que sa faune et sa flore, grâce à "L'éveil de la forêt". Yves Bossart, chargé de programmation au château du Haut-Koenigsbourg, en dit plus sur ces prochaines animations.Renseignements et billetterie sur le site internet haut-koenigsbourg.fr.Les interviews sont également à retrouver sur les plateformes Spotify, Deezer, Apple Podcasts, Podcast Addict ou encore Amazon Music.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

La classe de Terminale SAPAT du campus Provence Ventoux organise le vendredi 25 avril à Flassan, à la salle des fêtes Paul Peyre, des ateliers et animations sur le thème de Koh Lanta à destination d’une classe multi-niveaux de CE1/CM2.

This show is picking up! Listen to the conversation about the 2nd of season of X-Men Evo with Zack and Greg #xmen #xmenevolution #xmen2 Subscribe to my Wrestler Channel: @maurice_wrestle Buy a Shirt: https://brainbustertees.com/wrestlers/maurice/ Follow Me on X: https://x.com/MAURICE_Wrestle Follow Me on Insta: https://www.instagram.com/maurice_wrestle/

En avril, ne bredouille pas d'un fil ! Dans le Val d'Argent, ces prochaines semaines seront consacrées à un programme autour de l'éloquence. Exposition, atelier et divers spectacles seront proposés au public. En parallèle de ce projet, un autre événement incontournable sera proposé le samedi 05 avril, avec la Fête de la Graine. Pour en parler, Anne Richard, directrice de l'établissement, était à notre micro.Retrouvez le programme complet sur le site internet valdargent.bibenligne.fr.Les interviews sont également à retrouver sur les plateformes Spotify, Deezer, Apple Podcasts, Podcast Addict ou encore Amazon Music.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

« Ensemble pour nourrir le sol ! » Du 29 mars au 13 avril prochain, le Smictom d'Alsace centrale se joint au Réseau compost citoyen dans le cadre de la 12ème quinzaine nationale du compostage de proximité. En collaboration avec d'autres partenaires, diverses animations seront proposées au public dans l'objectif de valoriser les bienfaits du compostage. Les précisions de Pascal Strievi, maître composteur rattaché au Smictom d'Alsace centrale.Le lien vers notre article complet : https://www.azur-fm.com/news/alsace-centrale-un-programme-danimations-pour-sensibiliser-au-compostage-2429Les interviews sont également à retrouver sur les plateformes Spotify, Deezer, Apple Podcasts, Podcast Addict ou encore Amazon Music.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

L'association des P'tits Castinétains vous attend nombreux ce samedi 29 mars pour célébrer l'arrivée du printemps. Rendez-vous à l'espace Les Tisserands et au Parc Ergé pour un marché d'artisans et producteurs locaux, cumulé à de nombreuses animations. Chasse aux œufs sous forme de jeu de piste pour petits et grands, pêche aux canards, sculpture de ballons ou encore jeux en bois, il ne suffira que de soleil en plus pour profiter de cette merveilleuse journée. Émilie Fleurot, membre de l'association était dans nos studios.Infos pratiques :Samedi 29 mars 2025, 11h à 18hChâtenois, Espace Les Tisserands et Parc ErgéEntrée librehttps://www.intramuros.org/chatenois/agenda/502073Les interviews sont également à retrouver sur les plateformes Spotify, Deezer, Apple Podcasts, Podcast Addict ou encore Amazon Music.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Le programme d'animations de la Maison de la Nature du Vieux Canal à Hirtzfelden repart de plus belle en cette année 2025. Pour les prochains mois, de nombreux temps forts sont prévus. Ateliers, sorties natures, conférences, tout est là pour satisfaire toute la famille. Justine Chlecq, responsable pédagogique de la MNVC était dans nos studios.Les interviews sont également à retrouver sur les plateformes Spotify, Deezer, Apple Podcasts, Podcast Addict ou encore Amazon Music.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Batman: The Animated Series, arguably the pinnacle of Batman. Listen in as Zack and Greg review the first 15 episodes of BTAS. See us talk about Joker, Batman, Clayface and more.

Dieses Mal ist Moe Gouda im Gespräch mit der in Dresden beheimateten Animations- und Trickfilmkünstlerin Alma Weber. In Weimar aufgewachsen und an der Kunsthochschule Kassel ausgebildet, ist sie neben ihren diversen visuellen Kunstprojekten auch als Schlagzeugerin in mehreren Bands aktiv. Über dem Ostpol Dresden zwischen zwei Drumsets aufnehmend, rekonstruieren die beiden den Weg zu Almas eigenem Animations- und Produktionsstudio, Studio Animauz, dass sie mit zwei weiteren Künstlerinnen betreibt. Darüber hinaus besprechen die beiden u.a. was Rhythmus und bewegte Bilder miteinander zu tun haben und warum Dunderklumpen! der beste Film aller Zeiten ist. Eine Unterhaltung über männlich geprägte Kunst, alltägliche Inspiration für Abstraktionen und warum Menstruieren stärker tabuisiert ist als fiktiver Mord. Aktuelle Termine: The Male Gaze Recipe läuft 2025 beim 37. Filmfest Dresden im Nationalen und im Mitteldeutschen Wettbewerb Hey Püppi wird vom 15. - 17.05.2025 in Kassel und am 05.07.2025 in Leipzig gezeigt. Näheres dazu in unten aufgeführten Links. Studio Animauz Filmweberei Almas Instagram MenstruAliens - Instagram Hey Püppi - Instagram Bericht über Nudeln für Courbet - ARD Mediathek / MDR Bericht über Alma - MDR gránátèze - Bandcamp The Shna - Bandcamp

Voicing Animations with Kate HarbourThis month Rob and Helen Bee are joined by the legendary Kate Harbour – a voice actor with more iconic credits than you can shake a script at.You've heard her in Bob the Builder, Shaun the Sheep, Timmy Time, Octonauts, The Secret Show, Chip and Potato, Go! Go! Cory Carson… Honestly, the list goes on, and if we tried to name everything, we'd be here all day. She's also a master of radio drama (Doctor Who, Robin of Sherwood), comedy (Puckoon, The Big Ben Theory), and Netflix dubs – basically, if you've ever watched TV, you've probably heard Kate.But she's not just a voiceover powerhouse – she also teaches. Her Acting for Animation workshops have helped countless VOs bring characters to life, whether at top drama schools or in her own private coaching sessions. So, of course, we had to sit her down and grill her on everything: How did she get into animation? How does she build her characters? What makes a great animation performance? And crucially, how does her approach to teaching differ from everyone else's?It's a properly juicy episode, full of insights, laughs, and a fair bit of geeking out over characters. Let's get into it!We also chat to Annette Rizzo - from Equity's Audio Committee. She gives us the latest news on Equity's audio audit, BBC audio drama campaign, accessibility for blind/partially sighted for equity website, AI campaign and the Equity audio committee elections.Kate Harbour's links: Website - kateharbour.com  Acting for Animation workshop - info and booking link - https://www.kateharbour.com/a4a-book-ticketEquity Links: Save Audio Drama at the BBC campaign - https://www.change.org/p/save-audio-drama-at-the-bbcStop AI Stealing the Show campaign - https://www.equity.org.uk/campaigns-policy/stop-ai-stealing-the-show VO Social Links  Great Big VO Social blog -b-double-e.co.uk/the-first-great-big-vo-social-a-review/ Great Big VO Social photo gallery - greatbigvosocial.com/photo-gallery/For more information about The Voiceover Social visit: ⁠⁠⁠⁠⁠⁠⁠⁠The Voiceover Social Website⁠⁠⁠⁠⁠⁠⁠⁠ Email us ⁠⁠⁠⁠⁠⁠⁠⁠listen@thevosocial.com⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to our newsletter⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠See which events are coming soon⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠Find your closest VO Social group⁠⁠⁠⁠⁠⁠⁠⁠ Find us online: ⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠Facebook Page⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠Facebook Group⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠  Podcast sponsored by ⁠⁠⁠⁠⁠⁠⁠⁠B Double E⁠⁠⁠⁠⁠⁠⁠⁠.Theme tune by Rob Bee.All audio production by Rob Bee.

 It's episode 209 and time for us to talk about the genre of Design! We discuss graphic design, interior design, the line between design and art, fonts, kerning, footnotes, and more! Plus: Anna talks about evidence synthesis and search design! You can download the podcast directly, find it on Libsyn, or get it through Apple Podcasts or your favourite podcast delivery system. In this episode Anna Ferri | Meghan Whyte | Matthew Murray

A Little Red Book blogger's sci-fi animations were repeatedly reposted by Musk. Despite the time-consuming and lonely creative process, the blogger persisted in creation. His creative purpose is not for traffic, but to show his personal imagination of the sci-fi world.Join other motivated learners on your Chinese learning journey with maayot. Receive a daily Chinese reading in Mandarin Chinese, for your level. Full text in Chinese, daily quiz to test your understanding, one-click dictionary, new words, and more.Join other learners at https://www.maayot.com

Les animations de l'ACSL de Luxeuil

Send us a text and let us know what you think of our podcast!Lost to the mists of time...carelessly junked by the BBC without any thought to the future of TV...the lost episodes of Doctor Who are the stuff of legend!As of 2025, less than 100 episodes are still lost but they turn up from time to time - discovered in sheds in obscure parts of the world and some are rumoured to exist, held onto be selfish fans who wont share them with the world!Fortunately, the BBC have worked to create animated reconstructions of the stories that exist via audio recordings and tellysnaps (photos of the episodes during transmission) so as to bring us the next best thing to actually discovering the lost episode!In this podcast episode, we take a look at the animations and discuss them and the legends of the lost episodes...Support the show Subscribe to Who Corner to Corner on your podcast app to make sure you don't miss an episode! Now available to watch on YouTube! Join the Doctor Who chat with us and other fans on Twitter and Facebook! Visit the Who Corner to Corner website and see our back catalogue of episodes! Enjoying what we do? Consider joining our Explorers Subscription plan for more content! Who Corner to Corner: Great guests and 100% positive Doctor Who chat!

In ep 109 of “How Do You Say That?!” sponsored by britishvoiceover.co.uk, Bruce Duncan joins Sam and Mark to talk about alternative ways to create a power 30 second commercial, bringing out the pride in a script without going over the top and what to be wary of when your kid decides they want to be in front of the mic!Our VO question this week is all about keeping voiceover in the family!Get involved! Have you got a Wildcard suggestion that we should try or an idea for the show? Send it to us via Mark or Sam's social media or email it directly to podcast@britishvoiceover.co.ukScript 1Well, it just so happens that digital City have been doing all of this and more in 2024. The City served as a testbed for uncrewed aerial technology, thanks to a unique drone trial – an example of how innovators are using our sandbox environment in exciting ways. We made real progress with our LifeCycle data trust pilot project – a groundbreaking initiative that draws on the City's world-leading trust structure and applies it to data stewardship - an area that offers huge commercial potential for the city. Not only introducing innovation but by mapping route data, we can help make cycling across the city safer and more rider-friendly.Script 2Direction: Classy, 30s, relaxed, smart, measuredHarrington Smith has the perfect excuse to ‘take a look' at the stunning new BMW X5 50e xDriveIt's a ‘Personal Contract Hire Offer', hmm24-month agreement with an initial rental of £3,882 … hmm!?Monthly rental of £999 … nice!Plus the hybrid model is the perfect balance of petrol and electric …… thought that might do itThe new BMW X5 50e xDriveSearch Harrington Smith.co.ukTs & Cs apply. Harrington Smith is a credit brokerWe'd love your feedback - and if you listen on Apple Podcasts or Spotify, hit the follow button today! **Listen to all of our podcasts here - you can also watch on YouTube, or say to your smart speaker "Play How Do You Say That?!"About our guest: Bruce Duncan has been behind the mic since 2009 and has voiced thousands of Commercials, Corporate narrations, Explainers, and E-learning, Museum Audio Guides, Animations, Video Games, In-store and Telephone systems. You'll hear him greet you on the phone at Aviva (and a stack of other Insurance Companies), he'll advise you about special offers in B&Q, and try to sell you BMWs on the radio. In fact, the only place you're unlikely to hear him is narrating an Audiobook (if you exclude 'Penny the Penguin' sleep story on the Calm app).Bruce grew up in Surrey, and lives with his wife Rachel, two daughters (both Child VOs) and a newly adopted Saluki Sighthound. They enjoy holidays in their motorhome and given the chance, Bruce would spend his life mountain biking and snowboarding. Bruce's Website Bruce's Facebook page @BruceDuncanVO on X

Reanimated 4 is here to change the way we build animations in React Native. With CSS-style animations and transitions, animations are now easier to write, more predictable, and work more like they do on the web. In this episode, Ola Desmurs-Linczewska (https://x.com/p_syche_) sits down with Tomasz Zawadzki (https://x.com/tomekzaw_) and Mateusz Łopaciński (https://x.com/MatiPl01) from Software Mansion to discuss what's new in Reanimated 4, why React Native is moving toward web standards, and what these changes mean for developers. We talk about:

Subscriber-only episodeSend us a text and let us know what you think of our podcast!Lost to the mists of time...carelessly junked by the BBC without any thought to the future of TV...the lost episodes of Doctor Who are the stuff of legend!As of 2025, less than 100 episodes are still lost but they turn up from time to time - discovered in sheds in obscure parts of the world and some are rumoured to exist, held onto be selfish fans who wont share them with the world!Fortunately, the BBC have worked to create animated reconstructions of the stories that exist via audio recordings and tellysnaps (photos of the episodes during transmission) so as to bring us the next best thing to actually discovering the lost episode!In this podcast episode, we take a look at the animations and discuss them and the legends of the lost episodes... Subscribe to Who Corner to Corner on your podcast app to make sure you don't miss an episode! Now available to watch on YouTube! Join the Doctor Who chat with us and other fans on Twitter and Facebook! Visit the Who Corner to Corner website and see our back catalogue of episodes! Enjoying what we do? Consider joining our Explorers Subscription plan for more content! Who Corner to Corner: Great guests and 100% positive Doctor Who chat!

durée : 00:24:34 - Les goûts d'ici en Béarn Bigorre - Ce samedi 22 février, la garbure est à l'honneur à Bescat. dans une ambiance gourmande et festive.

durée : 00:11:22 - Les jardins de Marqueyssac avec des ateliers culinaires et le Château de Castelnaud avec des animations d'hiver

Les cavalcades se poursuivent en Alsace ! Après Wittisheim la semaine dernière, le rendez-vous est donné ce samedi 22 février du côté de la commune de Châtenois. Le top départ de ces festivités sera donné dès 15h, où le public pourra observer les pas moins de 512 carnavaliers qui défileront dans les rues. Une animation musicale sera proposée par la suite du côté du CCA, sans oublier également un spectacle de danse. Léandre Stoeckel, chargé de la communication du groupe carnavalesque D'narre Vo Kestaholtz, à l'organisation de cet événement, nous en dit plus.Informations pratiques : Samedi 22 février 2025, à partir de 15hCCA - 4, rue Saint-Georges 67730 ChâtenoisEntrée librePlus de renseignements sur la page Facebook D'narre Vo KestaholtzLes interviews sont également à retrouver sur les plateformes Spotify, Deezer, Apple Podcasts, Podcast Addict ou encore Amazon Music.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

In this episode of the Modern Web Podcast, host Rob Ocel talks with Mike Chen, Co-founder & CTO of Motivo, about the evolving world of web animations. Mike shares his enthusiasm for tools like Framer Motion (now Motion) and the View Transitions API, discussing how they make complex animations more accessible and intuitive. The conversation explores the practicality of animations in real-world applications, balancing user delight with business value, and avoiding unnecessary complexity. They also discuss the state of decentralized social media, with Mike sharing his thoughts on Blue Sky and its approach to user control. Finally, Mike talks about his Let's Get Technical Discord, a mentorship-focused space helping mid-level engineers sharpen their skills. Making Animations More Accessible – Mike Chen discusses how tools like - - Framer Motion (now Motion) and the View Transitions API simplify complex animations, making them easier to implement while maintaining performance.-Balancing Business Value and User Delight – The group explores when animations enhance UX versus when they become unnecessary, emphasizing the importance of practicality in real-world applications.- Decentralized Social Media Challenges – Mike shares his perspective on Blue Sky, its approach to user-controlled feeds, and the trade-offs between decentralization and usability.-The Power of Mentorship in Tech – Mike talks about his Let's Get Technical Discord, a space dedicated to helping mid-level engineers grow through in-depth discussions and guided learning.Chapters:00:00 - Introduction to the Podcast 00:23 - Guest Introduction: Mike Chen from Motivo 01:05 - Technologies That Have Caught Mike's Attention 01:40 - Web Animations and Their Growing Accessibility 02:13 - Framer Motion and the View Transitions API 02:50 - The Evolution of Animation Tools 03:31 - The Role of Spring Animations in UI 04:07 - Framer Motion vs. Framer as a Platform 05:05 - Expanding Animation Tools Beyond React 05:41 - Practical Use Cases for Animations in Development 06:17 - Business Justifications for Implementing Animations 07:02 - Subtle UI Enhancements vs. Overuse of Animations 08:04 - Good Animation Practices in UX 09:16 - How Companies Like Vercel and Linear Use Animations 10:55 - The Importance of Thoughtful UI Design 12:02 - The Impact of Animation on Brand Perception 13:27 - Animation as a Way to Reduce Cognitive Load 14:45 - Social Media Shifts and Blue Sky's Growth 16:09 - The Vision Behind Blue Sky's Decentralization Model 18:14 - The Challenges of Building User-Controlled Feeds 19:35 - Limitations of Blue Sky's Decentralization Approach 21:48 - Blue Sky vs. Mastodon: Usability and Adoption 24:05 - Scaling Challenges as Blue Sky Reaches 30M Users 26:43 - The Trade-offs Between Centralization and Convenience 28:59 - The Role of UI in Establishing Trust in Brands 30:27 - The Value of Mentorship in Engineering Growth 32:09 - The Struggles of Learning Without Proper Guidance 33:51 - Why Context Matters in Technical Decisions 35:15 - Where to Find Mike Online and Discord Community 36:02 - Closing Remarks and Sponsor MessageFollow Mike Chen on Social MediaBluesky: https://bsky.app/profile/chenmike.comLinkedin: https://www.linkedin.com/in/chenhmike/Sponsored by This Dot: thisdot.co

Comme chaque mois, Rémy de TV 3 Vallées présente les animations à venir du côté de Niederbronn-les-Bains. Voici le programme de ce mois de février.Les interviews sont également à retrouver sur les plateformes Spotify, Deezer, Apple Podcasts, Podcast Addict ou encore Amazon Music.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Catlin Miron joins Jamon and Robin to dive into React Native animations! From reanimated and layout animations to interpolation and motion accessibility, Catlin shares expert insights and best practices to bring your UI to life.  Show NotesCatalin's websiteReact Native Layout AnimationsReanimated InterpolateRNR 122RNR 168Connect With Us!React Native Radio: @ReactNativeRdioJamon Holmgren: @jamonholmgrenRobin Heinze: @robinheinzeCatalin Miron: @mironcatalin This episode is brought to you by Infinite Red!Infinite Red is an expert React Native consultancy located in the USA. With nearly a decade of React Native experience and deep roots in the React Native community (hosts of Chain React and the React Native Newsletter, core React Native contributors, creators of Ignite and Reactotron, and much, much more), Infinite Red is the best choice for helping you build and deploy your next React Native app.

Experience the frustration behind making episodes of the show - Alex recounts first hand how the storage bin project went horribly wrong! Matt complains about his BOSS flow sensor, Kyle makes some AMAZING product animations and we chat about our multi-part box generation series. We get a demo on multiple instances of LightBurn, dive into 3D height map generation and shout out the community settings library. All of that and more on this episode of the Laser Source Podcast! Big thanks to Johnson Plastics Plus for sponsoring this episode of the Laser Source Podcast. Here is a link to their store, check out their stock and show them some love! https://www.jpplus.com/affiliates?rfsn=7449563.a3057c (affiliate link) Use code YLR-ZDF for 15% off your order on eligible items. The channel, staff, communities, web services… everything. It's all here thanks to the LMA. ► Consider Supporting Us: https://masters.lasereverything.net/ ❤️ We're probably earning affiliate income when you buy stuff we link. ❤️ ✨As Amazon Associates we earn from qualifying purchases.✨ SFX handheld portable fiber marking machine: https://amzn.to/3UeBQ5S Check out design bundles for artwork! https://designbundles.net/?ref=ej0Vm5 Here is our affiliate link to the ninja transfers website for DTF transfers if you have a heat press and wanted to try some out: https://rb.gy/exmvp3 You're going to love this content too! ► Psst… we're on Odysee: https://odysee.com/$/invite/@lasereverything:9 ► NEW MakerREMIX Channel! @MakerREMIX ► MakerREMIX Network Dev Log: https://www.youtube.com/playlist?list=PLoBR3k35202aGHS6aqzFW_K-L5JktCJcP ► FULL Laser Source Podcast Playlist: https://www.youtube.com/playlist?list=PLoBR3k35202Yoskix8t3ibwd8gRFM5QPC ► LE Talk Radio: https://www.youtube.com/live/Gf5YHl4GqIA?si=xUNYTfOxGDaENqsF ► Laser Everything Linktree: https://linktr.ee/lasereverything ► Laser Source Linktree: https://linktr.ee/lasersource We have SO MANY Laser Resources: ► The 2024 Buying Guide: https://makearmy.io/scripts/buyingguide.php ► Join the FREE MakerArmy Network: https://makearmy.io/ ► Join the FREE Matrix Server: https://rb.gy/m4ca1j ► Join the FREE Discord Server: https://discord.gg/lasereverything ► Join the FREE Facebook Group: https://www.facebook.com/groups/lasereverything ► FREE Laser Engraving Starter Settings Packs: https://lasereverything.net/free-laser-settings ► Community Fiber Library: https://db.lasereverything.net/scripts/settings.php ► Community CO2 Library: https://db.lasereverything.net/scripts/settings.php ► Community UV Library: https://db.lasereverything.net/scripts/settings.php ► Laser Source Podcast: https://podcast.lasereverything.net/ Listen up! Laser engraving machines are inherently dangerous. The content this channel is for educational purposes only. Laser Everything LLC can not be held liable for any harm caused to any individual or personal property related to settings, activities, procedures, techniques, or practices described in whole or part on this YouTube channel. By watching this video you agree that you alone are solely responsible for your own safety and property as it pertains to this content. Do your own research before purchasing machines, materials or accessories. LE is viewer supported and probably earns commission when you buy stuff we link. Copyright @ Alexander Sellite/Laser Everything LLC. Any illegal reproduction of this content will result in immediate legal action.

One last Gold sponsor slot is available for the AI Engineer Summit in NYC. Our last round of invites is going out soon - apply here - If you are building AI agents or AI eng teams, this will be the single highest-signal conference of the year for you!While the world melts down over DeepSeek, few are talking about the OTHER notable group of former hedge fund traders who pivoted into AI and built a remarkably profitable consumer AI business with a tiny team with incredibly cracked engineering team — Chai Research. In short order they have:* Started a Chat AI company well before Noam Shazeer started Character AI, and outlasted his departure.* Crossed 1m DAU in 2.5 years - William updates us on the pod that they've hit 1.4m DAU now, another +40% from a few months ago. Revenue crossed >$22m. * Launched the Chaiverse model crowdsourcing platform - taking 3-4 week A/B testing cycles down to 3-4 hours, and deploying >100 models a week.While they're not paying million dollar salaries, you can tell they're doing pretty well for an 11 person startup:The Chai Recipe: Building infra for rapid evalsRemember how the central thesis of LMarena (formerly LMsys) is that the only comprehensive way to evaluate LLMs is to let users try them out and pick winners?At the core of Chai is a mobile app that looks like Character AI, but is actually the largest LLM A/B testing arena in the world, specialized on retaining chat users for Chai's usecases (therapy, assistant, roleplay, etc). It's basically what LMArena would be if taken very, very seriously at one company (with $1m in prizes to boot):Chai publishes occasional research on how they think about this, including talks at their Palo Alto office:William expands upon this in today's podcast (34 mins in):Fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours.In Crowdsourcing the leap to Ten Trillion-Parameter AGI, William describes Chai's routing as a recommender system, which makes a lot more sense to us than previous pitches for model routing startups:William is notably counter-consensus in a lot of his AI product principles:* No streaming: Chats appear all at once to allow rejection sampling* No voice: Chai actually beat Character AI to introducing voice - but removed it after finding that it was far from a killer feature.* Blending: “Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model.” (that's it!)But chief above all is the recommender system.We also referenced Exa CEO Will Bryk's concept of SuperKnowlege:Full Video versionOn YouTube. please like and subscribe!Timestamps* 00:00:04 Introductions and background of William Beauchamp* 00:01:19 Origin story of Chai AI* 00:04:40 Transition from finance to AI* 00:11:36 Initial product development and idea maze for Chai* 00:16:29 User psychology and engagement with AI companions* 00:20:00 Origin of the Chai name* 00:22:01 Comparison with Character AI and funding challenges* 00:25:59 Chai's growth and user numbers* 00:34:53 Key inflection points in Chai's growth* 00:42:10 Multi-modality in AI companions and focus on user-generated content* 00:46:49 Chaiverse developer platform and model evaluation* 00:51:58 Views on AGI and the nature of AI intelligence* 00:57:14 Evaluation methods and human feedback in AI development* 01:02:01 Content creation and user experience in Chai* 01:04:49 Chai Grant program and company culture* 01:07:20 Inference optimization and compute costs* 01:09:37 Rejection sampling and reward models in AI generation* 01:11:48 Closing thoughts and recruitmentTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and today we're in the Chai AI office with my usual co-host, Swyx.swyx [00:00:14]: Hey, thanks for having us. It's rare that we get to get out of the office, so thanks for inviting us to your home. We're in the office of Chai with William Beauchamp. Yeah, that's right. You're founder of Chai AI, but previously, I think you're concurrently also running your fund?William [00:00:29]: Yep, so I was simultaneously running an algorithmic trading company, but I fortunately was able to kind of exit from that, I think just in Q3 last year. Yeah, congrats. Yeah, thanks.swyx [00:00:43]: So Chai has always been on my radar because, well, first of all, you do a lot of advertising, I guess, in the Bay Area, so it's working. Yep. And second of all, the reason I reached out to a mutual friend, Joyce, was because I'm just generally interested in the... ...consumer AI space, chat platforms in general. I think there's a lot of inference insights that we can get from that, as well as human psychology insights, kind of a weird blend of the two. And we also share a bit of a history as former finance people crossing over. I guess we can just kind of start it off with the origin story of Chai.William [00:01:19]: Why decide working on a consumer AI platform rather than B2B SaaS? So just quickly touching on the background in finance. Sure. Originally, I'm from... I'm from the UK, born in London. And I was fortunate enough to go study economics at Cambridge. And I graduated in 2012. And at that time, everyone in the UK and everyone on my course, HFT, quant trading was really the big thing. It was like the big wave that was happening. So there was a lot of opportunity in that space. And throughout college, I'd sort of played poker. So I'd, you know, I dabbled as a professional poker player. And I was able to accumulate this sort of, you know, say $100,000 through playing poker. And at the time, as my friends would go work at companies like ChangeStreet or Citadel, I kind of did the maths. And I just thought, well, maybe if I traded my own capital, I'd probably come out ahead. I'd make more money than just going to work at ChangeStreet.swyx [00:02:20]: With 100k base as capital?William [00:02:22]: Yes, yes. That's not a lot. Well, it depends what strategies you're doing. And, you know, there is an advantage. There's an advantage to being small, right? Because there are, if you have a 10... Strategies that don't work in size. Exactly, exactly. So if you have a fund of $10 million, if you find a little anomaly in the market that you might be able to make 100k a year from, that's a 1% return on your 10 million fund. If your fund is 100k, that's 100% return, right? So being small, in some sense, was an advantage. So started off, and the, taught myself Python, and machine learning was like the big thing as well. Machine learning had really, it was the first, you know, big time machine learning was being used for image recognition, neural networks come out, you get dropout. And, you know, so this, this was the big thing that's going on at the time. So I probably spent my first three years out of Cambridge, just building neural networks, building random forests to try and predict asset prices, right, and then trade that using my own money. And that went well. And, you know, if you if you start something, and it goes well, you You try and hire more people. And the first people that came to mind was the talented people I went to college with. And so I hired some friends. And that went well and hired some more. And eventually, I kind of ran out of friends to hire. And so that was when I formed the company. And from that point on, we had our ups and we had our downs. And that was a whole long story and journey in itself. But after doing that for about eight or nine years, on my 30th birthday, which was four years ago now, I kind of took a step back to just evaluate my life, right? This is what one does when one turns 30. You know, I just heard it. I hear you. And, you know, I looked at my 20s and I loved it. It was a really special time. I was really lucky and fortunate to have worked with this amazing team, been successful, had a lot of hard times. And through the hard times, learned wisdom and then a lot of success and, you know, was able to enjoy it. And so the company was making about five million pounds a year. And it was just me and a team of, say, 15, like, Oxford and Cambridge educated mathematicians and physicists. It was like the real dream that you'd have if you wanted to start a quant trading firm. It was like...swyx [00:04:40]: Your own, all your own money?William [00:04:41]: Yeah, exactly. It was all the team's own money. We had no customers complaining to us about issues. There's no investors, you know, saying, you know, they don't like the risk that we're taking. We could. We could really run the thing exactly as we wanted it. It's like Susquehanna or like Rintec. Yeah, exactly. Yeah. And they're the companies that we would kind of look towards as we were building that thing out. But on my 30th birthday, I look and I say, OK, great. This thing is making as much money as kind of anyone would really need. And I thought, well, what's going to happen if we keep going in this direction? And it was clear that we would never have a kind of a big, big impact on the world. We can enrich ourselves. We can make really good money. Everyone on the team would be paid very, very well. Presumably, I can make enough money to buy a yacht or something. But this stuff wasn't that important to me. And so I felt a sort of obligation that if you have this much talent and if you have a talented team, especially as a founder, you want to be putting all that talent towards a good use. I looked at the time of like getting into crypto and I had a really strong view on crypto, which was that as far as a gambling device. This is like the most fun form of gambling invented in like ever super fun, I thought as a way to evade monetary regulations and banking restrictions. I think it's also absolutely amazing. So it has two like killer use cases, not so much banking the unbanked, but everything else, but everything else to do with like the blockchain and, and you know, web, was it web 3.0 or web, you know, that I, that didn't, it didn't really make much sense. And so instead of going into crypto, which I thought, even if I was successful, I'd end up in a lot of trouble. I thought maybe it'd be better to build something that governments wouldn't have a problem with. I knew that LLMs were like a thing. I think opening. I had said they hadn't released GPT-3 yet, but they'd said GPT-3 is so powerful. We can't release it to the world or something. Was it GPT-2? And then I started interacting with, I think Google had open source, some language models. They weren't necessarily LLMs, but they, but they were. But yeah, exactly. So I was able to play around with, but nowadays so many people have interacted with the chat GPT, they get it, but it's like the first time you, you can just talk to a computer and it talks back. It's kind of a special moment and you know, everyone who's done that goes like, wow, this is how it should be. Right. It should be like, rather than having to type on Google and search, you should just be able to ask Google a question. When I saw that I read the literature, I kind of came across the scaling laws and I think even four years ago. All the pieces of the puzzle were there, right? Google had done this amazing research and published, you know, a lot of it. Open AI was still open. And so they'd published a lot of their research. And so you really could be fully informed on, on the state of AI and where it was going. And so at that point I was confident enough, it was worth a shot. I think LLMs are going to be the next big thing. And so that's the thing I want to be building in, in that space. And I thought what's the most impactful product I can possibly build. And I thought it should be a platform. So I myself love platforms. I think they're fantastic because they open up an ecosystem where anyone can contribute to it. Right. So if you think of a platform like a YouTube, instead of it being like a Hollywood situation where you have to, if you want to make a TV show, you have to convince Disney to give you the money to produce it instead, anyone in the world can post any content they want to YouTube. And if people want to view it, the algorithm is going to promote it. Nowadays. You can look at creators like Mr. Beast or Joe Rogan. They would have never have had that opportunity unless it was for this platform. Other ones like Twitter's a great one, right? But I would consider Wikipedia to be a platform where instead of the Britannica encyclopedia, which is this, it's like a monolithic, you get all the, the researchers together, you get all the data together and you combine it in this, in this one monolithic source. Instead. You have this distributed thing. You can say anyone can host their content on Wikipedia. Anyone can contribute to it. And anyone can maybe their contribution is they delete stuff. When I was hearing like the kind of the Sam Altman and kind of the, the Muskian perspective of AI, it was a very kind of monolithic thing. It was all about AI is basically a single thing, which is intelligence. Yeah. Yeah. The more intelligent, the more compute, the more intelligent, and the more and better AI researchers, the more intelligent, right? They would speak about it as a kind of erased, like who can get the most data, the most compute and the most researchers. And that would end up with the most intelligent AI. But I didn't believe in any of that. I thought that's like the total, like I thought that perspective is the perspective of someone who's never actually done machine learning. Because with machine learning, first of all, you see that the performance of the models follows an S curve. So it's not like it just goes off to infinity, right? And the, the S curve, it kind of plateaus around human level performance. And you can look at all the, all the machine learning that was going on in the 2010s, everything kind of plateaued around the human level performance. And we can think about the self-driving car promises, you know, how Elon Musk kept saying the self-driving car is going to happen next year, it's going to happen next, next year. Or you can look at the image recognition, the speech recognition. You can look at. All of these things, there was almost nothing that went superhuman, except for something like AlphaGo. And we can speak about why AlphaGo was able to go like super superhuman. So I thought the most likely thing was going to be this, I thought it's not going to be a monolithic thing. That's like an encyclopedia Britannica. I thought it must be a distributed thing. And I actually liked to look at the world of finance for what I think a mature machine learning ecosystem would look like. So, yeah. So finance is a machine learning ecosystem because all of these quant trading firms are running machine learning algorithms, but they're running it on a centralized platform like a marketplace. And it's not the case that there's one giant quant trading company of all the data and all the quant researchers and all the algorithms and compute, but instead they all specialize. So one will specialize on high frequency training. Another will specialize on mid frequency. Another one will specialize on equity. Another one will specialize. And I thought that's the way the world works. That's how it is. And so there must exist a platform where a small team can produce an AI for a unique purpose. And they can iterate and build the best thing for that, right? And so that was the vision for Chai. So we wanted to build a platform for LLMs.Alessio [00:11:36]: That's kind of the maybe inside versus contrarian view that led you to start the company. Yeah. And then what was maybe the initial idea maze? Because if somebody told you that was the Hugging Face founding story, people might believe it. It's kind of like a similar ethos behind it. How did you land on the product feature today? And maybe what were some of the ideas that you discarded that initially you thought about?William [00:11:58]: So the first thing we built, it was fundamentally an API. So nowadays people would describe it as like agents, right? But anyone could write a Python script. They could submit it to an API. They could send it to the Chai backend and we would then host this code and execute it. So that's like the developer side of the platform. On their Python script, the interface was essentially text in and text out. An example would be the very first bot that I created. I think it was a Reddit news bot. And so it would first, it would pull the popular news. Then it would prompt whatever, like I just use some external API for like Burr or GPT-2 or whatever. Like it was a very, very small thing. And then the user could talk to it. So you could say to the bot, hi bot, what's the news today? And it would say, this is the top stories. And you could chat with it. Now four years later, that's like perplexity or something. That's like the, right? But back then the models were first of all, like really, really dumb. You know, they had an IQ of like a four year old. And users, there really wasn't any demand or any PMF for interacting with the news. So then I was like, okay. Um. So let's make another one. And I made a bot, which was like, you could talk to it about a recipe. So you could say, I'm making eggs. Like I've got eggs in my fridge. What should I cook? And it'll say, you should make an omelet. Right. There was no PMF for that. No one used it. And so I just kept creating bots. And so every single night after work, I'd be like, okay, I like, we have AI, we have this platform. I can create any text in textile sort of agent and put it on the platform. And so we just create stuff night after night. And then all the coders I knew, I would say, yeah, this is what we're going to do. And then I would say to them, look, there's this platform. You can create any like chat AI. You should put it on. And you know, everyone's like, well, chatbots are super lame. We want absolutely nothing to do with your chatbot app. No one who knew Python wanted to build on it. I'm like trying to build all these bots and no consumers want to talk to any of them. And then my sister who at the time was like just finishing college or something, I said to her, I was like, if you want to learn Python, you should just submit a bot for my platform. And she, she built a therapy for me. And I was like, okay, cool. I'm going to build a therapist bot. And then the next day I checked the performance of the app and I'm like, oh my God, we've got 20 active users. And they spent, they spent like an average of 20 minutes on the app. I was like, oh my God, what, what bot were they speaking to for an average of 20 minutes? And I looked and it was the therapist bot. And I went, oh, this is where the PMF is. There was no demand for, for recipe help. There was no demand for news. There was no demand for dad jokes or pub quiz or fun facts or what they wanted was they wanted the therapist bot. the time I kind of reflected on that and I thought, well, if I want to consume news, the most fun thing, most fun way to consume news is like Twitter. It's not like the value of there being a back and forth, wasn't that high. Right. And I thought if I need help with a recipe, I actually just go like the New York times has a good recipe section, right? It's not actually that hard. And so I just thought the thing that AI is 10 X better at is a sort of a conversation right. That's not intrinsically informative, but it's more about an opportunity. You can say whatever you want. You're not going to get judged. If it's 3am, you don't have to wait for your friend to text back. It's like, it's immediate. They're going to reply immediately. You can say whatever you want. It's judgment-free and it's much more like a playground. It's much more like a fun experience. And you could see that if the AI gave a person a compliment, they would love it. It's much easier to get the AI to give you a compliment than a human. From that day on, I said, okay, I get it. Humans want to speak to like humans or human like entities and they want to have fun. And that was when I started to look less at platforms like Google. And I started to look more at platforms like Instagram. And I was trying to think about why do people use Instagram? And I could see that I think Chai was, was filling the same desire or the same drive. If you go on Instagram, typically you want to look at the faces of other humans, or you want to hear about other people's lives. So if it's like the rock is making himself pancakes on a cheese plate. You kind of feel a little bit like you're the rock's friend, or you're like having pancakes with him or something, right? But if you do it too much, you feel like you're sad and like a lonely person, but with AI, you can talk to it and tell it stories and tell you stories, and you can play with it for as long as you want. And you don't feel like you're like a sad, lonely person. You feel like you actually have a friend.Alessio [00:16:29]: And what, why is that? Do you have any insight on that from using it?William [00:16:33]: I think it's just the human psychology. I think it's just the idea that, with old school social media. You're just consuming passively, right? So you'll just swipe. If I'm watching TikTok, just like swipe and swipe and swipe. And even though I'm getting the dopamine of like watching an engaging video, there's this other thing that's building my head, which is like, I'm feeling lazier and lazier and lazier. And after a certain period of time, I'm like, man, I just wasted 40 minutes. I achieved nothing. But with AI, because you're interacting, you feel like you're, it's not like work, but you feel like you're participating and contributing to the thing. You don't feel like you're just. Consuming. So you don't have a sense of remorse basically. And you know, I think on the whole people, the way people talk about, try and interact with the AI, they speak about it in an incredibly positive sense. Like we get people who say they have eating disorders saying that the AI helps them with their eating disorders. People who say they're depressed, it helps them through like the rough patches. So I think there's something intrinsically healthy about interacting that TikTok and Instagram and YouTube doesn't quite tick. From that point on, it was about building more and more kind of like human centric AI for people to interact with. And I was like, okay, let's make a Kanye West bot, right? And then no one wanted to talk to the Kanye West bot. And I was like, ah, who's like a cool persona for teenagers to want to interact with. And I was like, I was trying to find the influencers and stuff like that, but no one cared. Like they didn't want to interact with the, yeah. And instead it was really just the special moment was when we said the realization that developers and software engineers aren't interested in building this sort of AI, but the consumers are right. And rather than me trying to guess every day, like what's the right bot to submit to the platform, why don't we just create the tools for the users to build it themselves? And so nowadays this is like the most obvious thing in the world, but when Chai first did it, it was not an obvious thing at all. Right. Right. So we took the API for let's just say it was, I think it was GPTJ, which was this 6 billion parameter open source transformer style LLM. We took GPTJ. We let users create the prompt. We let users select the image and we let users choose the name. And then that was the bot. And through that, they could shape the experience, right? So if they said this bot's going to be really mean, and it's going to be called like bully in the playground, right? That was like a whole category that I never would have guessed. Right. People love to fight. They love to have a disagreement, right? And then they would create, there'd be all these romantic archetypes that I didn't know existed. And so as the users could create the content that they wanted, that was when Chai was able to, to get this huge variety of content and rather than appealing to, you know, 1% of the population that I'd figured out what they wanted, you could appeal to a much, much broader thing. And so from that moment on, it was very, very crystal clear. It's like Chai, just as Instagram is this social media platform that lets people create images and upload images, videos and upload that, Chai was really about how can we let the users create this experience in AI and then share it and interact and search. So it's really, you know, I say it's like a platform for social AI.Alessio [00:20:00]: Where did the Chai name come from? Because you started the same path. I was like, is it character AI shortened? You started at the same time, so I was curious. The UK origin was like the second, the Chai.William [00:20:15]: We started way before character AI. And there's an interesting story that Chai's numbers were very, very strong, right? So I think in even 20, I think late 2022, was it late 2022 or maybe early 2023? Chai was like the number one AI app in the app store. So we would have something like 100,000 daily active users. And then one day we kind of saw there was this website. And we were like, oh, this website looks just like Chai. And it was the character AI website. And I think that nowadays it's, I think it's much more common knowledge that when they left Google with the funding, I think they knew what was the most trending, the number one app. And I think they sort of built that. Oh, you found the people.swyx [00:21:03]: You found the PMF for them.William [00:21:04]: We found the PMF for them. Exactly. Yeah. So I worked a year very, very hard. And then they, and then that was when I learned a lesson, which is that if you're VC backed and if, you know, so Chai, we'd kind of ran, we'd got to this point, I was the only person who'd invested. I'd invested maybe 2 million pounds in the business. And you know, from that, we were able to build this thing, get to say a hundred thousand daily active users. And then when character AI came along, the first version, we sort of laughed. We were like, oh man, this thing sucks. Like they don't know what they're building. They're building the wrong thing anyway, but then I saw, oh, they've raised a hundred million dollars. Oh, they've raised another hundred million dollars. And then our users started saying, oh guys, your AI sucks. Cause we were serving a 6 billion parameter model, right? How big was the model that character AI could afford to serve, right? So we would be spending, let's say we would spend a dollar per per user, right? Over the, the, you know, the entire lifetime.swyx [00:22:01]: A dollar per session, per chat, per month? No, no, no, no.William [00:22:04]: Let's say we'd get over the course of the year, we'd have a million users and we'd spend a million dollars on the AI throughout the year. Right. Like aggregated. Exactly. Exactly. Right. They could spend a hundred times that. So people would say, why is your AI much dumber than character AIs? And then I was like, oh, okay, I get it. This is like the Silicon Valley style, um, hyper scale business. And so, yeah, we moved to Silicon Valley and, uh, got some funding and iterated and built the flywheels. And, um, yeah, I, I'm very proud that we were able to compete with that. Right. So, and I think the reason we were able to do it was just customer obsession. And it's similar, I guess, to how deep seek have been able to produce such a compelling model when compared to someone like an open AI, right? So deep seek, you know, their latest, um, V2, yeah, they claim to have spent 5 million training it.swyx [00:22:57]: It may be a bit more, but, um, like, why are you making it? Why are you making such a big deal out of this? Yeah. There's an agenda there. Yeah. You brought up deep seek. So we have to ask you had a call with them.William [00:23:07]: We did. We did. We did. Um, let me think what to say about that. I think for one, they have an amazing story, right? So their background is again in finance.swyx [00:23:16]: They're the Chinese version of you. Exactly.William [00:23:18]: Well, there's a lot of similarities. Yes. Yes. I have a great affinity for companies which are like, um, founder led, customer obsessed and just try and build something great. And I think what deep seek have achieved. There's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cash significantly. And then by being able to do that, they're able to significantly reduce their inference costs. And I think with kind of with AI, people get really focused on like the kind of the foundation model or like the model itself. And they sort of don't pay much attention to the inference. To give you an example with Chai, let's say a typical user session is 90 minutes, which is like, you know, is very, very long for comparison. Let's say the average session length on TikTok is 70 minutes. So people are spending a lot of time. And in that time they're able to send say 150 messages. That's a lot of completions, right? It's quite different from an open AI scenario where people might come in, they'll have a particular question in mind. And they'll ask like one question. And a few follow up questions, right? So because they're consuming, say 30 times as many requests for a chat, or a conversational experience, you've got to figure out how to how to get the right balance between the cost of that and the quality. And so, you know, I think with AI, it's always been the case that if you want a better experience, you can throw compute at the problem, right? So if you want a better model, you can just make it bigger. If you want it to remember better, give it a longer context. And now, what open AI is doing to great fanfare is with projection sampling, you can generate many candidates, right? And then with some sort of reward model or some sort of scoring system, you can serve the most promising of these many candidates. And so that's kind of scaling up on the inference time compute side of things. And so for us, it doesn't make sense to think of AI is just the absolute performance. So. But what we're seeing, it's like the MML you score or the, you know, any of these benchmarks that people like to look at, if you just get that score, it doesn't really tell tell you anything. Because it's really like progress is made by improving the performance per dollar. And so I think that's an area where deep seek have been able to form very, very well, surprisingly so. And so I'm very interested in what Lama four is going to look like. And if they're able to sort of match what deep seek have been able to achieve with this performance per dollar gain.Alessio [00:25:59]: Before we go into the inference, some of the deeper stuff, can you give people an overview of like some of the numbers? So I think last I checked, you have like 1.4 million daily active now. It's like over 22 million of revenue. So it's quite a business.William [00:26:12]: Yeah, I think we grew by a factor of, you know, users grew by a factor of three last year. Revenue over doubled. You know, it's very exciting. We're competing with some really big, really well funded companies. Character AI got this, I think it was almost a $3 billion valuation. And they have 5 million DAU is a number that I last heard. Torquay, which is a Chinese built app owned by a company called Minimax. They're incredibly well funded. And these companies didn't grow by a factor of three last year. Right. And so when you've got this company and this team that's able to keep building something that gets users excited, and they want to tell their friend about it, and then they want to come and they want to stick on the platform. I think that's very special. And so last year was a great year for the team. And yeah, I think the numbers reflect the hard work that we put in. And then fundamentally, the quality of the app, the quality of the content, the quality of the content, the quality of the content, the quality of the content, the quality of the content. AI is the quality of the experience that you have. You actually published your DAU growth chart, which is unusual. And I see some inflections. Like, it's not just a straight line. There's some things that actually inflect. Yes. What were the big ones? Cool. That's a great, great, great question. Let me think of a good answer. I'm basically looking to annotate this chart, which doesn't have annotations on it. Cool. The first thing I would say is this is, I think the most important thing to know about success is that success is born out of failures. Right? Through failures that we learn. You know, if you think something's a good idea, and you do and it works, great, but you didn't actually learn anything, because everything went exactly as you imagined. But if you have an idea, you think it's going to be good, you try it, and it fails. There's a gap between the reality and expectation. And that's an opportunity to learn. The flat periods, that's us learning. And then the up periods is that's us reaping the rewards of that. So I think the big, of the growth shot of just 2024, I think the first thing that really kind of put a dent in our growth was our backend. So we just reached this scale. So we'd, from day one, we'd built on top of Google's GCP, which is Google's cloud platform. And they were fantastic. We used them when we had one daily active user, and they worked pretty good all the way up till we had about 500,000. It was never the cheapest, but from an engineering perspective, man, that thing scaled insanely good. Like, not Vertex? Not Vertex. Like GKE, that kind of stuff? We use Firebase. So we use Firebase. I'm pretty sure we're the biggest user ever on Firebase. That's expensive. Yeah, we had calls with engineers, and they're like, we wouldn't recommend using this product beyond this point, and you're 3x over that. So we pushed Google to their absolute limits. You know, it was fantastic for us, because we could focus on the AI. We could focus on just adding as much value as possible. But then what happened was, after 500,000, just the thing, the way we were using it, and it would just, it wouldn't scale any further. And so we had a really, really painful, at least three-month period, as we kind of migrated between different services, figuring out, like, what requests do we want to keep on Firebase, and what ones do we want to move on to something else? And then, you know, making mistakes. And learning things the hard way. And then after about three months, we got that right. So that, we would then be able to scale to the 1.5 million DAE without any further issues from the GCP. But what happens is, if you have an outage, new users who go on your app experience a dysfunctional app, and then they're going to exit. And so your next day, the key metrics that the app stores track are going to be something like retention rates. And so your next day, the key metrics that the app stores track are going to be something like retention rates. Money spent, and the star, like, the rating that they give you. In the app store. In the app store, yeah. Tyranny. So if you're ranked top 50 in entertainment, you're going to acquire a certain rate of users organically. If you go in and have a bad experience, it's going to tank where you're positioned in the algorithm. And then it can take a long time to kind of earn your way back up, at least if you wanted to do it organically. If you throw money at it, you can jump to the top. And I could talk about that. But broadly speaking, if we look at 2024, the first kink in the graph was outages due to hitting 500k DAU. The backend didn't want to scale past that. So then we just had to do the engineering and build through it. Okay, so we built through that, and then we get a little bit of growth. And so, okay, that's feeling a little bit good. I think the next thing, I think it's, I'm not going to lie, I have a feeling that when Character AI got... I was thinking. I think so. I think... So the Character AI team fundamentally got acquired by Google. And I don't know what they changed in their business. I don't know if they dialed down that ad spend. Products don't change, right? Products just what it is. I don't think so. Yeah, I think the product is what it is. It's like maintenance mode. Yes. I think the issue that people, you know, some people may think this is an obvious fact, but running a business can be very competitive, right? Because other businesses can see what you're doing, and they can imitate you. And then there's this... There's this question of, if you've got one company that's spending $100,000 a day on advertising, and you've got another company that's spending zero, if you consider market share, and if you're considering new users which are entering the market, the guy that's spending $100,000 a day is going to be getting 90% of those new users. And so I have a suspicion that when the founders of Character AI left, they dialed down their spending on user acquisition. And I think that kind of gave oxygen to like the other apps. And so Chai was able to then start growing again in a really healthy fashion. I think that's kind of like the second thing. I think a third thing is we've really built a great data flywheel. Like the AI team sort of perfected their flywheel, I would say, in end of Q2. And I could speak about that at length. But fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours. And when we did that, we could really, really, really perfect techniques like DPO, fine tuning, prompt engineering, blending, rejection sampling, training a reward model, right, really successfully, like boom, boom, boom, boom, boom. And so I think in Q3 and Q4, we got, the amount of AI improvements we got was like astounding. It was getting to the point, I thought like how much more, how much more edge is there to be had here? But the team just could keep going and going and going. That was like number three for the inflection point.swyx [00:34:53]: There's a fourth?William [00:34:54]: The important thing about the third one is if you go on our Reddit or you talk to users of AI, there's like a clear date. It's like somewhere in October or something. The users, they flipped. Before October, the users... The users would say character AI is better than you, for the most part. Then from October onwards, they would say, wow, you guys are better than character AI. And that was like a really clear positive signal that we'd sort of done it. And I think people, you can't cheat consumers. You can't trick them. You can't b******t them. They know, right? If you're going to spend 90 minutes on a platform, and with apps, there's the barriers to switching is pretty low. Like you can try character AI, you can't cheat consumers. You can't cheat them. You can't cheat them. You can't cheat AI for a day. If you get bored, you can try Chai. If you get bored of Chai, you can go back to character. So the users, the loyalty is not strong, right? What keeps them on the app is the experience. If you deliver a better experience, they're going to stay and they can tell. So that was the fourth one was we were fortunate enough to get this hire. He was hired one really talented engineer. And then they said, oh, at my last company, we had a head of growth. He was really, really good. And he was the head of growth for ByteDance for two years. Would you like to speak to him? And I was like, yes. Yes, I think I would. And so I spoke to him. And he just blew me away with what he knew about user acquisition. You know, it was like a 3D chessswyx [00:36:21]: sort of thing. You know, as much as, as I know about AI. Like ByteDance as in TikTok US. Yes.William [00:36:26]: Not ByteDance as other stuff. Yep. He was interviewing us as we were interviewing him. Right. And so pick up options. Yeah, exactly. And so he was kind of looking at our metrics. And he was like, I saw him get really excited when he said, guys, you've got a million daily active users and you've done no advertising. I said, correct. And he was like, that's unheard of. He's like, I've never heard of anyone doing that. And then he started looking at our metrics. And he was like, if you've got all of this organically, if you start spending money, this is going to be very exciting. I was like, let's give it a go. So then he came in, we've just started ramping up the user acquisition. So that looks like spending, you know, let's say we're spending, we started spending $20,000 a day, it looked very promising than 20,000. Right now we're spending $40,000 a day on user acquisition. That's still only half of what like character AI or talkie may be spending. But from that, it's sort of, we were growing at a rate of maybe say, 2x a year. And that got us growing at a rate of 3x a year. So I'm growing, I'm evolving more and more to like a Silicon Valley style hyper growth, like, you know, you build something decent, and then you canswyx [00:37:33]: slap on a huge... You did the important thing, you did the product first.William [00:37:36]: Of course, but then you can slap on like, like the rocket or the jet engine or something, which is just this cash in, you pour in as much cash, you buy a lot of ads, and your growth is faster.swyx [00:37:48]: Not to, you know, I'm just kind of curious what's working right now versus what surprisinglyWilliam [00:37:52]: doesn't work. Oh, there's a long, long list of surprising stuff that doesn't work. Yeah. The surprising thing, like the most surprising thing, what doesn't work is almost everything doesn't work. That's what's surprising. And I'll give you an example. So like a year and a half ago, I was working at a company, we were super excited by audio. I was like, audio is going to be the next killer feature, we have to get in the app. And I want to be the first. So everything Chai does, I want us to be the first. We may not be the company that's strongest at execution, but we can always be theswyx [00:38:22]: most innovative. Interesting. Right? So we can... You're pretty strong at execution.William [00:38:26]: We're much stronger, we're much stronger. A lot of the reason we're here is because we were first. If we launched today, it'd be so hard to get the traction. Because it's like to get the flywheel, to get the users, to build a product people are excited about. If you're first, people are naturally excited about it. But if you're fifth or 10th, man, you've got to beswyx [00:38:46]: insanely good at execution. So you were first with voice? We were first. We were first. I only knowWilliam [00:38:51]: when character launched voice. They launched it, I think they launched it at least nine months after us. Okay. Okay. But the team worked so hard for it. At the time we did it, latency is a huge problem. Cost is a huge problem. Getting the right quality of the voice is a huge problem. Right? Then there's this user interface and getting the right user experience. Because you don't just want it to start blurting out. Right? You want to kind of activate it. But then you don't have to keep pressing a button every single time. There's a lot that goes into getting a really smooth audio experience. So we went ahead, we invested the three months, we built it all. And then when we did the A-B test, there was like, no change in any of the numbers. And I was like, this can't be right, there must be a bug. And we spent like a week just checking everything, checking again, checking again. And it was like, the users just did not care. And it was something like only 10 or 15% of users even click the button to like, they wanted to engage the audio. And they would only use it for 10 or 15% of the time. So if you do the math, if it's just like something that one in seven people use it for one seventh of their time. You've changed like 2% of the experience. So even if that that 2% of the time is like insanely good, it doesn't translate much when you look at the retention, when you look at the engagement, and when you look at the monetization rates. So audio did not have a big impact. I'm pretty big on audio. But yeah, I like it too. But it's, you know, so a lot of the stuff which I do, I'm a big, you can have a theory. And you resist. Yeah. Exactly, exactly. So I think if you want to make audio work, it has to be a unique, compelling, exciting experience that they can't have anywhere else.swyx [00:40:37]: It could be your models, which just weren't good enough.William [00:40:39]: No, no, no, they were great. Oh, yeah, they were very good. it was like, it was kind of like just the, you know, if you listen to like an audible or Kindle, or something like, you just hear this voice. And it's like, you don't go like, wow, this is this is special, right? It's like a convenience thing. But the idea is that if you can, if Chai is the only platform, like, let's say you have a Mr. Beast, and YouTube is the only platform you can use to make audio work, then you can watch a Mr. Beast video. And it's the most engaging, fun video that you want to watch, you'll go to a YouTube. And so it's like for audio, you can't just put the audio on there. And people go, oh, yeah, it's like 2% better. Or like, 5% of users think it's 20% better, right? It has to be something that the majority of people, for the majority of the experience, go like, wow, this is a big deal. That's the features you need to be shipping. If it's not going to appeal to the majority of people, for the majority of the experience, and it's not a big deal, it's not going to move you. Cool. So you killed it. I don't see it anymore. Yep. So I love this. The longer, it's kind of cheesy, I guess, but the longer I've been working at Chai, and I think the team agrees with this, all the platitudes, at least I thought they were platitudes, that you would get from like the Steve Jobs, which is like, build something insanely great, right? Or be maniacally focused, or, you know, the most important thing is saying no to, not to work on. All of these sort of lessons, they just are like painfully true. They're painfully true. So now I'm just like, everything I say, I'm either quoting Steve Jobs or Zuckerberg. I'm like, guys, move fast and break free.swyx [00:42:10]: You've jumped the Apollo to cool it now.William [00:42:12]: Yeah, it's just so, everything they said is so, so true. The turtle neck. Yeah, yeah, yeah. Everything is so true.swyx [00:42:18]: This last question on my side, and I want to pass this to Alessio, is on just, just multi-modality in general. This actually comes from Justine Moore from A16Z, who's a friend of ours. And a lot of people are trying to do voice image video for AI companions. Yes. You just said voice didn't work. Yep. What would make you revisit?William [00:42:36]: So Steve Jobs, he was very, listen, he was very, very clear on this. There's a habit of engineers who, once they've got some cool technology, they want to find a way to package up the cool technology and sell it to consumers, right? That does not work. So you're free to try and build a startup where you've got your cool tech and you want to find someone to sell it to. That's not what we do at Chai. At Chai, we start with the consumer. What does the consumer want? What is their problem? And how do we solve it? So right now, the number one problems for the users, it's not the audio. That's not the number one problem. It's not the image generation either. That's not their problem either. The number one problem for users in AI is this. All the AI is being generated by middle-aged men in Silicon Valley, right? That's all the content. You're interacting with this AI. You're speaking to it for 90 minutes on average. It's being trained by middle-aged men. The guys out there, they're out there. They're talking to you. They're talking to you. They're like, oh, what should the AI say in this situation, right? What's funny, right? What's cool? What's boring? What's entertaining? That's not the way it should be. The way it should be is that the users should be creating the AI, right? And so the way I speak about it is this. Chai, we have this AI engine in which sits atop a thin layer of UGC. So the thin layer of UGC is absolutely essential, right? It's just prompts. But it's just prompts. It's just an image. It's just a name. It's like we've done 1% of what we could do. So we need to keep thickening up that layer of UGC. It must be the case that the users can train the AI. And if reinforcement learning is powerful and important, they have to be able to do that. And so it's got to be the case that there exists, you know, I say to the team, just as Mr. Beast is able to spend 100 million a year or whatever it is on his production company, and he's got a team building the content, the Mr. Beast company is able to spend 100 million a year on his production company. And he's got a team building the content, which then he shares on the YouTube platform. Until there's a team that's earning 100 million a year or spending 100 million on the content that they're producing for the Chai platform, we're not finished, right? So that's the problem. That's what we're excited to build. And getting too caught up in the tech, I think is a fool's errand. It does not work.Alessio [00:44:52]: As an aside, I saw the Beast Games thing on Amazon Prime. It's not doing well. And I'mswyx [00:44:56]: curious. It's kind of like, I mean, the audience reading is high. The run-to-meet-all sucks, but the audience reading is high.Alessio [00:45:02]: But it's not like in the top 10. I saw it dropped off of like the... Oh, okay. Yeah, that one I don't know. I'm curious, like, you know, it's kind of like similar content, but different platform. And then going back to like, some of what you were saying is like, you know, people come to ChaiWilliam [00:45:13]: expecting some type of content. Yeah, I think it's something that's interesting to discuss is like, is moats. And what is the moat? And so, you know, if you look at a platform like YouTube, the moat, I think is in first is really is in the ecosystem. And the ecosystem, is comprised of you have the content creators, you have the users, the consumers, and then you have the algorithms. And so this, this creates a sort of a flywheel where the algorithms are able to be trained on the users, and the users data, the recommend systems can then feed information to the content creators. So Mr. Beast, he knows which thumbnail does the best. He knows the first 10 seconds of the video has to be this particular way. And so his content is super optimized for the YouTube platform. So that's why it doesn't do well on Amazon. If he wants to do well on Amazon, how many videos has he created on the YouTube platform? By thousands, 10s of 1000s, I guess, he needs to get those iterations in on the Amazon. So at Chai, I think it's all about how can we get the most compelling, rich user generated content, stick that on top of the AI engine, the recommender systems, in such that we get this beautiful data flywheel, more users, better recommendations, more creative, more content, more users.Alessio [00:46:34]: You mentioned the algorithm, you have this idea of the Chaiverse on Chai, and you have your own kind of like LMSYS-like ELO system. Yeah, what are things that your models optimize for, like your users optimize for, and maybe talk about how you build it, how people submit models?William [00:46:49]: So Chaiverse is what I would describe as a developer platform. More often when we're speaking about Chai, we're thinking about the Chai app. And the Chai app is really this product for consumers. And so consumers can come on the Chai app, they can come on the Chai app, they can come on the Chai app, they can interact with our AI, and they can interact with other UGC. And it's really just these kind of bots. And it's a thin layer of UGC. Okay. Our mission is not to just have a very thin layer of UGC. Our mission is to have as much UGC as possible. So we must have, I don't want people at Chai training the AI. I want people, not middle aged men, building AI. I want everyone building the AI, as many people building the AI as possible. Okay, so what we built was we built Chaiverse. And Chaiverse is kind of, it's kind of like a prototype, is the way to think about it. And it started with this, this observation that, well, how many models get submitted into Hugging Face a day? It's hundreds, it's hundreds, right? So there's hundreds of LLMs submitted each day. Now consider that, what does it take to build an LLM? It takes a lot of work, actually. It's like someone devoted several hours of compute, several hours of their time, prepared a data set, launched it, ran it, evaluated it, submitted it, right? So there's a lot of, there's a lot of, there's a lot of work that's going into that. So what we did was we said, well, why can't we host their models for them and serve them to users? And then what would that look like? The first issue is, well, how do you know if a model is good or not? Like, we don't want to serve users the crappy models, right? So what we would do is we would, I love the LMSYS style. I think it's really cool. It's really simple. It's a very intuitive thing, which is you simply present the users with two completions. You can say, look, this is from model one. This is from model two. This is from model three. This is from model A. This is from model B, which is better. And so if someone submits a model to Chaiverse, what we do is we spin up a GPU. We download the model. We're going to now host that model on this GPU. And we're going to start routing traffic to it. And we're going to send, we think it takes about 5,000 completions to get an accurate signal. That's roughly what LMSYS does. And from that, we're able to get an accurate ranking. And we're able to get an accurate ranking. And we're able to get an accurate ranking of which models are people finding entertaining and which models are not entertaining. If you look at the bottom 80%, they'll suck. You can just disregard them. They totally suck. Then when you get the top 20%, you know you've got a decent model, but you can break it down into more nuance. There might be one that's really descriptive. There might be one that's got a lot of personality to it. There might be one that's really illogical. Then the question is, well, what do you do with these top models? From that, you can do more sophisticated things. You can try and do like a routing thing where you say for a given user request, we're going to try and predict which of these end models that users enjoy the most. That turns out to be pretty expensive and not a huge source of like edge or improvement. Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model. Just a random 50%? Just a random, yeah. And then... That's blending? That's blending. You can do more sophisticated things on top of that, as in all things in life, but the 80-20 solution, if you just do that, you get a pretty powerful effect out of the gate. Random number generator. I think it's like the robustness of randomness. Random is a very powerful optimization technique, and it's a very robust thing. So you can explore a lot of the space very efficiently. There's one thing that's really, really important to share, and this is the most exciting thing for me, is after you do the ranking, you get an ELO score, and you can track a user's first join date, the first date they submit a model to Chaiverse, they almost always get a terrible ELO, right? So let's say the first submission they get an ELO of 1,100 or 1,000 or something, and you can see that they iterate and they iterate and iterate, and it will be like, no improvement, no improvement, no improvement, and then boom. Do you give them any data, or do you have to come up with this themselves? We do, we do, we do, we do. We try and strike a balance between giving them data that's very useful, you've got to be compliant with GDPR, which is like, you have to work very hard to preserve the privacy of users of your app. So we try to give them as much signal as possible, to be helpful. The minimum is we're just going to give you a score, right? That's the minimum. But that alone is people can optimize a score pretty well, because they're able to come up with theories, submit it, does it work? No. A new theory, does it work? No. And then boom, as soon as they figure something out, they keep it, and then they iterate, and then boom,Alessio [00:51:46]: they figure something out, and they keep it. Last year, you had this post on your blog, cross-sourcing the lead to the 10 trillion parameter, AGI, and you call it a mixture of experts, recommenders. Yep. Any insights?William [00:51:58]: Updated thoughts, 12 months later? I think the odds, the timeline for AGI has certainly been pushed out, right? Now, this is in, I'm a controversial person, I don't know, like, I just think... You don't believe in scaling laws, you think AGI is further away. I think it's an S-curve. I think everything's an S-curve. And I think that the models have proven to just be far worse at reasoning than people sort of thought. And I think whenever I hear people talk about LLMs as reasoning engines, I sort of cringe a bit. I don't think that's what they are. I think of them more as like a simulator. I think of them as like a, right? So they get trained to predict the next most likely token. It's like a physics simulation engine. So you get these like games where you can like construct a bridge, and you drop a car down, and then it predicts what should happen. And that's really what LLMs are doing. It's not so much that they're reasoning, it's more that they're just doing the most likely thing. So fundamentally, the ability for people to add in intelligence, I think is very limited. What most people would consider intelligence, I think the AI is not a crowdsourcing problem, right? Now with Wikipedia, Wikipedia crowdsources knowledge. It doesn't crowdsource intelligence. So it's a subtle distinction. AI is fantastic at knowledge. I think it's weak at intelligence. And a lot, it's easy to conflate the two because if you ask it a question and it gives you, you know, if you said, who was the seventh president of the United States, and it gives you the correct answer, I'd say, well, I don't know the answer to that. And you can conflate that with intelligence. But really, that's a question of knowledge. And knowledge is really this thing about saying, how can I store all of this information? And then how can I retrieve something that's relevant? Okay, they're fantastic at that. They're fantastic at storing knowledge and retrieving the relevant knowledge. They're superior to humans in that regard. And so I think we need to come up for a new word. How does one describe AI should contain more knowledge than any individual human? It should be more accessible than any individual human. That's a very powerful thing. That's superswyx [00:54:07]: powerful. But what words do we use to describe that? We had a previous guest on Exa AI that does search. And he tried to coin super knowledge as the opposite of super intelligence.William [00:54:20]: Exactly. I think super knowledge is a more accurate word for it.swyx [00:54:24]: You can store more things than any human can.William [00:54:26]: And you can retrieve it better than any human can as well. And I think it's those two things combined that's special. I think that thing will exist. That thing can be built. And I think you can start with something that's entertaining and fun. And I think, I often think it's like, look, it's going to be a 20 year journey. And we're in like, year four, or it's like the web. And this is like 1998 or something. You know, you've got a long, long way to go before the Amazon.coms are like these huge, multi trillion dollar businesses that every single person uses every day. And so AI today is very simplistic. And it's fundamentally the way we're using it, the flywheels, and this ability for how can everyone contribute to it to really magnify the value that it brings. Right now, like, I think it's a bit sad. It's like, right now you have big labs, I'm going to pick on open AI. And they kind of go to like these human labelers. And they say, we're going to pay you to just label this like subset of questions that we want to get a really high quality data set, then we're going to get like our own computers that are really powerful. And that's kind of like the thing. For me, it's so much like Encyclopedia Britannica. It's like insane. All the people that were interested in blockchain, it's like, well, this is this is what needs to be decentralized, you need to decentralize that thing. Because if you distribute it, people can generate way more data in a distributed fashion, way more, right? You need the incentive. Yeah, of course. Yeah. But I mean, the, the, that's kind of the exciting thing about Wikipedia was it's this understanding, like the incentives, you don't need money to incentivize people. You don't need dog coins. No. Sometimes, sometimes people get the satisfaction fro

In this conversation, Simon Grimm interviews Ben Awad, a successful YouTuber and co-founder of the app Voidpet. They discuss Ben's journey from content creation to app development, the challenges and successes he faced, and the technical aspects of building his applications. The conversation also touches on the importance of user experience, monetization strategies, and the evolution of Ben's career in the tech industry. In this conversation, Ben Awad discusses his experiences and insights into React Native, game development, and the integration of AI tools in programming. He shares his journey from Android development to embracing React Native, the challenges of real-time gaming, and the evolution of the developer experience. Ben also touches on animation techniques in game development, his literary interests, and the future of his projects, including Voidpet and the Voidlog series.Learn React Native - https://galaxies.devBen AwadBen X: https://x.com/benawadBen YouTube: https://www.youtube.com/@bawadBen Github: https://github.com/benawadLinksVoidpet: https://voidpet.comVoidpet garden: https://voidpet.com/o/gardenHands of Greed book: https://handsofgreed.comTakeawaysBen Awad transitioned from YouTube content creation to app development.Voidpet gained popularity on TikTok before any code was written.He has learned from both successful and failed projects.The Voidpet app focuses on mental health themes.Ben's cooking app, Saffron, is still active and successful.He emphasizes the importance of user experience in app development.Ben uses a simple tech stack for his apps to avoid over-engineering.He believes that offline capabilities in apps are not always necessary.Ben prefers native styling in React Native over other styles.A time API is essential for validating timestamps in games.Ben's early experiences with Android development were frustrating.React Native's developer experience has significantly improved over the years.Real-time gaming in React Native presents unique challenges.Animation techniques are crucial for enhancing game visuals.Choosing React Native for game development was a strategic decision.AI tools have become integral to Ben's coding workflow.

#GuidedMeditation #SleepMeditation #EnergyCleansing #Relaxation #Mindfulness Welcome to this Guided Meditation to Sleep and Cleanse Your Energy with me, Robert Aceves.

In this insightful episode, we had the pleasure of chatting with Chris Eidhof and Florian Kugler, the brilliant developers behind objc.io. Together, we explored the world of animations in SwiftUI, emphasizing the importance of understanding how SwiftUI creates and manages views and animations.

I interviewed co-directors Francisca Silva and Maria Jose Diaz about Ancestral Secrets VR that showed at IDFA DocLab 2024. See the transcript down below for more context on our conversation. This is a listener-supported podcast through the Voices of VR Patreon. Music: Fatality

In der dritten Staffel von Marvels Animationsserie tauchen wir wieder in die Tiefen des Multiversums ein. Bei Prime Video geht es um einen einsamen Jungen, der sich eine Zombie-Familie anlacht, um nicht mehr alleine zu sein. Und auf WOW wird es gesellschaftskritisch, wenn ein junger Afroamerikaner einem Geheimbund beitritt, der es weißen Menschen so einfach wie möglich machen will. Hier entlang geht's zu den Links unserer Werbepartner: https://detektor.fm/werbepartner/was-laeuft-heute >> Artikel zum Nachlesen: https://detektor.fm/kultur/was-laeuft-heute-marvel-animations-what-if-the-loneliest-boy-in-the-world-the-american-society-of-magical-negroes

In der dritten Staffel von Marvels Animationsserie tauchen wir wieder in die Tiefen des Multiversums ein. Bei Prime Video geht es um einen einsamen Jungen, der sich eine Zombie-Familie anlacht, um nicht mehr alleine zu sein. Und auf WOW wird es gesellschaftskritisch, wenn ein junger Afroamerikaner einem Geheimbund beitritt, der es weißen Menschen so einfach wie möglich machen will. Hier entlang geht's zu den Links unserer Werbepartner: https://detektor.fm/werbepartner/was-laeuft-heute >> Artikel zum Nachlesen: https://detektor.fm/kultur/was-laeuft-heute-marvel-animations-what-if-the-loneliest-boy-in-the-world-the-american-society-of-magical-negroes

Using Computer Animations in Trial I’m David Holub, an attorney focusing on personal injury law in northwest Indiana. Welcome to Personal Injury Primer, where we break down the law into simple terms, provide legal tips, and discuss personal injury law topics. In past episodes, we have discussed accident reconstructionists. If you have watched home remodeling […] The post Ep 291 Using Computer Animations in Trial first appeared on Personal Injury Primer.

The good, the bad ... & the utterly atrocious

Presenting the 116th episode of the Skwigly podcast! In this episode we meet some of the talents behind the 2024 holiday animated feature films 'The Night Before Christmas in Wonderland' (director Peter Baynton and producer Ruth Fielding of Lupus Films) and Locksmith Animation's 'That Christmas' (writer Richard Curtis, director Simon Otto and producer Nicole P. Hearon) as well as the 22-minute BBC special 'Tiddler' (producer Barney Goodland of Magic Light Pictures). Presented by Ben Mitchell and Steve Henderson Interviews conducted and edited by Steve Henderson Edited and produced by Ben Mitchell Music by Ben Mitchell

Learn Guide: https://learn.adafruit.com/creating-custom-led-animations Visit the Adafruit shop online - http://www.adafruit.com ----------------------------------------- LIVE CHAT IS HERE! http://adafru.it/discord Subscribe to Adafruit on YouTube: http://adafru.it/subscribe New tutorials on the Adafruit Learning System: http://learn.adafruit.com/ -----------------------------------------

In this conversation, Jeffrey Bradbury and Diane Manser explore the transformative power of digital tools in education, particularly focusing on Google animations. They discuss how these tools can enhance creativity, engagement, and learning across various subjects, while also addressing the emotional challenges teachers face and the importance of resilience and empowerment in the profession. If you are a new listener to TeacherCast, we would love to hear from you.  Please visit our Contact Page and let us know how we can help you today! [convertkit form=7230980] Conversation Takeaways Google Slides can be used creatively to make animations. Students can engage with animation concepts through familiar movies. Creating flipbook animations helps students understand the animation process. Using Google Slides allows for unique features not found in other presentation tools. The lesson plan for animations is available on TeacherCast. Students can express their creativity through animation projects. Animation festivals in classrooms can be a fun learning experience. Technical challenges can arise, but they can be managed effectively. Understanding the history of animation can enhance student interest. Collaboration and sharing ideas among educators can improve teaching strategies. The transition from physical to digital learning opens new avenues for creativity. Digital tools can be integrated into various subjects, enhancing student engagement. Teaching digital citizenship is essential in today's technology-driven classrooms. Students can take ownership of their learning through creative projects. Using animations in lessons can make complex topics more accessible and fun. Empowering students leads to better learning outcomes and classroom dynamics. Teachers can alleviate stress by incorporating engaging digital projects. Collaboration and sharing among educators can lead to innovative teaching practices. Resilience is key for teachers to navigate the challenges of the profession. Connecting with aspiring educators can inspire and motivate current teachers. Follow Our Podcast And Subscribe View All Episodes Apple Podcasts Spotify Follow Our Host Jeff Bradbury | @JeffBradbury TeacherCast | @TeacherCast Join Our PLN Are you enjoying the TeacherCast Network, please share your thoughts with the world by commenting on Apple Podcasts today? I enjoy reading and sharing your comments on the podcast each week. Let's Work Together Host: Jeff Bradbury @TeacherCast | @JeffBradbury Email: info@teachercast.net Voice Mail: **http://www.TeacherCast.net/voicemail** YouTube: **

In this episode our esteemed guest returns! This time to help us grok Scroll Driven Animation. Learn all about scroll(), view(), animation-timeline, timeline-scope, animation-range, and more. Power those animations with off-the-main-thread CSS scroll animation powers.   Resources: Bramus's Demos: All mentioned Demos + Tools + Video Course + DevTools Extension link → https://goo.gle/3Uw31up  Video Course direct link: https://goo.gle/learn-scroll-driven-animations   Adam's Demos: scroll() the hue wheel → https://goo.gle/4emb3NO  CSS scroll() feature time warp → https://goo.gle/4exH3yv  view() long bento list → https://goo.gle/4gtcCLx  view() scrolly telling → https://goo.gle/3TAq2vA  view() iOS-like app switcher → https://goo.gle/4etvCI6  view() variable font animation → https://goo.gle/4e8eJmd    Una Kravets (co-host) Twitter | Instagram | YouTube Making the web more colorful ✨

Sean, Sarah and Paul from History Rage are here to continue our Jaws series, it's downwards and downwards for Jaws 3-D (1983).Will Sean, Sarah and Paul be impressed or unimpressed with the film?Stick around for 'The Worst Shark Film You've Ever Seen' section, with responses from podcast friends of Review It Yourself. Discussion Points:-Sarah went from Jaws (1975) to Jaws 3-D (1983). -Sean paid £2.49 to rent this film and still didn't get his money's worth.-Paul discusses the Victorian Invention of 3-D and wonders why we haven't mastered it yet. -The dreadful water skiers and the obsession with millions of gallons of water.-The Brody family ageing phenomenon continues. -The idiocy of putting a shark in a petting pen. Raised Questions:-Is this the perfect litmus test for a podcast?-How did this get into cinemas?-Why is the transfer so bad?-How is that floating fish head still alive?-How on Earth did SeaWorld approve being in this film?-Can Leah Thompson cure aquaphobia?-Did Mike and Katherine ever go to Venezuela?-Do you have "Prometheus (2012) levels of disdain" for your audience?Thank You for your contributions:-'The Podcast That Wouldn't Die'. -'Sci-Fi Chronicles YouTube Channel'.-'Stew World Order' podcast.-'Coffee and Comments YouTube Channel'.-'Cinnamon Toast Crunch YouTube Channel'.-'Seismic Cinema' podcast.-'Talking SMAC: Superheroes, Movies, Animations and Comics Podcast'.-'Dissect That Film' podcast.Thanks for Listening! Review It Yourself is now on YouTube!Find us here:Twitter: @YourselfReviewInstagram: reviewityourselfpodcast2021YouTube: https://www.youtube.com/@ReviewItYourself⁠ Hosted on Acast. See acast.com/privacy for more information.

In this episode Chet, Romain and Tor chat with Doris Liu from the Compose team about animations in Compose -- covering everything from the basic primitives up to the recently added Shared Element Transitions.   Chapters: Intro (00:00) Animation capabilities of Compose (1:06) Different types of animation specs (3:43) Layers of functionality, transitions (7:49) TargetBasedAnimation (9:48) Vectors & velocity of color change (12:43) Second layer parallel to animation spec (16:39) Animation interruptions (18:48) Motion layout problem-solving (20:19) Both scale and move in question (25:45) Different mental models for layout animation in Compose vs. View (26:20) Shared element (31:05) Are there things you wish more people were aware of? (34:19) What's the tooling story for this? (41:57) What is Look Ahead? (43:16) All software is regret (48:49) New API: Modifier.animateBounds (51:52) How to reach Doris – leave a comment (55:57) Motion Frame of Reference Placement (57:29) Wrap up (59:10) Links: Shared element tutorial → https://goo.gle/3XrGYp5  Shared element talk → https://goo.gle/47tm3qm  A quick guide to compose animations → https://goo.gle/3Tm853p  The API layers except the highest level APIs we chatted about in the podcast → https://goo.gle/3MGsiNE Doris: @doris4lt Romain: @romainguy, threads.net/@romainguy, romainguy@androiddev.social Tor: threads.net/@tor.norbye and tornorbye@androiddev.social Chet: @chethaase, threads.net/@chet.haase, and chethaase@androiddev.social   Catch more Android Developers Backstage on YouTube → https://goo.gle/adb-podcast    Subscribe to Android Developers YouTube → https://goo.gle/AndroidDevs 

In this episode of Syntax, Wes and Scott talk about CSS' new scroll-driven animations, its implementation, uses, and potential pitfalls. They also discuss animation-timeline and animation-range, and how they can be utilized to control animations based on scroll positions. Show Notes 00:00 Welcome to Syntax! 00:46 Brought to you by Sentry.io 01:35 Scroll-driven animations Syntax 695: 5 New CSS Features You Should Know Scroll-driven animations demos and tools 04:13 @keyframes 05:22 animation-timeline 11:35 animation-range 08:49 View-based timelines 17:45 Neat uses: Dave Rupert on styling :stuck Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Scott and Wes serve up their reaction to the “State of React 2023” survey results, discussing the main API pain points like forwardRef and memo. They also explore the latest on state management, hooks pain points, and exciting new libraries in the React ecosystem. Show Notes 00:00 Welcome to Syntax! 01:41 Brought to you by Sentry.io. 02:28 The State of React 2023. 03:11 The Main API Painpoints. 04:31 forwardRef. 05:27 memo. 06:39 Context API. 07:18 StrictMode. 08:45 Double rendering. 09:36 State management. 11:58 Hooks Pain Points. 12:11 useEffect. 12:33 Dependency arrays. 13:11 New API Pain Points. 13:19 React Server Components. 14:40 Taint API. 15:19 Libraries. 17:02 Jotai. 17:45 Apollo Client. 19:05 Redux. 20:57 Redwood. 21:26 React Aria. 21:55 Astro. 22:04 The most negative. 23:35 Component Libraries. 25:50 Other Component Libraries. 25:53 Mantine. 27:47 Details element. Tolin.ski/demos. 28:59 Honorable mentions. 29:07 Animations. 29:28 Data Visualization. 31:26 CSS Tools and Libraries. 33:14 Styled Components. 34:16 Meta Frameworks. 38:50 Hosting. 40:08 Other Services. 40:45 Back-end language trivia. 43:00 State management. 43:40 Data Loading. 44:08 Other Tools. 44:09 Testing Libraries. 44:45 React Renderers. 47:58 Podcasts, thank you! 48:14 Sick Picks & Shameless Plugs. Sick Picks Scott: Thermacell. Wes: Nerf Guns Shameless Plugs Wes: Syntax.fm. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Hey Besties (and New Besties)! We will be returning next week with new episodes. Enjoy this trip down memory lane when Nikki and Brian were at the Super Bowl! Be sure to rate and review the podcast and subscribe to our YouTube channel! -- It's part two of our pre-Super Bowl podcasts from the iHeart and NFL stage. Things take a turn as both Nikki and Brian are recognized by their guests. Brian's shirt brings about a 'Jerry Maguire' moment with Rodney Thomas' agent, and Chris Long learns that the new sports broadcaster he thought looked like Nikki Glaser IS Nikki Glaser! Brian makes a move to catch Tyreek Hill. In the Final Thought, Nikki and Brian synthesize their experience while pinching themselves to make sure it's real. *Besties, you won't need to love sports to love this episode! xoxo. Subscribe to Big Money Players Diamond on Apple Podcasts to get this episode ad-free, and get exclusive bonus content: https://apple.co/nikkiglaserpodcast  Watch this episode on our YouTube channel: The Nikki Glaser Podcast Follow the pod on Instagram for bonus content: @NikkiGlaserPod Leave us your voicemail: Click Here To Record Nikki's Tour Dates: nikkiglaser.com/tour Brian's Animations: youtube.com/@BrianFrange More Nikki: IG More Brian: IG More producer Noa: IGSee omnystudio.com/listener for privacy information.

Nikki and Brian are joined by Chris and former co-host/Bestie Andrew Collin. They discuss various topics: billionaires owning islands, Nikki's music video coming out, and of course, her new special on HBO Someday You'll Die. They each share how they want to be buried and how they want their ashes spread. Some answers may surprise you! Final thought, Kim Kardashian slid a congratulations DM to Nikki this week. Subscribe to Big Money Players Diamond on Apple Podcasts to get this episode ad-free, and get exclusive bonus content: https://apple.co/nikkiglaserpodcast  Watch this episode on our Youtube Channel: The Nikki Glaser Podcast Follow the pod on Instagram for bonus content: @NikkiGlaserPod Leave us your voicemail: Click Here To Record Nikki's Tour Dates: nikkiglaser.com/tour Brian's Animations: youtube.com/@BrianFrange More Nikki: IG More Brian: IG More producer Noa: IGSee omnystudio.com/listener for privacy information.

Nikki and Brian return to the studio to talk more about the Tom Brady Roast and what jokes stood out. Both discuss their process and how much time and effort go into preparation. They both acknowledge the tremendous help that goes on behind the scenes like changing jokes on the fly or having to deliver a joke that someone has already said (looking at you Kevin Hart!) Nikki has been the talk of the town post roast and discusses how she handles compliments and major bookings. Whether it's Howard Stern or Conan, she knows it will be a good time. For final thought, Nikki may be too old for Bill Belichick. Subscribe to Big Money Players Diamond on Apple Podcasts to get this episode ad-free, and get exclusive bonus content: https://apple.co/nikkiglaserpodcast  Watch this episode on our Youtube Channel: The Nikki Glaser Podcast Follow the pod on Instagram for bonus content: @NikkiGlaserPod Leave us your voicemail: Click Here To Record Nikki's Tour Dates: nikkiglaser.com/tour Brian's Animations: youtube.com/@BrianFrange More Nikki: IG More Brian: IG More producer Noa: IGSee omnystudio.com/listener for privacy information.

Nikki and Brian are joined by Nikki's Boyfriend, Chris Convy, as they discuss the aftermath of The Roast of Tom Brady. Nikki discusses her moment of being all over social media and shows vulnerability about having overnight fame. Nikki discusses meeting comedy heroes at The Netflix brunch and Brian getting his invite reascended. Nikki plans future trips to Ohio and shares her story of her parents attending The Roast and how many cookies Nikki's mom fit in her bag. Final thought, thank goodness Julie found herself in the bathroom for a long time.  Subscribe to Big Money Players Diamond on Apple Podcasts to get this episode ad-free, and get exclusive bonus content: https://apple.co/nikkiglaserpodcast  Watch this episode on our Youtube Channel: The Nikki Glaser Podcast Follow the pod on Instagram for bonus content: @NikkiGlaserPod Leave us your voicemail: Click Here To Record Nikki's Tour Dates: nikkiglaser.com/tour Brian's Animations: youtube.com/@BrianFrange More Nikki: IG More Brian: IG More producer Noa: IGSee omnystudio.com/listener for privacy information.