Podcasts about Dau

This week in mobile gaming and entertainment:Unity risks another developer revolt with a new Enterprise fee, Warner Bros rejects a $108B Paramount bid in favor of Netflix, and Sensor Tower data shows casino, 4X and Solitaire hybrids dominating both revenue and downloads.What we cover:• Unity's new Enterprise Minimum Commitment Program• Why developers are calling it “blackmail”• How much Unity may charge top studios ($250k–$2M/year)• Netflix vs Paramount: who controls Warner Bros• Why streaming platforms now dictate entertainment power• US vs Global IAP revenue differences• Why 4X dominates globally• Why Solitaire & hybrid mashups are exploding in downloads• DAU charts that reveal ad-monetization giantsGet our MERCH NOW: 25gamers.com/shop--------------------------------------PVX Partners offers non-dilutive funding for game developers.Go to: https://pvxpartners.com/They can help you access the most effective form of growth capital once you have the metrics to back it.- Scale fast- Keep your shares- Drawdown only as needed- Have PvX take downside risk alongside you+ Work with a team entirely made up of ex-gaming operators and investors---------------------------------------For an ever-growing number of game developers, this means that now is the perfect time to invest in monetizing direct-to-consumer at scale.Our sponsor FastSpring:Has delivered D2C at scale for over 20 yearsThey power top mobile publishers around the worldLaunch a new webstore, replace an existing D2C vendor, or add a redundant D2C vendor at fastspring.gg.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipgChapters00:00 — Unity Enterprise fee backlash03:30 — Unity's broken incentives05:20 — Netflix vs Paramount for Warner Bros07:30 — US revenue weather09:00 — Global trends & DAU growth---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej LancaricDo you have UA questions nobody can answer? Ask ⁠⁠⁠⁠⁠⁠⁠⁠Matej AI⁠⁠⁠⁠⁠⁠ - the First UA AI in the gaming industry! https://lancaric.me/matej-ai

Épisode 1406 : “Reddit n'est plus seulement “le forum des geeks”, c'est en train de devenir un canal d'insights, de recommandation produit et de performance média très sérieux, avec des codes radicalement différents Et une croissance pub plus rapide que la plupart des autres plateformes sociales.Reddit, longtemps ignoré par les marketeurs, est en train de devenir une des plateformes les plus efficaces pour les marques qui savent jouer le jeu de la communauté : insights, considération, perf, gestion de crise. Pourquoi Reddit est en train de compter sérieusement pour les marquesEn quelques chiffres :•Reddit compte plus de 100 millions d'utilisateurs actifs quotidiens (DAU), autour de 101–116 millions selon les derniers trimestres 2024–2025.C'est l'équivalent de Threads ou Twitter•Le site génère plusieurs milliards de visites par mois (≈4,5 milliards en mai 2025), avec en moyenne 5+ pages vues par visite et environ 16–20 minutes passées par jour pour un utilisateurCôté revenus :•Reddit a généré environ 1,1–1,3 milliard de dollars de revenus en 2024, dont plus de 90% issus de la publicité.•Aux États‑Unis, les revenus pub de Reddit sont ceux qui croissent le plus vite parmi les plateformes sociales, autour de +30% à +50% par an selon les périodes.Qui sont les utilisateurs de Reddit et pourquoi c'est intéressant pour les marquesGlobalement, environ la moitié des utilisateurs sont basés aux États‑Unis.Aux US, 44% des utilisateurs ont entre 18 et 29 ans, et la grande majorité est dans le 18–34 ans.En 2025, il y a plus de 500 millions de comptes Reddit.C'est une augmentation de plus de 50% depuis 2019.2/3 des utilisateurs sont des hommes r/funny est le subreddit le plus populaire (67 millions d'abonnés)L'intérêt de Reddit pour les marquesComportements : le rôle clé de la recommandationReddit, c'est un énorme moteur de “social search” où les gens demandent “qu'est‑ce que je dois acheter ?”,I•Selon les données internes Reddit, environ 25% des posts sur la plateforme sont liés à des recommandations (produits, marques, services).•Dans 43% de ces conversations, les utilisateurs demandent explicitement de nouvelles options ou des alternatives produits – ils sont en phase active de recherche.•Pour une marque, ces moments sont beaucoup plus bas dans le funnel que le simple scroll d'un feed.Quel usage en faire ?Avant même de faire de la pub sur Reddit, tu peux l'utiliser comme un focus group permanent : qu'est‑ce que les gens disent de ta catégorie, de tes concurrents, de ton produit ? De plus en plus de marques utilisent Reddit comme un outil de recherche consommateurs : observer les conversations, identifier les “pain points”, la façon dont les gens comparent les produits, etc.•Reddit a lancé des outils comme Pro Trends, qui donnent aux annonceurs une synthèse des tendances de discussion par communautés et sujets, pour les aider à comprendre ce qui agite leurs audiences.Le case Sonos – r/Sonos :Subreddit d'environ plus de 250k membres (ordre de grandeur) rempli de clients, dont beaucoup très exigeants.Un Social Media Lead de Sonos est intervenu sous un handle personnel (KeithFromSonos), avec une posture “je suis là pour vous, pas pour vendre”, en répondant de façon honnête, en reconnaissant les problèmes, en escaladant certains cas en interne. Résultat : amélioration du climat global sur le subreddit, regain de confiance dans la marque.. . . Retrouvez toutes les notes de l'épisode sur www.lesuperdaily.com ! Le Super Daily est le podcast quotidien sur les réseaux sociaux. Il est fabriqué avec une pluie d'amour par les équipes de Supernatifs. Nous sommes une agence social media basée à Lyon : https://supernatifs.com. Ensemble, nous aidons les entreprises à créer des relations durables et rentables avec leurs audiences. Ensemble, nous inventons, produisons et diffusons des contenus qui engagent vos collaborateurs, vos prospects et vos consommateurs. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Felix covers this week's biggest gaming business headlines: Saudi Arabia's PIF slowing investments, King Shot surpassing $542M in revenue, AppLovin gaining ground on iOS, remarketing spikes, and Sensor Tower's November revenue & DAU charts.What you'll learn• Why PIF's cash slowdown could freeze gaming M&A• King Shot hits $542M in 9 months + $4.1M best day• Apple Ads losing share to AppLovin• Android still ruled by Google Ads• Remarketing up 20% YoY as CPIs climb• Top revenue games (Last War, Monopoly GO, Royal Match…)• Top DAU apps (Roblox, Free Fire, Block Blast…)Get our MERCH NOW: 25gamers.com/shop---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Podcast: Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipgChapters00:00 — PIF slows investments → gaming M&A winter03:00 — King Shot hits $542M → 4X meta dominance06:10 — AppsFlyer Index: AppLovin rises, remarketing up 20%08:50 — Monthly revenue rankings (Last War, Monopoly GO, King Shot…)10:20 — DAU rankings + final takeaways---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej LancaricDo you have UA questions nobody can answer? Ask ⁠⁠⁠⁠⁠⁠⁠⁠Matej AI⁠⁠⁠⁠⁠⁠ - the First UA AI in the gaming industry! https://lancaric.me/matej-ai

Estamos ON com mais um CienciON!No episódio de hoje, recebemos novamente a Profa. Dra. Ana Paula de Mattos Arêas Dau para uma conversa sobre o transumanismo. Neste segundo capítulo, vamos mergulhar ainda mais fundo nas grandes questões! “O que nos torna humanos?”, “Quais são os limites de uma transformação?” e “quais são  os dilemas neuroéticos?” Se essas perguntas te instigam, pegue o seu fone e venha explorar com a gente. CIENCION #110: Transumanismo II - O que nos faz humanos?Roteiro de: João Paulo Mantovan (UFABC), Ana Paula de Mattos Arêas Dau (UFABC).Convidada: Ana Paula de Mattos Arêas Dau (UFABC).Edição de áudio: André Luis Penha da Silva (UFABC).Participantes: Pedro Autreto (UFABC), João Paulo Mantovan (UFABC) e Ana Paula de Mattos Arêas Dau (UFABC).Revisão: Pedro Autreto (UFABC), João Paulo Mantovan (UFABC),  André Luis Penha da Silva (UFABC) e Ana Paula de Mattos Arêas Dau (UFABC).Edição de arte (capa): João Paulo Mantovan (UFABC).Divulgação e mídias: João Paulo Mantovan (UFABC).Coordenação Geral: Prof. Pedro Autreto (UFABC) Agradecimentos: Pró-Reitoria de Extensão e Cultura (PROEC) da UFABC

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In this episode of the GovDiscovery AI Podcast, host Mike Shanley speaks with Dr. Marina Theodotou, Executive Director of the newly launched Center for Frontier AI Security (CFAS). Drawing on her eight years at the Department of Defense, including leadership roles at the Defense Innovation Board and Defense Acquisition University, Dr. Theodotou discusses how the U.S. can maintain an AI advantage amid global competition. She highlights CFAS's mission to bridge the gap between AI policy and implementation by creating national security specific standards for AI safety, reliability, and alignment. The conversation also explores the challenges of compute power, interoperability, and the critical role of collaboration across government, industry, and academia in securing frontier AI systems. RESOURCES CFAS Website: www.cfas.online Connect on LinkedIn: https://www.linkedin.com/company/center-for-frontier-ai-security/ https://www.linkedin.com/in/marinatheodotou/ BIO: Dr. Marina Theodotou is an innovation and transformation leader with more than 30 years of experience advancing high-impact initiatives at the intersection of defense, finance, education, and consulting, spanning the US, EU, and Middle East. She is the Executive Director and Founder of the Center for Frontier AI Security (www.cfas.online) a non-profit focusing on operationalizing AI in national security. Dr. Theodotou also serves as a Senior Advisor with Kotter International, the premiere change management and innovation consultancy. As Executive Director of the Defense Innovation Board at the Pentagon, she advised top DoD leadership on innovation, digital transformation, and organizational change, delivering more than 150 actionable recommendations with 40% adoption rate. She also co-led a classified assessment of US-China AI competition shaping national defense strategy. Dr. Theodotou previously spearheaded people innovation at the Defense Acquisition University, (DAU) launching the groundbreaking People Innovation Readiness Assessment Framework to complement technology-centric innovation. She led efforts to scale digital products driving an exponential 1,720% increase in student digital engagement. Leveraging her leadership, she founded and led teams that scaled platforms like TEDx DAU, catalyzing widespread innovation throughout the DoD. She has published 150+ articles, served as a keynote speaker, mentor, and coach to tens of professionals in federal government and academia. As professor at the Jack Welch Management Institute, she taught over 120 Executive MBA Students. Dr. Theodotou won numerous innovation awards including the 2024 United States Distance Learning Association Gold Award, the 2023 Chief Learning Officer Innovation Trailblazer Gold Award and the DAU 2023 Senior Businessperson of the Year. Dr. Theodotou holds a doctorate in education on organizational change and leadership from the University of Southern California and continues to champion cross-sector innovation, organizational agility, and inclusive change as a board member, coach, and mentor.  LEARN MORE: Thank you for tuning into this episode of the GovDiscovery AI Podcast with Mike Shanley. You can learn more about working with the U.S. Government by visiting our homepage: Konektid International and GovDiscovery AI. To connect with our team directly, message the host Mike Shanley on LinkedIn. https://www.govdiscoveryai.com/ https://www.konektid.com/

En el episodio de hoy entrevistamos a Dàlia Crespo, coordinadora del Festival DAU Barcelona y con quien hablamos de muchos temas interesantes: cómo atender la diversidad en los Festivales, cómo organizar los espacios y las colas, cómo hacer crecer un festival cuando parece que no hay más sitio… Y también hablamos del juego en momentos de enfermedad y su poder emocional. Ah, y la pregunta más importante: ¿Qué va a hacer Elizabeth Hargrave en DAU? ;) En el Plapscanner hablamos de La Batalla de las Divas y de Carnival of sins y en Maridaje lúdico maridamos este último con nuestros propios pecados :O También os traemos ideas de maridaje de la mano de Patri (Caperucita lúdica), Afroféminas y las redes del DAU. Para acabar, os traemos un Amiga date cuenta express con temas muy interesantes como, por ejemplo, un estudio de la presencia de mujeres en el podcasting! Enlaces de interés: Entrevista a Cati y Noemí en la Trastienda de Shadowlands: https://shadowlands.es/1278-trastienda-113-rol-y-juegos-de-mesa-con-cati-y-noemi/ Espai tranquil del DAU: https://festivaldau.cat/dau-tranquil-un-espai-inclusiu-per-gaudir-del-joc-amb-calma/ Maridaje anticolonialista (Afroféminas): https://www.instagram.com/p/DPn3vWxDHo7/?igsh=MW11eGFianJ6bmt0dA%3D%3D Maridaje celebridades y juegos de mesa (DAU): https://www.instagram.com/reel/DP4OO4WiFLm/?utm_source=ig_web_copy_link&igsh=MzRlODBiNWFlZA== Estudio Podwoman: https://www.instagram.com/p/DPOHIXzDA2D/?igsh=MWFsZHB6Y2Q3ajV6 Diversity in gaming at Spiel ‘25: https://boardgamegeek.com/geeklist/362186/diversity-in-gaming-at-spiel-essen-25 Editorial Craneo en huelga: https://www.instagram.com/p/DPWEQO8EaKe/?igsh=MWt5ejl1c3lzZ2hmcQ%3D%3D Iniciativa Magic Mujeres: https://www.instagram.com/magicmujeresp/ https://www.instagram.com/reel/DPeh9OmiEmT https://bsky.app/profile/namurii.bsky.social/post/3m2mtzv3fac22 Juego Tax the rich: https://boardgamegeek.com/blog/1/blogpost/176717/designer-diary-taxing-the-rich-and-avoiding-politi Juego print&play Noticia Oculta: https://www.cultura.gob.es/cultura/areas/bibliotecas/mc/dia-bibliotecas-2025/recursos.html?fbclid=IwY2xjawNclsBleHRuA2FlbQIx https://www.youtube.com/watch?v=pE-QtnjOM9o ¿Qué leer para que no te coman los fachas? Edición fantasía (de Scriptibros): https://www.instagram.com/p/DPhAgszjWiI/?igsh=ZDNpY3F0dzE4NGlq ¡Gracias por escucharnos y hasta el mes que viene!

Épisode 1385 : Youpi c'est lundi et aujourd'hui on vous fait un récap de l'actu chaude ! On fait le point sur Adobe, Threads, Instagram et Linkedin ! Belle semaineInstagram lance une nouvelle fonctionnalité appelée « Competitive Insights ». C'est une fonctionnalité qui permet de comparer ses performances avec des comptes concurrents.Comme l'option qui existe déjà sur Linkedin.Concrètement, le module est disponible dans le tableau de bord professionnel d'Instagram. L'outil permet de comparer votre performance avec celle de dix comptes concurrents maximum. —Avec Firefly, Adobe passe la seconde La semaine dernière se tenait la conférence MAX 2025. C'est la grand messe d'Adobe. Une méga keynote durant laquelle le géant Adobe présentait l'intégralité des nouveautés à venir…Evidemment il a été beaucoup question d'intelligence artificielle..Ca se passe du côté d'Adobe Firefox qui propose désormais un nouveau modèle de génération d'images, plus réaliste et précis. Adobe génère désormais des images en 4 millions de pixels, bien davantage que Midjourney ou OpenAI.Accident de pianoSatire sociale qui met en lumière une créatrice de contenu qui se met en scène en se faisant mal. Enfin façon. De parler car elle présente une insensibilité congénitale à la douleurPendant des années sa renommée grandit car elle va toujours plus loin dans ses actes.Le film illustre le comportement t des fans, hardcore fans et d'une créatrice qui a depuis longtemps: perdu le sens de ce qu elle fait,Quentin Dupieux donc barge, Adèle Exarxopulos donc très fort LinkedIn enregistre une forte augmentation des commentaires et des publications vidéo.Comme chaque trimestre, LinkedIn vient de mettre à jour ses données trimestrielles et en profite pour faire un petit point sur LinkedIn.LinkedIn enregistre une progression marquée de l'engagement en 2025. Les commentaires sur les publications ont augmenté de 24 % par rapport à l'année précédente. Preuve selon le PDG que son algorithme contribue à stimuler plus d'engagement, en montrant aux utilisateurs plus de messages qui sont susceptibles de les intéresser.Les vidéos publiées sur la plateforme connaissent également une forte dynamique, avec trois trimestres consécutifs de croissance à deux chiffres sur le visionnage de vidéo.Threads atteint 150 millions d'utilisateurs actifs quotidiensThreads franchit une nouvelle étape en atteignant 150 millions d'utilisateurs actifs quotidiens (DAU), contre 100 millions en décembre 2024. En mensuel, la plateforme revendique 400 millions d'utilisateurs. C'est le moment de la comparaison avec XÀ titre de comparaison, X aurait 600 millions d'actifs mensuels et 250 millions d'utilisateurs quotidiens.Ce sont les chiffres communiqués officiellement mais selon plusieurs acteurs indépendants la réalité est plus proche d'un point de convergence. Selon les graphiques.. . . Le Super Daily est le podcast quotidien sur les réseaux sociaux. Il est fabriqué avec une pluie d'amour par les équipes de Supernatifs. Nous sommes une agence social media basée à Lyon : https://supernatifs.com. Ensemble, nous aidons les entreprises à créer des relations durables et rentables avec leurs audiences. Ensemble, nous inventons, produisons et diffusons des contenus qui engagent vos collaborateurs, vos prospects et vos consommateurs. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Yn 28 oed, bu'n rhaid i Joe Morrell dderbyn bod ei yrfa fel chwaraewr proffesiynol ar ben oherwydd anaf i'w ben-glin. Dau sy'n gallu uniaethu gystal â neb gyda'r ergyd enfawr hynny ydy Malcolm Allen ac Owain Tudur Jones.Am y tro cyntaf ers dros i ddegawd, mae Lerpwl wedi colli pedair gem yn olynol. Manchester United, o bawb, oedd y diweddaraf i gael o gorau o dîm Arne Slot. Be sydd wedi digwydd i rai o sêr amlycaf y Cochion? Oes 'na obaith o'r newydd i gefnogwyr Utd?A pharhau i ddisgwyl am fuddugoliaeth mae Wrecsam. Ydi'r rheolwr Phil Parkinson wir mewn peryg o golli ei swydd?

Welcome to another episode of BREAKING Protocol, our ongoing mini-series where we break down key updates and design questions straight from the development of SHADE Protocol.Kendall put this DAU out to the community, asking players directly what kinds of game music resonate with them — and what might best capture the essence of SHADE Protocol.⁠⁠https://linktr.ee/LittleLegendaryGames?utm_source=linktree_profile_shareHost: Jared Gonzalez. Executive Producer: Kendall Quiñones. Cohost: Chaz Hawkins, Mauro Piquera. Master Chief Engineer: Jared Gonzalez. Editor: Jared Gonzalez. Graphics Editor: Jared Gonzalez. Digital Media Editor: Jared Gonzalez. Producer: Jared Gonzalez. ⁠⁠https://linktr.ee/razzledazzleshowpodcast⁠

What's up guys. This is the final segment that will come out from the conversation between Rayvone, (it's me the editor man), Peter and Dau. We are discussing the Clippers organizational disturbance that is the present investigation over financial misdeeds. We recorded this conversation last Sunday (9/28) and because media day has happened since then, I also included my transcription of statements from Kawhi in interviews.Thanks for your time and have a great day!Find us online:Ray - @rayvonehackshawPeter - @bucketsince88Dau - @NiloticValor

What's up guys. Editing bay Ray here. This is the fourth segment from our conversation with Dau. We discuss the implications of the Fred Van Vleet injury on the Rockets' ability to contend in the 2025 - 2026 season.Find us online!Ray - @rayvonehackshawPeter - @bucketsince88Dau - @NiloticValor

What's up guys. Editing bay Ray here. This is the third segment from our conversation with Dau. We discuss Carmelo Anthony being inducted into the Hall of fame, what he had to say about Denver during his induction and how it reflects on his relationship with the organization going forward.Find us online!Ray - @rayvonehackshawPeter - @bucketsince88Dau - @NiloticValor

Felix dives into ad monetization trends, focusing on the surprising rise of prison-themed games dominating the simulation and idle arcade charts.Key insights:Prison Life (Supercent)~$107K/day ad revenue, ~$38M annual run rate.DAU ~2M, nearly 100% ad-driven.Rewarded impressions: ~4 per DAU (50% base) = $42K/day.Interstitials: ~6 per DAU (70% base) = $60K/day.Clever onboarding: first speed-up (Segway/tricycle) given for free → players experience premium benefit → later higher conversion.Takeaway: “Give it free first, then charge later.”Prison Survival (Highgame, Vietnam)DAU ~600K. Revenue ~$21–32K/day.Hypercasual-style “convert or die” model → if no rewarded ad, user is forced into interstitial.Core loop 50–80s → optimized for frequent ad exposure.Monetization mix: 6–8 interstitials per DAU = $12–15K/day; rewarded = $2–3K/day; banners/app open = $2K/day.Takeaway: Hypercasual ad-monetization loops still work in 2025.Prison Escape Journey (X Games Global, Vietnam)DAU ~560K. Revenue ~$10–15K/day.Essentially, a supermarket simulator reskinned with a prison theme.Interstitials on 180s loop; rewarded ads for ramen currency; “remove ads” IAP.Clever use of interstitial at app open instead of Google's app open ad → eCPM $8–15 vs $2.Prison Escape Simulator (Digital Melody)DAU ~220K. Revenue ~$6–10K/day.Softer ad strategy: fewer interstitials (~8 per DAU) + hidden rewarded ads.Monetizes heavier on rewarded app open format.Launched mid-August 2025, still scaling.Genre Trend: Simulation → Prison Theme TakeoverA year ago, supermarket manager sims dominated top simulation charts.Today: Prison Escape Journey is #1 in simulation.Prison themes resonate globally — from idle arcade to forex to supermarket clones.Takeaway: Prison is the new supermarket. Expect more reskins, higher conversion funnels, and creative UA exploiting the theme.Get our MERCH NOW: 25gamers.com/shop---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Podcast: Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipgChapters00:00 Introduction to Ad Monetization Trends03:36 The Rise of Prison-Themed Games08:57 Deep Dive: Prison Survival Game Analysis14:54 Exploring the Supermarket Simulator Influence20:43 Conclusion: Future of Simulation Games---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej LancaricDo you have UA questions nobody can answer? Ask ⁠⁠⁠⁠⁠⁠⁠⁠Matej AI⁠⁠⁠⁠⁠⁠ - the First UA AI in the gaming industry! https://lancaric.me/matej-ai

What's up guys. Editing bay Ray here. This is segment two from our conversation with Dau. We discuss our media day expectations and hopes for how the season starts with all of the new moving parts.Find us online!Ray - @rayvonehackshawPeter - @bucketsince88Dau - @NiloticValorTranscript

What's up guys. Editing bay Ray here. We are working on changes to how we upload the show and the timing of each episode. Stay tuned for more updates. Peter and I are joined by Dau and in this segment, we are discussing the MPJ trade in a bit more hindsight. Expect a media day preview tomorrow.Find us on the interwebsRay @rayvonehackshawPeter @bucketsince88Dau @NiloticValor

งานสร้างภาพยนตร์สักเรื่องก็เป็นเรื่องใหญ่แล้ว ทั้งการถ่าย สร้างฉาก ออกกอง ล้วนเป็นเรื่องที่ต้องคิดและจัดแจงกันอย่างละเอียด แต่ก็มีภาพยนตร์จำนวนหนึ่งที่เหมือนจะดันพรมแดนของงานสร้างหนังออกไปให้ไกลขึ้นอีก จนเราทึ่งว่างานภาพยนตร์มันจะมีข้อจำกัดอยู่ที่ตรงไหน อีพีนี้ของ Cinefile จัสพาโจ้บองโก้และชมพูเล่าฟังการถ่ายทำภาพยนตร์อลังการงานสร้างจำนวนหนึ่ง อาทิ Boyhood ที่ใช้เวลาถ่ายทำถึง 12 ปีกับแคสต์ชุดเดิม, Russian Ark ภาพยนตร์ขนาดยาว Long Take ที่แท้จริงเรื่องแรก, หรือ DAU ภาพยนตร์ที่ให้นักแสดงใช้ชีวิตในเมืองจำลองเป็นเวลามากกว่า 2 ปี #SalmonPodcast #Cinefile #CinefilePodcast #พอดแคสต์หนัง #รีวิวหนัง #ทุกประเด็นภาพยนตร์กับคนรักหนัง ---- ติดต่อโฆษณาได้ที่ podcast.salmon@gmail.com Follow Cinefile on Instagram Salmon Podcast https://www.instagram.com/salmon_podcast จัส https://www.instagram.com/teeraphanny/ มาร่วมรีวิว บอกรักหนังที่ชอบไปด้วยกันกับชาว Cinefile ได้ที่กรุ๊ป Cinefile Archive https://www.facebook.com/groups/340582525457844 Learn more about your ad choices. Visit megaphone.fm/adchoices

Mae gan Gwalia United gynlluniau uchelgeisiol iawn. Stadiwm newydd, chwaraewyr yng ngharfan Cymru ac, yn bennaf oll, cyrraedd prif adran clybiau Lloegr, y Women's Super League. Hyn oll o fewn y pum mlynedd nesaf. Dau yng nghanol y prosiect ydi Trystan Bevan a Casi Gregson. Tra bod Trystan yn defnyddio ei brofiad helaeth ym myd rygbi proffesiynol i geisio gosod y sylfaen am gynnydd a llwyddiant oddi ar y cae, sgorio goliau yw nod Casi er mwyn cychwyn y daith o drydedd haen Lloegr i'r brig. Mae'r ddau yn esbonio wrth Ows a Mal sut yn union maen nhw'n bwriadu gwneud hynny...

We dive into Coin Sort, a surprising hybrid-casual puzzler that blends merge + sorting mechanics with clever ad monetization.Flow-state puzzler: players merge and sort coins, driving a satisfying loop.Unique twist: players call in new coins themselves, creating agency.Difficulty ramps as more coins = higher-value merges = more chaos.Shallow progression today (daily rewards, calendar, boosters).Likely to expand into menu + cosmetic/meta systems if scale continues.Early launch style: prove core loop, meta comes later.Revenue ~$120K/day; ~65% IAP, ~35% ads.Banner ads: ever-present, likely a decoy to push “Remove Ads” IAP.Interstitials: after level 3, ~9.7 per DAU. Pop-up after 3rd = strong “remove ads” conversion.Rewarded ads: clever mechanic where frozen stacks of coins drop mid-level for 5s → high-pressure CTA. Also includes 2x post-level and daily reward.Explosive scaling since June 2025, 360K DAU → $40K/day ads + $80K/day IAP.Strong Tier 1 focus: US, UK, Canada, Australia, Japan.Scaling with playables, though creative variety still thin (3–4 pages).Coin Sort is IAP-heavy (~65% IAP vs HexaSort's 88% ads).Takeaway: Coin Sort proves that hybrid puzzlers don't need to be ad-first — if you build clever ad funnels that push players into IAPs, you can scale fast in Tier 1 markets.Get our MERCH NOW: 25gamers.com/shop--------------------------------------PVX Partners offers non-dilutive funding for game developers.Go to: https://pvxpartners.com/They can help you access the most effective form of growth capital once you have the metrics to back it.- Scale fast- Keep your shares- Drawdown only as needed- Have PvX take downside risk alongside you+ Work with a team entirely made up of ex-gaming operators and investors---------------------------------------For an ever-growing number of game developers, this means that now is the perfect time to invest in monetizing direct-to-consumer at scale.Our sponsor FastSpring:Has delivered D2C at scale for over 20 yearsThey power top mobile publishers around the worldLaunch a new webstore, replace an existing D2C vendor, or add a redundant D2C vendor at fastspring.gg.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Podcast: Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipgChapters00:00 Introduction and Vibe Check03:47 Game Discussion: Coin Sort06:47 Gameplay Mechanics and User Experience09:57 Monetization Strategies and Ad Revenue18:48 Game Launch and Initial Reception21:47 Creative Strategies and User Engagement24:47 Scaling Challenges and Market Dynamics27:55 Comparative Analysis of Game Performance30:47 Future Trends and Market Predictions---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me

Quem está ganhando a guerra da atenção nos apps financeiros?Apesar da proliferação de contas e carteiras digitais, os aplicativos que realmente engajam os usuários continuam sendo poucos — e os bancos tradicionais ainda dominam boa parte desse espaço.Neste episódio do Fintech Talks, recebemos Leandro Scalise, CEO da RankMyApp, para analisar os achados do mais recente estudo da empresa sobre engajamento em apps financeiros no Brasil. Discutimos como diferentes tipos de instituições performam em métricas como DAU, MAU, stickiness e volume de instalações, além das nuances que explicam a diferença entre boas interfaces e real fidelização.Conversamos sobre a influência da usabilidade, amplitude de serviços e confiança nas decisões de permanência dos usuários, os erros mais comuns cometidos por fintechs, e as oportunidades de engajamento por meio de estratégias como beyond banking e experiências conversacionais — que ganham tração com IA generativa e canais como WhatsApp.Um papo necessário para quem está de olho na evolução da experiência digital no setor financeiro e na disputa cada vez mais acirrada pela principalidade.Baixe o relatório no link abaixo:https://pages.rankmyapp.com/panorama-de-apps-financasAdquira o livro "Branch Tomorrow no link abaixo:https://amzn.to/3VeeX3I Confira!

Mermer ake ci dɛ̈ɛ̈p thok erin Chol ku Dau wakol laar ci keek kuöt paal bik keek nɔ̈k ku keek aa mith koor leŋ ruön 12 ku 15.

In this episode of DAU Contracting Conversations, Mark Jenkins discusses the On-the-Job Training (OJT) Tool for Contract Specialists. He provides a comprehensive overview of the tool, demonstrating how to access it via the DAU website and explaining its functionalities. The OJT tool is designed to help new contracting professionals track their progress and proficiency in various job tasks, ensuring they meet the Department of Defense's professional contracting competencies. Mark also highlights the importance of collaboration between supervisors and employees in using the tool to enhance on-the-job training and development. Tune in to learn more about how the OJT tool can support your contracting career.OJT Tool page:  https://www.dau.edu/tools/job-training-tool-contract-specialistsAvailable on DAU Media, Apple Podcasts, and YouTube. If you enjoy our content, please hit the like button to support us!  If you are watching this video on DAU Media, but rather watch on YouTube, go to https://www.youtube.com/channel/UCbF8yqm-r_M5czw5teb0PsA Apple Podcast:  https://podcasts.apple.com/us/podcast/contracting-conversations/id1621567225

In this episode of DAU Contracting Conversations, Mark Jenkins discusses best practices for sole source acquisitions. He shares insights on the development and implementation of a new interactive tool designed to help DoD contracting professionals and acquisition teams apply proven pricing strategies and stay compliant. The conversation covers the tool's features, including the acquisition type selector and real-life scenarios, and how it can streamline the sole source contracting process. Tune in to learn about the latest guidance from the Office of the Under Secretary of Defense for Acquisition & Sustainment and practical tips for navigating sole source contracts.Sole Source Best Practices/Lesson Learned:  https://www.dau.edu/tools/sole-source-best-practices/lessons-learnedAvailable on DAU Media, Apple Podcasts, and YouTube. If you enjoy our content, please hit the like button to support us!  If you are watching this video on DAU Media, but rather watch on YouTube, go to https://www.youtube.com/channel/UCbF8yqm-r_M5czw5teb0PsA Apple Podcast:  https://podcasts.apple.com/us/podcast/contracting-conversations/id1621567225

L'entrevista, amb l'actriu Anna Güell, que diumenge portarà a l'escenarid e l'auditori Sixto Mir de la Ràpita l'obra "Si surts amb vida", un monòleg escrit per Flavia Company. Serà l'assaig general, obert al públic amb entrada gratuïta i taquilla inversa, abans de l'estrena el proper mes d'octubre al teatre Dau al sec de Barcelona. podcast recorded with enacast.com

In this episode, we suffer (literally) through Wool Craze, a yarn-peeling 3D puzzler that's taking the charts by storm. The crew dissects why this painful yet brilliant game is generating ~$100-120K daily on a DAU of just ~140K — all while flooding the market with AI-generated creatives.Key highlights:Gameplay & Pain: Long, punishing levels (20–30 minutes each), fat-finger frustration, endless rewarded ads. A grind that monetizes sunk-cost fallacy.Monetization: Aggressive rewarded ads, interstitial spam, “Fail Packs” as top IAP. Smart combo of bundles + F2P pain points.Scale: ~$60K/day from IAP, $30–40K/day from ads = ~$100K/day total.Creative Strategy: Wild diversity (20+ different concepts). Heavy use of AI for characters, interviews, and banners. Street interview trend, talking AIs, even knitted dwarfs.UA Lessons: Chinese devs (HeroLinkage / SparkWish) pushing huge creative volume. AI lowers production cost, drives constant testing.Prediction: Could follow Screwdom's trajectory ($400K/day). With retention benchmarks and UA firepower, scaling is inevitable.Takeaway: Pain + AI + aggressive monetization = money machine.Get our MERCH NOW: 25gamers.com/shop--------------------------------------PVX Partners offers non-dilutive funding for game developers.Go to: https://pvxpartners.com/They can help you access the most effective form of growth capital once you have the metrics to back it.- Scale fast- Keep your shares- Drawdown only as needed- Have PvX take downside risk alongside you+ Work with a team entirely made up of ex-gaming operators and investors---------------------------------------For an ever-growing number of game developers, this means that now is the perfect time to invest in monetizing direct-to-consumer at scale.Our sponsor FastSpring:Has delivered D2C at scale for over 20 yearsThey power top mobile publishers around the worldLaunch a new webstore, replace an existing D2C vendor, or add a redundant D2C vendor at fastspring.gg.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Youtube: https://youtu.be/0ONl8Bj4jR4Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipgChapters00:00 Introduction and Game Overview02:18 Game Mechanics and Design05:16 Monetization Strategies07:28 User Experience and Retention10:31 AI in Game Development13:27 Comparative Analysis with Other Games16:03 Market Performance and Revenue Insights18:41 Creative Strategies and Advertising21:23 Conclusion and Future Prospects---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej LancaricDo you have UA questions nobody can answer? Ask ⁠⁠⁠⁠⁠⁠⁠⁠Matej AI⁠⁠⁠⁠⁠⁠ - the First UA AI in the gaming industry! https://lancaric.me/matej-ai

Kommt eine Ärztin oder ein Arzt neu aus dem Ausland in die Schweiz, dauert es rund ein halbes Jahr, bis sie hier arbeiten dürfen. Beim BAG herrscht Zulassungsstau. Der Spitalverband warnt vor den Folgen. +++ Weiteres Thema: Der Frust, wenn Gesetze nicht unserem Rechtsempfinden entsprechen.

This Contracting Conversations episode features a conversation with Kevin Linden, the DAU Center Director for Contracting and Small Business, discussing DAU Contracting Credentials. Kevin explains the purpose and benefits of these credentials, how they are developed and maintained, and the process for earning and renewing them. He also provides insights on how to find and apply for these credentials on the DAU.edu website. This episode is a valuable resource for anyone in the acquisition workforce looking to enhance their skills and career prospects. DAU: https://www.dau.edu/DAU iCatalog:  https://icatalog.dau.edu/icatalog_home.aspxIf you are watching this video on DAU Media, but would rather watch on YouTube, go to:   https://www.youtube.com/channel/UCbF8yqm-r_M5czw5teb0PsAApple Podcast:  https://podcasts.apple.com/us/podcast/contracting-conversations/id1621567225

「曖昧母音が分からない、音声変化や脱落が聞き取れない」これらは全て「英語のリズム」を理解することで解決できます！トピック紹介皆さんこんにちは、発音ディレクターDr. Dです。英語の発音には日本語には存在しない様々な要素が含まれます。例えば、短く曖昧に発音される母音（曖昧母音）、音がつながるときに起こる音声変化、また音が脱落するリダクションなど。これらが原因でネイティブの発音が聞き取れないことが多いわけです。今回はこれらを全て「英語のリズム」を理解することによって解決していきたいと思います。発音記号通りに発音しない英語英語はスペルや、また発音記号通りに発音されないことが多々あります。これはフレーズ全体を滑らかにつないで発音するために、音を変化させているからです。例えば次の様なケースがあります。・母音の曖昧化（曖昧母音）He is at school/heez ut skool/Classify/kla suh fai/・単語同士をつなぐ（リンキング）Take away/tei kuh wei/Take that/tei(k) tha(t)/・Tの変化（音声変化）Get it on/ge di daan/I want to do/uh wah nuh doo/・音が脱落する（リダクション）One of those people is…/wuh nuh thouz/Probably/Praab li/こういった音の変化が起こり、しかも速く発音されると日本人にとってはなかなか簡単に聞き取ることはできません。なので、あらかじめこういった発音の変化を知って、自分でも発音できる様にしておくことで徐々にパターンが読める様になり、聞き取れる様になってきます。そのためにはまず、英語のリズムを先に理解しておくことで、これら音の変化のパターンを全て包括的に理解することができます。英語はリズムで話す言語まず第一に知っておかなければならないこと。英語は “Stress-Timed Language”と呼ばれており、これは「ストレスで拍をとる言語」だということです。拍というのはリズムの単位のことです。簡単にいうと、「タン・タン・タン」とリズムをとると、それは３拍ということになります。「I'm coming right away」と発音すると、３拍で発音したことになります。これが拍です。手拍子は手で拍を取る動作です。英語はストレスと呼ばれる強調する音節を、この「拍」で捉えます。「I'm coming right away」というフレーズだと「com, right, way」の部分がストレスとなります。ストレスは意味が強い言葉に置かれ、単語の中のストレスの位置は決まっています。例えば、awayは /uh wei/ と２音節で発音されますが、/wei/ の音節がストレスです。このようにストレスでリズムをとるように発音すると、強い音と弱い音が交互に発音される感じになり、フレーズで発音した時に「リズミカルな発音」が生まれるわけです。Where are you?/wer AR yuh/I'm coming right away./um KUH min RAI da WEI/I'll be there in a minute./ul BEE ther na MI nut/母音の曖昧化英語を発音する時は、ストレスで声を張ったら脱力して、声を張ったら脱力しての繰り返しです。HAH ha HAH ha HAHこの「ha」の弱い音が曖昧母音になります。I'm coming right away./um KUH min RAI da WEI/ストレスが置かれない音節が曖昧化する感じです。この母音は特定の母音の音とかではなく、ただの脱力した声です。曖昧母音のエクササイズこの曖昧母音を出す感覚を、リズムで発音することで感じとってみましょう。同じ母音を強弱で発音して「はっきりした母音」と「曖昧な母音」を交互に発音します。ルール１、全体を一息で発音ルール２、脱力しながら弱音へ向かう(1) TAH ka TAH ka TAH kaTalk about itI'd like to talk about it.(2) BAH ta BAH ta BAH taI bought itI bought a lot of bottles.どうですか？母音が曖昧化するネイティブの発音感覚が少し分かりましたか？単語同士をつなぐリンキングリンキングに関しても英語のリズムが深く関わっています。例えば「Take it out」と発音する時も、全体を一息で「Take(強) it(弱) out(強)」のように強弱のリズムで発音します。このように発音すると自然と音がリンクしませんか？Take it out/TEI kih DAU(t)/これをカタカナ発音で発音すると「Take(強) it(強) out(強)」となってしまい抑揚がなくなってしまいます。Tの変化（音声変化）そして滑らかな抑揚を描くために、引っかかりやすい音を軽く濁らせて発音することがあります。これの代表的なのがFlap Tと呼ばれる「Tの変化」です。「Take it out」と発音する時、it の /t/ が弱い /d/ の音に変化しています。そうすることで滑らかに音をつないでいるわけです。この時に舌の位置は /t/ のままですが、少し弱く濁らせて発音しています。リンキングのエクササイズこの音声変化には色んなパターンがありますが、感覚は全て同じで、流れを出来るだけ止めないように、弱く濁らせて発音した結果、少し音が変化しています。(1) Get it/GE dih/Get it out of your mind/GE di DAU duv yer MAIND/(2) Wanted/WAH neh/I wanted to do that/uh WAH ne duh DOO thut/音が脱落する（リダクション）さらに英語は音が変化するだけでなく、そもそも脱落してしまう発音もあります。これがリダクションと呼ばれる現象です。One of those people/WUH na thouz/** of の /f/ が脱落している **Probably/PRAAB li/** 連続する /b/ が1つになっている **結局、これも英語のリズムなんです。ストレスが置かれる音節で拍をとり、それ以外の部分はスムーズに流れるように、少し省略されているに過ぎません。リダクションのエクササイズストレス音節に挟まれた、弱い発音が省略される感覚を感じてみましょう。(1) How's it going?/HAU zi GOU in/** it の /t/, または it そのものが省略されている **(2) I'm going to go grab some food before we get going./uh ma na gou/** going の /g/ が直前の m に吸収されている **(3) Where did you get that?/WER ju GEH/** did you が同化して /ju/ になっている **(4) I want to catch them right away./uh wa na KYAch em/** them の /th/ が脱落している **まとめというわけで今回は日本人にとって英語の聞き取りにくい要素となる、母音の曖昧化、リンキングや音声変化、音の脱落などの例を取り上げました。これらは結局ストレスを置いてリズミカルに発音した場合、自然とそのようになるといった感覚なんですね。どれだけ省略しても、ストレスがしっかりとリズムの軸となり発音されているから通じ合えているわけです。日本語は全ての音節を平坦に発音する言語なので、このストレスを置いてリズミカルに発音する感覚を得ることはそう簡単ではないかも知れません。しかし、こういった仕組みを理解して発音練習を２、３ヶ月も繰り返していけば、きっと感覚が掴めてくるはずです。そしてこの感覚が分かるとネイティブ発音がより鮮明に聞こえる様になります。そうすると動画を真似るコピーイングやシャドーイングの練習がちゃんとできる様になります。もし、自分ではどうしようもなければ、ドクターDイングリッシュでトレーナーをつけて少しの間やってみてください。きっとブレイクスルーできるはずです。応援していますので頑張って！それではまた来週お会いしましょう！

Want to instantly increase your app's retention and revenue in just 30 minutes?

Want to instantly increase your app's retention and revenue in just 30 minutes?

Julie Knechtel, Professor of Program Management, and Katie Keller, Professor of Contract Management, join Contracting Conversations to talk about the “PM & KO Communication Tool” they created. This tool provides questions to help team members quickly get up to speed with any program or portfolio. It includes questions to kick off conversations between requirements owners, program managers, and contracting officers, and ensures no important areas are missed. Emphasizes the three C's of establishing healthy work relationships: communication, collaboration and commitment. The tool offers tips, tools, and training courses to help users communicate and collaborate more effectively, building stronger relationships. It also includes suggested training for personal and team development covers essential workplace skills such as self-awareness, social skills, time management, teamwork, communication, giving and receiving feedback, effective listening, and conflict management. https://www.dau.edu/tools/dau-program-manager-and-contracting-officer-communication Available on DAU Media, Apple Podcasts, and YouTube. If you enjoy our content, please hit the like button to support us!  If you are watching this video on DAU Media, but rather watch on YouTube, go to https://www.youtube.com/channel/UCbF8yqm-r_M5czw5teb0PsA Apple Podcast:  https://podcasts.apple.com/us/podcast/contracting-conversations/id1621567225 

Inside Bending Spoons: the $2.5 billion app juggernaut behind Evernote, Remini, Splice, and more. Special guest Darius reveals the hacks behind viral UA, review scores, creative iteration, and the brutal playbook that's rewriting consumer SaaS.You'll learn:The secrets behind Bending Spoons' $500 million annual revenueWhy TikTok, Shorts, and Reels are more powerful than any ad network for app growthThe playbook for cutting costs, boosting review scores, and scaling faster than competitorsWhy retention is all about renewals, not DAU or D30Get our MERCH NOW: 25gamers.com/shop--------------------------------------PVX Partners offers non-dilutive funding for game developers.Go to: https://pvxpartners.com/They can help you access the most effective form of growth capital once you have the metrics to back it.- Scale fast- Keep your shares- Drawdown only as needed- Have PvX take downside risk alongside you+ Work with a team entirely made up of ex-gaming operators and investors---------------------------------------Vibe. Vibe is the leading Streaming TV ad platform for small and medium-sized businesses looking for actionable advertising campaign performance.https://www.vibe.co/---------------------------------------For an ever-growing number of game developers, this means that now is the perfect time to invest in monetizing direct-to-consumer at scale.Our sponsor FastSpring:Has delivered D2C at scale for over 20 yearsThey power top mobile publishers around the worldLaunch a new webstore, replace an existing D2C vendor, or add a redundant D2C vendor at fastspring.gg.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Special guest: Darius MoraYoutube channel: ⁨@DariusMora⁩ https://www.linkedin.com/in/moravcik/Youtube: https://youtu.be/xP3AziedKW0Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipgChapters00:00 Introduction to Bending Spoons and Guests02:49 Bending Spoons' Unique Business Model08:21 Hiring Practices and Company Culture11:19 Acquisitions and Market Strategy18:17 Transitioning from Gaming to App Development24:15 The Impact of Viral Trends on App Success29:37 User Acquisition and Marketing Techniques34:14 Subscription Models and User Retention Strategies37:31 Key Metrics for Success39:23 Understanding Retention and Monetization in Gaming41:11 Creative User Acquisition Strategies42:09 Manipulating Reviews for Better App Ratings44:07 Leveraging TikTok for User Acquisition50:19 Navigating Paid User Acquisition Challenges53:28 The Importance of Creative Testing55:39 The Power of Authentic Content in Marketing---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej Lancaric

This podcast episode discusses the Revolutionary FAR Overhaul (RFO), an initiative to streamline and simplify federal acquisition regulations, aiming to create a more efficient procurement system. It provides guidance on accessing resources and staying updated on the overhaul process. Information is given about the following areas: ·        RFO background and purpose ·        Key resource: acquisition.gov ·        Practitioner Albums ·        Feedback submission ·        DAU resources ·        Training and events https://www.acquisition.gov https://www.dau.edu https://www.dau.edu/revolutionary-far-overhaul https://www.dau.edu/eventsIf you are watching this video on DAU Media, but rather watch on YouTube, go to https://www.youtube.com/channel/UCbF8yqm-r_M5czw5teb0PsA Apple Podcast:  https://podcasts.apple.com/us/podcast/contracting-conversations/id1621567225

We dissect the phenomenon of FPS Strike, a low-poly, Counter-Strike-inspired mobile shooter racking up more than 4 million DAU, mostly across India, Pakistan, Brazil, Egypt, and Southeast Asia. Despite its massive scale, nearly everything about the game is stuck in the “admon nightmare” zone—zero iOS presence, almost no user acquisition, unoptimized ad stack, endless rewarded ads, and zero LiveOps. Yet, even with all those gaps, it is still a money-printing machine, generating $30,000 to $50,000 a day, primarily from video ads and interstitials.What's inside:Ad Stack TrainwreckAds Everywhere, Little MonetizationCreative and UA FailFake Counter-Strike, Real Results: The “nostalgia” is more about experiencing a shooter for the first time on mobile, not actual Counter-Strike memories. Simple graphics, bots for everyone, and a skin shop that barely matters.Key Takeaway:You do not need next-gen graphics or live PVP to win in Tier 3. But if you do not optimize your ad stack, creatives, and UA, you are burning money every day. FPS Strike is a case study in “good enough” that could be so much more.Get our MERCH NOW: 25gamers.com/shop--------------------------------------PVX Partners offers non-dilutive funding for game developers.Go to: https://pvxpartners.com/They can help you access the most effective form of growth capital once you have the metrics to back it.- Scale fast- Keep your shares- Drawdown only as needed- Have PvX take downside risk alongside you+ Work with a team entirely made up of ex-gaming operators and investors---------------------------------------Vibe. Vibe is the leading Streaming TV ad platform for small and medium-sized businesses looking for actionable advertising campaign performance.https://www.vibe.co/---------------------------------------For an ever-growing number of game developers, this means that now is the perfect time to invest in monetizing direct-to-consumer at scale.Our sponsor FastSpring:Has delivered D2C at scale for over 20 yearsThey power top mobile publishers around the worldLaunch a new webstore, replace an existing D2C vendor, or add a redundant D2C vendor at fastspring.gg.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Podcast: Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipgChapters00:00 Introduction to the Game Landscape04:42 Analyzing Game Metrics and User Engagement07:38 Gameplay Mechanics and User Experience10:38 Monetization Strategies and Ad Integration13:36 Game Features and User Retention Strategies18:27 The Importance of Monetization Strategies21:08 Ad Revenue Insights and Optimization24:51 Maximizing Ad Stack Efficiency27:37 User Engagement and Retention Strategies31:38 Influencer Marketing and Organic Growth35:34 Game Mechanics and Player Experience38:42 Future of FPS Games and Industry Trends---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me

Paul West, founder of Fumb Games, joins Two and a Half Gamers to share the real growth story behind the studio's breakout year. From Bitcoin Miner's $10 million run to the launch of Idle Mine and a creative rebrand that hit $1 million in sixty days, Paul explains how ruthless UA strategy, creative expansion, and a “cut what doesn't work” mindset allowed a lean team to scale tenfold—right in the middle of the industry's hardest year.What's inside:UA: Bigger Risks, Smarter FocusFumb Games ditched the “three-day payback only” mindset, stretched paybacks, and invested in UA even when it was scary. They ran tons of networks, then cut to the four that moved the needle.Creative Volume and ExperimentationAI tools, influencer gigs on Fiverr, and a nonstop creative testing culture delivered more winning ads and allowed for higher spend. The team tested everything—static, video, and even reskinned gameplay for new themes.LiveOps and Community as Retention FuelWeekend and midweek events, Discord and Reddit codes, and direct player rewards kept ARPDAU and DAU rising together—unusual for a scaling idle studio.Monetization UpgradesSubscriptions became 80 percent of purchase revenue in new titles. Side revenue from play gap (offline ad fill), Zebedee (crypto microtransactions), and Discord monetization all helped lift margins.Market Read and Thematic FocusWhen Bitcoin Miner started to plateau, they rebranded a casual project into Idle Mine, doubled down on the Bitcoin theme, and saw instant traction and monetization in a proven niche.Key Takeaway:You do not 10x by playing it safe. Cut what doesn't work, invest big in what does, and let your UA and creative testing do the heavy lifting. If the theme and the community are hot, pour fuel on that fire.Get our MERCH NOW: 25gamers.com/shop---------------------------------------Vibe. Vibe is the leading Streaming TV ad platform for small and medium-sized businesses looking for actionable advertising campaign performance.https://www.vibe.co/---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Host: Paul Westhttps://www.linkedin.com/in/paulkwest/Podcast: Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipgChapters00:00 Introduction to Fum Games and Growth Strategy03:35 Challenges in the Gaming Industry06:41 Core Pillars for Future Growth09:33 Innovative Monetization Strategies13:13 User Acquisition and Community Engagement15:59 Rebranding and New Game Launch18:11 Key Takeaways and Future Outlook---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej LancaricDo you have UA questions nobody can answer? Ask ⁠⁠⁠⁠⁠⁠⁠⁠Matej AI⁠⁠⁠⁠⁠⁠ - the First UA AI in the gaming industry! https://lancaric.me/matej-ai

Ben Walker, a contract price cost analyst at Robbins Air Force Base, Georgia joins this episode of Contracting Conversations. Ben shares his expertise on the ProPricer Automation Tool (PPAT), an Excel add-in he created that automates and standardizes the cost modeling process. Learn how PPAT can help build multi-position cost models in Excel, making it easier to manage PPAT proposal files submitted by contractors. Whether you're a contracting officer, contract specialist, or part of a technical evaluation team, this episode offers valuable insights into how PPAT can streamline your work and enhance accuracy. ProPricer Automation Tool (PPAT) | www.dau.eduAvailable on DAU Media, Apple Podcasts, and YouTube. If you enjoy our content, please hit the like button to support us!  If you are watching this video on DAU Media, but rather watch on YouTube, go to https://www.youtube.com/channel/UCbF8yqm-r_M5czw5teb0PsA Apple Podcast:  https://podcasts.apple.com/us/podcast/contracting-conversations/id1621567225  

In this Contracting Conversations episode, Christina Jalbert, DAU's Learning Asset Manager for CON 7130, "Introduction to Profit or Fee Analysis," and CON 7170V, "Analyzing Profit or Fee." She discusses the importance of understanding profit or fee analysis in acquisition roles, emphasizing the subjective nature of these determinations and the need for professionals to grasp the DFARS criteria. Christina explains the structure and objectives of both courses, highlighting the interactive learning experiences and the significance of incorporating industry perspectives. Tune in to learn how these courses can benefit acquisition professionals and enhance their understanding of profit or fee analysis. Available on DAU Media, Apple Podcasts, and YouTube. If you enjoy our content, please hit the like button to support us!  If you are watching this video on DAU Media, but rather watch on YouTube, go to https://www.youtube.com/channel/UCbF8yqm-r_M5czw5teb0PsA Apple Podcast:  https://podcasts.apple.com/us/podcast/contracting-conversations/id1621567225

In this episode, the 2.5 Gamers crew — Matej, Felix, and Jakub — dissect the top mobile ad creative trends spotted across platforms in April 2025.From AI-generated hooks dominating 5-second ad intros, to IP-mirroring creatives, TikTok-style POV playables, and Freezing Families never going away — this is a masterclass in what's hot (and not) in mobile game UA right now.You'll see:⚙️ The rise of AI-generated 5-second hooks — and how top studios use them

【本期嘉宾】杨恒、姚凯飞、程苓峰杨恒（投过一些头部大deal）姚凯飞（跨境电商行业从业者）程苓峰（科技自媒体）主播：潘乱（「乱翻书」主理人）⏰【时间线】102:53 京东当前面临的核心困境是什么？09:28 京东做外卖，是真心实意还是围魏救赵？15:29 定位品质外卖但大部分订单都是茶咖快餐，是品质升级还是薅羊毛？16:25 品质≠高价，连锁相对于更独立的品牌，可能更品质20:46 京东最优秀的是物流、品质和售后服务，品质外卖跟京东的心智有连接24:03 京东做外卖，原有的仓配采销有哪些能力是可复用的？28:23 闪电仓和京东大仓的商品成本，哪个更有优势32:22 抖音、滴滴为什么搞不好外卖？34:47 京东做外卖的战略意义有哪些？38:34 美团增长从DAU买量到业务促活42:39 美团的“神会员”的认知、辨识度和推广力度都是不足的44:38 京东和美团直球对决，会让饿了么边缘化吗？47:53 今天是哪些人在用饿了么？50:09 饿了么相较于美团有哪些劣势和优势？56:17 京东外卖直接把库迪咖啡从ICU拉到了KTV。59:53 京东的百亿补贴，钱都从哪来？65:50 京东能否复制拼多多的“百亿补贴”神话？72:18 把水搅浑，市场格局在变。淘宝、抖音和拼多多的即时零售尝试282:07 京东外卖给骑手交五险一金，刘强东这做法非常值得肯定。如果美团也给全职骑手交“五险一金”，会对美团产生什么影响？89:19 虽然刘强东说不打口水仗，但发檄文、送外卖、策反美团饿了么小哥动作不断，怎么看京东的公关舆论战术？91:51 网民关注刘强东在干什么，但可能更关注有人站出来打美团。92:41 这次大战很像当年的“3Q大战”96:35 今天短视频情绪主导的舆论场，美团应对并不擅长，需要补课吗？100:05 理科生和文科生的舆论攻防复盘，京东的道德高地有些无解？107:56 动员起社会情绪后谁都可能被反噬，连雷军都不能例外113:53 两个月=20年，京东外卖突破1,000 万单/天118:45 京东的百亿补贴能够持续多久？这是战略投入还是无底洞？128:13 京东即时零售的挑战134:38 AI赋能，会带来哪些帮助？138:21 最后演进成什么样？让子弹再飞一会✍【本期文字版】《京东和美团的直球对决》by乱翻书mp.weixin.qq.com

From childhood coding to founding one of the most played Web3 games today, Luke Barwikowski shares his journey, the vision behind ⁨@pixels_xyz⁩ and what it takes to build a sustainable play-to-earn economy.Discover how games like Stardew Valley and RuneScape inspired Pixels, why community engagement and transparency are everything, and how AI could reshape the future of Web3 gaming.Takeaways:- Pixels is leading in DAU & revenue- True play-to-earn powered by community- AI, transparency, and sustainability are key pillars- Inspired by nostalgic favorites—reimagined for Web3Don't miss this exclusive deep dive into the future of gaming!#play2earn #web3gaming

Ljósmyndarinn Hallgerður Hallgrímsdóttir segir að það eina sem henni hafi dottið í hug að gera þegar hún kom tómhent heim af fæðingardeildinni hafi verið að taka sjálfsmynd. Nokkrum mánuðum síðar opnaði hún skáp sem áður hafði geymt svarthvítar filmur og áttaði sig á því, án þess að muna almennilega eftir því, að hún hafði tekið á þær allar. Dóttir hennar, Dýrleif, fæddist og dó sama daginn, í lok september árið 2015. Ljósmyndabókin Dauðadjúpar sprungur er tileinkuð henni og Hallgerður segir okkur frá bókinni í þætti dagsins. Þá færir Freyja Þórsdóttir okkur fyrstu heimspekilegu hugleiðinguna í nýrri pistlaröð og fjallar um undrun, ólíkar birtingarmyndir fegurðar og forréttindi. Við kynnum okkur líka slavneskan þjóðlagasöng og söngtækni hjá slóvensku söngkonunni Zvezdönu Novakovich. Umsjón: Melkorka Ólafsdóttir

Most practices stop at routine eye exams — but what if your practice could unlock new (cash-pay) revenue streams and provide high-value specialty care without feeling like you're selling to patients?  Dr. Jordan Dau started Dau Family Eyecare from scratch, transforming it into a technology-driven, specialty-focused practice that sees 100-150 patients per week and is expanding to a second location. In this episode, recorded live from Vision Expo East in Orlando, Dr. Dau shares how he structures patient interactions to seamlessly integrate specialty services — so they feel like the next natural step, not a sales pitch. 

Need retention curves, LTV predictors, or DAU predictors for your mobile game or app? Don't want to learn about to build retention curves or LTV predictions or DAU predictions, but need the data?You're in luck: Russell Ovans, author of Game Analytics, has just built out these tools for free use. And in this edition of Growth Masterminds, he shows us exactly how they work and why they're useful.Ovans demos his set of free online tools designed for game marketers, including the retention curve creator, LTV predictor, daily average user predictor, and a geographical pricing tool that does have a cost. Perfect for anyone in game marketing or analytics who needs this kind of data but prefers not to learn exactly how to create it themselves :-) 00:00 Introduction to Growth Masterminds00:59 The Goal of Making Game Analytics Simple02:09 Challenges and Feedback on the Book03:39 Launching ARPDAU's Free Tools04:54 Demo of Retention Curve Creator10:51 Predicting Customer Lifetime Value (LTV)16:52 Estimating Daily Active Users (DAU) and Revenue21:57 Future Enhancements and Feedback24:05 Introduction to the Big Mac Index24:30 Country-Specific Pricing Strategies25:08 Challenges with Global Pricing26:31 Implementing the Big Mac Index28:06 Google Play Console Pricing Features30:39 Using Professor ARPDAU's Tool31:52 Adjusting Prices with CSV Files39:33 Final Thoughts and Q&A

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

Last time we spoke about the South China Sea Raid. In January, General Krueger reinforced the American beachhead at Lingayen Gulf while Admiral Halsey launched Operation Gratitude, targeting enemy ships based on faulty intelligence. Despite not finding the expected battleships, American forces decimated a Japanese convoy, sinking numerous vessels and claiming 113 enemy aircraft. Meanwhile, on Luzon, the 1st and 14th Corps advanced against Japanese defenses, capturing key positions despite fierce resistance. As both sides prepared for counteroffensives, the battle intensified, marking a pivotal moment in the Philippines campaign. On January 17, the 58th Brigade and supporting regiments launched a daring operation to destroy enemy positions. As American forces advanced, they faced fierce resistance, but some regiments achieved notable successes. Task Force 38 executed airstrikes on Formosa and Hong Kong, inflicting damage despite heavy losses. Meanwhile, Japanese forces struggled to regroup amid American pressure. General Suzuki devised a plan to fortify Leyte, but ongoing air raids hampered supply efforts. Tensions escalated as both sides prepared for decisive confrontations in the ongoing battle for control. This episode is the Mandalay Offensive Welcome to the Pacific War Podcast Week by Week, I am your dutiful host Craig Watson. But, before we start I want to also remind you this podcast is only made possible through the efforts of Kings and Generals over at Youtube. Perhaps you want to learn more about world war two? Kings and Generals have an assortment of episodes on world war two and much more  so go give them a look over on Youtube. So please subscribe to Kings and Generals over at Youtube and to continue helping us produce this content please check out www.patreon.com/kingsandgenerals. If you are still hungry for some more history related content, over on my channel, the Pacific War Channel you can find a few videos all the way from the Opium Wars of the 1800's until the end of the Pacific War in 1945.  We last left off with, General Krueger strategically positioned General Mullins' 25th Division on the right flank of the 43rd Division to bolster the assault forces at the Rosario front. This maneuver was also intended to facilitate the continuation of the 14th Corps' advance to the south. Following the successful repulsion of General Nishiyama's local counterattack, General Wing ordered the 63rd, 158th, and 172nd Regiments, which had been stalled, to launch an offensive from the west along the Damortis-Rosario road. Simultaneously, the 103rd and 169th Regiments were tasked with advancing northward along Route 3, originating from Pozorrubio. In contrast, while the 58th Independent Mixed Brigade and the 23rd Division worked to contain General Swift's 1st Corps, General Yamashita was reinforcing the San Jose sector. He further instructed the Shigemi Detachment to maintain a defensive position in Binalonan. Although the 27th and 161st Regiments had successfully relieved the 103rd Regiment in the Binalonan area, Major-General Shigemi Isao ultimately opted to halt further troop movements. He decided to leave only a small garrison in Binalonan while the majority of his forces prepared for a decisive stand at San Manuel.   As the situation unfolded, the 3rd Battalion of the 161st Regiment encountered minimal resistance, allowing them to advance into the northern half of Binalonan by nightfall on January 17. In a parallel effort, General Patrick directed the 1st Regiment towards Urdaneta, where they successfully eliminated a small outpost belonging to the Shigemi Detachment. Additionally, he dispatched the 20th Regiment to the Cabaruan Hills, where they achieved their objective by reaching Lunec and securing the central area of the hills by the end of the day. On January 18, the offensive momentum of the 6th and 25th Divisions persisted. The 20th Regiment advanced to a low ridge approximately 2,500 yards west of Cabaruan, where American forces identified the primary defenses of the 2nd Battalion, 71st Regiment. Meanwhile, the 161st Regiment successfully cleared Binalonan, and the 27th Regiment moved forward to seize control of the Bactad area, further consolidating their gains in the region. Simultaneously, Wing's units were gearing up for a significant new offensive. In line with this strategy, the 2nd Battalion of the 169th Regiment advanced along Route 3, deftly circumventing the town of Sison, and finally reached a crucial road junction located to the northeast of the town. Meanwhile, to the north, the 172nd Regiment executed a successful nighttime ambush against a Japanese artillery battalion. Following this victory, they dispatched a company to seize control of a strategically important hill, rising 600 feet and situated approximately 1,000 yards north of Rosario. This position enabled American forces to exert control over a substantial portion of the surrounding area. By the conclusion of the day, the 158th and 63rd Regiments established contact through patrols about a mile south of Amlang, as they prepared for a coordinated assault that was set to launch on January 19. This offensive culminated in the collapse of the last Japanese defenses just two days later. Concurrently, the 172nd Regiment successfully established a patrol base on Hill 606. From this vantage point, patrols ventured into Rosario, discovering the town was heavily mined, riddled with booby traps, and defended by concealed machine-gunners and riflemen hiding amidst the rubble of the buildings. On January 19, the 103rd Regiment initiated an assault on Hill 600, located at the southern end of the ridge line east of Route 3. At the same time, the 2nd Battalion of the 169th Regiment faced several intense counterattacks from retreating Japanese forces that had been bypassed at Mount Alava and Sison. Despite the fierce resistance, the relentless pressure from Japanese troops and increasingly heavy artillery fire ultimately compelled the battalion to withdraw by noon. Nevertheless, the American operations succeeded in diminishing the Japanese presence in the region. Looking southward, the 161st Regiment cautiously advanced toward San Manuel, while the 27th Regiment effectively moved into Asingan, successfully cutting off Shigemi's escape route. Further south, the 1st Regiment rapidly progressed along Route 3 toward the Agno River, capturing the towns of Villasis, Carmen, and Rosales. Meanwhile, the 20th Regiment commenced an assault on the 2nd Battalion of the 71st Regiment stationed in the Cabaruan Hills. On the front commanded by General Griswold, the 14th Corps was in the process of mobilizing along the Camiling-Anao line, preparing to initiate an advance toward the Tarlac-Victoria line. The 160th Regiment began its march, covering seven miles southward from Camiling along Route 13, while the 129th Regiment took a strong position in Anao, establishing vital contact with other outposts of the 37th Division stationed at Paniqui. The advances made on January 20 were notably swift, with the 148th Regiment successfully capturing the towns of Gerona and Pura. Meanwhile, the 37th Reconnaissance Troop made significant progress by entering the guerrilla-occupied area of Victoria, and units from the 40th Division advanced to within four miles of Tarlac, signaling a promising push toward their objectives. In the area around Cabaruan, the Japanese forces had sustained heavy losses during the initial attack. In response, Colonel Patrick made the strategic decision to withdraw two battalions from the hills to reinforce the southern advance. Probing slowly through the roughest ground in the Cabaruan Hills on January 20 and 21, the 20th Regiment's reinforced battalion jumped off in the morning of the 22nd in what was expected to be the last attack, its way paved by an especially heavy artillery and air bombardment. But from the start, operations did not go as planned. The air strike, conducted by Fifth Air Force A-20s was four hours late, subjecting the infantry to "a nerve racking wait," and did not include requested napalm. Air and artillery concentrations were, however, well placed, and it seemed improbable to the waiting infantry that many Japanese could have lived through them. A combined tank-infantry assault began about 12:30 and proceeded slowly but steadily for almost two hours. Then the attackers were stopped cold by a tremendous burst of rifle, machine-gun, and light artillery fire from the very hillsides that had received the weight of the bombardments. Company E, in the lead, fell back; Company G's officers were all either killed or wounded, and the company was temporarily scattered; Company F was pinned in place; and two supporting tanks were knocked out. Casualties mounted quickly to 10 men killed and 35 wounded. As a consequence, Patrick found it necessary to redirect one battalion from the 1st Regiment to support the ongoing attack. Simultaneously, the remainder of the 20th Regiment pressed forward toward Cuyapo, while the bulk of the 1st Regiment continued its eastward movement toward the guerrilla-held Balungao. Additionally, the 6th Reconnaissance Troop reached Guimba, successfully establishing contact with patrols from the 14th Corps, which was crucial for coordinating their efforts. On another front, with Mount Alava now vulnerable, the 169th Regiment launched a vigorous assault on January 20, managing to secure the summit of the mountain by nightfall. In contrast, the 103rd Regiment continued to face heavy casualties during their frontal assaults against Hill 600, ultimately gaining only a precarious foothold on the exposed southern slopes. Meanwhile, in a significant naval development, Admiral Halsey's Task Force 38 exited the South China Sea through the Balintang Channel on January 20. The task force was poised to execute further strikes against Formosa, aiming to disrupt enemy operations and bolster the Allied offensive in the region. The following day, with significantly improved weather conditions, Admiral McCain's aircraft carriers launched a coordinated series of airstrikes targeting Formosa, the Pescadores Islands, and the southern Ryukyu Islands. These operations resulted in the destruction of 104 Japanese aircraft on the ground, the sinking of seven oil tankers, and the loss of seven transport ships, along with additional damage inflicted on another seven vessels.  For the first time since November 1944, TF 38 felt the sting of kamikazes. Operating just 100nm east of Formosa, TF 38 was not difficult to find. Just after noon, a single aircraft appeared to conduct a conventional bombing attack on TG 38.3's light carrier Langley. One bomb hit forward. Personnel casualties were light, but the carrier was conducting flight operations three hours later. Within minutes, another aircraft also evaded radar detection and the CAP to commence a suicide dive against Ticonderoga. The kamikaze struck the flight deck and penetrated where its bomb exploded. An impending strike was spotted and ready to launch; now these aircraft provided fuel for the fire which was quickly spreading. Just before 1300hrs, another group of eight kamikazes and five escorts resumed the attack on TG 38.3. Only two suicide aircraft survived the CAP to dive on the wounded Ticonderoga. One was sent spinning into the water by antiaircraft fire, but the final attacker crashed into the carrier's island. More fires were started. The crew succeeded in putting out the flames by 1415hrs and correcting a nine-degree list by 1800hrs. Though the ship was saved, the cost was high. Some 143 men were killed and 202, including her captain, were wounded. In addition, the air group lost 36 aircraft. As a final farewell, kamikazes attacked the two destroyers on picket duty just 65nm off Formosa at 1310hrs. A single Zero had joined a returning strike. Before it could be identified as enemy, it dove on destroyer Maddox, striking her amidships. This and the explosion of the bomb aboard created a fire that was quickly extinguished. After a final day of strikes on January 22 against the Ryukyus, during which eight ships were sunk, Task Force 38 set course for Ulithi. Upon arrival, Admiral Halsey transferred command of the Fast Carrier Force to Admiral Spruance, who would lead the final offensives in the Central Pacific.  Meanwhile, back on Luzon, on January 21, the 160th Regiment swiftly cleared the town of Tarlac and began its advance toward San Miguel. Simultaneously, the 145th and 148th Regiments moved unopposed toward La Paz. The speed of 14th Corps' advance had stretched Griswold's supply lines abnormally and had exposed his left from Cuyapo to La Paz, a distance of nearly 25 miles. He had no definite information about suspected Japanese concentrations in the vicinity of Cabanatuan, on Route 5 just 15 miles east of La Paz. His worries about the security of his flank were hardly put to rest by reports of new contacts with Japanese forces at Moncada, now 20 miles behind the front, and at La Paz. Elements of the 129th and 145th Regiments easily took care of the Japanese in the Moncada area; but during the night of January 21, a pitched battle developed at La Paz when a platoon of Japanese infantry, supported by one tank, attacked a 148th Regiment perimeter at a road junction a mile west of town. The Japanese finally withdrew after destroying a bridge that carried a secondary road across a river a mile east of La Paz; but because of this, Griswold reported to General Krueger that it would be impossible to extend 14th Corps' left any further south until he had more information about Japanese forces east of La Paz. To mitigate this risk, he decided to keep General Beightler's 37th Division positioned in reserve while General Brush's 40th Division continued its advance southward along Route 3 toward Bamban. As a result, elements of the 160th Regiment and the 40th Reconnaissance Troop reached Capas and conducted patrols toward Camp O'Donnell on January 22. Meanwhile, the 161st Regiment began probing the defenses established by Japanese forces under Shigemi. The 103rd Regiment faced severe losses during their assault on Hill 600, necessitating a withdrawal to reorganize. Concurrently, the 169th Regiment launched an attack on Hill 355, making slow and costly progress against fierce resistance on the steep, barren slopes. By January 24, most of Hill 355 had been cleared, allowing the 3rd Battalion of the 103rd Regiment to move in and conduct mop-up operations in the area. As the 158th Regiment looked northward and secured the area around Amlang, they initiated an eastward push towards the positions held by the 172nd Regiment. However, their advance was slow, with only a modest gain of 500 yards achieved. Over the next two days, the 158th faced a grueling battle, making painstaking progress as they advanced foot by foot across exposed hills and ridges, all while enduring relentless machine-gun, mortar, and artillery fire. It wasn't until January 26 that they successfully broke through to Cataguintingan, where they provided much-needed support to the 172nd Regiment's ongoing assaults into Rosario. Meanwhile, by the evening of January 23, the 161st Regiment had managed to secure the southern slopes of the barren ridge north of San Manuel. They established trail blocks in the Aboredo Valley, effectively controlling movement in the area. To the south, the 108th Regiment had taken control of the Capas region, while the 160th Regiment successfully captured Bamban. However, it became increasingly evident that the American forces were now confronting the well-fortified and organized defenses of the Kembu Group. The Kembu Group's strength lay in the terrain it held, in the depth of its defenses, and in the great number of automatic weapons (aircraft and anti-aircraft) it possessed. Its major weaknesses were its immobility; the inadequate training and armament of the bulk of its troops; shortages of food, ammunition, and field artillery; and the rudimentary state of many defensive installations, a state deriving from the late start in establishing the positions at and west of Clark Field. The health of the command was poor from the start, and medical supplies were short. Morale was not of the highest order, and many of the troops were easily disaffected Formosan, Okinawan, and Korean labor personnel. In brief, the Kembu Group was the poorest armed, prepared, and supplied of Yamashita's three defense commands. On the other hand, as the 40th Division was soon to learn, even poor service troops, whatever their state of training and armament, can put up stiff resistance in good defensive terrain.  In preparation for the defense of Clark Field, General Tsukada assembled a diverse array of forces, totaling approximately 30,000 troops, although the majority consisted of air and naval personnel. He strategically divided his Army units into four distinct detachments. The Takayama, Takaya, and Eguchi Detachments were positioned along the first and second lines of defense, facing eastward toward Highway 3. Their defensive line extended from the hills west of Bamban to the vicinity of Fort Stotsenburg. In contrast, the Yanagimoto Detachment maintained its mobile units at Angeles and Porac, ready to respond to any potential enemy paratrooper landings on the southern flank of the Clark Field defenses. Additionally, Rear-Admiral Sugimoto Ushie commanded naval units comprising about 15,000 men, tasked with defending positions behind the two forward lines. In light of this formidable opposition, General Griswold made the strategic decision to utilize January 24 for consolidation and regrouping. This involved preparing for further advances southward while also probing into the enemy defenses that had already been uncovered. The 160th Regiment was able to secure Lafe Hill, although they were unable to establish a foothold on Hill 500, highlighting the challenges that lay ahead. To the north, American forces initiated another offensive against the Cabaruan Hills. The units of the 1st Regiment made only modest progress, yet they managed to advance closer to the main defensive positions as night fell. Meanwhile, at San Manuel, the 161st Regiment launched its first assault against the Shigemi Detachment, which encountered unexpectedly fierce resistance. As a result, the Americans were only able to establish a fragile foothold in the northern section of the town. Further north, Wing directed the 103rd Regiment to set up a line of departure along Route 3, aiming to strike eastward at the northwestern slopes of Hill 600. At the same time, they were tasked with advancing up the southwestern slopes of the exposed Hill 700 to secure that strategic location, as well as Hill 800 to the northwest. The 169th Regiment, advancing to the left of the 103rd, was assigned to capture Question Mark Hill. Concurrently, the 63rd and 172nd Regiments were ordered to launch simultaneous assaults on Hills 900 and 1500, respectively, while the reserve 3rd Battalion of the 63rd Regiment was also committed to clear Benchmark Hill. This coordinated attack was scheduled for January 25. The initial phases of the assault showed promise, with the supporting forces making significant headway. However, the 103rd Regiment faced considerable difficulties, managing to secure Hill 800 only by nightfall. Notably, the 172nd Regiment achieved tactical surprise, successfully clearing most of Hill 900. Over the next two days, the 63rd Regiment regrouped around Hill 1500 in preparation for its own offensive. Simultaneously, the 161st Regiment continued its slow advance southward through San Manuel, facing intense opposition. Patrick's units on the Cabaruan Hills managed to gain a mere 300 yards against determined resistance. Meanwhile, Brush pressed his attack on Clark Field, with the 160th Regiment clearing Hill 500 and advancing nearly a mile along the ridge from Lafe Hill, while the 108th Regiment secured Hills E and G. The following day, the 40th Division continued its southward maneuver. Any movement by American troops along the generally open ridges west of Route 3 inevitably brought down Japanese machine-gun and mortar fire, often augmented by fire from the dismounted aircraft automatic weapons, anti-aircraft guns, and light artillery. Seeking cover and usually pinned in place, the American infantry would call for close-in mortar and artillery support, wait for the concentrations to be fired, and then drive forward a few yards, when the process had to be repeated. Each time, the Americans managed to overrun a few Japanese machine-gun or rifle strongpoints. There was little choice of routes of advance. Draws, providing some concealment in scrub growth or bamboo thickets, were usually covered by well-emplaced Japanese weapons both within the draws and on the ridges to each side. Possession of the high ground, as ever, was essential. Yet the troops had to employ draws whenever possible to outflank Japanese ridgeline strongpoints, and often draws and ravines proved to be the only routes by which tanks, tank destroyers, and cannon company self-propelled mounts could get to the front to fire against Japanese cave positions along the sides of the ridges. The capture of one Japanese-held cave served only to disclose another, and one machine-gun position was overrun only to provide access to the next. Dislodging the Kembu Group from such defenses in depth was to prove a slow, laborious, and costly process, demanding the closest teamwork between the infantry and its supporting arms. Casualties, as a rule, would not be heavy on any one day--progress would be too slow and the troops would spend too much of their time pinned down awaiting fire from supporting weapons. But a daily attrition rate of about 5 men killed and 15 wounded for each battalion engaged would soon begin to have its effect. Meanwhile the 160th Regiment swiftly captured Hills 636 and 600 in rapid succession. However, the 108th Regiment lost control of Hill G during the engagement. On January 26, Griswold committed the 37th Division to the fight, with the 145th Regiment successfully capturing Mabalacat and Mabalacat East Airfield. They then shifted westward across Route 3, overrunning Clark Field Runway Number 1. In the Cabaruan Hills, American forces gained only 150 yards at a considerable cost. In response, Patrick decided to deploy another battalion from the 1st Regiment to eliminate this pocket of resistance. The following day, this two-battalion assault proved successful, resulting in the destruction of an entire battalion of Japanese troops, with over 1,400 enemy soldiers killed. Further north, the 161st Regiment finally broke through the main defenses of Shigemi's forces. Before dawn most of the Japanese left in San Manuel scrambled across the draw on the east side of town and fled to join the 10th Reconnaissance Regiment north of San Nicolas, but not before launching a final counterattack to cover their escape. At 0930 the 161st Infantry's two battalions resumed the drive southward through the town, and by 1330 San Manuel was clear. In a heroic but tactically unimportant stand the Shigemi Detachment had virtually fulfilled its self-imposed desire for annihilation in place. The detachment had lost 750 men killed; all its tanks, artillery, trucks, machine guns, and mortars had been either captured or destroyed. Probably no more than 250 troops escaped, and many of them were unarmed and wounded. The 161st Infantry and attached units had lost approximately 60 men killed and 200 wounded; the 716th Tank Battalion lost 3 tanks. Meanwhile, Wing's offensive continued on January 27, with the 103rd Regiment successfully reaching the crest of Hill 700 and the northwestern slopes of Hill 600. Unfortunately, they lost both positions to a brutal Japanese counterattack amidst a violent tropical downpour. On January 28, the 172nd Regiment captured Rosario, while the 63rd secured the southern crest of Hill 1500. Both regiments completed the capture of this strategic feature by January 30, thereby finalizing the occupation of the crucial road junction area. Further south, on January 27, the 160th Regiment advanced only 500 to 800 yards to the west and southwest. The 108th Regiment made a more substantial advance of about 1,000 yards southwest from Hills E and G but failed to reach Hill 5. The 145th Regiment pushed south along Route 3 for an additional three miles, reaching Culayo and Dau before taking control of the guerrilla-occupied Angeles, which had recently been abandoned by the retreating Yanagimoto Detachment. In a similar vein, the 148th Regiment secured Magalang without encountering any resistance. As we shift our focus from Luzon, we turn our attention to Burma, where we will delve into the ongoing developments of Operation Capital. Picking up from our previous discussions, we find ourselves in Central Burma, where the 2nd British Division and the 19th Indian Division are making significant strides toward Shwebo. The 2nd British Division successfully captured Ye-u on January 2, followed by the establishment of a crucial bridgehead across the Mu River just three days later. Meanwhile, the 19th Indian Division also advanced, reaching the Shwebo area by January 5. On January 8, a coordinated assault was launched by units from both divisions, culminating in the capture of Shwebo after two days of intense and brutal combat. To the west, General Festing's 29th Brigade began probing the northern flank of the 15th Division at Twinnge. Concurrently, other elements of the 19th Division worked to solidify their positions by establishing additional bridgeheads over the Irrawaddy River at Thabeikkyin and Kyaukyaung. On January 10, the 20th Indian Division captured Budalin and subsequently pushed towards Monywa, where the 33rd Division had only left a small contingent to serve as a rearguard. However, the campaign faced unexpected challenges; heavy rainfall during the first week of January brought all transport operations of the 4th Corps to a standstill, significantly hampering the Lushai Brigade's planned assault on Gangaw. After enduring a heavy aerial bombardment, the attack on Gangaw finally commenced on January 10. The Lushai Brigade managed to overpower the limited defending forces, forcing them to retreat after a brief skirmish. With Gangaw now under their control, the Lushai Brigade refocused their efforts on reconnaissance, monitoring the flanks of the 7th Indian Division. Meanwhile, the 28th East African Brigade took the lead in the advance, successfully displacing a Japanese garrison at Tilin on January 22. By this time, the 114th Brigade had begun to follow in the wake of the East Africans, while the 89th Brigade executed a long maneuver to the left, advancing toward Pauk in parallel with the other offensives. Although General Kimura was aware of some movements on his southern flank, he perceived these as mere feints by minor forces intended to divert his attention southward. Following a relentless barrage of artillery and air strikes, the 20th Division launched its offensive against Monywa on January 20. This assault faced fierce resistance, and it took two days of intense and bloody combat before the division was able to secure control of the town. After capturing Monywa, the 80th Brigade advanced towards Myaung, while the 110th Brigade shifted its focus to Ayadaw before launching an attack on Myinmu. By January 25, they had successfully established a bridgehead in that area. Meanwhile, to the east, the 2nd Division commenced its assault on Sagaing on January 14. They made significant headway against the forward defenses of the 31st Division, with other units managing to secure a bridgehead at Ywathitgyi. General Katamura, concerned about the expanding bridgeheads established by the 19th Division across the Irrawaddy River, ordered the 15th and 53rd Divisions to neutralize these positions before they could become fortified. As a result, during the last week of January, the Japanese forces executed a series of coordinated night attacks on Kyaukmyaung. By this time, British-Indian troops had dug in deeply, supported by formidable artillery and machine-gun positions. The ensuing conflict was marked by brutal carnage, with the 15th Division suffering a staggering loss of one-third of its personnel, while the 53rd Division was compelled to withdraw to Kyaukse after incurring heavy casualties. In parallel, the 89th Brigade successfully occupied Pauk on January 28, as General Messervy's forces geared up for a decisive push towards Meiktila. Looking northward, General Sultan was also advancing his own offensive aimed at reopening the Burma Road to China. He ordered the 50th Chinese Division to move towards Lashio and deployed the Mars Task Force to Hosi. Additionally, he dispatched the 36th British Division towards Mongmit, although the British advance was expected to be slow until additional forces could be brought into alignment for a more coordinated effort. General Sun's newly established 1st Army initiated a delayed offensive against Namhkam, which resumed in early January. This resurgence was marked by the 90th Regiment's strategic capture of the hill that overlooks the southwestern entrance to the Shweli River valley. Concurrently, the 112th Regiment advanced through Loiwing, subsequently crossing the river to approach Namhkam from the northeast. Meanwhile, the 88th Regiment entered the valley via the main road, making a direct push across the small plain toward Namhkam. The 89th and 114th Regiments executed a broader maneuver around the southern end of the Shweli valley; the 89th crossed the river on January 7 and advanced northward toward Namhkam, while the 114th crossed three days later, navigating through the hills toward the Namhkam-Namhpakka trail. To the south, the 475th Regiment progressed through Mong Hkak and reached Mong Wi on January 6, preparing for another challenging march across the hilly terrain toward Hosi, with the 124th Cavalry Regiment following closely behind. So close is Tonkwa to the mountains that the 475th found the trail rising steeply on the 1st day's march east. Like a crazily twisted drill it bored its way farther east and ever higher. In some places it was 15 to 20 feet across; in others, just wide enough for a man and a mule. As they rounded the turns, the men would peer ahead and look out across the valleys to where lay row on row of hills. Trees were everywhere. In flat places carved by erosion, the Burmese had cut and farmed terraces, and little villages clung to the mountains like limpets to a rock. Because existing maps were unreliable, so that map reconnaissance could not locate water and bivouac areas, and because the sheer fatigue of climbing the steeper slopes was formidable, march schedules went down the mountain side, with quite a few steel helmets and an occasional mule. Halts were a matter of common sense leadership at platoon or company level. The march was tactical but no Japanese were encountered, though rumor of their nearness kept the men alert. The Chinese had passed that way before, while a screen of Kachin Rangers was preceding the American column. Speaking the local dialects and carrying radios and automatic weapons, the Kachins were an excellent screen which masked the MARS Task Force while reporting anything that might be suspicious. Despite the difficult march, crossing the 400-foot wide Shweli was not too hard. The bridge built by the Chinese some weeks before still stood, a triumph of Oriental ingenuity, with bundles of bamboo for pontons and vines for cable. The Shweli was beginning to tear it apart, but work parties from the 475th kept it operable.  Meanwhile, spurred into action by General Wedemeyer after a month of inactivity, General Wei's Y-Force finally resumed its offensive operations in late December. They promptly dispatched the 2nd, 6th, and 71st Armies to launch an assault on the forward positions of the 56th Division at Wanting. Simultaneously, the 53rd Army executed a wide flanking maneuver to the west, aiming to encircle and attack the Japanese forces from the rear. Faced with the intense pressure of this four-pronged offensive, General Matsuyama was compelled to withdraw the 148th Regiment to a position north of Mongyu. He also ordered the Yoshida Force to mount a counteroffensive toward Muse and committed the reserve 2nd Regiment to secure Namhpakka. On January 5, the 53rd Army reached the vicinity of Muse and began crossing the river; however, they were met with fierce resistance from Matsuyama's timely counterattack, which thwarted their advance. Ten days later, Sun's forces initiated a well-coordinated offensive against Namhkam, which ultimately succumbed on January 16 as the 55th Regiment retreated toward Khonung. With the Shweli Valley now firmly under Allied control, Matsuyama began to tighten his defensive perimeter in anticipation of a final withdrawal toward Hsenwi. On January 17, the Mars Task Force made significant progress by reaching the Hosi sector, where they immediately engaged Japanese outposts. The 475th Regiment successfully secured the advantageous high ground near Nawhkam village. Over the next two days, American forces clashed with the 4th Regiment, capturing the strategically important Loikang Ridge and the elevated terrain overlooking Namhpakka. On January 19, the Mars Task Force attempted to disrupt Japanese supply lines by blocking the Burma Road through demolition and artillery bombardment, coinciding with the arrival of the 55th Regiment, which was sent to bolster the defenders. In a parallel effort, the 114th Regiment managed to sever the Namhkam-Namhpakka trail at Loilawn on the same day. Faced with this escalating threat from the south and the intensifying Chinese assaults on Wanting, the 56th Division was compelled to further contract its defensive perimeter. As a result, Wanting fell on January 20. Fortunately for Matsuyama, General Wei received orders from the Generalissimo to conclude the Salween campaign immediately, which meant that the Chinese forces would remain in their positions until they could be relieved by Sultan's units. Over the course of nearly nine months of intense combat, Wei's Y-Force had successfully reoccupied an impressive 24,000 square miles of Chinese territory and had defeated one of the most elite divisions of the Japanese army, along with elements from two additional divisions. However, the fighting was far from over. Sun continued to dispatch the 112th and 113th Regiments toward Wanting and Mongyu, aiming to clear the final stretch of the road leading to China. Meanwhile, the 89th and 114th Regiments pressed eastward to cut off the Burma Road north of Namhpakka, further complicating the situation for the Japanese forces. In the southern region, from January 20 to January 24, the Mars Task Force continued its strategic operations, executing ambushes and demolition missions while successfully repelling several intense counterattacks from enemy forces. By late January, pressure by MARS Task Force and that of the Chinese forces in the north began to register on the Japanese. The soldiers of the 4th Regiment could see the aerial activity that kept MARS supplied. Not recognizing what they saw, they were so impressed by a big supply drop on the 24th that they sent a report to the 56th Division of a large airborne force being landed along the Burma Road. Accepting this report, General Matsuyama decided to destroy his ammunition and retreat south. His superiors on January 24 agreed to let him retreat, but only after he had evacuated casualties and ammunition. Forty vehicles with gasoline accompanied by a Major Kibino of the 33rd Army staff were sent north to support the 56th in its withdrawal. The Japanese truck convoy made its run north the night of January 24. The trucks were heard, and the Americans placed heavy fire on the road. Kibino had been making the trip in a tankette. Hit by a 4.2-inch mortar shell, it burst into flames clearly visible from the American lines. Kibino clambered out, jumped on a truck, and succeeded in getting his convoy through to the 56th Division. Next day the derelict tankette was credited to the 2nd Battalion, 475th Regiment. But  Encouraged by the additional supplies of gasoline and inspired by the heroic examples of Major Kibino and the personnel of the truck companies, the 56th Division renewed its efforts and, during the next four days effected the evacuation of over 1000 casualties and moved several tons of ammunition to Hsenwi.  Meanwhile, General Matsuyama began to systematically reposition his forces toward Namhpakka, a strategic maneuver that would enable Chinese troops to occupy Mongyu on January 27. In a broader context, by the end of January, the 36th and 50th Divisions were also engaged in crossing the Shweli River, preparing to advance their offensives further southward. In the Arakan region, Operation Romulus exceeded expectations. The 1st Battalion of the 111th Regiment had been defending Akyab. On December 31, as the rear guard of the Sakura Detachment crossed the Kaladan River and moved eastward the Battalion was ordered to withdraw to Ponnagyun. As intelligence suggested very few Japanese were left on Akyab island, a recce aeroplane reported the locals showing no anxiety and on January 2 messages were dropped in Urdu and Burmese asking them to sit on the ground if the island was still occupied or stand with their hands in the air if not. Captain Jimmy Jarrett of ‘C' Flight, 656 AOP Squadron, then landed to a rousing reception and found the Japanese had quit on December 31, although nobody believed him until a senior officer flew in to confirm it. This prompted General Christison to swiftly initiate an amphibious invasion. Notably, this operation was executed without the anticipated naval bombardment and without deploying the reserve 26th Indian Division. As a result, the 3rd Commando Brigade successfully captured Akyab on January 3, facing no resistance, and the 25th Indian Division soon followed, reinforcing the area. From Akyab, the 9th York and Lancasters were transported by boat to establish a strategic blockade along the Yo River at Ponnagyun. There, they encountered significant Japanese forces. After a fierce engagement, however, the Japanese defenders were compelled to retreat toward Myohaung by January 11. In response to the evolving situation, Admiral Mountbatten devised a plan to land the 3rd Commando Brigade and the 25th Division on the Myebon Peninsula. This operation aimed to sever the primary lines of communication for Japanese forces, while preparations were made for the 26th Division to conduct a landing on Ramree Island. In response to the urgent military situation, Operation Passport was swiftly conceived and executed on January 12. British-Indian forces successfully landed at the southern tip of the peninsula, supported by both air and naval operations. Once ashore, the commandos advanced inland, facing intense resistance from fortified hill positions. Their efforts culminated in the capture of Pagoda Hill and the village of Myebon. However, as they pushed forward, opposition intensified, making it increasingly difficult for the British-Indian troops to reach Hill 831. Simultaneously, the 82nd West African Division, now commanded by Major-General Hugh Stockwell, entered the Kaladan Valley to relieve the 81st Division, which had been engaged in combat for over a year. The West African forces began to apply pressure against the Matsu Detachment units stationed at Myohaung and Minbya, although these Japanese forces managed to maintain their positions despite the mounting assaults. Meanwhile, planning was underway for the deployment of the 3rd Commando Brigade and the 51st Indian Brigade to land at Kangaw, coinciding with General Lomax's invasion of Ramree Island.  Early on January 21 the naval bombardment group opened fire. Christison and the other Force Commanders were watching through field glasses from the bridge of HMS Queen Elizabeth in her first engagement since the Dardanelles in 1915. Christison later said: ‘Some shells fell on a marsh behind the Jap defences, and I saw a number of duck spring up. “Duck”, I shouted. “The Royal Navy never ducks”, said the Admiral.' With her second salvo Queen Elizabeth scored a direct hit on the Japanese ammunition depot, which facilitated the landing operations. The 71st Indian Brigade successfully captured Kyaukpyu with minimal resistance. This victory enabled them to advance toward Minbyin and Kyaupyauk, both of which fell into their hands by January 23. In the subsequent days, the brigade continued its advance toward the Yanbauk Chaung, where they encountered fierce Japanese defenses. At the same time, other elements of the division worked to secure Cheduba and Sagu Kyun Islands, further consolidating their strategic position in the region. On January 22, the commandos and the 51st Brigade successfully landed in the Kangaw area near the Min River. However, they faced violent and frequent counterattacks from Japanese forces, which hindered their ability to expand their beachhead. Despite these challenges, the intense pressure from the commandos ultimately forced the Japanese defenders at Hill 831, Myohaung, and Minbya to retreat toward Kani, marking a significant shift in the operational landscape. I would like to take this time to remind you all that this podcast is only made possible through the efforts of Kings and Generals over at Youtube. Please go subscribe to Kings and Generals over at Youtube and to continue helping us produce this content please check out www.patreon.com/kingsandgenerals. If you are still hungry after that, give my personal channel a look over at The Pacific War Channel at Youtube, it would mean a lot to me. General Krueger's forces advanced against Japanese defenses, capturing key positions despite heavy resistance and casualties. Meanwhile, in Burma, British-Indian troops advanced, seizing key locations despite heavy resistance. Both fronts faced intense combat, leading to significant territorial gains against Japanese forces by the end of January.

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