Podcasts about Sora

MILEI TEM TUDO PARA SER REELEITO – MAS LULA TAMBÉMNeste episódio do Stock Pickers, Paolo Di Sora, da RPS Capital, tem uma conversa franca sobre o cenário político e macroeconômico da Argentina e do Brasil, os efeitos das decisões de Javier Milei e Lula, e o que isso significa para investidores. A conversa também passa por juros, Bolsa, risco fiscal, popularidade, valuation e oportunidades de investimento.Se você acompanha mercado financeiro, política, macroeconomia, ações, Argentina, Brasil, Milei, Lula, Bolsa e oportunidades de investimento, este episódio é para você.

Food can be the loudest voice in your head, even when you are not hungry. We sit down with best-selling author Sora Vernikoff, who healed her own compulsive overeating and built a no-diet weight loss program around one core skill: learning how to eat and stop. If you are tired of binge eating, yo-yo dieting, and that constant “what's next to eat” loop, this conversation offers a different path that focuses on behavior change rather than deprivation.We dig into Sora's framework for why overeating and overthinking often feel identical: both are driven by repeating thoughts you do not feel able to release. She breaks down the role of the subconscious mind, why willpower alone keeps failing, and how calming food thoughts can free up the strength to handle the uncomfortable feelings you have been trying to escape. If you have ever felt stuck in a mental replay, whether it's donuts in the kitchen or a fight you cannot stop rehashing, you will recognize yourself here.Sora also teaches a practical technique you can test immediately: the Green Technique. You ask “How much is enough?” and “How much is too much?” before you eat, then you set aside a clear “marker” amount you do not touch. That one move changes the moment from automatic eating to intentional portion control, while still allowing the foods you love. We also cover why diets so often backfire, how rigid rules can trigger binges, and where to find Sora's tools at nodieting.net and OverthinkersCoach.com.If this helps, subscribe, share the episode with a friend who feels stuck, and leave a review so more people can find real-world support for overeating, overthinking, and sustainable weight loss. Support the show Thank you to our sponsor Complete Coverage Football - http://www.completecoveragefootball.com

How Ankit Nayal scaled organic TikTok to 50 million views with an AI content factory, and why half of them were wasted until conversion came first.Most founders who burn through their paid ad budget pivot to organic with one or two accounts and hope something works.Ankit Nayal pivoted to organic and went to 150 to 200 TikToks a day.He runs this for his app Flamme. He has crossed 50 million views. He told me more than half of those views were wasted, because conversion was not in his framework yet. The episode is about what he built once that became obvious.The path there started in a cave. After losing his ad budget in 2025, Ankit scrolled TikTok for four hours a day for two months. He compared it to having McDonald's every meal. Out of that came the VSC framework. Viral: an under-5,000-follower account with a 100K-view post that is still picking up trend score. Scalable: a format that replicates cleanly across accounts. Convertible: a video that actually pulls downloads. Memes pulled 0.1% conversion. A girl reacting to a hook pulled 0.5%. A 100K-view reaction beat a 2M-view meme on bang for buck.The system around the framework is more cumbersome than most posts about it admit. He started by filming himself and concluded that a brown man with an Indian accent was not the best fit for the American market. He moved to Russian creators sourced through Kwork.ru at one dollar a minute and twenty-five cents per ten-to-fifteen-second reaction. ChatGPT translation overhead killed that workflow. He moved to Sora 2, then to Seedance. Every clip gets broken into five-second blocks because the model starts hallucinating past five seconds. A CapCut filter layer with ten effects scrubs the plastic skin off AI faces. Phones get lined up on physical farms because the TikTok API gets content flagged.The funnel sequence he ends on is the part that stuck with me. Organic first, then UGC, then paid. Most founders run it backwards.Video Chapters: 00:00 Introduction03:00 Losing the paid ad budget on a dating app06:00 Four hours of TikTok a day for two months11:00 The VSC framework14:00 Why memes converted nothing18:00 Russian creators on Kwork20:00 Moving to Sora 2 and Seedance22:00 The CapCut plastic-skin filter23:00 The five-second hallucination limit26:00 Why lip sync breaks scale31:00 The phone farm38:00 Which products should not run organic TikTok39:00 Organic, then UGC, then paidTopics covered:- Organic TikTok at scale for consumer apps- The VSC framework: viral, scalable, convertible- AI UGC production with Sora, Seedance, and CapCut- Creator sourcing on Kwork and the limits of real UGC- Phone farms and TikTok content flagging- Why B2B founders should not run organic TikTokLearn more:https://mobileuseracquisitionshow.com/episode/[slug]/ - Episode page https://www.linkedin.com/in/annayal/ - Connect with Ankit on LinkedIn https://www.annayal.com/ - Ankit's website https://intelligentartifice.kit.com - Newsletter

In marshes across the country, birds awaken on a summer morning. Tall dense grasses and reeds often make marsh birds hard to see, but their voices carry easily across the lush, green landscape. You can hear birds like the Redhead, the Sora, the American Bittern, the Ruddy Duck, this Yellow-headed Blackbird, and many more. More info and transcript at BirdNote.org. Want more BirdNote? Subscribe to our weekly newsletter. Sign up for BirdNote+ to get ad-free listening and other perks.  BirdNote is a nonprofit. Your tax-deductible gift makes these shows possible.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Fluent Fiction - Japanese: Haruto's Honest Vote: Art, Mistakes, and Integrity Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ja/episode/2026-06-20-07-38-20-ja Story Transcript:Ja: ハルトは元気な若者です。En: Haruto is an energetic young man.Ja: 彼はいつも楽しそうに絵を描いています。En: He always seems to enjoy drawing pictures.Ja: 美術を使って町を良くしたいと思っています。En: He wants to use art to improve the town.Ja: その日は、町の小さな投票所で夏の暑さが彼を包んでいました。En: On this day, the summer heat enveloped him at the town's small polling station.Ja: 「今日は大事な日だ。」ハルトは思いました。En: "Today is an important day," Haruto thought.Ja: 新しい地域美術プロジェクトを支持するために、投票をしたいと決めていました。En: He had decided to vote in support of a new local art project.Ja: 投票所は地域センターの一角にありました。En: The polling station was located in a corner of the community center.Ja: 部屋はシンプルですが、明るく温かい雰囲気でした。En: The room was simple, but it had a bright and warm atmosphere.Ja: そこで彼は友人のユキとソラと出会いました。En: There, he met his friends Yuki and Sora.Ja: 「ハルト、今日は投票の日だね。」ユキが微笑みました。En: "Haruto, today is voting day, isn't it?" Yuki smiled.Ja: 「うん、僕はアートプロジェクトのために投票するよ。」彼は嬉しそうに答えました。En: "Yeah, I'm going to vote for the art project," he replied happily.Ja: 彼は投票用紙を受け取り、個室に入りました。En: He received a ballot and entered a booth.Ja: ついつい鉛筆を手にすると、いつもの癖で絵を描いてしまいました。En: Almost instinctively, he picked up a pencil and, out of habit, started drawing.Ja: 気がつくと、投票用紙にはかわいい動物の絵がいっぱいでした。En: When he realized what he was doing, the ballot was filled with cute animal drawings.Ja: 「あ、やばい！」彼は驚きました。En: "Oh no!" he was surprised.Ja: 絵を描いてしまい、有効な投票ができませんでした。En: He had drawn pictures on it and couldn't cast a valid vote.Ja: 窮地に陥ったハルトは考えました。En: Caught in a dilemma, Haruto thought about what to do.Ja: 恥ずかしい思いをするのは嫌だけど、新しい用紙をお願いしないと投票ができません。En: He didn't want to feel embarrassed, but he couldn't vote without requesting a new ballot.Ja: 彼はどうするか悩みましたが、誠実に行動することを選びました。En: He was troubled but chose to act honestly.Ja: 彼はボランティアの方に近寄り、静かに事情を説明しました。En: He approached a volunteer and quietly explained the situation.Ja: 「すみません、用紙に絵を描いちゃいました。新しい用紙をいただけますか？」En: "Excuse me, I ended up drawing on the ballot. Could I have a new one, please?"Ja: ボランティアの女性は優しく微笑みました。En: The volunteer lady smiled kindly.Ja: 「大丈夫ですよ。正直に話してくれてありがとう。こちらが新しい用紙です。」En: "It's okay. Thank you for being honest. Here's a new ballot."Ja: 新しい用紙を受け取ったハルトは、今度こそきちんと投票しました。En: With a new ballot in hand, Haruto voted correctly this time.Ja: 彼の心は軽くなり、自己主張する自信がついてきました。En: His heart felt lighter, and he gained confidence in expressing himself.Ja: 投票所を出た彼は、ユキとソラに向かって微笑みました。En: As he left the polling station, he smiled at Yuki and Sora.Ja: 「うまくいったよ。間違えても大丈夫、正直でいることが大切だね。」En: "It went well. Even if you make a mistake, being honest is what matters."Ja: その日、ハルトは一つ大切なことを学びました。誠実さが評価されること、自分の考えを素直に表現することの大切さ。En: That day, Haruto learned something important: the value of honesty and the importance of expressing his thoughts honestly.Ja: そして彼は、これからも町のために頑張ることを決意しました。En: And he resolved to continue working hard for the sake of the town. Vocabulary Words:energetic: 元気なdrawing: 絵を描くenveloped: 包まれたpolling station: 投票所ballot: 投票用紙booth: 個室instinctively: ついついhabit: 癖dilemma: 窮地embarrassed: 恥ずかしいtroubled: 悩んだvolunteer: ボランティアhonestly: 誠実にexpressing: 表現するconfidence: 自信resolved: 決意したimportance: 大切さatmosphere: 雰囲気instinctively: ついついinvalid: 無効なhonest: 正直なsake: ためにvolunteer: ボランティアkindly: 優しくexpress: 表現するyoung man: 若者improve: 良くするvote: 投票するexplanation: 説明community center: 地域センター

In this special live episode recorded at SynthBee headquarters in South Florida, hosts Charlie Fink, Ted Schilowitz, and Rony Abovitz bring listeners inside a special gathering of neuroscientists, philosophers, and technologists debating the future of AI. Moving beyond hype, the conversation focuses on "Collaborative Intelligence" vs. Artificial General Intelligence (AGI), exploring whether we are building tools that amplify humanity or autonomous systems that will eventually replace it.Instead of traditional interviews, the hosts invite workshop speakers to the hot seat for rapid-fire insights on the deepest questions in tech: Can we measure an AI's true intentions? Is consciousness a physics problem? And how do we ensure these systems remain compatible with human flourishing?News HighlightsDisney invests $1B in OpenAI & licenses IP: The hosts debate whether this is a masterstroke to engage fans with user-generated Sora content or a "Yahoo powered by Google" mistake that hands the keys to the kingdom to a rival.Valve launches new PCVR hardware: A quick look at the attempt to revive the high-end PC VR market.Meta adds real-time vision to Ray-Bans: The next step in multimodal AI wearables.Guest HighlightsDr. Uri Maoz (Neuroscientist, Chapman/Caltech): Discusses the "black box" problem of neural networks, comparing the opacity of AI to the human brain, and how neuroscience tools might help us detect deception in AI systems.Dr. Walter Sinnott-Armstrong (Ethics Professor, Duke): Argues that ethical AI regulation shouldn't be a monolith; different cultures need "sovereignty of ethics" to allow diverse moral frameworks to coexist rather than one centralized Silicon Valley standard.Dr. Julio Frenk (Chancellor, UCLA): Frames the AI race as a battle between "Computational Democracy" (distributed, transparent power) and "Computational Autocracy" (centralized control), warning that universities must preserve critical thinking or risk losing the ability to govern AI at all.Reed Maxwell & Laura Condon (Hydrologists, Princeton/Arizona): Reveal how AI is modeling the planet's water crisis, predicting "black swan" climate events, and why funding for this critical earth-science work is mysteriously disappearing.Danny M (12-Year-Old Prodigy): Steals the show with a stunningly articulate take on AI consciousness, "trapped man" experiments, and how fractal geometry might map neural weights—proving the next generation is more ready for this future than we are.Dr. Aaron Schurger (Psychology, Chapman): Explores the neuroscience of spontaneous action and free will, debating whether "telepathic" connections and quantum effects in the brain could be the missing link for true human-AI compatibility.Jared Ficklin (Chief Product Officer, SynthBee): The former Frog Design fellow argues we must shift the conversation from AI "capability" to "compatibility," using the intuitive connection humans have with dogs or horses as the benchmark for successful AI interfaces.Thanks to our sponsor Zappar!Subscribe for weekly insider perspectives from veterans who aren't afraid to challenge Big Tech.New episodes every Tuesday. Watch full episodes on YouTube. Hosted on Acast. See acast.com/privacy for more information.

Shelley Palmer,media technologist, advisor, and author with over 700,000 daily newsletter subscribers, returns to the show. He's one of the sharpest thinkers writing about AI today, and this conversation covers the full arc: from social media liability to the trust collapse coming for all of us, and into the real productivity gains and surveillance trade-offs of living inside an AI-first workflow.The episode opens with the Google and Meta lawsuit verdict and quickly moves past the legal question. Shelley's position is precise: you can't legislate parenting, but you can legislate transparency, and the tech industry has failed on that front entirely. The $6 million judgment against Meta and Google is a rounding error — not a deterrent. What matters is what platforms actually engineered: engagement above all else, backed by neuroscience, probabilistic math, and dopamine feedback loops optimized for shareholders, not users.AI XR News You Should Know: OpenAI is ending Sora and pivoting hard to Codex and enterprise. Ben Affleck secured $900 million from Netflix for a custom AI filmmaking tool. Epic Games cut 1,000 jobs as Fortnite loses audience. NVIDIA's Jensen Huang introduced Nemo Claw and Open Shell at GTC — a corporatized framework for personal AI agents.Key Moments[00:01:15] – Charlie opens noting the show missed one episode in nearly 300 — his daughter's wedding[00:01:55] – OpenAI kills Sora; the Critters director goes dark before the episode[00:04:45] – Google and Meta lose their social media addiction lawsuit; Meta also loses in New Mexico[00:08:07] – Shelley on what can actually be legislated: not parenting, but transparency[00:11:42] – Shelley on Zuckerberg: he genuinely believed connection would be net positive; ask him today[00:13:31] – "Planetarily net negative. No matter what good it does, it does more harm."[00:18:16] – Rony on dopamine engineering: neuroscientists studying pixel size, color, sound to refine addiction[00:19:40] – Shelley reframes it: engagement maximization for shareholders, no more insidious than that[00:23:19] – The physiological change argument: humans evolved to default to trust; AI-generated everything breaks that[00:31:50] – Rony's counterpoint: trust will reset local; the software ecosystem will follow[00:36:53] – Shelley: "Our business increased last year. Everyone on my staff is doing 400 times the work."[00:44:42] – AI-first means automating every workflow you can honestly automate — and knowing what isn't ready[00:45:06] – Jensen's Nemo Claw and Open Shell: the safer path to personal AI agents, and what it actually costs[00:49:42] – The surveillance trade-off: an effective AI agent requires more personal data exposure than anything before it[00:51:24] – Apple's Secure Enclave play: why Tim Cook may win the AI trust war in the endThe productivity gains are real, but so is the privacy exposure, and the systems that earn trust — at every level — are the ones that will survive.This episode is brought to you by Zappar, the company behind Mattercraft — the leading visual development environment for building immersive 3D web experiences across mobile, headsets, and desktop. Mattercraft now features an AI assistant that helps you design, code, and debug in real time, right in your browser.Start building at mattercraft.io. Subscribe to the AI XR Podcast wherever you listen.Watch the full episode for the full breakdown. Available where podcasts are. Full videos available on YouTube. https://youtu.be/S_AECjELYyo Hosted on Acast. See acast.com/privacy for more information.

Lucas Rizzotto is one of the most distinctive artists working at the intersection of technology and human experience. He built Where Thoughts Go, a VR piece that proved genuine connection was possible inside a headset when everyone said it wasn't. He followed it with Pillow, a mixed reality app designed around the bedroom. He then spent months letting an AI algorithm run his life — wearing Mantra smart glasses, building a surveillance and memory system on himself, and documenting it as an ongoing series on Instagram and TikTok. Now he's making a live cinematic experience called Escape the Internet, which he calls Broadway crossed with a video game crossed with standup comedy. It premiered as a ghost debut at SXSW this year.Mike Boland, analyst and founder of AR Insider, sits in for Rony Abovitz in this episode. The conversation opens on the Rec Room shutdown — $250 million raised, a $3.5 billion valuation, and now a wind-down. The panel connects the collapse to a pattern: VR has always been an exotic pursuit sold as a mainstream one, and the unit economics of concurrent immersive social spaces are nearly impossible. The discussion moves to OpenAI shutting down Sora, the AI video generation race between Google VO3 and Kling, the rise of AI slop in social feeds, and Lucas confirming he quit LinkedIn because it's unreadable.AI XR News: Rec Room is shutting down after raising $250M at a $3.5B peak valuation. Snapchat is acquiring its remaining assets. OpenAI closed down Sora, overwhelmed by competition from Google VO3 and Kling. AI-only social feeds from Meta and Grok are not gaining traction — users are tuning them out.Key Moments:[05:37] – Ted's thesis: VR is an exotic pursuit that was never going to be mainstream, and Rec Room would have been healthier if it accepted that early[07:33] – Lucas: Ready Player One was the worst thing to happen to XR — it gave executives a fictional roadmap to fund[18:38] – Ted asks whether Apple can do for mixed reality what it did for the smartphone — and the panel is skeptical[27:42] – Mike on physics as the hard ceiling: Moore's Law doesn't apply to waveguides and optics the way it applies to chips[29:02] – Lucas explains why he dropped display glasses for his wearable AI experiment — they increase engineering complexity by 50x[32:17] – Lucas's AI-controlled life series: a complex algorithm watches him, mines personal data, and tells him what to do to find happiness — including an unplanned trip to Lithuania[34:12] – Ted asks if the experiment is a net positive or negative. Lucas: neutral if you're in control, net negative if Meta or OpenAI are running the system[37:52] – Lucas on convenience as a death by a thousand cuts: he optimized his life in Berlin to have everything within three minutes and became miserable[41:00] – Charlie on Where Thoughts Go: assigned it to students every semester; it only works if you surrender to it[47:15] – Escape the Internet: hundreds of people in a movie theater, all on their phones, playing a shared cinematic narrative. Lucas calls it a modern version of church[53:40] – The standup model applied to software: Lucas tested Escape the Internet at SXSW and cut 50% of the material that didn't get a reactionThis conversation sits at the intersection that the AI XR Podcast lives for: technology as creative material, not just commercial tool. Lucas's view that we've been building things people use all the time when we should be building things that blow their minds for two hours and then get out of the way is one of the sharper critiques of the attention economy you'll hear this year.This episode is brought to you by Zappar and Mattercraft — the leading visual development environment for building immersive 3D web experiences on mobile, headsets, and desktop. Mattercraft now includes an AI assistant that helps you design, code, and debug in real time, right in your browser. Start building at mattercraft.io.Subscribe to the AI XR Podcast so you never miss a conversation. Hosted on Acast. See acast.com/privacy for more information.

YEAR 6 IS FINALLY HERE!  GO CHECK OUT OUR YOUTUBE TO SEE OUR BRAND-NEW INTRO!  You can find the animator using the link below! https://www.fiverr.com/syedahumna56/do-professional-pixel-art-animation-of-your-choice?utm_medium=shared&utm_source=copy_link&utm_campaign=gig&utm_term=AyNLxkP   *Intro includes minor edits not provided by the original animator. All animated assets were provided by the animator listed above, with some text assets added in post by Keeping Up With The Nerds.   Check out our affiliated links! Opus clips Partner link: https://www.opus.pro/?via=Nerd   Check out our Website: Keepingupwiththenerds.com   The summer heat is bringing the ultimate gaming heat!

Most duck hunters have flushed a Sora rail from the cattails at some point, but few know much about these secretive marsh birds. In this episode, wildlife biologist Eamon Harrity joins the show to discuss the fascinating world of rails. We cover Sora migration, nesting habits, habitat needs, population trends, and the unique adaptations that allow these birds to thrive in dense wetland environments. Eamon also shares stories from his research on Ridgway's Rails and discusses some of the biggest unanswered questions surrounding rail behavior and conservation. If you've ever heard the distinctive call of a Sora echoing across a marsh and wondered what you were hearing, this episode is for you. Topics discussed:• What exactly is a rail?• Sora migration and wintering grounds• Nesting and breeding behavior• Why rails are so difficult to study• How rails find isolated wetlands during migration• Rail hunting history and regulations• Wetland management and conservation• The future of rail research Follow the North American Waterfowler Podcast for new episodes every week. Contact Elliott: freelanceduckhunting@gmail.com Support the Show: Patreon.com/freelanceduckhunting Partners of the Show Flight Day Ammunition www.flightday.com Code NAW10 Shotty Gear www.shottygear.com Code: FDH10 Weatherby www.weatherby.com Mammoth Guardian Dog Kennels www.mammothpet.com Search Mammoth Guardian Dog Create on Amazon Learn more about your ad choices. Visit megaphone.fm/adchoices

Arboral Year 1294. It was an age when wars were fought over the spires. In the shadows of the battlefield lies one young boy, shunned by the mercenary troupe he accompanies because of a single horn growing from his head and bleeding out from a wound in battle, he desperately tries to sound the retreat... only for his song to unlock something within him he didn't know was possible. His name is Luca and he is one of the Branch-hexed. In a world plagued by war, all he wants is to stop using music as a tool for war and play for pleasure, but first he must help The Pontiff, the head of religion called Spiralism, defeat a mysterious power known only as The Garland. Does Luca have what it takes? And even if he succeeds, will The Pontiff keep his word?On this week's episode, Mat is joined once again by SonMangaKing to chat about Vol. 1 of The Bugle Call: Song of War written by Mozuku Sora and illustrated by Higoro Toumori. Join us as we discuss the book's clear influences in other Seinen fantasy, how Luca's powers are used to dazzling effects on the page and why we'll be reading on with the story despite some initial apprehension! ---Show Notes---Attack on Titan by Hajime IsayamaBerserk! by Kentaro MiuraVagabond by Takehiko InoueVinland Saga by Makoto YukimuraChainsaw Man by Tatsuki FujimotoClaymore by Norihiro Yagi---Thanks to Juliano Zucareli for our theme music!Find us on:X: Manga Tak PodBluesky: Manga Tak PodInstagram: Manga Tak Pod

Chris reminds us that we don't Sora 'bout Bruno. Kelley buys a privacy screen for the litterbox. Robert tells us about the game From those guys who made the one Software. RIP our wallets this September. Question of the Week Do you want them to make a 3D-2D remake of the first six Final Fantasy games? Check out the show notes here! The post RPG Cast – Episode 816: “Summer Games Infestation” appeared first on RPGamer.

Fluent Fiction - Japanese: Awakening in Kyoto: A Journey Through Art and Tradition Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ja/episode/2026-06-13-22-34-01-ja Story Transcript:Ja: 春の終わり、京都の美しい美術館で、そらとカイトは家族の再会の後、絵画展を訪れていた。En: At the end of spring, in a beautiful museum in Kyoto, Sora and Kaito visited an art exhibition after a family reunion.Ja: そらは久しぶりに京都に来た大学生で、美術に興味があるが、家族の期待に少し疲れていた。En: Sora was a university student who had not been to Kyoto in a while and was interested in art, but she felt a bit weighed down by her family's expectations.Ja: 一方、カイトは美術館のキュレーターであり、アートと家庭の伝統の間で揺れていた。En: On the other hand, Kaito was a curator at the museum, torn between art and familial traditions.Ja: 美術館は静かで落ち着いた空間だった。En: The museum was a quiet and serene space.Ja: 大きな窓から自然光が入り込み、芸術作品を優しく照らす。En: Natural light streamed in through large windows, gently illuminating the art pieces.Ja: 訪れる人々は、浮世絵から現代のインスタレーションまで、多様な作品を見て回る。En: Visitors wandered around, viewing a diverse range of works, from ukiyo-e to modern installations.Ja: そらはカイトに言った。「家族のこと、もっと理解したい。でも、何だか今まで受けてきた期待が重く感じるの。」En: Sora said to Kaito, "I want to understand my family more. But somehow, the expectations I've had until now feel heavy."Ja: カイトは微笑んで答えた。「そら、自分のペースで楽しんでいいよ。作品を見て、自分の感じたことを大事にしてね。」En: Kaito smiled and replied, "Sora, it's okay to enjoy at your own pace. Look at the works and cherish what you feel."Ja: そらは作品の前に立ち、心を開いた。En: Standing in front of a piece, Sora opened her heart.Ja: ある絵に目が留まった。En: Her eyes rested on one particular painting.Ja: それは静かな田園の風景で、彼女の心に響いた。En: It was a quiet rural landscape that resonated with her heart.Ja: 思わず足を止め、カイトに尋ねた。「この絵は、どんな意味があるの？」En: Unable to help herself, she stopped and asked Kaito, "What does this painting mean?"Ja: カイトは少し考え、話し始めた。「この絵は、自然と人の共生を描いている。En: Kaito thought for a moment and began to speak, "This painting depicts the coexistence of nature and people.Ja: 昔の人々は、小さな自然から多くを学んで生きていたんだ。」En: People in the past lived learning a lot from small aspects of nature."Ja: そらは驚いたように言った。「この絵、私の心が自由になる感じがするよ。En: Surprised, Sora said, "This painting makes me feel like my heart is becoming free.Ja: でも、どうしたら家族と自分の自由を両立できるのかな？」En: But how can I balance my family and my personal freedom?"Ja: カイトは優しく言った。「伝統を受け入れることは大切だけど、それを超えて自分自身を大事にすることも大切なんだ。」En: Kaito said gently, "Accepting tradition is important, but it's also important to cherish yourself beyond that."Ja: 二人は長い間その絵の前で話し、そらは次第に自分の道を見つける勇気を得た。En: The two talked in front of the painting for a long time, and Sora gradually gained the courage to find her own path.Ja: 彼女はカイトの言葉に力をもらい、家族の伝統を受け入れつつ、自分らしさを失わないことの大切さを学んだ。En: Inspired by Kaito's words, she learned the importance of embracing her family's traditions without losing her own identity.Ja: 美術館を出ると、そらは風に揺れる桜を見上げた。En: As they left the museum, Sora looked up at the cherry blossoms swaying in the wind.Ja: 「ありがとう、カイト。おかげで新しい視点を得たよ。」En: "Thank you, Kaito. Thanks to you, I've gained a new perspective."Ja: カイトも微笑んで、「こちらこそ、そらの考える力に感動したよ。」と返した。En: Kaito also smiled and responded, "Thank you, too. I'm impressed by your capacity for thought, Sora."Ja: こうして、二人は互いに新しい理解と絆を持ち、心温まる一日を終えた。En: Thus, the two ended their heartwarming day with a new understanding and bond.Ja: 春の空は穏やかに、彼らの未来を照らしていた。En: The gentle spring sky shone upon their future. Vocabulary Words:reunion: 再会serene: 落ち着いたilluminating: 照らすdiverse: 多様なweighed down: 疲れていたexpectations: 期待rural: 田園resonated: 響いたcoexistence: 共生surprised: 驚いたbalance: 両立accepting: 受け入れるtradition: 伝統embracing: 受け入れつつidentity: 自分らしさcapacity: 力perspective: 視点curator: キュレーターgentle: 優しくpersonal freedom: 自分の自由cherish: 大事にするopened her heart: 心を開いたcoexistence: 共生heartwarming: 心温まるbond: 絆swaying: 揺れるimpressed: 感動したfamilial traditions: 家庭の伝統art installations: インスタレーションnatural light: 自然光

Linktree: ⁠⁠https://linktr.ee/Analytic⁠⁠Join The Normandy For Ad-Free NME, Additional Bonus Audio And Visual Content For All Things Nme+! Join Here:⁠⁠ ⁠⁠https://ow.ly/msoH50WCu0K⁠⁠ In this segment of Notorious Mass Effect, Analytic Dreamz reacts to the official Kingdom Hearts IV Teaser Trailer released in June 2026.Analytic Dreamz delivers a detailed breakdown of the long-awaited teaser, covering Sora's new design and abilities, the mysterious new Keyblade, stunning updated graphics, and the first major story hints for the next chapter in the Kingdom Hearts saga. The segment explores potential new Disney worlds, returning characters, and the evolving lore following Kingdom Hearts III.From the emotional tone and cinematic visuals to gameplay implications and Square Enix's direction for the series, Analytic Dreamz analyzes every key moment and what it means for fans.Join Analytic Dreamz for a full trailer reaction, frame-by-frame breakdown, theories, and predictions for Kingdom Hearts IV as the franchise enters its next era.This segment is essential listening for Kingdom Hearts fans excited about the future of Sora and the fight for light.Privacy & Opt-Out: https://redcircle.com/privacy

Anime Was (Not) A Mistake is the champions! It's another summer and you know what that means, chilling with friends and taking in a well-deserved vacation...or does it? Dan and Jonathan are suddenly flung into a digital world in an unprecedented event we like to call Prodigious Summer: Volume I. Join us for the summer of digivolution as we examine EVERY episode of Digimon Adventure 01 and 02. (Skipping around but discussing them all) As the newly dubbed "Digi Destined" Tai, Matt, Izzy, Sora, Joe, Mimi and T.K. befriend partner Digimon and seek to destroy the forces of evil through friendship we will be there with them every step of the way.  With new bonds created with their Digimon friends watch as Digivices, tags, and crests work together to ascend to higher levels. We go from fighting a digidevil on File Island, taking down an Elvis impersonator ape, and finally return to the real world to confront a vampiric threat! Relive the excitement of a well-timed Pepper Breath. Mourn the loss of some close allies... Wizardmon, here's looking at you. And most importantly know that the power to digivolve lives inside your heart! Jonathan definitely misses his shopping and social life, but Dan seems to be fitting right in... wait...where did he get those goggles from?  Rate, Review, Subscribe, and Listen to Us on Podbean/iTunes/Stitcher/Spotify Follow us on Instagram:@animewasnotamistakepodcast Or on Facebook:@animewasnotamistakepod Music Provided: “Digimon Are The Champions” – Shuki Levy and Paul Gordon – Saban Entertainment – Digimon: Adventure - Digimon: Adventure OST - 1999 “Previously on Digimon” Takanori Arisawa  – Saban Entertainment – Digimon: Adventure - Digimon: Adventure OST - 1999 “Digimon's Heroic Theme” – Project Trinity Covers - Digimon: Adventure - 2012

Connect with Us: Follow us for updates, bonus content, and discussions about all things South Park. On Facebook: @SouthParkPod On YouTube : @SouthParkPod On TikTok : @SouthParkPodOn X: @SouthParkPodsOn Blue Sky: @smbsouthparkreview.bsky.social On Instagram: @SouthParkPodcastSubscribe and Support: Subscribe to SMB South Park Review Crew on your favorite podcast platform to never miss an episodeContact: Got a question, suggestion, or just want to share your thoughts on South Park? Reach out to us at suckmyballspod@gmail.co or visit us at linktr.ee/southparkpod

June is here so guess what? It's officially Hot AI Summer.

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

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Most of the AI timeline debate happens in software. Benchmark scores, model releases, the shape of the capability curve. Jon Billow watches a different number for a living: lead times.Billow is on the leadership team at BNS, a firm that manufactures and installs electrical and communication infrastructure. The same critical power equipment his teams put into data centers also goes onto Navy and Coast Guard ships, more than 150 of them. He emailed John Sherman because he thinks the people forecasting AI's arrival are missing what he sees on the construction side every week. The buildout can only move as fast as its slowest part, and right now almost every part is backed up for years.That email is what got him on the show. Here is the heart of what he laid out.The constraint nobody prices inTo bring a large data center online, Billow says, a long list of things has to land at the same time: permitting, grid interconnect, critical power, cooling, and the compute itself. Miss one and the whole project waits. And nearly every item on that list carries a backlog measured in many months, sometimes years.The pinch point he keeps returning to is critical power equipment. According to Billow, the orders all funnel back to roughly five manufacturers, Eaton, ABB, Schneider, GE Vernova among them, and all of them are slammed. He notes that even the US government is having a hard time getting its allocation for ship programs, because it is standing in the same line as every hyperscaler. On top of that, more municipalities are now requiring data centers to bring their own behind-the-meter power generation, which adds another category of equipment backlog and a skill most operators have never needed before. Hooking up to the grid is one thing. Building gas turbines and finding electricians who can parallel generators is another, and the skilled trades are already stretched thin.A factor of five to sevenSherman pushed him to put a number on the gap. If a company says a project lands in a year, how far off is that really?Billow's read: the US has roughly 50 gigawatts of total data center capacity today, with about a quarter of it allocated to AI. Around five gigawatts are under active construction and another seven to twelve sit in backlog. Set that against the order-of-magnitude jumps the labs are talking about and his estimate is blunt. “If I was to be a betting man I would say it's in the order of five to seven years.” Whatever timeline you have been handed, in other words, multiply it.The tells from inside the labsHe pointed to two recent signals that the infrastructure is already the limiting factor. OpenAI walking back a large commitment tied to its Sora video product, which Billow reads as a company looking at finite compute and deciding where to spend it. And Anthropic delaying a model, which he attributes partly to security concerns and partly to the reality of constrained compute capacity. The software keeps leapfrogging. The ground underneath it does not move at the same speed.Why this could be good newsBillow does not frame any of this as a reason to relax. He frames it as time. If the physical buildout runs years behind the hype, that is runway to get governance and alignment right rather than scrambling after the fact. He drew the parallel Sherman's audience knows well, comparing the moment to how the world slowly built doctrine around nuclear risk, and argued the work now is to use the delay deliberately.His closing image stuck with us. He said he wants to tell his grandkids that we were building the car while it was going down the road at 55 miles an hour, but we had the presence of mind to put in seat belts because we knew who was in the back seat.Where they did not agreeThe conversation did not paper over the tension. Sherman described his time in Holly Ridge, Louisiana, a town of about 2,000 mostly elderly people living next to a data center he compared to the size of Manhattan, with construction dust in the air and water residents will not drink. He found it overwhelmingly sad. Billow sees the same structures differently, as a testament to human ingenuity that can be sited and built responsibly if we choose to. Both things sat in the room at once, and the episode is better for letting them.Going deeperWe pulled the headline argument into this piece. The full breakdown for paid subscribers goes into the parts that get more technical and more political:* Compute governance as the most feasible near-term guardrail, including chip tracking and why the industry pushes back hard* The anonymous-compute problem and why “confidential computing” worries safety researchers* China's narrow-AI approach and what it implies about the data center race* Recursive self-improvement, Jevons paradox, and whether you even need new data centers to reach the danger zone* The regulatory carve-out tech enjoys, and the NDA story coming out of LouisianaIf you want that version, upgrade your subscription and it lands in your inbox. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com/subscribe

In 2018, researchers at MIT unveiled an artificial intelligence so disturbing it earned a name straight out of a psychological thriller: Norman (as in Bates). Unlike typical AIs, Norman was exposed to some of the darkest corners of the internet, causing it to see horror in the mundane. Though designed as an experiment, Norman became a cautionary tale about how artificial minds can mirror humanity's most disturbing tendencies. For a full list of sources, please visit: sosupernaturalpodcast.com/dark-web-norman-the-psychopathic-ai   Did you know you can listen to So Supernatural ad-free? Join the Crime Junkie Fan Club! Visit https://crimejunkiepodcast.com/fanclub/ to view the current membership options and policies. So Supernatural is an Audiochuck and Crime House production. Find us on social! Instagram: @sosupernaturalpod Twitter: @_sosupernatural Facebook: /sosupernaturalpod Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of Reading With Your Kids, Jed welcomes Jimmy Vee, author, magician, ventriloquist, marketer, and proud "weirdo," to celebrate his new series beginning with There Are No Dinos In This Book. Jimmy shares how his background in magic, ventriloquism, and marketing copywriting fuses into a unique creative voice for kids—funny, interactive, and packed with personality. He explains how the classic children's magic idea of "look no see"—where kids see something the magician "doesn't"—became the structural engine of his book. On the page, the narrator insists there are no dinosaurs, while kids spot visual clues and "argue" with the narrator, recreating the energy of a live magic show in a read‑aloud experience. Jed notes that it's the kind of book you can't read flat; it demands performance, voices, and engagement. Jimmy walks through the challenge of capturing live-show energy in static text, drawing on his experience writing mass‑media ads and picturing himself on stage as he drafts. He talks about tailoring humor across ages, the joy of "selfish jokes" that mostly please the performer, and the wild differences between intimate school shows and massive, anything‑goes crowds in places like Puerto Rico and El Salvador. They also dive into titles and covers as marketing hooks, unpacking how Jimmy built memorable names like PD Perfect Pants and Professor Nincompoop, using alliteration, rhythm, and a clear hook to stand out in a tiny thumbnail. In the final segment, Jed briefly visits with returning guest Helena Ku Rhee to spotlight her new picture book Sora's Seashells, a gentle, name-centered story about identity, kindness, and family love.

The Keyblade War begins with a flurry of opening attacks from the Organization. When all seems lost, Sora recalls the cursed plot device at his disposal: Time travel!You can find us on social media under khbhpodcast or use your Gummiphone to email us at khbhpodcast@gmail.com

I denne episoden utforsker vi skjæringspunktet mellom avansert maskinlæring, anvendt etikk og sosiopolitisk transformasjon. Vi har besøk av Preben Monteiro-Ness, en ledende kapasitet innen AI-sikkerhet for å dekonstruere mekanismene bak moderne nevrale nettverk og de systemiske risikoene som følger i kjølvannet av superintelligens. Sentrale tekniske begreper Explainable AI (XAI): Feltet innen kunstig intelligens som søker å gjøre maskinlæringsmodellers beslutningsprosesser transparente og forståelige for mennesker. Essential for å unngå "snø-bias" (hvor modellen identifiserer en hund som en husky kun basert på bakgrunnen). World Models: En tilnærming der AI-en bygger en intern, abstrakt representasjon av det fysiske eller konseptuelle miljøet den opererer i, snarere enn bare å utføre statistiske sannsynlighetsberegninger på sekvensielle data. RLHF (Reinforcement Learning from Human Feedback): En treningsmetode hvor menneskelige operatører rangerer modellens svar for å finjustere dens sosiale og faglige kompetanse. Zero Marginal Cost Society: En økonomisk teori (popularisert av Jeremy Rifkin) om en fremtid der kostnaden ved å produsere en ekstra enhet av en vare eller tjeneste nærmer seg null, drevet av automatisering og AI. Filosofiske og litterære referanser Kants Kategoriske Imperativ: Drøftet i lys av AI-ens intensjonalitet; er en handling moralsk hvis den utføres av riktige grunner, eller er resultatet det eneste som teller? Bostroms "Paperclip Maximizer": Et tankeeksperiment som illustrerer risikoen ved feiljusterte mål (alignment problem), hvor en superintelligens kan ødelegge verden i et forsøk på å utføre en triviell oppgave. Alasdair MacIntyre & Telos: Diskusjon rundt menneskelivets formål (telos) i en post-arbeid-æra. Oppenheimer-analogien: En refleksjon over det teknologiske "point of no return" og behovet for globalt samarbeid for å unngå eksistensiell risiko. Kulturelle nedslagspunkter Satanisme og AI-protopia: En anekdotisk analyse av hvordan visjoner om kunstig intelligens har manifestert seg i subkulturer på Grünerløkka lenge før LLM-revolusjonen. Steinerskole-utopien: En metafor for et samfunn fokusert på kreativitet, smal kunst og menneskelig samhandling fremfor industriell produktivitet. Sora og AI-generert video: Status for nåværende generative modeller for levende bilder og de tekniske begrensningene knyttet til minne og beregningskraft (compute). Anbefalt litteratur: Nick Bostrom: Superintelligence: Paths, Dangers, Strategies Jeremy Rifkin: The Zero Marginal Cost Society Alasdair MacIntyre: After Virtue See omnystudio.com/listener for privacy information.

Fluent Fiction - Japanese: Embracing Uncertainty: A Journey of Renewal in Kamikochi Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ja/episode/2026-05-17-22-34-01-ja Story Transcript:Ja: 春の陽射しが降り注ぐ中、カミコウチのトクモリは柔らかい緑で覆われ、美しい桜が風にそよいでいました。En: Under the spring sunshine, Kamikochi's Tokumori was covered in soft greenery, and the beautiful cherry blossoms swayed in the breeze.Ja: リナ、カイト、そしてソラは、ゴールデンウィークのタイミングで家族ハイキングに出かけました。En: Rina, Kaito, and Sora embarked on a family hike during Golden Week.Ja: 「お姉ちゃん、見て見て！あっちにリスがいるよ！」ソラがカメラを構えながら叫びます。En: "Big sis, look, look! There's a squirrel over there!" Sora shouted while pointing his camera.Ja: ソラはいつも写真を撮るのが好きで、自然の美しさを捉えるのが得意です。En: Sora always enjoyed taking pictures and had a knack for capturing the beauty of nature.Ja: 年下のソラの無邪気さが、一瞬リナの心を軽くしました。En: The innocent enthusiasm of her younger brother momentarily lightened Rina's heart.Ja: リナは大学生で、将来のことを考えるといつも不安でした。En: Rina was a university student, constantly anxious about her future.Ja: このハイキングも、本当は楽しむつもりだったのに、頭の中は悩みでいっぱいです。En: Though she intended to enjoy the hike, her mind remained filled with worries.Ja: 家族と過ごすこの時間を本当に楽しめていない自分にリナはもどかしさを感じています。En: She felt frustrated at her inability to truly enjoy this time with her family.Ja: 「リナ、調子はどう？」カイトが気軽な声で聞きます。En: "How are you doing, Rina?" Kaito asked casually.Ja: リナは歩き続けながら、兄の顔をちらりと見ました。En: Rina glanced at her brother's face while continuing to walk.Ja: カイトはいつも自然を愛し、どんなことでもあまり心配しない人です。En: Kaito always loved nature and rarely worried about anything.Ja: 「うん、まぁまぁかな…」リナは一瞬ためらいましたが、正直に話すことに決めました。En: "Well, so-so..." Rina hesitated for a moment but decided to speak honestly.Ja: 「将来が不安なんだ。En: "I'm anxious about the future.Ja: 何をしたいのかわからなくなってきて。」En: I've started to feel unsure about what I want to do."Ja: 「そっか、でもそれっていいことかもしれないよ」とカイトは優しく答えました。En: "I see, but maybe that's a good thing," Kaito replied gently.Ja: 「未確定なことには無限の可能性があるからね。En: "Uncertainty holds limitless possibilities.Ja: 山登りだって、頂上に一歩ずつたどり着くように、人生も一歩ずつでいいと思うよ。」En: Just like reaching the summit one step at a time in mountain climbing, it's okay to approach life step by step."Ja: 頂上に着くころには、空は晴れ渡り、心地よい風が吹いていました。En: By the time they reached the summit, the sky was clear, and a pleasant breeze was blowing.Ja: リナはカイトの言葉を思い出し、目の前に広がる風景を見つめます。En: Recalling Kaito's words, Rina gazed at the expansive scenery before her.Ja: 飛騨山脈（ひださんみゃく）の壮大さと季節の移り変わりを肌で感じ、自分のこれからの道にも新たな視点を見出しました。En: Feeling the grandeur of the Hida Mountains and the change of seasons on her skin, she found a new perspective on her path ahead.Ja: 「ありがとう、カイト。En: "Thank you, Kaito.Ja: 今は不安定でも、この不確実さを楽しんでみるよ。」リナは微笑み、自然の広がる景色に深呼吸をしました。En: I'll try to enjoy this uncertainty, despite the instability," Rina said with a smile, taking a deep breath of the nature-filled scenery.Ja: 新しい気持ちで未来の自分を見つける、長い旅が始まったのです。En: A long journey to discover her future self had begun with renewed spirit.Ja: リナはこれからのことを考えると前向きな気持ちになり、ソラやカイトと一緒に過ごす優しい時を一層楽しむことができるようになりました。En: Thinking about what lay ahead, Rina felt more positive and could enjoy the gentle moments spent with Sora and Kaito even more.Ja: リナの心にある春の訪れは、新しい始まりを告げていたのです。En: The arrival of spring in Rina's heart heralded a new beginning. Vocabulary Words:greenery: 緑swayed: そよいでembarked: 出かけましたknack: 得意innocent: 無邪気anxious: 不安frustrated: もどかしさhesitated: ためらいましたuncertainty: 不確実possibilities: 可能性summit: 頂上expansive: 広がるgrandeur: 壮大さinstability: 不安定renewed: 新しいheralded: 告げていたtiming: タイミングcapturing: 捉えるmomentarily: 一瞬constantly: いつもrarely: あまりintended: つもりapproach: たどり着くperspective: 視点expansive: 広がるheralded: 告げていたenjoyed: 楽しむbreeze: 風discover: 見つけるjourney: 旅

In this week's episode, Liam & Ben must:

The plot strikes back when Sora, Donald, and Goofy receive a distressing call from Chip and Dale.

The colors be hoisted once Sora plays crab catch up and sails into action. Meanwhile, the Organization continues searching for the secret box.

Selkies rise from the sea in Celtic legend. Wild. Powerful. Unstoppable. This week on the Irish & Celtic Music Podcast, we celebrate the women of Celtic music who carry that same energy. From the north shore to the black water, Show 757 is an hour of music that will pull you under in the best possible way. It's the Irish & Celtic Music Podcast #757  -  -  Subscribe now at CelticMusicPodcast.com! One Street Over, Gillian Boucher & Bob McNeill, Fialla, Eloise & Co., The Leftovers, The Bow Tides, Low Lily, Tara's Folk, Sue Spencer, Eimear Arkins, Sora, Louise Bichan, Kim Carnie, THE DIVINERS GET CELTIC MUSIC NEWS IN YOUR INBOX The Celtic Music Magazine is a quick and easy way to plug yourself into more great Celtic culture. Enjoy seven weekly news items with what's happening with Celtic music and culture online. Subscribe now and get 34 Celtic MP3s for Free. VOTE IN THE CELTIC TOP 20 FOR 2026 This is our way of finding the best songs and artists each year. You can vote for as many songs and tunes that inspire you in each episode. Your vote helps me create this year's Best Celtic music episode. You have just three weeks to vote this year. Vote Now! You can follow our playlist on YouTube to listen to those top voted tracks as they are added every 2 - 3 weeks. THIS WEEK IN CELTIC MUSIC 0:08 - Boxing Robin "Ned Coleman's #2/The Orphan" from Land of the Noon - Day Moon Gypsy Youngraven - vocals, guitar, and bodhrán 3:28 - WELCOME 5:25 - Gillian Boucher & Bob McNeill "Mountain Road #2" from Race for the Sun Gillian Boucher: fiddle, piano 11:22 - Fialla "Maid in Her Father's Garden" from Home & Away Katie: Vocals, Guitar, Bodhrán, Irish Stepdancing 14:44 - Eloise & Co. "Hanter Dro 1953 Kraozon/Hanter Dro 1930 Gregam/Meetinghouse Hanter Dro" from avec Elodie Becky Tracy (fiddle, octave fiddle) Rachel Bell (accordion) Rachel Aucoin (piano) 18:35 - The Leftovers "Down By the Glenside" from Heart of Buffalo Elizabeth Shea: vocals 21:34 - FEEDBACK 24:17 - The Bow Tides "Trip to Gaelicia" from Sailing On Ellery Klein: fiddle Jessie Burns: fiddle Katie Grennan: fiddle, champion Irish dancer 28:29 - Low Lily "Where We Belong" from Angels in the Wreckage LIZ SIMMONS: Guitar & Vocals NATALIE PADILLA: Fiddle, Banjo & Vocals 32:06 - Tara's Folk "How many Roads" from remember how we fall Julien Casanova -  fiddle Catherine de Vençay -  cello 36:19 - Sue Spencer "Free in the Harbour" from North Shore Sue Spencer: Guitar, Vocals 40:22 - THANKS 42:51 - Eimear Arkins "The St. Louis Waltz (Waltz)" from Here & There Eimear Arikins: Fiddle, Vocals 47:10 - Sora "Selkie" from Ghostlines Sora -  Voice, Piano, Violin 50:09 - Louise Bichan "Auch" from The Lost Summer Louise Bichan: fiddle 53:58 - Kim Carnie "Eolas Gradhaich" from A' Chailleach Kim Carnie: vocals 57:20 - CLOSING 58:40 - THE DIVINERS "Daychovo Horo" from earshot (EP) ILSE DE ZIAH: Cello; Fiddle; Vocals 1:01:23 - CREDITS Support for this program comes from Hank Woodward. Support for this program comes from Dr. Annie Lorkowski of Centennial Animal Hospital in Corona, California. Support for this program comes from John Sharkey White, II. Support for this program comes from International speaker, Joseph Dumond, teaching the ancient roots of the Gaelic people. Learn more about their origins at Sightedmoon.com Support for this program comes from Cascadia Cross Border Law Group, Creating Transparent Borders for more than twenty five years, serving Alaska and the world. Find out more at   www.CascadiaLawAlaska.com The Irish & Celtic Music Podcast was produced by Marc Gunn, The Celtfather and our Patrons on Patreon. The show was edited by Mitchell Petersen with Graphics by Miranda Nelson Designs. Visit our website to follow the show. You'll find links to all of the artists played in this episode. Todd Wiley is the editor of the Celtic Music Magazine. Subscribe to get 34 Celtic MP3s for Free. Plus, you'll get 7 weekly news items about what's happening with Celtic music and culture online. Best of all, you will connect with your Celtic heritage. Please tell one friend about this podcast. Word of mouth is the absolute best way to support any creative endeavor. Clean energy is the single most powerful tool we have to fight climate change. Solar, wind, hydro  -  every kilowatt of clean power displaces the fossil fuels warming our planet. The big picture matters. So do the small choices you make every day. This week's tip comes from the 5 Rs of Sustainability: Refuse. Before you buy something new, ask yourself if you actually need it. Every item you don't buy is one that never had to be made, shipped, or eventually thrown away. Refusing is the most underrated act of sustainability there is. Start there. Your wallet and the planet will both thank you. Promote Celtic culture through music at http://celticmusicpodcast.com/. WELCOME THE IRISH & CELTIC MUSIC PODCAST * Helping you celebrate Celtic culture through music. I am Marc Gunn. I'm a Celtic musician and also host of Pub Songs & Stories. Every song has a story, every episode is a toast to Celtic and folk songwriters. Discover the stories behind the songs from the heart of the Celtic pub scene. This podcast is for fans of all kinds of Celtic music. We are here to build a diverse Celtic community and help the incredible artists who so generously share their music with you. If you hear music you love, please email the artists to let them know you heard them on the Irish & Celtic Music Podcast. These musicians are not part of some corporation. They are small indie groups that rely on people just like you to support their music so they can keep creating it. Please show your generosity. Buy a CD, Album Pin, Shirt, Digital Download, or join their community on Patreon. You can find a link to all of the artists in the shownotes, along with show times, when you visit our website at celticmusicpodcast.com. ALBUM PINS ARE CHANGING THE WAY WE HEAR CELTIC MUSIC Looking for a fresh way to support the music you love? Meet the Album Pin. Album Pins are lapel pins themed to a specific album — and each one comes with a digital download. Wear your music. All of my latest pins are wood - burned and locally produced, which means a smaller footprint and a one - of - a - kind feel you won't find anywhere else. Pick yours up at magerecords.com THANK YOU PATRONS OF THE PODCAST! Every episode of the Irish & Celtic Music Podcast exists because of you. Your support makes this possible, week after week, year after year. That is not a small thing. Your generosity covers real costs: audio engineering, graphic design, the Celtic Music Magazine, show promotion, and buying music directly from the independent Celtic artists we feature. You are the reason this music reaches new ears every single week. Not a patron yet? Here is what you are missing. Patrons get early access to episodes, music - only editions, free MP3 downloads, exclusive stories and artist interviews, and a vote in the Celtic Top 20. Join us today and help keep Celtic music alive, independent, and growing. Every single patron matters. Slainte! A special thanks to our Celtic Legends: Fuzzy, Dave and Rosie Donnelly, Rick Boyce, Bruce, Daniel Ide, Brian McReynolds, Marti Meyers, Alan Schindler, Margreta Silverstone, Emma Bartholomew, Dan mcDade, Jeff A, Gerald F Boyle, Miranda Nelson, Nancie Barnett, Gary R Hook, Lynda MacNeil, Kelly Garrod, Mike Schock, Shawn Cali HERE IS YOUR THREE STEP PLAN TO SUPPORT THE PODCAST Go to our Patreon page. Decide how much you want to pledge every month, $4, $12, $25. Keep listening to the Irish & Celtic Music Podcast to celebrate Celtic culture through music. You can become a generous Patron of the Podcast on Patreon at SongHenge.com. TRAVEL WITH CELTIC INVASION VACATIONS Every year, I take a small group of Celtic music fans on the relaxing adventure of a lifetime. We don't see everything. Instead, we stay in one area. We get to know the region through its culture, history, and legends. You can join us with an auditory and visual adventure through podcasts and videos. Learn more about the invasion at http://celticinvasion.com/ #celticmusic #irishmusic #celticmusicpodcast I WANT YOUR FEEDBACK What are you doing today while listening to the podcast? Send me a photo. If you're in a Celtic band, send me an audio recording of you performing live. Just audio. I'll use it in a podcast episode later this year. Email me at follow@bestcelticmusic. Asa Swain commented on Patreon: "I like hearing you talk, but thanks for releasing a "music only" version for everyone. I appreciate it." woodland folk replied to question, "how does the podcast make your life better?": I listen to ur podcast on my phone. on my closed fiddle case, mingled with birdsong, gentle hissing wood,the sun comes up early over the Mendips.the wind is still fresh... A battle of wills, my playlist rarely is enough....two tunes I play in the city right now I heard on one of my favourite episodes to date(man of the house),"the silver spear"& the blue idol.... I listened to this episode in a wood near the coast. maby five yrs ago, a deep cashcrop, scented pine. the needles leave a sponge rug moss covers old stumps & oaks, older by far than the rest of the wood that grow in crearings. deer whistle & bark in the night "home is were the heart is" & have on occasion gone back to listen again... The music u play suits the wood my friend..."  

Rogério Montanare, Thiago Siqueira e Central Pandora (Matheus e Sora) conversam sobre um gênero que lança todos os anos grandes filmes: ficção-científica. Dessa vez decidimos conversar sobre os melhores filmes lançados dos anos 2000 pra cá! Listinha? LISTONA! Vamos bater papo sobre os 30 melhores filmes de sci-fi do século 21!!! Quem é o rei do gênero: Christopher Nolan ou Denis Villeneuve? Quem escreve melhor que Alex Garland?Falamos sobre "Ela" (2013), "Distrito 9" (2009), "Planeta dos Macacos: O Confronto" (2014), "Wall-E" (2008), "Interestelar" (2014), "Brilho Eterno de uma Mente Sem Lembranças" (2004), "Mad Max: Estrada da Fúria" (2015), "Expresso do Amanhã" (2013), "Um Lugar Silencioso" (2018), "Filhos da Esperança" (2006) e muito mais!!|| ASSINE O SALA VIP DO RAPADURACAST- Escute um podcast EXCLUSIVO do RapaduraCast toda semana! http://patreon.com/rapaduracast

Sora, Donald, and Goofy are back in the Caribbean and simply along for the ride. Jack Sparrow died, everyone has a pirate-sona, and now there's a weird squid guy.

OpenAI woke up this week and chose violence.

Get your tickets to our L.A. live show here! After the smash success of ChatGPT, OpenAI positioned its video generation model Sora as AI's next consumer-friendly frontier. Disney signed on to the vision, promising a huge investment and allowing the studio's characters to appear in Sora videos. Then OpenAI abruptly shut Sora down. WSJ's Berber Jin takes us inside the pivot and explores what it means for the AI industry. Jessica Mendoza hosts. Further Listening: - OpenAI's 'Code Red' Problem - Is the AI Boom… a Bubble? - Artificial: The OpenAI Story Sign up for WSJ's free What's News newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

We love a ranking here on The Vergecast, and it's time for the hardest one yet: David and Nilay compare notes on the 50 best products Apple has ever made, and see how their answers stack up to the many, many voters on The Verge this week. Before that, though, it's time for a bit of AI news — surprise, it's enterprise software! — and the comeback of the Hype Desk. After all that, and after the rankings, we do a round of Brendan Carr is a Dummy, talk about the fediverse, and repurpose our old iMacs. Vote for The Vergecast in the Webby Awards! A vote for The Vergecast is a vote that Brendan Carr is a dummy, that buttons are good, and that party speakers rule the world. Voting is open until April 16.  ⁠https://vote.webbyawards.com/PublicVoting#/2026/podcasts/shows/technology⁠ Further reading: ⁠OpenAI's big numbers: $122 billion funding round, 900 million weekly ChatGPT users. ⁠ ⁠Why OpenAI killed Sora ⁠ ⁠I think Google is taking a couple digs at OpenAI about Sora. ⁠ ⁠Apple's third-party Siri Extensions could lead to an AI App Store. ⁠ ⁠Microsoft's new ‘superintelligence' game plan is all about business⁠ ⁠OpenAI acquires TBPN | OpenAI⁠ ⁠Apple turns 50: celebrating five decades of the tech giant ⁠ ⁠Everything is iPhone now ⁠ ⁠Steve Jobs and the greatest run of products in tech history ⁠ ⁠How the invention of QuickTime changed computers forever ⁠ ⁠The triumphs and failures of Apple without Steve Jobs ⁠ ⁠The Apple product that really changed the industry: the MacBook Air ⁠ ⁠Apple at 50: a visual history ⁠ ⁠The origin story of Apple's long-running relationship with Foxconn ⁠ ⁠Apple's long, bitter App Store antitrust war ⁠ ⁠Snazzy Labs' iMac - Studio Display Mod Guide⁠ ⁠Flipboard Surf launches social websites combining Bluesky, Mastodon, RSS, and more⁠ ⁠These Raspberry Pi price hikes are no joke ⁠ ⁠Today is the final day to save up to $150 on a PS5 before the price goes up ⁠ ⁠Sony temporarily suspends memory card sales due to shortages ⁠ ⁠The White House has an app now, and Trump wants you to report people to ICE on it ⁠ ⁠What's inside the White House app? ⁠ Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed.We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. Learn more about your ad choices. Visit podcastchoices.com/adchoices

This Week in AI, JCal sits down with three CEOs building the infrastructure, intelligence, and interfaces for the next era of AI: Jeremy Fraenkel (CEO, Fundamental), Victor Riparbelli (CEO, Synthesia), and Nick Harris (CEO, Lightmatter). We break down what's actually happening beneath the AI hype: the data modality LLMs completely missed, why copper is the real bottleneck in AI data centers, OpenAI shutting down Sora, the build vs. buy debate for AI tools, and how close we really are to AGI.AI's Biggest Blind Spot, Tabular Data: LLMs transformed text, images, and code, but 70-80% of enterprise data lives in rows and columns.Copper Can't Keep Up: Nick explains why AI data centers are hitting a wall. GPUs compute faster than they can communicate. Lightmatter's photonic chips push 1.6 terabits per fiber and can 3x training speed.Why OpenAI Killed Sora & Anthropic's Focus is Winning: Victor breaks down why even OpenAI had to learn the lesson of focus, and why Claude Code has every founder talking.Vibe Coding Your Own CRM vs. Buying Salesforce: Jeremy reveals Fundamental built their own internal CRM using vibe coding. The panel debates when building beats buying and when it's a distraction.The Omnipresent CEO: Jason shares how he's using AI agents for root access to Slack, Gmail, and Notion, resurrecting former employees as AI personas, automating SDR workflows, and summarizing employee inboxes while they're on vacation.Are We Already at AGI?: Nick says the rate of progress is a double exponential. Jeremy argues AGI is a moving goalpost. Victor warns of "Future Shock" and societal disruption.

You better lean in before a 25 year old beats you to the punch! This week we're talking about developments in Sheryl Sandberg's business, Diner Goths, Sophie Rain, and more! 13 min: Sheryl Sandberg 23 min: Alpine Divorce 30 min: Diner Goths  40 min: RIP Sora AI  49 min: Who is Sophie Rain  65 min: Caps Off ___________________________________ Keep up with all the latest: https://www.goodnoticings.com/ Read our many musings on Substack: https://goodnoticings.substack.com/ Join the Patreon for new, exclusive episodes every Friday! https://www.patreon.com/c/goodnoticings Follow us on: TikTok- @goodnoticingspod Instagram- @goodnoticingspod Theme song by: Bri Connelly  ___________________________________ SORA:  https://www.theverge.com/ai-artificial-intelligence/902368/openai-sora-dead-ai-video-generation-competition https://techcrunch.com/2026/03/29/soras-shutdown-could-be-a-reality-check-moment-for-ai-video/ Alpine Divorce:  https://www.theguardian.com/lifeandstyle/ng-interactive/2026/mar/17/alpine-divorce-abandoned-hiking-trail Diner Goths:  https://www.thenewatlantis.com/publications/american-diner-gothic Sophie Rain:  https://www.gq.com/story/sophie-rain-profile Learn more about your ad choices. Visit podcastchoices.com/adchoices

Luke finally heard from the court about the erroneous parking ticket he received. He's somewhat satisfied with the results. He and Andrew also discuss the end of the A.I. app called Sora, which could let anyone make convincing deep fakes depicting real people. And, speaking of robots, Luke is ready for them to fully take over at least one aspect of Major League Baseball.

OpenAI shocked many last week with its decision to shutter its video generation app Sora. WSJ reporter Berber Jin joins us for an exclusive look behind the scenes of the decision. Plus, at the WSJ Leadership Institute's recent Chief People Officer Summit, IBM's HR chief explained the company's plan to hire more entry-level workers in a move to prioritize growth, widely contrasting with other companies which look to reduce headcount amid the AI boom. Julie Chang hosts. Sign up for the WSJ's free Technology newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

OpenAI is shutting down its video generator Sora less than six months after it launched, and just three months since it signed a deal with Disney. Is this an A.I. company fine tuning its offerings, or the long-awaited popping of the A.I. bubble?Guest: Jason Koebler, cofounder of 404 Media.Want more What Next? Subscribe to Slate Plus to access ad-free listening to the whole What Next family and across all your favorite Slate podcasts. Subscribe today on Apple Podcasts by clicking “Try Free” at the top of our show page. Sign up now at slate.com/whatnextplus to get access wherever you listen.Podcast production by Elena Schwartz, Paige Osburn, Anna Phillips, Madeline Ducharme, and Rob Gunther. Hosted on Acast. See acast.com/privacy for more information.

P.M. Edition for Mar. 30. The Labor Department proposed a new rule that would make it easier to invest in private markets through 401(k)s. It comes as investors pull money from some private-credit funds. WSJ retirement reporter Anne Tergesen explains the risks. Plus, last year OpenAI hyped up its new AI video product, Sora. So why did it abruptly pull the plug last week? WSJ tech reporter Berber Jin tells us. And the CEO of Air Canada is stepping down after he offered condolences for the LaGuardia Airport crash in English and not in French. Alex Ossola hosts. Sign up for the WSJ's free What's News newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

OpenAI is shutting down its video generator Sora less than six months after it launched, and just three months since it signed a deal with Disney. Is this an A.I. company fine tuning its offerings, or the long-awaited popping of the A.I. bubble?Guest: Jason Koebler, cofounder of 404 Media.Want more What Next? Subscribe to Slate Plus to access ad-free listening to the whole What Next family and across all your favorite Slate podcasts. Subscribe today on Apple Podcasts by clicking “Try Free” at the top of our show page. Sign up now at slate.com/whatnextplus to get access wherever you listen.Podcast production by Elena Schwartz, Paige Osburn, Anna Phillips, Madeline Ducharme, and Rob Gunther. Hosted on Acast. See acast.com/privacy for more information.

This week: Just minutes before Trump posted about talks with Iran, oil markets saw a flurry of activity. Conspiracy theories followed. Felix Salmon, Elizabeth Spiers, and Emily Peck dissect the suspicious timing of those trades and the possibility of insider trading within the Trump administration. Then, the hosts react to the surprising ruling on Meta and social media addiction. And: OpenAI's sudden decision to shut down its consumer-facing video generation platform, Sora. In the Slate Plus episode: The treasury market rom-comWant to hear that discussion and hear more Slate Money? Join Slate Plus to unlock weekly bonus episodes. Plus, you'll access ad-free listening across all your favorite Slate podcasts. You can subscribe directly from the Slate Money show page on Apple Podcasts and Spotify. Or, visit slate.com/moneyplus to get access wherever you listen. Podcast production by Jessamine Molli. Hosted on Acast. See acast.com/privacy for more information.

This week on PREVIOUSLY ON…, Jason shares his review of Project Hail Mary. Then he and Rosie break down the news that Wonder Man has been greenlit for a second season, with Yahya Abdul-Mateen and Ben Kingsley set to return, making it only the third live-action Marvel TV series to receive an additional season, alongside Loki and Daredevil: Born Again. Next, they discuss the recent announcement that Disney is backing out of a proposed $1B deal with OpenAI as the company shuts down Sora, its standalone video generation app. Finally, they wrap up with news that Sony is raising prices across the entire PS5 product line amid economic uncertainty and rising RAM and memory costs, as well as Netflix once again increasing prices across all of its subscription plans. Follow Jason: IG & Bluesky Follow Rosie: IG & Letterboxd Follow X-Ray Vision on Instagram Join the X-Ray Vision DiscordSee omnystudio.com/listener for privacy information.

It's Indicators of the Week (now on YouTube!). It's our weekly look at some of the most fascinating economic numbers from the news. On today's episode: The US ain't doing too hot in attracting European tech workers; OpenAI takes its video generator Sora behind the barn; and a rapper, pound cake, and the police. Related episodes: OpenAI's deals are looking a little frothy We're about to lose a lot of foreign STEM workers For sponsor-free episodes of The Indicator from Planet Money, subscribe to Planet Money+ via Apple Podcasts or at plus.npr.org. Fact-checking by Julia Ritchey and Vito Emanuel. Music by Drop Electric. Find us: TikTok, Instagram, Facebook, Newsletter.  To manage podcast ad preferences, review the links below:See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

Meta fined $375M for child safety failures. Musk lost 3 lawsuits in a week. Sam Altman compared to a Nazi. Netflix raised prices again. The Pentagon can't quit Claude. Reddit wants your face scan. Star Trek's streaming era is over. But the thin black line holds!

Kara and Scott unpack the Trump administration stacking an AI council with Big Tech names, the market-moving chaos around shifting Iran statements, and surprising Democratic wins in Florida — including in Trump's own backyard. Then, the TSA mess continues, Meta and YouTube are found liable in landmark social media addiction cases, and OpenAI calls it quits on Sora, just as Scott predicted. Watch this episode on the ⁠⁠Pivot YouTube channel⁠⁠.Follow us on Instagram and Threads at ⁠⁠@pivotpodcastofficial⁠⁠.Follow us on Bluesky at ⁠⁠@pivotpod.bsky.social⁠⁠Follow us on TikTok at ⁠⁠@pivotpodcast⁠⁠.Send us your questions by calling us at 855-51-PIVOT, or email pivot@voxmedia.com Learn more about your ad choices. Visit podcastchoices.com/adchoices

We start with some important business: Nilay has a flight to catch, and is very worried he won't catch it. Also, it's Apple's 50th anniversary next week, and we're going to spend the week debating which Apple products are the best Apple products. (Head to the ad-free Vergecast feed to hear our selection show!) But mostly, this episode is about social media. In two key trials this week, juries found social platforms liable not for the content they display but for the actual structure and features of the platform. That could change the way social media companies act, and how users fight back. After that, it's time for the silliness of the router ban, the latest in the chatbot wars, and an update on what's happening with Grammarly's Expert Voices feature. Further reading: Rank your top 50 Apple products Verge subscribers, here's how to set up ad-free podcasts  The TSA is broken — is privatization next?  What is ICE actually doing at American airports?  Meta misled users about its products' safety, jury decides  Meta and YouTube found negligent in landmark social media addiction case  Social media on trial: tech giants face lawsuits over addiction, safety, and mental health What it was like to watch grieving parents stare down Mark Zuckerberg in court  A bombshell child safety leak changed Meta — for the worse  Internal chats show how social media companies discussed teen engagement  2026 is the year of social media's legal reckoning  The US government just banned consumer routers made outside the US  The United States router ban, explained FCC green-lights Nexstar's $6.2B merger with rival TV station owner Tegna Cox Communications not liable for pirated music, Supreme Court rules  Confronting the CEO of the AI company that impersonated me  North Carolina man pleads guilty to AI music streaming fraud.  Apple is testing a standalone app for its overhauled Siri  OpenAI is planning a desktop ‘superapp'  This is Microsoft's plan to fix Windows 11  OpenAI just gave up on Sora and its billion-dollar Disney deal The age of piracy ended with LimeWire | Version History Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed.We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this edition of You Trend Do That On TV, Jack and Miles discuss the numerous special elections, the "MAGA activist" who pushed 2020 election fraud claims getting busted for election fraud, Open AI shuttering Sora, an update on Trump & Bibi's war with Iran and much more!See omnystudio.com/listener for privacy information.

OpenAI is shutting down Sora and its video generation models to focus on enterprise customers and coding. Meanwhile, Coinbase and Circle are crashing as congress considers a bill that could eliminate stablecoin rewards. The irony is, Coinbase could be more profitable without rewards.Travis Hoium, Lou Whiteman, and Rachel Warren discuss:- Sora is shutting down- Stablecoins in congress- Amazon's latest robot acquisitionsCompanies discussed: Disney (DIS), Coinbase (COIN), Circle (CRCL), Amazon (AMZN).Host: Travis HoiumGuests: Lou Whiteman, Rachel WarrenEngineer: Kristi WaterworthAdvertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠megaphone.fm/adchoices⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices