Podcasts about Io

🧭 REBEL Rundown Click here for Direct Download of the Podcast. 💨 What Is Nitrous Oxide? Nitrous Oxide (N2O) is a colorless, odorless inhaled anesthetic that has been used for centuries, particularly in the surgical world. Mechanistically, it can induce euphoria, anxiolysis, and intoxication via NMDA receptor antagonism.During the late twentieth century, nitrous oxide was increasingly used recreationally due its accessibility and perceived benign nature.The modern day slang term for nitrous oxide is “whippets” – which tends to refer to the canisters that contain this agent and are frequently used as whipped cream foaming agents.Despite the legal nature and benign perception of nitrous, frequent use can lead to lasting and permanent neurologic effects. 🧠 How Nitrous Oxide Causes Toxicity Nitrous oxide toxicity results from its ability to oxidize the cobalt moiety in Vitamin-B12, thus leading to a functional B12 deficiency, despite adequate consumption and absorption.1Functioning B12 is needed as a cofactor for methionine synthase.2 This enzyme has two critical roles:The conversion of 5-methyl tetrahydrofolate to tetrahydrofolate; tetrahydrofolate is essential for the synthesis of our DNA.And the conversion of homocysteine to methionine; methionine is needed to maintain the integrity of the myelin sheath of our axons.As a result, nitrous toxicity leads to: a megaloblastic anemia and demyelination of both the dorsal columns and the lateral corticospinal tracts (also known as subacute combined degeneration). 🚶‍️ Clinical Manifestations of Nitrous Oxide Toxicity These patients will have a combination of both upper and lower motor neuron symptoms due to demyelination of the dorsal columns, lateral corticospinal tracts, and peripheral nerves. As a result, the following may manifest:Dorsal Columns: diminished sense of proprioception, vibration, and fine touch.Lateral Corticospinal Tracts: upgoing plantars, hyperreflexia, weakness of voluntary distal muscle controlPeripheral Nerves: numbness/tingling and weakness in a glove and stocking pattern (symptoms that start initially in the feet and hands that progressively spread proximally to the ankles and wrists)Taking all of this into account, patients may present with difficulty ambulating, positive Romberg sign, dysmetria (difficulty with finger to nose or heel to shin), upgoing Babinski reflex, and decreased strength and sensation in a glove and stocking pattern. 🔍 How to Diagnose Nitrous Oxide Neurotoxicity History is key! As with a lot of pathologies in toxicology, identifying the exposure will expedite management.A thorough neurologic exam will narrow the differential – with a particular focus to fine, peripheral motor and sensory deficits, dysmetria, proprioception, and ability to ambulate.Magnetic resonance imaging of the spine may identify enhancement and/or edema of the dorsal columns, specifically on T2 weight axial imaging – sometimes referred to as the “inverted V” or “inverted rabbit ears appearance.”3Serum B12 concentrations may be normal as the issue is with a functional deficiency as opposed to a vitamin absence. However, patients have elevated concentrations of both homocysteine and methylmalonic acid, both of which are metabolized in the presence of functional B12. 💉 Management of Nitrous Oxide Toxicity First and foremost, cessation of nitrous oxide abuse is crucial to limit/prevent toxicity.While there is no universally agreed upon treatment regimen, supplementation with intramuscular B12 is recommended.Approaches vary from daily or every other day injections until symptoms improve at which point injections can be spaced out to weekly and then monthly.Physical and occupational therapy may be needed depending on the degree of functional debility.It is important to note, that depending of the severity and chronicity of toxicity, some proportion of patients may not fully return to their baseline. 📌 Take-Home Points Though legal and seemingly benign, nitrous oxide abuse can lead to permanent neurologic dysfunction.Nitrous oxide toxicity can affect the dorsal columns, lateral corticospinal tracts, and peripheral nerves.Thus leading to a constellation of both upper and lower motor neuron deficits, particular in a glove and stocking pattern: deficits in proprioception and fine motor skills, positive Romberg, upgoing Babinski, peripheral numbness, tingling, and weakness.Magnetic resonance imaging may identify symmetric high signal intensity in the dorsal columns.Treatment includes B12 supplementation and physical/occupational therapy as needed. 📚 References Long H. Chapter 81. Inhalants. In: Nelson LS, et al. Goldfrank’s Toxicologic Emergencies. 11th ed. New York: McGraw-Hill; 2019Shah K, Murphy C. Nitrous Oxide Toxicity: Case Files of the Carolinas Medical Center Medical Toxicology Fellowship. J Med Toxicol. 2019 Oct;15(4):299-303. doi: 10.1007/s13181-019-00726-x. Epub 2019 Aug 6. PMID: 31388940; PMCID: PMC6825085.Schmitz ZP, Hoffman RS. Magnetic resonance imaging in a patient with nitrous oxide-induced subacute combined degeneration of the spinal cord. Clin Toxicol (Phila). 2023 Nov;61(11):1006-1008. doi: 10.1080/15563650.2023.2286205. Epub 2023 Dec 19. PMID: 38060330. Post Peer Reviewed By: Marco Propersi, DO (Twitter/X: @Marco_propersi), and Mark Ramzy, DO (X: @MRamzyDO) 👤 Associate Editor Anand Swaminathan MD, MPH All Things REBEL EM Meet The Team 🔎 Your Deep-Dive Starts Here REBEL Core Cast – Pediatric Respiratory Emergencies: Beyond Viral Season Welcome to the Rebel Core Content Blog, where we delve ... Pediatrics Read More REBEL Core Cast 143.0–Ventilators Part 3: Oxygenation & Ventilation — Mastering the Balance on the Ventilator When you take the airway, you take the wheel and ... Thoracic and Respiratory Read More REBEL Core Cast 142.0–Ventilators Part 2: Simplifying Mechanical Ventilation – Most Common Ventilator Modes Mechanical ventilation can feel overwhelming, especially when faced with a ... Thoracic and Respiratory Read More REBEL Core Cast 141.0–Ventilators Part 1: Simplifying Mechanical Ventilation — Types of Breathes For many medical residents, the ICU can feel like stepping ... Thoracic and Respiratory Read More REBEL Core Cast 140.0: The Power and Limitations of Intraosseous Lines in Emergency Medicine The sicker the patient, the more likely an IO line ... Procedures and Skills Read More REBEL Core Cast 139.0: Pneumothorax Decompression On this episode of the Rebel Core Cast, Swami takes ... Procedures and Skills Read More Showing Slide 1 of 7 The post REBEL Core Cast—Nitrous Oxide Toxicity: Whippets and Neurologic Injury appeared first on REBEL EM - Emergency Medicine Blog.

It’s that time of the year when JOY asks for your support to remain being out, loud and proud – JOY Radiothon. This year, we’re mixing it up with JOY by combining JOYEurovision and babblePOP! to bring back babbleVISION! Michael and Io play some Eurovision classics alongside new bops and bangers in the same languages to help you celebrate with JOY. Io’s enjoying the World Cup, while Michael’s shopping for a pirate shirt. And we’re looking forward to inviting (soon to be) famous drag queen Roy Jadiothon to JOY for next year’s Radiothon spectacular. Get involved You can show your support during JOY Radiothon by becoming a member or donating at joy.org.au/radiothon Follow JOYEurovision across Facebook, Instagram, Threads, TikTok, Bluesky and X at linktr.ee/joy_eurovision Playlist Spanish Eurovision 2007: D’Nash ️‍ – I Love You Mi Vida        Brand-new babble: Lucenzo – Limoncello        Danish Eurovision 1963: Grethe & Jørgen Ingmann – Dansevise [Dance song]        Brand-new babble: Katinka – På Tværs [Across]        Finnish Eurovision 1983: Ami Aspelund – Fantasiaa [Fantasy]        Brand-new babble: Benjamin ️‍ – Badabim (My Kind of Terapiaa)        Polish Eurovision 1995: Justyna – Sama [Alone]        Brand-new babble: Lor – obcy (1979) [alien]        Italian Eurovision 1977: Mia Martini – Libera [Free]        Brand-new babble: Orietta Berti ft il rosso & IAEM – QUADRI CUORI PICCHE FIORI [DIAMONDS HEARTS SPADES CLUBS]         The post JOY Radiothon 2026: Celebrate with JOY (babbleVISION pt 2) appeared first on JOY Eurovision.

Patrick Moorhead and Daniel Newman cover Tim Cook's final WWDC as CEO and Apple's Gemini-powered Siri strategy, the $35 billion Apollo and Blackstone deal backing Anthropic's capacity expansion, Intel's packaging wins with Google and NVIDIA, SpaceX's IPO at a $1.77 trillion valuation, Anthropic's Claude Fable 5 and Mythos 5 launch across every major cloud, and earnings reactions from Oracle, Micron, and Adobe. The handpicked topics for this week are: Apple's Siri AI Will Run on Gemini, Closing Out Tim Cook's Final WWDC as CEO: At WWDC, Apple confirmed Siri AI will run on Gemini through a new billion-dollar per year, multi-year deal, while Apple's Foundation Model Cloud Pro runs on NVIDIA GPUs inside Google Cloud. The announcement marks Tim Cook's last WWDC as CEO before John Ternus takes over on September 1. Apple isn't building its own AI cluster or competing on CapEx. They're betting that by owning the consumption layer, backed by access to health data and private messaging through iMessage, Apple will have a moat that compute spending can't replicate. (The Decode) Apollo and Blackstone Close the Largest Private Credit Deal Ever Backing Anthropic's Capacity Expansion: A $35 billion deal, the largest private credit transaction on record, will fund Google TPU capacity tied to Anthropic's compute needs, with Broadcom backstopping senior debt tranches and Google backstopping lease payments. The structure treats compute as a lendable asset class and signals more than 20 gigawatts of demand still being built out through 2028. Circular financing between chipmakers, cloud providers, and AI labs has moved from controversial to standard practice. (The Decode) Intel's Foundry Wins Packaging Work on Google's TPUs, Not a Full Fab Deal: Reports that Intel landed a deal tied to Google and NVIDIA reframe what's actually being handed off. Intel gets the packaging work on over 3 million TPUs, the compute die stays with TSMC, and the I/O die is being negotiated with Samsung at 2nm. INTC rose 12% Monday. The deal represents a low-risk path for Intel to augment, not replace, TSMC, while raising questions about anti-competitive dynamics in the foundry market. (The Decode) SpaceX Becomes an AI Infrastructure Company With a $1.77 Trillion IPO: SpaceX's IPO priced amid oversubscribed demand, with its valuation now reflecting not just Starlink connectivity and launch dominance but a newly material AI business, including AI1 orbital data center tests planned for late 2027 and a $920 million per month Google compute contract running through 2029. A sum-of-the-parts breakdown of the connectivity, launch, and AI segments lands well short of the trading price, with the gap largely explained by confidence in Elon Musk's track record of execution. (The Decode) Anthropic Launches Claude Fable 5 and Mythos 5 Across Every Major Cloud: Anthropic shipped Claude Fable 5 and Mythos 5 with same-day availability across Snowflake, AWS Bedrock, Vertex AI, and Microsoft Foundry, pricing at $10 and $50 per million tokens. The hyperscaler-neutral distribution strategy lands ahead of Anthropic's anticipated IPO. The models represent a real step up in research capability over Opus 4.8, but they come with a significant change. Users no longer have the option to opt out of data sharing with Anthropic, a shift some enterprises, including Microsoft, are already responding to. (The Decode) Is SpaceX a Once-in-a-Generation Entry or the Top of the Market? One side argues SpaceX represents a generational opportunity on par with early Amazon or Netflix, with interplanetary travel and off-world resource extraction as the long-term payoff that justifies looking past current valuation math. The other side argues this is peak euphoria: a company trading at roughly 95 times sales, propped up in part by circular investment from Google into both SpaceX and its AI segment, with a steep drawdown likely before any sustained climb. (The Flip) The Chip and Security Trade Reverses From Broken to Bifurcated: The semiconductor sector posted its biggest single-day gain since 2020, with the SOX up 5% on Monday, June 8, as a prior selloff in names like Broadcom, CrowdStrike, and Palo Alto Networks fully reversed. Intel rose 12%, Marvell 10%, and Corning 7%. The rebound reframes the AI trade narrative from a broad breakdown to a split between winners and laggards within the same sector. (Bulls & Bears) Oracle Posts a Record Quarter, But the Market Focuses on a $50 Billion Funding Plan: Oracle delivered record revenue of $19.2 billion, up 21 %, with EPS of $2.11, beating estimates of $1.89. IaaS grew 93 %, the fastest pace among hyperscalers, and RPO hit $638 billion, up $85 billion quarter over quarter, including $75 billion in AI contracts. FY27 guidance of $90 billion was maintained, and EPS guidance was raised, yet the stock fell 5% after hours amid concerns about Oracle's capital spending plans. Oracle's AI cloud backlog now exceeds those of AWS, Google, and Microsoft, built heavily on commitments from Anthropic and OpenAI. (Bulls & Bears) Micron's Profit Trajectory Puts It in Google's Earnings Tier: Micron is projected to generate nearly as much profit in 2027 as Google, with Q2 revenue of $23.86 billion, up 22 % and beating estimates, and Q3 guidance of $33.5 billion in revenue, $19.15 EPS, and 81 % gross margin. The stock is up 776%, with Wall Street firms, including UBS, raising price targets. The open question is whether memory has broken its historically cyclical pattern given sustained AI demand. (Bulls & Bears) Adobe Beats Across the Board, But the Stock Drops on CEO Departure and Freemium Pivot: Adobe posted record revenue of $6.62 billion, up 13 % and beating consensus of $6.45 billion, with non-GAAP EPS of $5.96, topping estimates of $5.81. AI first ARR tripled year over year to over $500 million, with total ARR reaching $27.1 billion, and FY26 guidance was raised. The stock still fell 5.5 % after hours, driven by the CFO's departure to Marvell and market concern over a strategic shift toward freemium pricing that delays near-term profitability. (Bulls & Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. The Decode Apple WWDC- Apple Caves to Google AND NVIDIA — Siri AI Runs on Gemini ($1B/yr) + Apple Foundation Model Cloud Pro Runs on NVIDIA GPUs in Google Cloud; Tim Cook's Final WWDC as CEO Before John Ternus Succeeds Him Sept 1 https://www.cnbc.com/2026/06/08/apple-wwdc-2026-live-updates.html Google's $35B Infra Deal — Apollo + Blackstone Close the Largest Private Credit Deal Ever; Broadcom Backstops Senior Tranches; Google Backstops Lease Payments https://www.reuters.com/business/apollo-blackstone-back-anthropics-35-billion-capacity-expansion-new-broadcom-tie-2026-06-09/ Intel's Foundry Reportedly Wins Google Packaging (Not Full Fab) — The Information Reframed: 3M+ TPU Packaging by Intel, Compute Die Still TSMC, I/O Die Being Negotiated With Samsung 2nm; INTC +12% Monday; Pat Calls Out TSMC Anti-Competitive Risk https://www.trendforce.com/news/2026/06/09/news-intel-foundry-gains-momentum-as-google-reportedly-orders-3m-tpus-nvidia-evaluates-18a-for-multi-die-gpu-design/ SpaceX Becomes an AI Infrastructure Company — Friday IPO at $1.77T; AI1 Orbital Data Center Tests Late 2027; Google $920M/mo Compute Contract Through 2029 https://finance.yahoo.com/markets/stocks/articles/spacex-poised-history-record-75-100000402.html Anthropic Ships Claude Fable 5 + Mythos 5 — Same-Day Distribution Across Snowflake, AWS Bedrock, Vertex AI, Microsoft Foundry; Hyperscaler-Neutral by Design Ahead of IPO; $10/$50 per M Tokens https://www.anthropic.com/news/claude-fable-5-mythos-5 The Flip FOR: https://www.cnbc.com/2026/06/11/spacex-billionaire-investing.html AGAINST: https://www.nytimes.com/2026/05/20/technology/elon-musk-spacex-ipo.html Bulls & Bears The Chip + Security Tape Recovery — SOX +5% Monday June 8 (Biggest Day Since 2020); AVGO/CRWD/PANW Selloff Reversed; Intel +12%, Marvell +10%, Corning +7%; the AI Trade Pivots From "Broken" to "Bifurcated" https://www.investopedia.com/stock-market-today-dow-jones-s-and-p-500-06082026-11992852 Oracle (ORCL) Q4 FY26 ACTUALS — Record $19.2B Rev (+21%), EPS $2.11 Beat ($1.89); IaaS +93%; RPO HITS $638B (+$85B QoQ, $75B AI Contracts); FY27 $90B Guide Maintained, EPS Guide Raised; Stock −5% AH on Massive Capex Plan https://www.tradingkey.com/analysis/stocks/us-stocks/261959450-oracle-record-q4-2026-earnings-report-cloud-data-center-stock-tradingkey "$MU Will Generate Almost As Much Profit in 2027 as $GOOGL"; Q2 Rev $23.86B (+22% Beat), Q3 Guide $33.50B / $19.15 EPS / 81% GM; MU Stock +776%; UBS Among Wall Street Raising Targets https://247wallst.com/investing/2026/06/11/wall-street-just-put-a-monster-target-on-micron-is-the-stock-still-too-cheap/ Adobe (ADBE) Q2 FY26 ACTUALS — Record $6.62B Rev (+13%) Beats Consensus $6.45B; Non-GAAP EPS $5.96 Beats $5.81; AI-First ARR Triples YoY to $500M+; Total ARR $27.10B; FY26 Guide RAISED; Stock −5.5% AH Despite Beat-and-Raise https://www.businesswire.com/news/home/20260611677110/en/Adobe-Reports-Record-Q2-Results    

Beth GMs for Ellie, Crash, Io, and Paul. This episode: The Technicalities follow the pipes towards a supposed destination and learn things along the way. Follow this series on… RSS: https://aaronbsmith.com/cogwheel/tag/gurpswars/podcast Patreon: https://www.patreon.com/cogwheelgaming Mastodon: https://is.aaronbsmith.com/@cogwheel Not on Mastodon? Consider these instances: gamepad.club dice.camp mastodon.art chirp.enworld.org tabletop.vip MP3 Download: GURPS Wars: Technicalities S1 Ep 09: A Series of Tubes Music Used: “biotech” by Kokesz is Public Domain and can be downloaded from http://modarchive.org. Keep us ad free by supporting us on Patreon! Thanks to our current Patreon Patrons (as of this upload…): Ellie, Liv Dromen, Paul, ShanShen, Walter, & Patron Emeritus Cindy!

Oggi Ilaria ci accompagna nella lettura di Io sono di legno di Giulia Carcasi, un romanzo intenso che affronta temi profondi come il dolore, la perdita, la fragilità e la capacità di andare avanti.Una riflessione delicata su come la sofferenza non si cancelli, ma si attraversi, diventando parte del nostro percorso di vita.Scopri di più su https://www.radiosoffio.it

It’s that time of the year when JOY asks for your support to remain being out, loud and proud – JOY Radiothon. This year, we’re mixing it up with JOY by combining JOYEurovision and babblePOP! to bring back babbleVISION! Michael and Io play some Eurovision classics alongside new bops and bangers in the same languages to help you sing with JOY. Get involved You can show your support during JOY Radiothon by becoming a member or donating at joy.org.au/radiothon Follow JOYEurovision across Facebook, Instagram, Threads, TikTok, Bluesky and X at linktr.ee/joy_eurovision Playlist Croatian Eurovision 1999: Doris Dragović – Marija Magdalena [Mary Magadelene]        Brand-new babble: Detour – Pusti me da spavam [Let me sleep]        French Eurovision 1956: Dany Dauberson ️‍ – Il Est Là [He is Here] Brand-new babble: kissed – reviens me voir [come back and see me]        Icelandic Eurovision 1994: Sigga ️‍ – Nætur [Nature]        Brand-new babble: Tatjana – Háð þér [Depends on you]        Slovenian Eurovision 2002: Sestre ️‍ – Samo ljubezen [Only love]        Brand-new babble: Damjan Murko – Moj Mali Ku… [My Little Pup…]        Turkish Eurovision 1980: Ajda Pekkan – Pet’r Oil [Petrol]        Brand-new babble: manifest & Ajda Pekkan – Hileli [Fraud]        The post JOY Radiothon 2026: Sing with JOY (babbleVISION pt 1) appeared first on JOY Eurovision.

La compassione è segno evidente di amore; si soffre per chi e con chi si ama. Gesù oggi posa il suo sguardo sulla folla che lo segue e il suo spirito di Uomo-Dio si muove a compassione per loro perché egli vede quella gente come pecore senza pastore. Il suo sguardo va oltre il tempo e oltre i confini dello spazio. È urgente per loro, per tutti, che abbiano guide sicure, pastori sapienti e santi. Egli ha detto di se stesso: “Io sono la via”, “Io sono il buon pastore”; si è detto disposto a dare la vita per le sue pecorelle, si è messo alla ricerca della pecora smarrita e tutte le ha difese dagli assalti dei lupi. La sua presenza nel mondo è però limitata nel tempo; vuole perciò assicurare un prolungamento senza fine del suo annuncio di salvezza e ha quindi urgente bisogno di ottimi pastori da inviare in sua vece e nel suo nome per quella folla e per tutte le genti di tutti i tempi. Gesù convoca a sé i dodici e dà loro poteri speciali, gli stessi che egli esercita durante il suo peregrinare nel mondo: scacciare i demoni e guarire ogni genere di infermità. Quindi l’evangelista Matteo elenca i nomi dei dodici; sono i primi di una serie interminabile e meritano questa citazione speciale. Loro si muovono sulle orme di Cristo e tutti gli altri che seguiranno percorreranno le stesse orme, compiranno gli stessi prodigi, annunceranno lo stesso Vangelo. Ha così origine la schiera dei missionari e così nasce la Chiesa missionaria. Cristo continua, nei suoi ministri, a essere presente e vivo nelle strade del mondo. L’annuncio del Regno ha ormai la sua continuità. La loro missione gradualmente si aprirà al mondo intero finché, in ogni angolo del mondo, non sarà udito il messaggio della redenzione. Strada facendo devono dare un annuncio essenziale di salvezza: “Il Regno di Dio è vicino”. Le pecore smarrite e senza pastori troveranno così le loro guide, i dispersi potranno tornare all’ovile, i malati potranno recuperare la salute e i peccatori potranno sperimentare il dono della misericordia, gli affamati potranno saziarsi del pane di vita. Cristo si affida alla fragilità degli uomini; potrà quindi accadere che talvolta gli stessi pastori rischieranno momentaneamente di smarrirsi e di cedere alla tentazione di avventurarsi in pascoli non buoni, ma il Signore si è fatto garante per tutti loro: egli è disposto a cercare non solo la pecora smarrita, ma anche i pastori, anche quelli che si renderanno indegni del loro mandato. Lo ha dichiarato esplicitamente: “Io sarò con voi sempre, sino alla fine dei tempi”.

ぬるぽ放送局おたより投稿フォーム https://forms.gle/6tbmBzK6wbyavJG47 2026年6月パワープレイ 「Phantasmagoria mystical expectation」 アレンジ・ギター・ベース　ARM ボーカル　悠 杏李 作詞　kiku　夕野ヨシミ 原曲：風神少女 音楽ジャンル：ミクスチャーポップ 収録アルバム：東方風櫻宴 2006・5・21 Release https://www.iosysos.com/discographyportal.php?cdno=IO-0090 https://www.youtube.com/watch?v=fOmaLZDp3y0 番組時間：86分22秒 出演者：夕野ヨシミ、たくや VOICEVOX:ずんだもん VOICEVOX:四国めたん ---- 2026/6/11に公開録音したものを配信いたします。 ラジオ記事はリスナーのEEチャンピオンさんが書いてくれているので楽してます。 ＜オープニング＞ ・札幌も夏が始まりました ・外は、暑いんでしょうね ・喉の肉離れ ・VDONinjaの調子が悪い ・今日はアイドリングがないから事故っちゃう ・ポッドキャストの人は待ってないよ ・イオシスくんの活動をあれしますか ・かつ丼と活動って似てますよね ・＜楽曲提供＞ 　カバー楽曲 　「天ノ弱」／ドラゴンブラッド：スレイヤーズ学院 　歌唱：花たん 　作詞・作曲：164 　編曲：コバヤシユウヤ(IOSYS) 　ギター：三浦公紀 　ベース：john=hive(IOSYS) ・ドラゴンブラッドを始めるなら今！ ・正解はじゃがポックル ・じゃがポおじさん ・楽曲提供のお知らせ 　「私たちは、花になる／イロドリミドリ｜HaNaMiNa｜S.S.L.」 　作詞：七条レタス 　作曲：D.watt 　編曲：fu_mou(Hifumi,inc.) ・楽曲提供のお知らせ 　「きゅんキラ☆ネバギバ行進曲／あぴゃりちゃん」 　作編曲：コバヤシユウヤ 　作詞：john=hive 　Guitar：三浦公紀 ・トピックチャンネルとは ・やはり、かわいいキャラは必要 ・自由の女神を女性枠ととらえるとは ・ガワだけのwiki ・追加されたよ 　Nintendo Switch『グルーヴコースター フューチャーパフォーマーズ』 　2026/6/11 無料アップデート 　「HG魔改造ポリビニル少年」 　作詞・作編曲：IOSYS TRAX 　歌：さきぴょ ・YouTubeタイトーチャンネルにて試聴動画が公開されました 　「DX超性能フルメタル少女」 　作詞・作編曲：IOSYS TRAX 　歌：ちよこ 　「HG魔改造ポリビニル少年」 　作詞・作編曲：IOSYS TRAX 　歌：さきぴょ ・もう、12,3年前 ・アメリカニキは現金を持ってきてください ・​ありったけのキャッシュをかき集め ・何をやります？ ・1分将棋を盤面もなく初心者が？ ・歩が8枚集まってキング歩 ・マイクラ将棋 ・ムダ話を雑談力って言いました？ ・新日本将棋連盟作ろう ＜Aパート＞ ・ふつおたです ・歯医者で引き分け ・ぬるぽもギネスいけるのでは？ ・急なニンテンドーダイレクト ・強引に同意を求める ・ビールおかわりした直後にワインを飲む ・生ビール放送 ・東方projectすげーな ・ニュークラだとキャバクラになっちゃうな ・ぴっちりした服はみんな好きだから ・歯って欠けませんか？ ・吉野家がタッチパネルに ・梅干しとチーズと炭酸水しかない冷蔵庫 ・え？ネットスーパーで2万も？何を？ ・ウイスキーは普通1本で済むから ・お便り1通で何分やってるのか ・ホラー映画をご所望 ・ミーガン ・女性Vならホラーゲームは映えますよね ・英語のタイトルなら自信がない ・東方アレンジっぽい単語を組み合わせる ・穴からは離れてほしい ・マスパ音頭はありそう ・バニーガーデンを買ってしまいました ・重い過去のキャラに定評のあるキュリエイトさん ・今日は漫才をやりますか ・そのお店がグレーだったとしても？ ・片玉から紹介されました ・バター犬牧場ってなんだよ ＜Bパート＞ ・みつをたです ・水道管が壊れたので送ります ・おっきなゴンってなんだよ ・シアンさんどうしたの？ ・減った骨は食べちゃったの？ ・暗殺の母のＣＶが柴田理恵さん ・ばんちょーがせくちーな件について ・豆柴でごまかせる ・供給の多いブルアカ ・ブルアカ始めるなら今！ ・にじさんじピックアップニュース ・にじさんじストーンズ ・小ジョッキで水を飲みましょう ・でび様の新曲 ・カラオケでオケツブンブンフェスティバル ・ほな、エンドラ討伐がええんじゃないかな ・ボーイは食べ物じゃないんだよな ・ホロピックアップニュース ・しぐれういだから ・75万円のエレキギター ・イオシスは1万日ですけどね（マウント ・100万円のＰＣも使ったことない ・合体してもスペックは大したことはない ・お家で核融合発電 ・Ｖピックアップニュース ・ローソンのVTuber ・いろんなＶがいるんだね ・ガッツ石松さんご冥福をお祈りします ・ロリ3人組 ・今はフローラ ・ポロって出るゆうじ ・おにぎりスライムとは ・ゲーム実況をやる曜日が足りない ・冥曜日 ・朝配信でおやすみなさーい ・お便りお待ちしてます ＜エンディング＞ ・Forza Horizon 6やりますか ・あまりテクテクライク知識は生かせない ・梅雨はやる気あるんですか？ ・もう、ほぼ水 ・キリン5番絞り ・体内で石の錬成しないようにしましょう

Full show notes and transcript  - https://bit.ly/google-agentic-eraWatch on YouTube - https://youtu.be/eamMBmm6oTU-----Episode Summary:Dara and Matthew open with a breaking-news bulletin on Anthropic's newly released Fable, the consumer sibling to Mythos, covering its safety off-ramp to Opus 4.8, its pricing, and the looming switch from subscription to usage-based access. The main episode is a deep dive on Google Cloud Next '26 and I/O '26, unpacking the Gemini Enterprise Agent Platform, Gemini 3.5 Flash, Omni, Antigravity 2.0, WebMCP, and the shift to generative AI search. The thread running through it all: agents are the headline, but governance and a solid semantic layer are the subplot that makes them actually useful.-----About The Measure Pod:The Measure Pod is your go-to fortnightly podcast hosted by seasoned analytics pros. Join Dara Fitzgerald (Co-Founder at Measurelab) & Matthew Hooson (Head of Engineering at Measurelab) as they dive into the world of data, analytics and measurement, with a side of fun.-----If you liked this episode, don't forget to subscribe to The Measure Pod on your favourite podcast platform and leave us a review. Let's make sense of the analytics industry together!

AI can finally write back to the plant floor, but only if you can trust it. Chris Stevens and Annemarie Breu of Siemens explain how orchestration makes that safe.Industrial AI has reached a turning point. Manufacturers can already collect data, contextualize it, and surface insights, but the hardest step has always been turning insight into action on real control equipment. Chris Stevens and Annemarie Breu of Siemens explain how an orchestration layer finally closes that loop. Annemarie frames the tension clearly. Automation depends on determinism, while large language models are probabilistic by design, so the goal is to bring that discipline into AI and validate any suggestion before it changes a set point.Most executive conversations start with return on investment, and two forces are making the case easier to prove. The workforce shortage has stretched the expected payback window from 18 months toward 36 months, and when a line cannot run for lack of people every idle minute costs thousands of dollars. The other driver is overall equipment effectiveness, since most plants run near 70 percent OEE and even a fraction of a percent of gain can justify a project. Energy is a standout case too. A BorgWarner sustainability effort used a digital twin to flatten demand peaks and reportedly paid for itself in under six months, even as data center growth pushes electricity demand higher through 2040.On trust and safety, Annemarie borrows a principle from industrial safety. Just as fail safe IO modules rely on two channel evaluation, every AI suggestion is validated against a state machine, a workflow, or a physics based digital twin before the orchestration layer passes it to a controller. With virtual commissioning and soft PLCs a change can be tested virtually, approved by a human in the loop, and only then written to control, an approach PepsiCo and NVIDIA echoed at CES when they called the digital twin a must have. Making AI real, the pair argue, comes down to discipline, clear scope, acceptance criteria, and focused 90 day challenges, plus the change management and user experience that drive adoption. Their favorite quick win is preventive maintenance driven by machine data, which both BorgWarner and Maersk tied to millions in savings.About Chris StevensChris Stevens is President of US Automation at Siemens, where he leads a roughly one billion dollar business spanning software, services, and hardware. He brings more than 25 years across Siemens Digital Industries, starting in the field selling assembly and test equipment, moving into the software and digital twin world, and returning to automation to bring the hardware and software sides of the business together.About Annemarie BreuAnnemarie Breu is a senior technology leader at Siemens Digital Industries focused on automation software deployment and customer technology partnerships in the US. She began at Siemens about a decade ago as a systems engineer in the San Francisco Bay Area, working with consumer electronics manufacturers on virtual commissioning and digital twins. Her work today centers on bringing the determinism and reliability of automation into industrial AI.Timestamps0:00 Introduction and Automate 2026 preview2:50 Meet Chris Stevens and Annemarie Breu9:30 The first AI question is always ROI14:00 Workforce gaps and OEE drive the business case19:30 Energy management and the data center demand surge23:20 Data, sensors, and contextualization requirements28:00 Guardrails, hallucinations, and two channel validation32:40 The digital twin and the human in the loop37:40 How partners and integrators move up the stack45:30 What it takes to make AI real on the floor55:50 Preventive maintenance as a quick win59:40 Predictions, career advice, and book picksAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Edge Computing and the Value of AI in Manufacturing Data: https://www.joltek.com/blog/edge-computing-ai-value-manufacturing-dataIT and OT Architecture Integration: https://www.joltek.com/services/service-details-it-ot-architecture-integrationDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

PHP Podcast – June 11, 2026 Guest Hosts: Sara Golemon, Elizabeth Barron & Holly Schilling Eric and John are out this week — Sara, Elizabeth, and Holly take over. Here’s what they covered: PHPVerse Recap PHPVerse just wrapped up, and Elizabeth was there in Amsterdam. The format is unusual — all speakers are flown to one location, but the audience is entirely virtual. It was a class act: professional TV crew, studio lighting, and a makeup and hair team on site. Around 2,500–3,000 people watched the live stream. Everything was broadcast as one long block; individual talk segments and possibly the documentary trailer will be cut and released separately. The full stream is available now — the PHP documentary trailer (produced by Jet Breeze, covering 30+ years of PHP history) appears around the 2:24:30 mark. PHP Foundation 2026 Strategy Document Elizabeth and the PHP Foundation released their 2026 strategy document the same day as this recording. The foundation gathered community input across numerous conversations and conferences, synthesized it into findings, and has now published a plan for the rest of the year. Key themes: repositioning PHP’s public perception (which Elizabeth calls a solvable problem), creating six special interest groups, and launching an Onboarding Initiative to build a real on-ramp for new PHP developers. Elizabeth’s view is that the two things giving her the most hope for PHP’s future are the passion and expertise of the community, and how good the language itself has gotten. Visit thephp.foundation to read the full document. The Onboarding Initiative One of the six special interest groups the foundation is launching is specifically focused on bringing new developers into PHP. Goals include creating a true learning path (not just a reference manual that assumes existing knowledge), improving educational resources, and potentially working with the php.net website to improve the first-time experience. Holly made the point that PHP’s barrier to entry is genuinely lower than almost any other language — the Hello World program is 11 characters — but that story isn’t being told outside the PHP bubble. New developers are turning to JavaScript as a first language and running into minified spaghetti instead of something approachable. AI Writing PHP — And PHP as a Second Language Holly built the entire PHP Tek conference app backend in Laravel without writing a single line of code herself — AI-generated throughout, which she reviewed and approved. The code held up to peer review at the conference with only minor style nits. She ran it on PHP 8.3 and used modern standards throughout (one piece of feedback: stop using empty()). The consensus: AI models write good modern PHP because of the vast amount of open source PHP they were trained on. The caveat Sara raised is worth thinking about — how much of that training data is PHP 4-era code and WordPress 3 repositories? Either way, Holly’s case for PHP as a second language is strong: low ceremony, low boilerplate, readable syntax, and it’s a language where you can do something useful in minutes. PHP’s Reputation Problem (and Why It’s Fixable) The group dug into PHP’s perception gap — the mismatch between how good the language actually is and how it’s perceived outside the community. Holly’s experience as a mobile developer who recommends PHP to others: the pushback is immediate (“isn’t that slow?”, “isn’t that dead?”). The benchmarks don’t support that reputation — PHP outperforms Python on most comparable workloads — but data alone doesn’t shift perception. Elizabeth’s point is that this is primarily a storytelling and coordination problem, not a language problem, and that the foundation’s repositioning work is exactly aimed at closing that gap. The community has the passion. It just needs to tell the story outside its own bubble. PHP Polling API RFC Sara walked through the RFC for a new Polling API in PHP (wiki.php.net/rfc/poll_API). The short version: PHP currently has five or six different ways to do I/O multiplexing (watching multiple streams and acting on whichever one is ready first), and which one works depends on the OS, available extensions, and PHP version. The Polling API proposal creates a single, unified interface that abstracts all of that. The immediate beneficiaries are async frameworks like Amp PHP, ReactPHP, and Revolt, which currently have to maintain multiple backend implementations to cover different environments. The bigger picture: this is a building block on the path toward true async PHP, likely contributing to something more complete in PHP 9.0. Most app developers won’t use it directly — but the libraries they depend on will. RFCs are all listed at wiki.php.net/rfc. PHP.net: Do As We Say, Not As We Do Sara, who has contributed to php.net, copped to the state of the codebase: some of it dates to the PHP 3 era, there are functions.inc files, and it is very much “do as we say, not as we do.” The historical reason is that php.net used to rely on community-administered mirrors (r-synced servers running everything from PHP 5.1 to 5.6 simultaneously), so modernizing the code was impossible without controlling the runtime. That’s changed with CDN-based load balancing — they can now control what PHP version runs on php.net — and the code has been getting better. But it’s a slow process. PHP Podcasts Past, Present, and Future Holly asked about the PHP Town Hall podcast (Ben Edmonds and Phil Sturgeon), and the group did a quick tour of PHP podcast history. The PHP Roundtable — originally started by Sammy, taken over by Eric — has produced about three episodes. Sara and producer Joe are planning to take it off Eric’s hands and actually do it properly. And Elizabeth announced that the PHP Foundation is launching a new podcast: tentatively called PHP at Scale, hosted by Ben Marx, focused on telling the stories of organizations pushing PHP to its limits. No launch date yet, but there’s already a queue of interested guests. Next Week’s Show — Moved to Wednesday Sara will be on a boat off the coast of Galicia on Thursday, so next week’s episode is moving to Wednesday. Guests will include Paul Reinheimer and (hopefully) Sean Coase — two veterans from PHP’s podcasting past. Elizabeth is going to try to make it work around the Canadian Grand Prix. Mac Mini M4 for Local LLMs Holly picked up a refurbished Mac Mini M4 (16GB RAM, 512GB storage) specifically to run LLM models locally via Ollama. Apple Silicon is a solid choice for this because the unified memory architecture gives the neural cores access to far more RAM than a discrete GPU setup. Sara is waiting for the M5, which is reportedly not coming until fall — and is already resigned to spending too much on it when it lands. Links from the show: PHP Foundation — 2026 Strategy Document PHP RFC: Polling API PHP RFC Wiki — All RFCs Under Discussion Amp PHP — Async framework ReactPHP — Event-driven async PHP Revolt — Event loop for PHP php.net website source code (github.com/php/web-php) PHP Architect Discord Guest Hosts: Sara Golemon Based in Lisbon, Portugal PHP core contributor; code contributor via the Curl project (which means she technically has code on Mars) Elizabeth Barron Executive Director, PHP Foundation Based in Germany Holly Schilling Primary mobile developer; built the PHP Tek 2026 conference app Based near Chicago, IL Streams: Youtube Channel Twitch Connect & Hire PHP Architect Website Twitter/X Mastodon Hire PHP Developers Looking to hire PHP developers? Email support@phparch.com – Joe and the team are available for consulting, infrastructure work, Ansible playbooks, and code review. Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ PHPScore Put Your Technical Debt on Autopay with PHPScore Music Provided by Epidemic Sound https://www.epidemicsound.com/ Join Us Live Next Week Note: Next week’s show is on Wednesday (not Thursday) with guests Paul Reinheimer and Sean Coase. Youtube Channel Got feedback? Join us on Discord at discord.phparch.com The post The PHP Podcast 2026.06.11 appeared first on PHP Architect.

Kraken: Start met investeren in crypto en ontvang €15 gratis crypto cadeau

Io non darò la mia gloria a un altro. Isaia 48:11

Marco Dané "Se potessimo fare un bel gioco"Dal Paese di Giocagiò a Tandem. La tv dei ragazzi raccontata da uno dei suoi protagonisti.Manni Editoriwww.mannieditori.itLa televisione per bambini era diventata il mio mondo. E per molti bambini, quella televisione è stata una finestra sul possibile. Abbiamo parlato con loro, non solo per loro. Abbiamo giocato, raccontato, cantato, spiegato, ma abbiamo anche ascoltato. E quando oggi un adulto mi ferma per strada e mi dice: «Io ti guardavo da piccolo, mi hai insegnato a sorridere», sorrido anch'io. Perché so che in quel viaggio, io non ero solo. Eravamo in tanti. E tutti abbiamo imparato qualcosa. Marco Dané è stato un protagonista dei programmi televisivi per ragazzi come autore e conduttore: dall'esordio nel 1969 nel Paese di Giocagiò al fianco di Gianni Rodari, a Trentaminutigiovani, il tg per ragazzi di Rai2, a Tandem negli anni Ottanta con Fabrizio Frizzi, fino al ruolo di giudice in Paroliamo (quello in Rai e poi all'interno di Non è la Rai di Gianni Boncompagni), ha contribuito alla nascita di un'epoca d'oro della televisione italiana, in cui l'intento educativo sapeva coniugarsi con l'intrattenimento intelligente.In queste pagine Dané ripercorre le sue trasmissioni che hanno segnato intere generazioni, restituendo la magia dei personaggi come Signor Coso, Scarabocchio, Buendìa, il Pagliaccio... È un viaggio nella storia della tv per ragazzi e una scoperta delle prime sperimentazioni tecniche, come l'evoluzione dei mezzi di ripresa, l'elettronica, le nuove possibilità di interazione con i telespettatori.Per gli adulti di oggi, questo libro è un ponte con l'infanzia: una galleria di ricordi, emozioni e nostalgia ma anche la dimostrazione di come la fantasia, il gioco e l'intrattenimento possano essere una cosa seria.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.IL POSTO DELLE PAROLEascoltare fa pensarehttps://ilpostodelleparole.it/

Welcome to a show about death. We've got some unfinished business as Alex Garday returns (from the dead?) for an all new fully improvised musical. Crashing cars, mystical mirrors, liminal limerence, and more as we put the FUN in funeral on this week's Charm Scene! Alex Garday is a performer from Phoenix, Arizona. He has performed in Chicago for the past 16 years at all of the major comedy theaters including recently as an understudy for The Second City's Mainstage production, Don't Quit Your Daydream. He regularly can be seen performing with Baby Wine at The Annoyance, Blank! The Musical at The Revival, Phony Award Winning Musical at iO, and Baby Wants Candy at the Second City. He has worked across North America as a host/emcee/facilitator for corporate events for Fortune 500 companies. He is the Talent/Product Coordinator and Marquee Host for Game Night Out a company that provides entertainment and curated in-person game nights for clients from across the Chicagoland area. He is 6'5″ and ethnically ambiguous. You can find him on social media platforms @alexgarday. Cast: Lily Ludwig, Austin Packard, Alex Garday Music Director: Sam Scheidler Drums: Chris Ditton Charm Scene is performed entirely by humans in sunny Chicago, IL. For more on the podcast, follow us @CharmScenePod on Instagram, visit us online at charmscenepod.podbean.com, or email us at CharmScenePod@gmail.com. In listening to this show, we hope you continue to support live human art wherever you find it. Stay charming!

Francesca ha 53 anni, vive a Senigallia e per quasi tutta la vita ha fatto l'insegnante. Oggi non lavora più: si è licenziata e vive con 23.000 euro l'anno, frutto dei suoi investimenti. Il suo rapporto con il denaro nasce da bambina, quando suo padre, impiegato di banca a Ravenna, le regala uno dei primi bancomat per bambini. «Da quel momento ho imparato a gestire i soldi: sapevo quanto potevo spendere in una settimana, e in che cosa». Cresce così tenendo la contabilità di ogni spesa e mette da parte tutto con una direzione sola: i viaggi, l'unica voce davvero preponderante nel suo bilancio.Diventa insegnante, compra casa, e poi si trasferisce in un casolare nelle Marche con il compagno, per inseguire il sogno di una vita in collina. Ma per dieci anni a lavorare è solo lei, mentre lui si licenzia per scrivere. «L'orto lo curavo io, della casa mi occupavo io, guadagnavo io. Lo squilibrio economico ha fatto saltare il piatto». Dopo la separazione conosce quello che è oggi il suo compagno, un ingegnere che da anni vive dei propri investimenti, e che le insegna la cosa che le mancava: smettere di affidare i risparmi alla banca. «Io non faccio trading, sono più una cassettista: compro titoli e li tengo lì, per far lavorare l'interesse composto». Comincia così a investire da sola e a ricalibrare ogni voce delle sue spese. Vende la casa in collina, si trasferisce a Senigallia, prova un anno sabbatico senza stipendio per capire come si vive senza un'entrata fissa. E quando capisce che regge, nel 2024 si licenzia. Oggi dei 23.000 euro annuali di cui ha bisogno per vivere, 9mila euro sono spesi in viaggi, e una parte finisce nel risparmio già a inizio mese, prima ancora di spendere il resto. «Io voglio godermi la vita adesso. Ho 24 anni in meno dei miei genitori: quando me la godo, a ottant'anni?».

TRASCRIZIONE E VOCABOLARIOPuoi sostenere il mio lavoro con una donazione su Patreonhttps://www.patreon.com/italianosiPer €2 al mese riceverai le trascrizioni di tutti i PodcastPer €3 al mese riceverai, oltre alle trascrizioni, anche una lista dei vocaboli più difficili, con spiegazione in italiano e traduzione in inglese.CONTENUTIIn questa puntata, dopo un'introduzione su come ho passato il fine settimana, mi/vi pongo una domanda: parlo troppo lentamente per il livello di questo podcast?TRASCRIZIONECiao a tutti e ciao a tutte! Bentornati, bentornate o benvenuti, benvenute nel podcast di italiano sì. Io sono Elisa, questo è un podcast pensato per voi che imparate l'italiano, ma avete già un livello intermedio B1. Forse solo B1, forse B2? In realtà parleremo proprio di questo più tardi. Che poi magari avete un livello A2, ma con l'aiuto delle trascrizioni, del vocabolario riuscite a seguire senza problemi. Ho una studentessa nuova (ciao Eileen), che è partita da 0 due mesi fa circa e ha un altissimo livello di comprensione, ma di produzione, naturalmente, non ha nemmeno un A1. Ha appena cominciato. La differenza tra comprensione e produzione può essere molto alta. Nel mio caso, per esempio, lo è sempre. Io ho sempre un livello altissimo di comprensione e magari anche quasi inesistente di produzione. [...]MY YOUTUBE CHANNELSupport the show

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubA N M Bazlur Rahman - Java Champion & Author of "Modern Concurrency in Java"Michael Redlich - Java Champion & Lead Java Queue News Editor at InfoQCheck out more here:https://gotopia.tech/episodes/443RESOURCESBazlurhttps://bsky.app/profile/bazlur.cahttps://x.com/bazlur_rahmanhttps://github.com/rokon12https://www.linkedin.com/in/bazlurhttps://bio.site/bazlurhttps://bazlur.caMichaelhttps://twitter.com/mpredlihttps://github.com/mpredli01https://www.linkedin.com/in/michael-redlich-13a966https://about.me/mpredliDESCRIPTIONIn this GOTO Book Club episode, Java Champion A N M Bazlur Rahman joins host and fellow Java Champion Michael Redlich to discuss Modern Concurrency in Java — the first comprehensive update to Java concurrency literature in 20 years. Bazlur traces his motivation to the arrival of virtual threads in JDK 21, which he describes as a fundamental shift in Java's concurrency cost model: platform threads were expensive and scarce, demanding careful pooling; virtual threads are cheap, plentiful, and behave like ordinary threads from the developer's perspective, without requiring a new programming model. The book covers this evolution end-to-end, from the history of threads through to structured concurrency, scope values, and the modern frameworks that have already adopted virtual threads — most with a single config change.The conversation also takes a nuanced look at reactive programming's future. Bazlur's conclusion is that reactive remains compelling in specific contexts — event-driven streaming systems, architectures needing end-to-end back-pressure — but it's no longer the default answer to scalability. For most microservices doing blocking I/O, virtual threads are now the stronger default, and reactive becomes a deliberate architectural choice rather than an automatic one. The book's goal is to give developers both the conceptual grounding and the practical guidance to make that choice confidently — understanding the tool one level deep, so they can design better systems, not just configure their way through a framework.RECOMMENDED BOOKSA N M Bazlur Rahman • Modern Concurrency in Java • https://amzn.to/42w8cOkBen Evans & Jim Gough • Optimizing Cloud Native Java • https://amzn.to/41nivD9Ben Evans, Jason Clark & David Flanagan • Java in a Nutshell • https://amzn.to/43FDoMAIan F. Darwin • Java Cookbook 5th ed. • https://amzn.to/3QH0NZyVictor Grazi & Jeanne Boyarsky • Real-World Java • https://amzn.to/4oCEeBRBlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Nearly four years after taking office, Giorgia Meloni remains one of the most intriguing politicians in Europe. While presenting herself as a reliable partner in Brussels, the prime minister of Italy promotes a far right conservative and nationalist agenda at home, summarized in the slogan ‘Io sono Giorgia, sono una donna, sono una madre, sono cristiana' (‘I am Giorgia, I am a woman, I am a mother, I am Christian,). Is the ‘Meloni model' becoming a new norm for the European right?Leading Fratelli d'Italia, a party with post-fascist roots, Meloni's rise to power initially sparked controversy and skepticism among EU member states. Today, however, she operates effectively within European and international institutions, embracing NATO and the EU, and positioning herself as a strong supporter of Ukraine. Has Meloni truly shifted her worldview, or is this part of a long-term strategy? At the same time, Meloni has cultivated close ties with President Donald Trump, whose ‘America First' politics echo her own nationalist agenda. The Italian prime minister has sought to position herself as the European “Trump whisperer”. But as transatlantic tensions rise, can she sustain this balancing act between Washington and Brussels?During this evening, we explore the paradoxes of Meloni's politics and place them in the context of Italy's political culture and Europe's evolving power dynamics. What does her success reveal about the direction in which Europe is heading? And is the recently lost referendum on reforming the Italian judiciary system a first crack in Meloni's success?Programme editor: Britt van RossumModerator: Katarina SchulZie het privacybeleid op https://art19.com/privacy en de privacyverklaring van Californië op https://art19.com/privacy#do-not-sell-my-info.

[Thai: Four hundred and one – Think of each other] Tell your mates: The babble’s back! Michael and Io line up some of the biggest songs going around right now, along with some bops that are brand spankin’ new. Buckle up and enjoy the ride! Liked a particular track? Click the link to check out the video. And don’t forget to follow across social media: Facebook | X (Twitter) | Threads Playlist Kuba Szmajkowsky – Ale u mnie też [Polish: But for me too]        marguerite – bellevie [French: Beautiful life]        Los Mirlos & Mireya – Cumbia Pa Olvidar [Spanish: Cumbia to Forget]        SENIDAH – Ti i Ja [Serbian: You and Me]        Oscar Zia – Ful [Swedish: Ugly]        babble2babble: Thai BNK48 – Ponytail to Shushu        PROXIE – ฮ็อบ [Hob]        Samurai Jay & Vito Salamanca – OSSESSIONE [Italian: OBSESSION]        Fiki & Azis – Ima li, nyama li [Bulgarian: Is there, or is there not?]        Drifting Clouds – Rarrandharr [Liyawulma'mirr-Djambarrpuyngu]        The post สี่ร้อยหนึ่ง – เอาใจเขามาใส่ใจเรา appeared first on babble POP!.

Predicazione espositiva del Pastore Jonathan Whitman di Matteo capitolo 11 versetti da 25 a 30. Registrata presso il Centro Evangelico Battista di Perugia il 31 maggio 2026.Titolo del messaggio: "Tre gioiose verità sulla misteriosa e meravigliosa grazia di Dio"MATTEO 11 V25-3025 In quel tempo Gesù prese a dire: «Io ti rendo lode, o Padre, Signore del cielo e della terra, perché hai nascosto queste cose ai sapienti e agli intelligenti, e le hai rivelate ai piccoli. 26 Sì, Padre, perché così ti è piaciuto. 27 Ogni cosa mi è stata data in mano dal Padre mio; e nessuno conosce il Figlio, se non il Padre; e nessuno conosce il Padre, se non il Figlio, e colui al quale il Figlio voglia rivelarlo. 28 Venite a me, voi tutti che siete affaticati e oppressi, e io vi darò riposo. 29 Prendete su di voi il mio giogo e imparate da me, perché io sono mansueto e umile di cuore; e voi troverete riposo per le anime vostre; 30 poiché il mio giogo è dolce e il mio carico è leggero».

Industrial network protocols decide whether a machine talks or stays silent. Chuck from Horner Automation breaks down how they win, fade, and converge.Chuck has spent 36 years at Horner Automation and lived through what the industry once called the fieldbus wars. Before Horner became known for its all in one controllers, it spent a decade building specialty IO modules for GE Fanuc during the era of DeviceNet, SDS, InterBus S, PROFIBUS, and CANopen. His core argument is that most of those early protocols were technically fine. The ones that became standards won on the commercial weight of the companies backing them, not on superior specifications, with EtherCAT a rare exception that succeeded largely on technical merit.Trust is the recurring theme. Industry adopts slowly, and for years Ethernet was dismissed as too unreliable and not deterministic enough for control until Ethernet/IP, PROFINET, and Modbus TCP proved themselves. Today the market has settled around a big four set of protocols, and Chuck does not expect it to narrow further. For high speed motion he points to EtherCAT and PROFINET IRT as the implementations he most respects, since both step away from standard Ethernet at the device level to reach submillisecond timing.The episode is also a reality check on building your own hardware. Chuck and Dave describe how custom development routinely costs teams hundreds of thousands to millions of dollars, and how the real trap is obsolescence and maintenance rather than the first build. On the product side, the standout is FPD-Link, a serialization technology borrowed from automotive that carries video, touch, and power over one coaxial cable. Working with Safe Fleet, a maker of ambulances and fire trucks, Horner now mounts rugged displays up to seven meters from the PLC while still programming everything as one device.Looking ahead, Chuck argues that every PLC should now be treated as a data device first, because digitizing the process is the prerequisite for doing anything useful with AI. He also flags cybersecurity as the next burden for application engineers, with new mandates forcing both manufacturers and integrators to implement protections that were once optional. At Automate, Horner is showing HMI Connect and a 300 dollar CPU 151 that packs 18 IO points, wireless connectivity, and edge capability into a micro PLC.About Chuck and Horner AutomationChuck is a technical brand ambassador at Horner Automation, where he has spent 36 years across applications, product management, and education. An electrical engineer who started in the automotive industry, he now produces in depth tutorials on industrial protocols for the Horner APG YouTube channel. Horner Automation is a privately held controls manufacturer best known for its all in one PLC and HMI controllers, edge ready PLCs, and rugged hardware for industrial and mobile applications.Timestamps0:00 Introduction2:20 Chuck's Background and 36 Years at Horner Automation9:20 End User Engineer vs OEM Manufacturer Perspective13:20 New at Automate: HMI Connect and the CPU 151 Edge PLC21:30 The Fieldbus Wars and the History of Industrial Protocols24:20 What It Takes to Implement a Protocol Stack29:30 Why Protocols Win: Commercial Force vs Technical Merit32:40 Will Industrial Protocols Ever Converge?40:30 High Speed Motion: EtherCAT, PROFINET IRT, and Ethernet/IP44:40 FPD-Link: Rugged Remote HMI for Ambulances and Fire Trucks55:00 PLCs as Data Devices and the Push Toward AI1:02:40 Cybersecurity Mandates Coming for Application EngineersReferencesHorner Automation: https://www.hornerautomation.comAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Understanding Plant Networks: https://www.joltek.com/blog/understanding-plant-networks-how-industrial-connectivity-evolvedIndustrial Ethernet Reliability: https://www.joltek.com/blog/industrial-ethernet-reliabilityDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

Today on the 5: After Google's I/O conference and the announcement of changes coming to the search engine, DuckDuckGo saw a rise in the installs of the iPhone app and the usage of their search engine. Most outlets see this as an anti-AI response, but I tyhink that's only part of the reason.

In questa terza e ultima parte della chiacchierata con Francesco Fedelfio entriamo nel cuore pratico della progettazione di un giardino.Parliamo di tempi, scelte, cantiere, piante, maestranze e responsabilità. Perché un giardino fatto bene non nasce in fretta.Ha bisogno di pensiero, osservazione, progetto e rispetto dei tempi naturali.Io e Francesco partiamo da un punto chiaro: se vuoi un giardino sano, bello e duraturo, devi smettere di ragionare con la logica del “tutto e subito”. Il periodo ideale?Progettare con calma tra inverno e primavera, preparare il terreno e le opere durante l'estate, piantare in autunno e poi accompagnare il giardino nella sua crescita.Parliamo anche del ruolo del progettista paesaggista, che non dovrebbe essere un semplice disegnatore di aiuole, ma una figura di fiducia per il committente.Una specie di direttore d'orchestra: coordina giardinieri, vivaisti, impiantisti, muratori, fornitori e tiene insieme il risultato finale.E poi tocchiamo un tema scomodo: il valore del lavoro progettuale.Troppo spesso nel nostro settore il progetto viene regalato, nascosto dentro la vendita delle piante o trattato come un dettaglio.Ma un buon progetto può evitare sprechi, errori, discussioni e brutte sorprese.Chiudiamo con una riflessione sul giardinaggio ecologico: meno chimica, meno soluzioni facili, più capacità di leggere il giardino come un piccolo ecosistema vivo.Perché il giardino non è un prodotto da comprare al volo.È un processo da guidare con competenza, pazienza e buon senso.Se ti serve un appoggio sensato nel progettare, realizzarere e gestire il tuo giardino, scrivimi a robertomassai@giardinofuturo.it

Google recently announced its 2026 algorithm updates at I/O in May 2026, and that made me stop and ask: Is my website actually built for how people are going to find me now?And how can I incorporate this for website copywriting for my clients?Google just fundamentally shifted how people discover businesses online. Information agents are scanning the web 24/7. Agentic booking is expanding to pull real-time pricing and availability. Conversational search is remembering context and surfacing deep-dive content. These affect how your business gets found online.So I audited my own website against these new realities. And I made three specific changes that all business owners can make today.In this episode, I'm walking you through the three specific website copywriting changes I made after Google's 2026 algorithm updates announced at I/O in May. These updates (information agents, agentic booking, and especially conversational search) are actively reshaping how AI finds, evaluates, and recommends service providers online. And the businesses who act on this now have a real advantage.Let's make sure the website you've already built is actually working for you in 2026 and beyond.Want me to make these website copywriting changes for you so that YOU will be found through Google with its new updates? Book a call here to get started.➡️ SHOW NOTES: Grab all the links and resources mentioned in this episode on the blog here! https://www.megankachigan.com/website-copywriting-google-algorithm-updates-2026CONNECT WITH MEGAN:Join My Inbox Community → www.megankachigan.com/email Website → www.megankachigan.comLinkedIn → https://www.linkedin.com/in/megan-kachigan-loehr-9957684b/Threads → https://www.threads.net/@megankachiganInstagram → https://www.instagram.com/megankachigan/Know exactly what to fix in your copywriting with this "Why Isn't This Converting?" Free 5-Day Challenge. You'll get bite-sized email prompts where you'll apply one simple, high-impact fix in just minutes to make your content convert without having to re-write everything or constantly guess at what's going to work.

It's Christmas in June as charming guest Rob Grabowski (Clued In, Hitch*Cocktails) joins us for a merry murder mystery. Was it the sinister son? The revengeful rabbit? The horrible head of HR? Everyone has a motive on this week's fully improvised cluesical. Rob Grabowski is a Michigan native but has called Chicago home for over 15 years. He performs regularly with Hitch*Cocktails: an improved thriller; Clued In: an improvised murder mystery; Comedy Sportz Chicago; and Kohl's Cash, an iO house team. Follow him on instagram @robgrabo. He recommends visiting your local independent bookstore.  Cast: Lily Ludwig, Austin Packard, Rob Grabowski Music Director: Sam Scheidler Drums: Chris Ditton Charm Scene is performed entirely by humans in sunny Chicago, IL. For more on the podcast, follow us @CharmScenePod on Instagram, visit us online at charmscenepod.podbean.com, or email us at CharmScenePod@gmail.com. In listening to this show, we hope you continue to support live human art wherever you find it. Stay charming!

Vorresti ricevere notizie, saluti, auguri dalle Apostole della Vita Interiore?Lasciaci i tuoi contatti cliccando il link qui sotto e con la nostra nuova rubrica digitale potremo raggiungerti.https://www.it.apostlesofil.com/database/- Premi il tasto PLAY per ascoltare la catechesi del giorno e condividi con altri se vuoi -+ Dal Vangelo secondo Marco +In quel tempo, vennero da Gesù alcuni sadducei - i quali dicono che non c'è risurrezione - e lo interrogavano dicendo: «Maestro, Mosè ci ha lasciato scritto che, se muore il fratello di qualcuno e lascia la moglie senza figli, suo fratello prenda la moglie e dia una discendenza al proprio fratello. C'erano sette fratelli: il primo prese moglie, morì e non lasciò discendenza. Allora la prese il secondo e morì senza lasciare discendenza; e il terzo egualmente, e nessuno dei sette lasciò discendenza. Alla fine, dopo tutti, morì anche la donna. Alla risurrezione, quando risorgeranno, di quale di loro sarà moglie? Poiché tutti e sette l'hanno avuta in moglie».Rispose loro Gesù: «Non è forse per questo che siete in errore, perché non conoscete le Scritture né la potenza di Dio? Quando risorgeranno dai morti, infatti, non prenderanno né moglie né marito, ma saranno come angeli nei cieli. Riguardo al fatto che i morti risorgono, non avete letto nel libro di Mosè, nel racconto del roveto, come Dio gli parlò dicendo: "Io sono il Dio di Abramo, il Dio di Isacco e il Dio di Giacobbe"? Non è Dio dei morti, ma dei viventi! Voi siete in grave errore».Parola del Signore.

durée : 00:27:33 - Les émissions culturelles de France Culture - par : Marie Labory - Dans ce débat critique, un programme dédié à la littérature étrangère avec les déambulations de narrateur.rices à Paris et à New York, entre narrative nonfiction et roman. Au menu : "Lonely City" d'Olivia Laing et "Une année à Paris avec Gertrude Stein" de Deborah Levy. - réalisation : Laurence Malonda, Boris Pineau, Aïssatou N'Doye, Jules Barbier, Zohra Vignais, Lise Ripoche, Mathi Adjinsoff - invités : Marie Sorbier Productrice du "Point Culture" sur France Culture, et rédactrice en chef de I/O, Céline du Chéné Productrice à France Culture Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

durée : 00:14:03 - Les émissions culturelles de France Culture - Publié en Grande-Bretagne il y a dix ans, "Lonely City" sort enfin en France. L'occasion pour les lecteur.rice.s de l'héxagone de découvrir Olivia Laing, voix majeure de la non-fiction narrative. - invités : Marie Sorbier Productrice du "Point Culture" sur France Culture, et rédactrice en chef de I/O, Céline du Chéné Productrice à France Culture Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

durée : 00:13:31 - Les émissions culturelles de France Culture - par : Marie Labory - L'autrice britannique Deborah Levy dresse dans son nouveau roman le portrait par petites touches de Gertrude Stein, mère de la scène artistique parisienne des années 1920, celle de la Génération Perdue. - réalisation : Laurence Malonda, Boris Pineau, Aïssatou N'Doye, Jules Barbier, Zohra Vignais, Lise Ripoche, Mathi Adjinsoff - invités : Marie Sorbier Productrice du "Point Culture" sur France Culture, et rédactrice en chef de I/O, Céline du Chéné Productrice à France Culture Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

Sofia ha cinquant'anni, vive in provincia di Como e ogni mattina attraversa la frontiera per andare a lavorare in Svizzera. Per capire la sua storia, bisogna partire da molto più indietro - da una famiglia in cui i soldi non c'erano.Sofia nasce da due genitori appena diciottenni. I soldi sono pochi, le tensioni molte, e quando i suoi si separano lei frequenta il liceo classico di Como in mezzo a figli di medici e avvocati. È lì che la mancanza di denaro diventa una sofferenza vera. «Le mie compagne avevano le Superga del colore della maglietta. Io avevo i vestiti dei sacchi. Ho fatto il primo anno di superiori con il Montgomery che mi era stato comprato e quando ero in classe non l'ho mai tolto». È in quegli anni che si forma la convinzione che ancora oggi guida Sofia: i soldi danno libertà, e quella libertà bisogna guadagnarsela da soli. Sua madre trova un lavoro come bibliotecaria e non vacilla: i suoi figli faranno l'università. Sofia ottiene la borsa di studio massima, si laurea in Scienze dell'Educazione alla Cattolica, poi si iscrive a un master a Venezia in Integrazione degli stranieri. Per fare lo stage obbligatorio, nessuno in Italia risponde. Così prova a telefonare in Svizzera e trova subito lavoro in un centro, dove lavora ancora adesso. Oggi Sofia dirige venticinque persone e guadagna seimila euro al mese. È una posizione che gestisce con una discrezione quasi assoluta: quasi nessuno sa quanto guadagna. E quando la differenza con gli altri emerge, cerca di stemperarla. Ma dentro la famiglia il meccanismo si complica: paga sempre lei le pizze, fa sempre i regali più grandi, e sente che qualcosa nelle relazioni, piano piano, viene falsato. «Se guadagni tre volte quello che guadagna la persona con cui mangi la pizza, è anche normale che a un certo punto paghi e basta. Ma poi le relazioni vengono condizionate da questo, perché si insinua quel tarlo secondo cui sono sempre io quella che paga».Il privilegio di cui sa di godere, Sofia cerca di farlo ricadere indirettamente anche sul resto della società. «Di mestiere lotto contro l'ingiustizia sociale tutto il giorno. E cerco anche di fare in modo che la qualità della vita delle persone che mi sono più vicine sia migliore grazie al mio lavoro».

Worn intake valves, pitted camshafts, shock cooling, and AD compliance are on the docket. Email podcasts@aopa.org for a chance to get on the show. Join the world's largest aviation community at aopa.org/join Full notes below: Norm wonders whether condition-based maintenance and inspections failed him. He is co-owners in an airplane with a Lycoming IO-360, and after a few years they found a crack in the crankcase. The engine was torn down and found to have some rust on the cylinder walls, scoring on the crankshaft, and a worn and pitted lifter. They had been borescoping, doing oil analysis, looking at the filter, and never found any concerns. The hosts say the approach worked perfectly. The point of condition-based maintenance is to fix safety related problems, and they argue that all Norm's issues were financial issues. Mike argues that the lifter wear could have been found with by measuring the valve opening, but that it wouldn't have necessarily resulted in a teardown. The oil analysis wouldn't have found anything because the metal chunks were too large, and although a magnet over the filter material may have helped, he's not sure that would have resulted in a teardown either. The lesson is that the airplane was safe, despite the condition concerns. Jay has an RV with an experimental IO-540 that he loves. A look at the cylinder data found that one of his intake valves was eroding. As the shop dug into the engine they found a few other issues, including pitting on the camshaft. An IRAN is going to cost him maybe $20,000 or $30,000 less than an overhaul, so he's wondering if it's ok to save the money or should he just overhaul the engine while it's off. The hosts tell him to save his money. The only reason they would overhaul now is to increase the market value if he were planning on selling. Otherwise there's little benefit. Ronan wonders how to interpret the data on his friend's Piper Arrow as regards shock cooling. They often get the alerts on the Garmin engine analyzer, and they are wondering if there's anything they can do to avoid it. Paul jokes that he should just turn that feature off. Mike said the only time you have to worry about this is when the cylinders are at high temperature, such as cruise to chopping the power. But in a descent the cylinders are already cooling, so he's not worried about it. Bill is wondering if his club is documenting too much on AD compliance. The hosts give some detailed information on how they document ADs and why it matters. They tend to document everything in a large spreadsheet and note whether or not it applies. If it doesn't, they say so on the document and leave it for a future mechanic or owner. Doing so helps with hours of research, they say. They are also careful to document parts and accessories, especially those inside the engine, as you don't want to have to take the prop off to check a crankshaft serial number every year, for example.

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,

McKenzie and Io talk with friend of the pod and genuine movie pervert Vicky Osterweil about the history of cinema, copyright, and how that fucking mouse wound up owning everything. BUY THE BOOK!! https://www.haymarketbooks.org/books/2525-the-extended-universe Vicky can also be found on bluesky @vickyacab.bsky.social Io can be found https://twitter.com/bum_lungon Instagram @Bum.Lung, bluesky @bumlung.bsky.social or you can buy their prints at https://www.etsy.com/shop/BumLung This show is published by Strangers in A Tangled Wilderness. We can be found at www.tangledwilderness.org, or on Twitter @TangledWild and Instagram @Tangled_Wilderness. You can support the show on Patreon at www.patreon.com/strangersinatangledwilderness. Our logo is by Robin Savage. And our theme music is by a lovely mountain goblin.

Join us for one of the most POWERFUL and VALUABLE Self-Liberation Saturday transmissions thus far — essentially fully LIVE with a brand new show. First off, on Cloak & Dagger, Thane is joined by Alena from Gart.io, the first stealth alert app, enabling you to share your location with trusted… The post [P.A.Z.NIA RADIO NETWORK] Self-Liberation Saturday! Cloak & Dagger with Alena Vranova of Satoshi Labs/Gart.IO; Tinker Tribe LIVE with Greg Doud; P.A.Z.NIA Monthly News Show! appeared first on The Vonu Podcast.

Google acaba de presentar uno de los eventos MÁS importantes de los últimos años… y sinceramente creo que mucha gente todavía no entendió lo que realmente pasó. Gemini Omni, agentes inteligentes, Android con IA total, nuevas gafas, generación de contenido, automatización extrema, búsquedas que podrían reemplazar páginas web… y una transformación gigantesca que afecta directamente a creadores, empresas, trabajos y al futuro entero de Internet. En este episodio analizamos TODO lo presentado en el Google I/O 2026, pero desde una mirada profunda, crítica y realista. Porque sí… lo que mostró Google es impresionante. Pero también abre preguntas enormes sobre privacidad, monetización, el futuro del trabajo y el rol de la inteligencia artificial en nuestras vidas. ¿Estamos entrando en una nueva era tecnológica… o en el comienzo de una crisis silenciosa para millones de personas? 

The Sweet Side of Tasty Caffeine™. Your go-to flavors when your treat cravings call for a boost! The sweetest 5-hour ENERGY flavors are back. Three mouthwatering flavors: Confetti Craze, Fruity Rainbow, and Cotton Candy, full snack break vibes with zero-sugar and a Tasty Caffeine boost. Add some fun to your caffeine break. Taste the Fun: https://click2cart.com/274100bu?utm_campaign=swtflvr&utm_medium=paid_video&utm_source=kf&utm_content=allLet Rocket Money help you reach your financial goals faster. Join at https://rocketmoney.com/kindafunny Thank you for the support! Run of Show - 00:00:00 - Start00:02:52 - Fable is delayed to 202700:21:00 - New Xbox Boss Asha Sharma Reportedly Warns Staff 'Hard Choices' Are Ahead, but Insists Recent Game Pass Changes Are Helping00:30:38 - Ad00:32:20 - 007 First Light is already IO's fastest-selling game ever00:38:00 - Activision Files Trademark For Crash Bandicoot Motion Pictures00:40:55 - Balatro publisher Playstack is being sold to GameSpot and Fandom parent company00:47:01 - FIFA announces new "Digital Football" vision, an ecosystem of games from multiple publishers and developers00:50:56 - Wee News!01:03:01 - SuperChats & You‘re Wrong Learn more about your ad choices. Visit megaphone.fm/adchoices

It's here – the James Bond game we've been waiting almost six years for! How much Hitman is in First Light? And how successfully does IO borrow from Naughty Dog? We discuss these subjects and many more across almost two hours.We've done our best to keep this episode free of spoilers. You'll hear about none of the major story beats in this one. Hosted on Acast. See acast.com/privacy for more information.

In this episode: Why the ten blue links era of the internet is ending The shift from search to ask, and what it means for discovery What AI is actually rewarding now (and why volume is no longer the moat) The Interpretation Gap: the silent reason smart founders are being skipped The ChatGPT client story that made this real for Monique What Google announced at I/O and the one line of news nobody is talking about Why smaller, clearer experts are now outranking bigger, vaguer brands The new definition of a brand: retrieval infrastructure, not marketing asset Who wins the next era of the internet, and who quietly disappears Quotes worth pulling: "People stopped searching. They started asking." "AI is not reading your content the way a human fan does. It is trying to categorize you." "A smaller but clearer expert can now beat a bigger but vaguer brand. That has never been true before." "Your brand is not just a marketing asset anymore. It is retrieval infrastructure." "The people who win next are not the loudest. They are the clearest." Next week on the podcast: Monique sits down with Carol Cox, founder of Speaking Your Brand, whose business is already 20 to 25 percent ChatGPT-referred. They get specific about what AI is reading, what women specifically need to protect in this shift, and what becomes more valuable as machines get smarter. Who Knows You is hosted by Monique Bryan, brand authority strategist and built for founders, operators, and experts who are doing real work and ready to be picked for it. Take the AI Visibility Audit to find out where your positioning is breaking down and what to fix: [RUN YOUR AUDIT] Connect with Monique:Before we build, let us talk. https://moniquebryan.com/book/ - Website: moniquebryan.com LinkedIn: Monique Bryan Instagram: @moniquebryan

You'll need a map, compass and legend to understand all the new AI Google announced at its I/O conference last week. (They literally wrote a blog post called, "100 things we announced at I/O 2026” and most of them were AI based.) Luckily for you, we spend hours each day going through the latest in AI to cut the fluff from the real. So on today's ‘AI Working Wednesdays' series, we break down 3 of Google's biggest AI updates you can use today: Google Omni, Gemini 3.5 Flash and Antigravity 2.0. What's new and how do they work? We'll show you the ins and outs live. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Gemini 3.5 Flash Model Hands-On DemoGemini 3.5 Flash Pricing and Token UsageBenchmarks: Gemini 3.5 Flash vs. 3.1 ProIntelligence vs. Cost in Gemini 3.5 FlashGemini 3.5 Flash for API and DevelopersGoogle Gemini Omni Flash Video Model ReviewOmni Anything-to-Anything Multimodal FeaturesGoogle Omni vs. Video Model CompetitorsAnti Gravity 2.0 Agent Desktop App OverviewAnti Gravity 2.0 Pros, Cons, and Use CasesUsage Limits in Google Gemini and Anti GravityChain of Thought Transparency in Gemini ModelsCanvas Mode Interactive Web App DemonstrationsTimestamps:00:00 Key AI updates from Google IO04:58 New Google AI updates discussed08:57 Google's anti gravity desktop use10:01 Touring Google's Anti Gravity App14:40 Testing a new AI prompt18:06 Critiquing vibe coding aesthetics21:28 Discussing Google's Gemini 3.1 Pro Model24:40 Comparing AI model performances and costs29:13 Google's advancements in video AI30:13 Future of Google's AI Technology33:58 Exploring Google Gemini features36:51 Google Gemini chain of thought feature42:02 Google Gemini's new model features44:23 River crossing puzzle gameplay48:25 Discussing Google Gemini 3.5 flash drawbacks51:10 Feedback on an AI releaseKeywords: Gemini 3.5 Flash, Google Gemini, AI updates, Google I/O 2026, Gemini Omni, Gemini Omni Flash, anti gravity 2.0, AI video model, hands-on AI demo, agentic coding, desktop AI app, benchmarking, AI model comparison, Gemini Spark, Gemini Pro 3.5, Gemini 3.1 Pro, token usage, API users, Google Workspace, always-on agent, AI cost efficiency, intelligent agents, world model, multimodal AI, generative video creation, video editing, scheduled tasks, Google Daily Brief, model usage limits, thinking steps, chain of thought, artificial analysis intelligence index, token inefficiency, cost to run AI, OpenAI GPT-5.5, Claude Sonnet, Claude Opus, open source AI models, AI-powered creativity, robotics, embodied AI, front-end AI tools, Canvas mode, conversational editing, interactive website builder, AI-powered app creation.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

I love James Bond movies. I grew up with them. I just talked about having gone through every Bond film a few weeks ago (there's 28, if you include The Rock, and you should!). When I watched the marketing for First Light, it looked like a Hitman game. A very well done, excellently made, Hitman game. And that meant I was out. I'm not hating on Hitman. Blood Money is a classic, and as far as I'm concerned the peak of the series because after that I never got into it again. World of Assassination is supposed to be incredible - if you love Hitman and haven't played it - get after it. This is by no means trying to talk you out of anything Hitman. But... Is James Bond right for a Hitman game? It didn't land for me. For whatever reason, James Bond means car chases, fast action, gun fights. In game form, that screams shooter to us. First or Third person, but shooter. Not stealth, not sandbox, not puzzles. None of those things are bad, and judging by the reviews IO interactive did a fantastic job. So, why is the game tracking low sales? Well, who is this game for? Hitman fans? James Bond fans? Uhhh... Uncharted fans??

Google just stood on stage at I/O 2026 and named pet care by name. Starting this summer, Google's AI agent will call your business on a client's behalf to check availability and pricing — and the businesses it can't read won't make the list. This episode breaks down what changed, the 7.22-word stat that just broke traditional SEO, the six-rule blog structure AI actually cites, the four places your reviews need to live, and the four things every pet business owner needs to do this week before the rollout hits. Timestamps [0:00] — Welcome + the CC story from February (the strainer in action) [3:00] — Why AI literacy is the new business literacy [4:30] — Google I/O: the biggest change to Search in 25 years [6:00] — The 7.22-word stat that just broke traditional SEO [8:30] — AI Mode hits one billion users — what that means for your visibility [10:30] — The new game: ranking vs. being citable [13:30] — The first 100 words rule + the brochure problem [16:00] — The 6-rule blog structure AI will actually cite [20:00] — Why Google Analytics is lying to you (and where to look instead) [22:30] — The Google quote: pet care named by name [25:30] — What it looks like when Google's AI agent calls your business [27:30] — Daily Brief + Gemini Spark for pet business owners [30:30] — Four things you can do this week (with the 4-place reviews framework) [33:30] — Close + Keep jumping In This Episode You'll Discover Why Google named pet care — by name, on stage — at I/O 2026, and what's actually rolling out this summer The 7.22-word AI search stat (and what your clients are actually typing into Google now) The 6-rule blog structure that gets your pet business cited by ChatGPT, Perplexity, and Google AI Mode The 4 places your reviews need to live — and why having them only on Google looks suspicious to AI Why Google Analytics is hiding your AI traffic — and where the real fingerprints live Four things every pet business owner needs to do this week before the summer rollout About This Episode Bella Vasta — founder of Jump Consulting and host of Bella in Your Business — sits down to break down everything Google announced at I/O 2026, the biggest developer event of the year. Bella translates the keynote into pet-business plain English: what changed in Search, why the average AI Mode query is now 7.22 words instead of 4, the six-rule blog structure that AI engines actually cite, the four places your reviews need to live for AI to trust you, what it means that Google named pet care by name as one of the first categories its AI agent will call on behalf of clients, and exactly what business owners need to do this summer to stay in the conversation. She also closes the loop on a Google Labs experiment she flagged for The Jumpers community back in February — and now lives on the keynote stage. Resources Mentioned in This Episode Ep 428: ChatGPT Is Not Google Ep 433: 13 AI Pet Sitting Business Mindset Shifts Ep 421: Why AI Will Save Your Pet Business The AI Brain: The One File That Makes Every AI Sound Like You Google I/O 2026 keynote recap (Google blog) Book a website + AI visibility session with Bella Connect with Bella Website Sessions with Bella The Jumpers Mastermind Subscribe to Bella in Your Business Bella's Website Find Bella on Instagram + Facebook ? search Bella Vasta Frequently Asked Questions Q1: Is Google's AI really going to call my pet business? Yes. At Google I/O on May 19, 2026, Google announced that AI Mode will start performing tasks on behalf of users — including making reservations, booking appointments, and getting quotes. They named three industries to start: home services, beauty, and pet care. The agent will call businesses, check availability and pricing, and bring the results back to the searcher. Rollout begins in the United States this summer. Q2: What is the difference between SEO and AIO (AI Optimization)? SEO is about ranking — getting your page to the top of the blue-link results so a human clicks. AIO is about being citable — making sure an AI engine like ChatGPT, Perplexity, or Google AI Mode can read your website, understand what you do, and confidently recommend you when someone asks. Old SEO chased the click. AIO is about being in the answer itself. Both still matter, but AIO is now the gate. Q3: Why is my pet care business invisible on Google AI Mode? Most pet care websites read like a brochure — vague phrases like 'passionate care for your beloved pets' or 'tailored services for your pet's unique needs.' AI engines cannot cite that language because it does not answer a specific question. To show up in AI Mode, your pages need specific facts in the first 100 words: city, zip codes, services, prices, availability, and what kind of pets you specialize in. Specific. Real. Answerable. Q4: Why doesn't my Google Analytics show AI traffic? Google Analytics runs on JavaScript. The crawlers from ChatGPT, Perplexity, and Google AI Mode do not execute JavaScript, so they never trigger your Analytics tracking. That means even when AI bots visit your site every single day, your Analytics dashboard shows nothing. The only place AI bot visits show up is in your server logs. Ask your web host or developer for access to your raw server logs — that is where the AI fingerprints live. Q5: How long is the average AI Mode search now? According to Google's own one-year AI Mode data published in May 2026, the average AI Mode query is 7.22 words — almost double the average traditional Google search at 4 words. The top words used to begin an AI Mode search are What, How, I, Is, and Can. The top action words inside the search are find, information, identify, explain, and summarize. Pet care clients are no longer typing 'pet sitter Phoenix' — they are typing full conversational questions, which is why brochure-style websites built around three-word keywords are losing visibility fast. Q6: How do I structure a pet care blog so AI will cite it? Six rules. One — make your headline a question a real client would type. Two — answer that question in the first 100 words with a specific number, city, or service. Three — make every H2 heading a question too. Four — add an FAQ block with six to ten real Q&As and FAQ schema markup. Five — internally link to one other blog on your site and link back from it. Six — include an author bio with credentials, photo, years in business, and service area. That signals E-E-A-T (experience, expertise, authoritativeness, trust) — what AI engines look for when deciding what to cite. Q7: Where should I put my pet business reviews so AI can find them? Four places. Place one — your Google Business Profile (the floor). Place two — embedded on your website as real text (not screenshots), on a dedicated Reviews page AND on every service page, with schema markup. Place three — woven into your FAQ answers so reviews function as proof inside your actual responses. Place four — cross-platform on Yelp, Nextdoor, Facebook, and Bark, because AI engines look for citation consistency. A pet business with 300 reviews on Google and zero anywhere else looks suspicious to AI. The one with reviews distributed across four platforms looks like a real business. Q8: What are the four things every pet business owner needs to do this week? First, be your own client — open ChatGPT, Perplexity, and Google AI Mode and search 'best pet sitter in [your city].' See whether you appear. Second, read your homepage like an AI would and audit the first 100 words for specifics: city, services, prices, availability. Third, lock down your Google Business Profile — hours, phone number, services, service area, photos. Fourth, distribute your reviews across the four places listed above so AI sees you consistently cited as a real business. Full Episode Transcript You guys, on February 26th, I was inside my mastermind with the jumpers and I was talking about this tiny little what they call Google Labs, right? It's an experiment that they were doing. It's called CC. And CC was this email feature that it was so cool because every morning it would read your Gmail and your calendar and then hand you a prioritized summary of your day. What was urgent, what was next, all in one place with links to go to it. So now you're not having to read through your emails and your ? appointments and requests and things that had deadlines and not know it it just it was amazing. I was fired up and I told all my jumpers that like they all needed to be on it right now. And the response was also excitement, and other people signed up for it too. Some people had to get on the wait list because There was a wait list for it, but it was a really cool thing. And since February, I personally have been doing it. Now let's fast forward to May 19th, which you're gonna hear a lot about today. Google stood on a stage at their biggest developer conference of the year and announced it to the world. It was a new name. It was built into their Gemini app on the keynote stage in front of a billion people. And guys, this is exactly what I do. I take this stuff. That is out there, that is overwhelming, that is just like there's so much that you become paralyzed. And I put it through a strainer. I decide what is actually gonna be important to you, the small business owner. I distill it and I give it straight to you. That's exactly what I did. Okay. And I filter out the noise. I bring you the things that actually matter before they matter, before the headlines, before everyone else gets on top of it. That's what I've been doing since 2023, okay? And today's no different because AI literacy is the new business literacy. And if you're listening to this, you are one of the special people in the small business world that wants to learn and wants to know. You're not one of the ones that are sticking your head in the sand or paralyzed by fear. Do you have fear? Probably.

Connecting with Google CEO Sundar Pichai at I/O every year is one of my favorite Decoder traditions. This was our fifth year doing it, and there's always a whole slew of new things to talk about. This year, in addition to the news, we talked about Google Zero; picking fights with YouTube creators and publishers; and what being at “the foothills of the singularity" even means.  Links:  If Google can't make AI agents useful, maybe no one can | The Verge The future of Google is a search box that does everything | The Verge Large language mistake | The Verge You can now remix other people's YouTube Shorts with AI | The Verge Condé Nast calls Google Zero | The Verge Demis Hassabis said this may be the ‘foothills of the singularity' | The Verge Google I/O 2026: All the news and announcements | The Verge Subscribe to The Verge to access the ad-free version of Decoder! Credits: Decoder is a production of The Verge and part of the Vox Media Podcast Network. Decoder is produced by Kate Cox and Nick Statt. This episode was edited by Kabir Chopra. Our editorial director is Kevin McShane.  The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices

The Pope said WHAT about AI?

On this week's Marketplace Tech Bytes: Week in Review, we take a look at how college graduates do not wanna hear about AI. Plus, what we all learned from the Musk v. Open AI case. But first, AI was unsurprisingly front and center at Google's annual I/O developer conference. Among a suite of new AI products, Google said it updated its iconic search bar. Now, when searching in AI mode, the bar will expand as you ask a question. It will also provide suggestions about what you might wanna ask. Google says this is the biggest change to its search box since it debuted over 25 years ago. Marketplace's Stephanie Hughes spoke with Anita Ramaswamy, a columnist at The Information, about how this could change how people experience the internet. Check out our YouTube page to watch more episodes of “Tech Bytes.”

On this week's Marketplace Tech Bytes: Week in Review, we take a look at how college graduates do not wanna hear about AI. Plus, what we all learned from the Musk v. Open AI case. But first, AI was unsurprisingly front and center at Google's annual I/O developer conference. Among a suite of new AI products, Google said it updated its iconic search bar. Now, when searching in AI mode, the bar will expand as you ask a question. It will also provide suggestions about what you might wanna ask. Google says this is the biggest change to its search box since it debuted over 25 years ago. Marketplace's Stephanie Hughes spoke with Anita Ramaswamy, a columnist at The Information, about how this could change how people experience the internet. Check out our YouTube page to watch more episodes of “Tech Bytes.”

Google dropped like 197 new AI features this week.

Apple has shown off the new Accessibility features coming in iOS 27, which did nothing to stem the torrent of rumors about what we'll see in Apple Intelligence, but possibly did steal a little bit of thunder from Google's peculiar mishmash of an I/O conference, on the AppleInsider Podcast.Contact your hosts:@williamgallagher_ on Threads@WGallagher on TwitterWilliam's 58keys on YouTubeWilliam Gallagher on emailWes on BlueskyWes Hilliard on emailWes's blog HillitechSponsored by:Bartender:  Check out the new Bartender Pro at macbartender.com/appleinsiderNordStellar: Unlock your 10% discount at nordstellar.com/appleinsider with the coupon code nordappleinsider-10-NORDSTELLARLinks from the Show:Owning an Apple Home: implementing smart pet solutionsVision Pro wheelchair control & more accessibility features detailed ahead of WWDCHikawa Grip & Stand for iPhone launches globally at a new lower priceRevamped Siri may launch in beta, despite two year delayPrivacy & data security will remain central to Apple's 2026 AI pushGenmoji in iOS 27 will use what you type and what's in Photos for suggestionsImproved Writing Tools, generated wallpapers, & easier Shortcut creation rumored for iOS 27AI is making smartphones verifiably worse by designDon't expect new Macs at WWDC 2026Google I/O 2026 had nothing to say and said it badly ahead of Apple's WWDCProblematic hinge could delay the iPhone FoldApple's iPhone Fold hinge design may become industry standard Latest Apple Immersive rollout exemplifies Apple Vision Pro's entire problemSupport the show:Support the show on Patreon or Apple Podcasts to get ad-free episodes every week, access to our private Discord channel, and early release of the show! We would also appreciate a 5-star rating and review in Apple PodcastsMore AppleInsider podcastsTune in to our HomeKit Insider podcast covering the latest news, products, apps and everything HomeKit related. Subscribe in Apple Podcasts, Overcast, or just search for HomeKit Insider wherever you get your podcasts.Subscribe and listen to our AppleInsider Daily podcast for the latest Apple news Monday through Friday. You can find it on Apple Podcasts, Overcast, or anywhere you listen to podcasts.Those interested in sponsoring the show can reach out to us at: advertising@appleinsider.com ★ Support this podcast on Patreon ★

Google dominated I/O with Gemini 3.5 Flash, its fastest agentic model yet, plus Gemini Spark as a 24/7 personal agent. It also launched Gemini Omni for video generation, overhauled its search box, shipped Antigravity 2.0, and added Street View to Project Genie. Google rolls out Gemini 3.5 Flash, its "strongest agentic and coding model yet", for tackling long-horizon agentic tasks, in the Gemini app and Search's AI Mode (Google) Google announces Gemini Spark, a "24/7 personal AI agent" that is powered by Gemini 3.5 and supports integrations with Google Workspace apps, including Gmail (Engadget) Google launches Gemini Omni, a multimodal model it says can "create anything from any input", starting with video generation, for Google AI Plus, Pro, and Ultra (VentureBeat) Google overhauls its search box, letting users input longer queries, including with photos and videos, and automate searches with Gemini 3.5 Flash-based agents (NYT) Google introduces Antigravity 2.0, featuring an updated desktop app that lets users orchestrate agents, an Antigravity CLI tool, and an SDK for custom workflows (TechCrunch) Google adds Street View integration to Project Genie, its interactive world builder, and expands Genie from the US to adult Google AI Ultra subscribers globally (Engadget) Learn more about your ad choices. Visit megaphone.fm/adchoices