Podcasts about Ic

Hello and welcome to episode 98 of TheDX Mentor – a discussion with Steve, W0ZB, Violletta, KN2P, and Joe, W8GEX, about the upcoming VP0SG DXpedition to South Georgia. December 2025 - Amateur Radio DXpeditions (ARD) (⁠https://www.ardxpeditions.com/⁠ ) is pleased to announce a major milestone in the preparation of the VP0SG South Georgia 2027 DXpedition. We have successfully signed the vessel contract and first deposit is being paid, officially securing our expedition vessel: the MV MEREDIAN, operated by 60° South Expeditions.  The MV MEREDIAN is a robust, ice-strengthened expedition vessel capable of safely supporting land-based operations in the challenging sub-Antarctic environment. The experience of the 60° South team, combined with their long track record in the region, makes them an ideal partner for this ambitious project. Securing the vessel represents a crucial step that moves VP0SG from planning to reality. We are pleased to report positive progress in our ongoing dialogue with the Government of South Georgia & the South Sandwich Islands (GSGSSI). Our early planning has been well received, and the framework of our proposed operation is considered consistent with current guidance. While the formal permit process will open in 2026, GSGSSI has expressed a willingness to continue working with us as we refine our plans throughout the coming months. This represents a highly encouraging step toward obtaining the final landing permit required for onshore operations in 2027. If you have a comment or a question, please drop me a note at thedxmentor@gmail.comBelow are the links that we alluded to: https://www.ardxpeditions.com/Website: www.aj8b.comYoutube & Podcast TheDXMentorReal Time DX Info (DailyDX https://www.dailydx.com/Southwest Ohio DX Assoc. https://www.swodxa.orgDaily DX https://www.dailydx.com/DX Engineering https://www.dxengineering.com/Icom https://www.icomamerica.com/IC-7760 Product Page: https://www.icomamerica.com/lineup/products/IC-7760IC-PW2 Product Page: https://www.icomamerica.com/lineup/products/IC-PW2IC-7300 MK2 Product Page: https://www.icomamerica.com/lineup/products/IC-7300MK2/IC-9700 Product Page: https://www.icomamerica.com/lineup/products/IC-9700/IC-905 Product Page: https://www.icomamerica.com/lineup/products/IC-905/IC-R8600 Product Page: https://www.icomamerica.com/lineup/products/IC-R8600/IC-52A Plus Product Page: https://www.icomamerica.com/lineup/products/ID-52APLUS/ 

Test steps out with Trish and asks Mick Foley to wait. November 28, 1999, the night Triple H took the money, the power, and Stephanie, Test sat back. The smile on his face when those cops came. He made the call. Tommy Blacha and Rob Pasbani recap the September 4, 2000 Labor Day edition of WWF Monday Night Raw, live from the Rupp Arena in Lexington, Kentucky, airing at 11pm due to the US Open on USA Network.Foley opens the show with a full investigation, narrowing the spousal abuse complaint down to 373 suspects, then bringing out Stephanie, Angle, Chyna, and Eddie one by one. The Test reveal lands as clever soap opera with real nostalgic stakes. Triple H then immediately Pedigrees both Test and Albert in the same night and leaves them on the mat.In the IC title triple threat, Angle knocks Chyna out cold with the belt. Eddie dropkicks Angle out, turns around, and finds Mamacita laid out. He chooses her over Angle. They fall together in a heap, the referee counts three, and Eddie Guerrero is the new Intercontinental Champion. The camera pans to catch a devious grin on his face while Chyna cannot see it, a reveal Tommy and Rob say undercuts everything the pairing has built.Other major discussion points include:Christian pins The Rock with a Conchairto to close the show while Kane joins JR on commentary and delivers emo monologues about being dragged from the gutter and pain becoming your friendThe Dudleys beat the APA and Kaientai in a tables match, prompting Tommy to push for consistent rules: one member through the table ends the match, and anything requiring both should have its own nameRight to Censor kidnaps Val Venus after his promo defending personal freedom, and Lawler's RC Cola live read accidentally plays over Road Dogg's entrance promoTommy opens the episode comparing WWF's late-2000 slide to the current moment in WWE, using a JP Morgan stock analysis and a Fightful report about Netflix executive pressure on the 2025 creative teamPlease support the show, and join our brand new Patreon, featuring watch-alongs for classic matches. New watch along added: Gorilla Monsoon Mixtape!Previous watch alongs include RVD/Sabu vs. Hayabusa/Hakushi, Verne Gagne vs. Dick the Bruiser, Rock vs Hogan and Buddy Rogers vs. Pat O'Connor.You also get these episodes one day early!Please subscribe at https://patreon.com/talesfromtheattitudeeraChapters:00:00 Intro01:39 WWE 2025 vs. WWF 2000: Tommy's Exit, the TNN Move, and the Business Slide03:44 JP Morgan Analysis / Netflix Execs Pressure WWE Creative12:27 How to Watch on archive.org / Complete 2000 WWF Collection15:27 Show Overview / Aired at 11pm on Labor Day16:03 Opening Segment: Foley Investigates the Arrest / 373 Suspects21:42 Test's Confession: "November 28, 1999"29:24 Foley Books Triple H vs. Test / "I'm Not the Champion, But Okay"32:45 Number One Contender's Match: Kane vs. Benoit / Rock on Commentary37:17 Tables Match: Dudleys vs. APA / Rules Discussion41:27 Eddie Begs Foley / Triple Threat IC Match Booked42:27 SmackDown Your Vote / WWF Invites Bush and Gore to Debate44:29 IC Title Triple Threat: Eddie Guerrero vs. Chyna vs. Kurt Angle47:11 Test Hyped / Triple H and Stephanie Backstage48:52 Eddie Explains to Chyna / Offers the Belt Back / The Devious Face52:27 Triple H vs. Test (w/ Albert and Trish) / Angle Sneaks Up on Stephanie57:26 Naked Mideon with Foley / The Implication58:01 Val Venus vs. Right to Censor / "Stick It Where the Sun Don't Shine"1:00:20 Crash and Malenko Backstage / Edge and Christian: "Genital Warts Rule"1:02:46 Hardcore Title: Boss Man vs. Steve Blackman1:04:40 Road Dogg and Val vs. Right to Censor / RC Cola Disaster / Val Kidnapped1:07:15 Lawler and Jericho vs. Tazz and Mideon1:09:25 King Rolls Up Tazz / Austin Statement Teased for SmackDown1:11:00 Kane on Commentary / Emo Kane / "Pain Can Become Your Friend"1:11:48 Main Event: Rock and Undertaker vs. Edge and Christian / Conchairto / Christian Pins Rock1:14:52 Final Thoughts and Sign-Off Hosted on Acast. See acast.com/privacy for more information.

Astronomy Daily · S05E157 · “The Edge of Everything” · Monday 3 August 2026 In this episode •      The largest galaxy, measured at last. Ultra-deep imaging of IC 1101 in the Abell 2029 cluster has traced its outer edge for the first time: an edge radius of ~260 kpc, a diameter near 520 kpc (~1.7 million light-years), and ~3.4 trillion solar masses in stars — about 17× the width of the Milky Way. Faint outer structures aligned with cluster X-ray disturbances show it is still accreting. Source: Marrero-de la Rosa et al., “How large can galaxies be? Ultra-deep imaging of IC 1101”, accepted A&A (arXiv:2607.15340, 16 Jul 2026); phys.org, 31 Jul 2026; The Debrief, 1 Aug 2026. •      Hayabusa2's record asteroid flyby. JAXA confirmed (press conference 30 Jul) that its Hayabusa2 probe passed just 400 m from the surface of near-Earth asteroid Torifune on 5 July — 744 m from centre at >18,000 km/h — the closest asteroid flyby by any mission, plus the first-ever laser ranging of an asteroid during a flyby. Torifune proved to be a contact binary. Source: JAXA press conference, 30 Jul 2026; phys.org / Japan Today (AFP), 31 Jul 2026; autoevolution, 1 Aug 2026. •      UK's first orbital launch on hold. Rocket Factory Augsburg de-stacked its RFA One at SaxaVord Spaceport (Unst, Shetland) after finding a vehicle issue during hot-fire test prep (announced 28 Jul). The certified launch window opens 10 August; a successful flight would be the first orbital launch from UK soil. Source: RFA statement (X), 28 Jul 2026; Space.com, 1 Aug 2026; Shetland Times, 28 Jul 2026. •      Andromeda is winding down. A new Hubble study mapping ~200 million stars across two-thirds of Andromeda's disk finds star formation has declined over ~500 million years (from ~1 to ~0.2 solar masses/year), with the steepest drop in the last 40 million years — possibly linked to an interaction with satellite galaxy M32. Source: Williams et al., The Astrophysical Journal, 27 Jul 2026; NASA/STScI release, 27 Jul 2026. Skywatch — both hemispheres •      Comet 10P/Tempel 2 — perihelion 2 Aug; closest approach to Earth tonight (3 Aug). Binoculars/small scope, dark skies. Best pre-dawn from the Southern Hemisphere; ~9:30–10:30 pm local for mid-northern latitudes before moonrise. •      Moon near Saturn tonight (3 Aug) — naked-eye pairing, both hemispheres, up into dawn. •      Mercury at greatest western elongation (2 Aug) — low eastern horizon ~1 hr before sunrise, toward Gemini; Mars higher. Better placed for northern observers. •      Diary (2 days out): Falcon 9 upper stage (2025-010D) lunar impact near Einstein Crater, 5 Aug ~06:35 UTC — ~2:35 am EDT / 11:35 pm PDT (4 Aug); North America best-timed on the dark limb. Southern Hemisphere: ~4:35 pm AEST (daylight) — await orbiter after-images. •      Looking ahead: 12 Aug total solar eclipse (Greenland/Iceland/Spain) + moonless Perseid peak + six-planet dawn alignment. View partial phases only through certified ISO 12312-2 filters. Links & follow •      Website, back catalogue, news feed & newsletter: astronomydaily.io •      Social: @AstroDailyPod  ·  Part of the Bitesz.com Podcast NetworkBecome a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-latest-space-news--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.astronomydaily.io/nordvpn. You'll be glad you did!Get the best secure and private email on the planet. Stop your Government, google and who knows who else spying on every email you write. Do what we did and use ProtonMail. They beleive in privacy and there are no ads in their business model...yet they still provide a free forever service. Check them out and get out special deal at www.astronomydaily.io/protonmailBecome a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

Good Morning Portugal!

掌握前瞻趨勢與科技脈動，立即訂閱 IC之音電子報：https://pse.is/8wpwwx--從晶片的精密製程，到歐洲街角的日常美學，如果兩者其實是同一種創造力的不同語言？本集邀請陽明交通大學教授、跨域創作者顧廣毅，談他如何把「思維污染」帶進新竹——讓科技人不只會算，也開始會感受、會質疑、會創作。從旅行經驗到跨界展覽，他提出一種顛覆想像的學習方式：偷學歐洲，把科學活成一種美學。 ________________  企劃、製作 | 謝美芳

Tom Verrilli is the chief product officer at Whatnot, a live shopping platform that's become the fastest-growing U.S. marketplace business in history, with over $8 billion in GMV. Before joining Whatnot, Tom was CPO at Twitch and director of product growth at Twitter (during one of the most turbulent periods in the company's history).In our in-depth conversation, we discuss:1. Why Whatnot's product team was founded on the premise “we regret that product management exists”2. How AI is reshaping the PM role3. What Tom looks for when hiring PMs4. The shift toward senior ICs doing the work5. How AI has transformed data science at Whatnot6. Tom's “play the accordion” mental model7. Why “hire great people and get out of their way” fails8. His biggest lessons from his time at Twitter—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and moreMercury—Radically different banking, now with Command—Episode transcript: https://www.lennysnewsletter.com/p/this-cpo-regrets-that-product-management—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Tom Verrilli:• X: https://x.com/tdrobbo• LinkedIn: https://www.linkedin.com/in/tom-robertson-042—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction(02:40) “We regret that product management exists”: what it means and why(08:30) When specialization makes sense, and when it doesn't(15:20) How Whatnot structures its PM org(17:28) What 31,832 PM applications revealed about the function(19:40) How to develop systems thinking(22:10) The shift to senior ICs doing IC work(32:26) Advice for PMs struggling in today's market(35:22) How AI has transformed work at Whatnot(41:14) The data scientist problem(42:48) Which roles are trending up and down(44:48) What the product team of the future looks like(46:29) Why core PM skills are the most durable in an AI world(49:23) How to get the most out of the people you hire(53:32) Navigating the CPO-founder relationship(57:34) Advice for aspiring CPO's(59:16) How to know when to stand firm and when to step back(01:01:38) Play the accordion: balancing strategic vision with fast iteration(01:06:35) Agentic commerce vs. live commerce(01:09:46) Lessons from Twitter(01:13:07) Failure corner(01:15:45) Lightning round and final thoughts—Referenced:• Whatnot: https://www.whatnot.com• Building, and Whatnot: https://www.linkedin.com/pulse/building-whatnot-tom-verrilli-bwkdc• Twitch: https://www.twitch.tv• Why Netflix is betting on systems thinkers—not specialists—in the AI era | Elizabeth Stone (CPTO): https://www.lennysnewsletter.com/p/netflix-cpto-on-ai-and-the-future• Peter Bailis on LinkedIn: https://www.linkedin.com/in/pbailis• Mike Krieger on LinkedIn: https://www.linkedin.com/in/mikekrieger• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Ben Kus on LinkedIn: https://www.linkedin.com/in/benkus• Henry Shi on LinkedIn: https://www.linkedin.com/in/henrythe9th• Hex Threads: https://hex.tech/product/threads• Product management theater | Marty Cagan (Silicon Valley Product Group): https://www.lennysnewsletter.com/p/product-management-theater-marty• Grant LaFontaine on LinkedIn: https://www.linkedin.com/in/grantlafontaine• Logan Head on LinkedIn: https://www.linkedin.com/in/logan-head• Emmett Shear on LinkedIn: https://www.linkedin.com/in/emmettshear• Kara Swisher on X: https://x.com/karaswisher• Jeff Bezos: Amazon and Blue Origin—Lex Fridman Podcast: https://www.youtube.com/watch?v=DcWqzZ3I2cY• Star City on AppleTV+: https://tv.apple.com/us/show/star-city/umc.cmc.2l8p785osmtmiyk64bh6tfde1• For All Mankind on AppleTV+: https://tv.apple.com/us/show/for-all-mankind/umc.cmc.6wsi780sz5tdbqcf11k76mkp7• Service NSW Mobile App: https://www.service.nsw.gov.au/services/service-nsw-mobile-app• Rudyard Kipling: https://en.wikipedia.org/wiki/Rudyard_Kipling• E-fish.com: https://www.e-fish.com—Recommended books:• The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers―Straight Talk on the Challenges of Entrepreneurship: https://www.amazon.com/dp/0062273205• The Purpose Driven Church: Every Church Is Big in God's Eyes: https://www.amazon.com/dp/0310201063• Babel: Or the Necessity of Violence: An Arcane History of the Oxford Translators' Revolution―An Historic Fantasy of Dark Academia: https://www.amazon.com/Babel-Necessity-Violence-Translators-Revolution/dp/0063021439—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

為日常注入質感與溫度，邀你訂閱 IC之音電子報：https://pse.is/8wpwy6--主持人曾陽晴本集分享，使徒保羅在凱撒利亞受審的歷程。《本集經文》使徒行傳25:20-26:19這些事當怎樣究問，我心裡作難，所以問他說：你願意上耶路撒冷去，在那裡為這些事聽審嗎？但保羅求我留下他，要聽皇上審斷，我就吩咐把他留下，等我解他到該撒那裡去。亞基帕對非斯都說：我自己也願聽這人辯論。非斯都說：明天你可以聽。第二天，亞基帕和百尼基大張威勢而來，同著眾千夫長和城裡的尊貴人進了公廳。非斯都吩咐一聲，就有人將保羅帶進來。非斯都說：亞基帕王和在這裡的諸位啊，你們看這人，就是一切猶太人，在耶路撒冷和這裡，曾向我懇求、呼叫說：不可容他再活著。但我查明他沒有犯什麼該死的罪，並且他自己上告於皇帝，所以我定意把他解去。論到這人，我沒有確實的事可以奏明主上。因此，我帶他到你們面前，也特意帶他到你亞基帕王面前，為要在查問之後有所陳奏。據我看來，解送囚犯，不指明他的罪案是不合理的。亞基帕對保羅說：准你為自己辯明。於是保羅伸手分訴，說：亞基帕王啊，猶太人所告我的一切事，今日得在你面前分訴，實為萬幸；更可幸的，是你熟悉猶太人的規矩和他們的辯論；所以求你耐心聽我。我從起初在本國的民中，並在耶路撒冷，自幼為人如何，猶太人都知道。他們若肯作見證就曉得，我從起初是按著我們教中最嚴緊的教門作了法利賽人。現在我站在這裡受審，是因為指望神向我們祖宗所應許的；這應許，我們十二個支派，晝夜切切的事奉神，都指望得著。王啊，我被猶太人控告，就是因這指望。神叫死人復活，你們為什麼看作不可信的呢？從前我自己以為應當多方攻擊拿撒勒人耶穌的名，我在耶路撒冷也曾這樣行了。既從祭司長得了權柄，我就把許多聖徒囚在監裡。他們被殺，我也出名定案。在各會堂，我屢次用刑強逼他們說褻瀆的話，又分外惱恨他們，甚至追逼他們，直到外邦的城邑。那時，我領了祭司長的權柄和命令，往大馬色去。王啊，我在路上，晌午的時候，看見從天發光，比日頭還亮，四面照著我並與我同行的人。我們都仆倒在地，我就聽見有聲音用希伯來話向我說：掃羅！掃羅！為什麼逼迫我？你用腳踢刺是難的！我說：主啊，你是誰？主說：我就是你所逼迫的耶穌。你起來站著，我特意向你顯現，要派你作執事，作見證，將你所看見的事和我將要指示你的事證明出來。；我也要救你脫離百姓和外邦人的手。我差你到他們那裡去，要叫他們的眼睛得開，從黑暗中歸向光明，從撒但權下歸向神；又因信我，得蒙赦罪，和一切成聖的人同得基業。亞基帕王啊，我故此沒有違背那從天上來的異象；

We talk AEW Redemption and Night 1 of WWE SummerslamNew womens IC champion. WWE Radio MCMG Debut Grayson WallerMara SadeStreet Clothes Wrestler of the WeekThis and much more linktr.ee/boot2theface Become a supporter of this podcast: https://www.spreaker.com/podcast/boot-2-the-face--3558574/support.

✉️掌握前瞻趨勢與科技脈動，立即訂閱 IC之音電子報：https://pse.is/8wpwwx從產業經濟到AI時代的推論經濟，看得見的變化來自於產業供應鏈的合縱連橫，同時也不能夠忽略的是，隱藏在其中的價值鏈轉變的趨勢。當情境變化時，價值的產製者、消費者，以及決定價值的方式也與以往不同，台灣如何看懂當中可能出現的新規則與新挑戰？媒體在這樣的情境下，又該如何重新探索真正的價值所在？黃欽勇Facebook https://www.facebook.com/hwangchinyeong

Hello and Welcome to the DX Corner for your weekly Dose of DX. I'm Bill, AJ8B.The following DX information comes from Bernie, W3UR, editor of the DailyDX, the WeeklyDX, and the How's DX column in QST. If you would like a free 2-week trial of the DailyDX, your only source of real-time DX information, just drop me a note at thedxmentor@gmail.comLike many of you, I am an avid CQ DX Marathon participant. To help identify those stations that may not be rare, but also not common, I will bring it to your attention by announcing MARATHON ALERT. This will let you know that based on my experience, this activation is one that you will want to make part of your Marathon Chase! Let's get started.{Marathon Alert} OH0 – Aland Islands - OH0ERF will be activated from August 5 to August 12, by a group of German hams operating holiday style. The team plans to operate all bands from 160 m through 10 m, using CW, SSB, and FT8, with 6 m added if conditions allow. Equipment includes an IC-7610, two IC-7300 transceivers, and an ACOM 700S amplifier. Planned antennas include a 3-band delta loop, a 3-element 3-band Mosley beam, 40 m and 160 m verticals, and various wire antennas.{Marathon Alert} R1FJ – Franz Jozef Land - R7AL, Vasily, and his team should be QRV around August 15 for 15 days. Five hundred kilograms of gear is already in Murmansk, their departure point, with the ops bringing another 300 kg.{Marathon Alert} UZBEKISTAN – UK - Darek, SP9DLM and his son Greg SN9GM are QRV using UK/calls until August 15.{Marathon Alert} T2 – Tuvalu - JK1JXZ, Masaaki “Aki” Iwasawa, is QRV with the callsign T2JK from Funafuti until August 7, 80-6M. On weekdays he expects to be on the air after 5 PM local time, and on the weekend, he will be on all day long. {Marathon Alert} E5/S – South Cook Islands - ZL2KE, Steve, is QRV as E51KEE from the South Cook Islands until August 14th, mainly on CW with some SSB, likely focusing on 30 and 40 meters unless propagation improves. Another Steve, ZL4CZ, is QRV as E51CZZ until August 6th on SSB. Neither operator is using FT8 or other digital modes. {Marathon Alert} TU – Ivory Coast - F5HPE, Fredy, is underway to the Ivory Coast, where he has the TU5MM callsign. He will be joining TU5JZ, Mathruin. Fredy expects to be there until the 22th, operating from the Motobé Radio Club on 80, 40, 20, 15 and 10M SSB and FT8, and will give FT4 a try.{Marathon Alert} OJ0 - Märket Reef - OH6BG says a DXpedition is coming up August 15-22, with OJ0JR, Henri; and OJ0YL, Anne. He points out that the reef is the most exposed location in the Baltic Sea, “more rock than island, more sea than shelter.” To be dealt with before the first QSO is made is the weather, landing conditions, antennas, power and constant wind and waves, he says. They plan 80-10 CW, SSB and FT8.This week, the DX Mentor Podcast will feature a discussion with two operators from the upcoming 2027 VP0SG DXpedition to the South Georgia Islands. Check it out and let me know what you think. If you have questions or need information, just drop me a note at thedxmentor@gmail.comUntil next week, this is Bill, AJ8B saying 73 and thanks to my XYL Karen for her love and support. I Hope to hear you in the pileups! Have a great DX week!

Rediffusion Plongez dans la traque de Gilles Bertin, chanteur punk devenu braqueur. Icône du groupe Camera Silens dans les années 80, il se perd peu à peu dans la drogue et la marginalité. Jusqu'à ce jour de 1988, où il participe à l'un des braquages les plus spectaculaires de l'époque : le hold-up d'une fourgonnette Brink's à Toulouse, sans un coup de feu. Pendant près de trente ans, il disparaît, échappant à la justice, vivant sous une fausse identité en Espagne. Quand il refait surface, c'est un vieil homme malade, décidé à affronter enfin son passé. Entre cavale, silence et rédemption, son histoire interroge sur la frontière fragile entre révolte et perdition. Crédits : Production : Bababam  Textes : Pierre Serisier Voix : Anne Cosmao, Aurélien Gouas Learn more about your ad choices. Visit megaphone.fm/adchoices

Observación de cielo profundo realizada cerca de la luna nueva de julio con el Dobson de 250 mm f/6,3 y 1.600 mm de distancia focal. La noche presenta unas condiciones típicamente veraniegas: calor, un cielo algo turbulento y menos contrastado de lo deseable. Previendo que el Dobson de 40 cm no iba a poder aprovechar todo su potencial, decido sacar el telescopio de 25 cm, que aparece menos de lo que merece en estas sesiones y también tiene derecho a salir de vez en cuando a pastar fotones. El recorrido transcurre principalmente por las regiones de la Vía Láctea del Cisne, Vulpecula y Cefeo. En lugar de acudir a los grandes objetos que suelen concentrar toda la atención, la propuesta consiste en detenerse ante cúmulos abiertos y nebulosas planetarias menos visitados, además de levantar la vista del ocular para observar una enorme región oscura de nuestra galaxia. Objetos observados: ✨ NGC 6819 – Cúmulo abierto en el Cisne. Pequeño, rico y muy concentrado, a bajos aumentos puede recordar a un cúmulo globular antes de comenzar a resolverse en multitud de estrellas débiles. NGC 6818, la Pequeña Joya – Nebulosa planetaria en Sagitario. Un diminuto disco de elevado brillo superficial que admite aumentos y puede llegar a insinuar una estructura anular. ✨ NGC 6866, el Cúmulo del Ave Fragata – Cúmulo abierto en el Cisne. Una concentración inicialmente nebulosa que se va desgranando poco a poco y en la que algunos observadores reconocen la figura de un ave con las alas extendidas. ✨ NGC 6940 – Cúmulo abierto en Vulpecula. Extenso, rico y lleno de cadenas, parejas y pequeñas agrupaciones que invitan a recorrer lentamente todo el campo. Saco de Carbón del Norte – Nebulosa oscura en el Cisne. Una gran acumulación de polvo interestelar que interrumpe visualmente la Vía Láctea y que se observa mejor apartando el ojo del telescopio. NGC 7008, la Nebulosa del Feto – Nebulosa planetaria en el Cisne. Irregular, moteada y con varias condensaciones y estrellas superpuestas que le proporcionan un aspecto especialmente complejo. NGC 7027, el Rectángulo Verde – Nebulosa planetaria en el Cisne. Muy pequeña, brillante y exigente con la estabilidad atmosférica; a altos aumentos puede revelar una forma alargada y aproximadamente rectangular. ✨ Trumpler 37 – Gran cúmulo abierto en Cefeo, asociado a la nebulosa IC 1396. Una agrupación joven, enorme y poco concentrada cuyos límites se confunden con el fondo estelar de la Vía Láctea. El episodio terminó siendo más largo de lo previsto. La localización de los objetos apenas consumió tiempo y disponer de la información preparada en la tableta permitió dedicar bastantes minutos a observar y comentar cada uno de ellos. La comodidad durante la locución no se correspondió con la postura ante el telescopio. La silla-escalera que utilizo con el Dobson de 40 cm apenas resulta útil con el de 25, así que buena parte de la noche transcurrió doblando el lomo de mala manera. Otra cuestión pendiente de resolver, porque la comodidad al ocular influye directamente en el tiempo y la calidad de la observación. Antes de recoger todavía quedaron unos minutos para Saturno, que comenzaba a ganar una altura razonable sobre el horizonte. Una noche sin grandes clásicos, sin condiciones perfectas y sin heroicidades, pero con suficientes objetos interesantes como para recordar que, en regiones tan ricas como el Cisne, siempre merece la pena mirar un poco más allá. Enlaces y formas de contacto: https://linktr.ee/luces_x

Welcome back to our weekly episodes, in today's episode we talk about wrestlers who have lost some steam, we cover SNME, Smackdown and RAW. We give our predictions for SummerSlam and we get into some storylines for Paige!!! All this and so much more!!! CHEERS!!!JERKING THE CURTAINROUND TABLE OF TOPICSNEWSWWE has a social influencer problem Wrestlers kissing in the ring???Santana is now Cruz Montana??? Unreal season 3 is here and it doesn't suck Tom Brady WWE bound???Grayson Waller cooks NXT…..last opportunity to save his character???“You Just Made the List” Give me a storyline for Paige SMACKDOWNThe exchange between Nick and Gunther has me salivating…..could they take match of the night???Kathy Kelley yelling at Sami Zayn is great for business I'm liking angry Sami and everyone getting tired of him Nikki Bella returns for a 6 woman tag against Fatal Influence Gunther is cooking in the locker room Solo spittin like LA Knight is great for business Too many Barbie's in the locker room, is it Chelsea's time???Priest sends a message from the locker room and Cody likes the heat Cody getting booed is interesting, helluva exchange between him and Punk…..exactly what we expected Trying to wrap my brain around Charlottes win to move forward???Finn closes the show with a win SNMEStephanie McMahon blesses SNME , always great to have her back Fatal Influence is your new tag team champions…..how long do they hold them and what's next for Paige and Brie The human head JD and Doms disrespect of NY, acting exactly like their fans….DanHausen finds a KAT for the win Lyra continues to cook with this new character, what a wild match and interesting ending Punk and Cody look great together……let's get them against Seth and Oba YESSSSS NICK ALDIS!!!!Why does Seth get stuck with the loser……nobody wants Haliburton on their team, hell I would have taken Kittles or GronkRAWThe games continue with Brock, Oba weighs in at 302!!! Oba looks in great shapeVision defeats Alpha Academy……that chair shot was vicious Right choice by not pushing Bron, needs more time Vignettes were solid tonight, gets me more excited for SS Not sure about what I saw with Hendry and DanHausen??? Ryan Garcia was a terrible addition Has J'Von Evans hype died down? Moneys on the pole could be fire!!! LA Knight is cooking the Bloodline……just saying Speaking of wrestlers who lost heat…..let's add Jacob Fatu to the list Great match, Raquel is your new IC champion!!! Let's gooooooSeth stomps his way to Summerslam, gotta give this last round to him TNAOrder 4 is cooking like never before with the new addition of Mila Moore Correction Elayna Black is not your first TNA TV Knockouts Champ, the tourney is still going The Righteous lose to the Hardy's in the Righteous Deletion match 10 count with Always Ready AriannaCheck out the Smackdown Siblings on TikTok @ariannaandthomasEpisodes dropping weekly!!!Follow us on TikTok @the.funkaholiks.pod THEE POD THAT TALKS WHAT THEY LOVE 

This week the boys reconvene to talk the latest in professional wrestling. The 1 count is AEW specifically the AEW Redemption PPV. Kenny Omega retains over Kevin Knight but the tension between Kenny and Will Ospreay boils over after the match. Willow finally reaches the mountaintop by defeating Thekla for the AEW Women's title. The Demand achieve their goal of championship gold beating the Conglomeration for the Trios titles. The 2 count is WWE. The boys discuss the Joe Hendry/Danhausen concert. Chelsea Green and Charlotte Flair advance to the Ladder Match at SummerSlam. Raquel Rodriguez wins the IC title over Sol Ruca. Nick Aldis vs Gunther is official after their contract signing. SummerSlam predictions made along the way. The 3 count is a review of nights 6-8 of the G1 Climax Tournament from New Japan Pro Wrestling. Odds and ends to close the pod! Available on all audio podcast platforms. Listen Share Subscribe Repeat! Rate and review on Apple and Spotify! AEW WWE 49:35 G1 1:22:45

✉️精彩話題與深度觀點，訂閱 IC之音電子報一手掌握：https://pse.is/8wpx6m★★歡迎點此寫電子小紙條給主持人，分享您對節目的感想。失智的人，真的只是遺忘嗎？還是那些看似零碎的片段，正悄悄訴說一段被時代掩埋的人生故事？劇作家、劇場編導周慧玲老師，分享2026臺北藝術節創作社劇團最新作品《孃孃狂言》。本劇由徐堰鈴、黃宇琳主演，以兩位九旬高齡女性為主角，透過失智、失語與夢境交錯的敘事，逐步拼湊她們自1947年上海遷居臺灣的人生軌跡。「孃孃」的方言意涵究竟是……？它不僅指涉姑姑，更象徵超越血緣的親密情感與文化傳承。劇中藉由兩位女性「狂言囈語」般的對話，引領觀眾走進記憶斷裂的縫隙，重新思索個人史、家族史與集體歷史如何彼此交織。同時，作品也回望四〇年代上海女性追求自主的「不嫁主義」，描繪她們如何在動盪年代堅持人生選擇，卻也承受孤獨與漂泊的人生代價。《孃孃狂言》大量運用上海話與夢境結構，營造如記憶拼圖般層層展開的劇場語言，讓觀眾在追尋真相的過程中，重新凝視那些被遺忘的人與故事。演員卡司、角色與劇情發展有哪些意想不到的碰撞呢？它來自於真實事件嗎？本集將帶您走入周慧玲充滿人文關懷的創作世界，一同思索——當歷史終將遺忘，我們又該如何記住自己，以及那些曾經活過的人？別忘了2026年9月4日至9月6日走進臺北表演藝術中心球劇場，欣賞2026臺北藝術節：創作社劇團《孃孃狂言》。————————————企劃︱王安祈、羅仕龍、周信宏製作︱周信宏

Join SantiZap and SanchoWest as they break down the latest episode of WWE RAW, including Raquel Rodriguez capturing the Intercontinental Championship, the debate over whether Finn Bálor is finally receiving a meaningful push, Vince McMahon's ongoing arbitration discussion, WWE's new SiriusXM radio channel, the unforgettable Joe Hendry and Danhausen segment, what's next for Brock Lesnar after SummerSlam, and the latest Heat Order rankings. Plus, plenty of laughs, hot takes, and wrestling discussion from start to finish.TIMESTAMPS0:00 Club WWE membership joke begins12:50 Raquel Rodriguez wins IC title29:54 Is Finn Balor being pushed?41:54 Vince McMahon arbitration discussion51:52 SiriusXM channel becomes WWE Radio1:11:06 Dan Housen and Joe Hendry segment1:24:21 What comes after Brock Lesnar?1:33:37 New Heat Order rankings

This week the boys reconvene to talk the latest in professional wrestling. The 1 count is AEW specifically the Redemption PPV. Kenny Omega retains over Kevin Knight but the tension between Kenny and Will Ospreay boils over after the match. Willow finally reaches the mountaintop by defeating Thekla for the AEW Women's title. The Demand achieve their goal of championship gold beating the Conglomeration for the Trios titles. The 2 count is WWE. The boys discuss the Joe Hendry/Danhausen concert. Chelsea Green and Charlotte Flair advance to the Ladder Match at SummerSlam. Raquel Rodriguez wins the IC title over Sol Ruca. Nick Aldis vs Gunther is official after their contract signing. SummerSlam predictions made along the way. The 3 count is a review of nights 6-8 of the G1 Climax Tournament from New Japan Pro Wrestling. Odds and ends to close the pod!Available on all audio podcast platforms. Listen Share Subscribe Repeat! Rate and review on Apple and Spotify!AEWWWEG1

This week the boys reconvene to talk the latest in professional wrestling. The 1 count is AEW specifically the Redemption PPV. Kenny Omega retains over Kevin Knight but the tension between Kenny and Will Ospreay boils over after the match. Willow finally reaches the mountaintop by defeating Thekla for the AEW Women's title. The Demand achieve their goal of championship gold beating the Conglomeration for the Trios titles. The 2 count is WWE. The boys discuss the Joe Hendry/Danhausen concert. Chelsea Green and Charlotte Flair advance to the Ladder Match at SummerSlam. Raquel Rodriguez wins the IC title over Sol Ruca. Nick Aldis vs Gunther is official after their contract signing. SummerSlam predictions made along the way. The 3 count is a review of nights 6-8 of the G1 Climax Tournament from New Japan Pro Wrestling. Odds and ends to close the pod!Available on all audio podcast platforms. Listen Share Subscribe Repeat! Rate and review on Apple and Spotify!AEWWWE 49:35G1 1:22:45

This week the boys reconvene to talk the latest in professional wrestling. The 1 count is AEW specifically the Redemption PPV. Kenny Omega retains over Kevin Knight but the tension between Kenny and Will Ospreay boils over after the match. Willow finally reaches the mountaintop by defeating Thekla for the AEW Women's title. The Demand achieve their goal of championship gold beating the Conglomeration for the Trios titles. The 2 count is WWE. The boys discuss the Joe Hendry/Danhausen concert. Chelsea Green and Charlotte Flair advance to the Ladder Match at SummerSlam. Raquel Rodriguez wins the IC title over Sol Ruca. Nick Aldis vs Gunther is official after their contract signing. SummerSlam predictions made along the way. The 3 count is a review of nights 6-8 of the G1 Climax Tournament from New Japan Pro Wrestling. Odds and ends to close the pod!Available on all audio podcast platforms. Listen Share Subscribe Repeat! Rate and review on Apple and Spotify!AEWWWE 49:35G1 1:22:45

Uma FEVE de 39% é IC com fração reduzida. Uma de 41%, não. Você já parou para pensar em que evidência fisiopatológica esse corte se apoia? A resposta incomoda: nenhuma. Ele é arbitrário e a 2ª Definição Universal de Insuficiência Cardíaca finalmente assume isso.Neste episódio, Dr. William Batah e Dr. Plínio Wolf destrincham o documento que reorganiza o modo como diagnosticamos, classificamos e acompanhamos IC. Não é um episódio sobre tratamento. É sobre o alicerce conceitual em cima do qual todo o tratamento é construído e que acabou de mudar.

Inside Carolina basketball analyst Rob Harrington joins Tommy Ashley to discuss the current state of the Carolina basketball program, focusing on Michael Malone's hiring and its impact within the Carolina Family and beyond. Harrington highlights Malone's creative staffing approach, blending new and old voices, and his use of both aspects of his coaching staff in  recruiting both here and abroad. Harrington also points to Malone's emphasis on alumni outreach and the inclusion of the branches of the family tree as positive points for the program's future. Finally, the IC duo touches on the need for instate talent on the roster as a means to secure fan support and engagement in the new era of athletics. Visit the No. 1 site for UNC sports coverage and community: http://www.InsideCarolina.com Founded in 1994, Inside Carolina is universally viewed as the authority on Tar Heel sports and recruiting. With relentless, unparalleled year-round coverage, and the largest online community of always-engaged UNC fans, the slogan is true: “There is no offseason at Inside Carolina.” **Call to Action:** **Subscribe:** Follow 'Inside Carolina' wherever you get your podcasts to never miss an episode! **Review:** Leave us a 5-star review on Apple Podcasts or Spotify to help us reach more Tar Heel fans! **Visit:** Explore http://www.InsideCarolina.com for breaking news, recruiting updates, and expert commentary on all things UNC sports.This show is brought to you by Inside Carolina, the No. 1 site for UNC sports coverage and community. Visit http://www.InsideCarolina.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Da Sharpshooters Wrestling Podcast as they recap this weeks episode of #WWE #WWERAW #SUMMERSLAM NEWS I don't know. Why come here for news. Like go to the Sapp man or someone like that for real news. We just talking nonsense son. RAW REVIEW Roman and Rollins face-off OBA and Brock face-off Danhausen and Hendry has a horrible concert Sol Ruca loses title. Another short black title reign Raquel wins IC title Maxine remains evil. The Vision takes down Otis We may cover AEW Redemption We may not. Also it's the Lord's Birthday wish him happy birthday folks

啟發多元思考與學習成長，歡迎訂閱 IC之音電子報：https://pse.is/8wpx2c現在的人都健忘，有一說是AI太順手！隨時問就好了！唾手可得。但恩寵，可不是這樣，蒙恩、得福，輕易健忘，就太可惜了。　＊＊＊馬可福音 8:13-21他就離開他們，又上船往海那邊去了。門徒忘了帶餅；在船上除了一個餅，沒有別的食物。耶穌囑咐他們說：你們要謹慎，防備法利賽人的酵和希律的酵。他們彼此議論說：這是因為我們沒有餅吧。耶穌看出來，就說：你們為什麼因為沒有餅就議論呢？你們還不省悟，還不明白嗎？你們的心還是愚頑嗎？你們有眼睛，看不見嗎？有耳朵，聽不見嗎？也不記得嗎？我擘開那五個餅分給五千人，你們收拾的零碎裝滿了多少籃子呢？他們說：十二個。又擘開那七個餅分給四千人，你們收拾的零碎裝滿了多少筐子呢？他們說：七個。耶穌說：你們還是不明白嗎？。 

On this jam packed episode of the TFW podcast, we discuss our thoughts on season 3 of ‘WWE Unreal' and what our biggest takeaway's were, this past Monday's head scratching episode of ‘WWE RAW', we also preview this weekend's 2 night SummerSlam event that we'll be in attendance for, AEW Redemption from this past Sunday, + MUCH MORE!⏱️ Chapters:0:00 Intro/SummerSlam Personal Memories9:43 Unreal Season 3 thoughts and biggest takeaways40:13 Bron Breakker and how he was presented on Unreal47:03 Sol Ruca losing the IC title this past Monday on ‘RAW' and WWE continuing to drop the ball on their young stars57:45 ‘RAW' talk including the bad SummerSlam go home segments and discussion around younger talent getting title reigns early in their career1:17:04 Sami Zayn's incredible backstage promo from ‘SmackDown' this past Friday1:19:58 SummerSlam night 1 preview1:41:50 SummerSlam night 2 preview2:00:20 AEW Redemption talk including Matt's thoughts on Kyle Fletcher, the post main event match angle with Jon Moxley, Kenny Omega, and Will Ospreay, along with discussing Thekla dropping the world title a month before ‘All In' & what should happen with Willow vs Mercedes Moné at ‘All In'

In deze aflevering van De Nieuwe Wereld gaat Talitha Muusse in gesprek met emeritus hoogleraar IC-geneeskunde Armand Girbes en GZ-psycholoog Roelien den Ouden over de huidige transgenderzorg in Nederland.Aanleiding voor het gesprek is het recente rapport van de Gezondheidsraad, dat concludeerde dat de Nederlandse aanpak op dit gebied zorgvuldig genoeg is. Girbes en Den Ouden, mede-ondertekenaars van een kritisch opiniestuk hierover in Trouw, uiten hun ernstige zorgen over deze conclusie. Volgens hen ontbreekt de medisch-wetenschappelijke onderbouwing voor de ingrijpende en onomkeerbare behandelingen bij minderjarigen zoals puberteitsblokkers en chirurgische ingrepen nagenoeg volledig.----------------Steun DNWMaak het geluid van de Nieuwe Wereld mogelijk. Zonder uw steun geen DNW! Word lid of doneer:

Tommy Dreamer reacts to the go-home RAW to SummerSlam including Raquel Rodriguez winning the Women's IC title, along with the final war of words between Roman Reigns and Seth Rollins ahead of their match at SummerSlam. To visit our partners at Chewy, click here. The Master's Class is now available on its own podcast feed! SUBSCRIBE NOW to hear over 50 episodes of Dave, Bully, Mark, and Tommy taking you behind the scenes like only they can, plus BRAND NEW episodes every week. Subscribe to SiriusXM Podcasts+ to listen to new episodes of Busted Open ad-free and get exclusive access to bonus episodes. Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Wai Ting & Jack Wannan review the final WWE Raw before SummerSlam as Reigns & Rollins go face-to-face, Brock & Oba weigh-in, Sol Ruca defends the IC title against Raquel Rodriguez, and Danhausen & Joe Hendry perform in concert.The XL Edition continues at POSTwrestlingCafe.com with News of the Day and Feedback, ad-free and timestamped.Redemption final thoughtsAEW announces ‘Rebel Heart' Dynamite specialSummerSlam Night 1 & 2 lineups announcedMike Santana reveals new ring nameG1 updateNXT and AEW lineups

VOV1 - Việc tái tạo giọng nói giống thật là ước mơ của nhiều người bị mất giọng nói do bệnh tật và mới đây, một nhóm nghiên cứu của Australia đã biến giấc mơ này thành sự thật và hứa hẹn sẽ tạo ra niềm vui cho nhiều gia đình trong thời gian tớiCác nhà nghiên cứu thuộc công ty công nghệ y tế Laronix có trụ sở tại thành phố Brisbane của Australia đã sử dụng trí tuệ nhân tạo để chế tạo ra một thiết bị có thể có tái tạo giọng nói giống như thật. Thiết bị có tên gọi là AVA Voice. Đây là thiết bị không xâm lấn, sử dụng trí tuệ nhân tạo để tái tạo giọng nói tự nhiên của người bệnh dựa trên đoạn ghi âm giọng nói thật trước đó. Với sự tiến bộ của trí tuệ nhân tạo, giọng nói tái tạo được chuyển đổi trong thời gian thực và có độ chân thực cao, giống với giọng nói của người bệnh trước khi bị mất giọng.Ưu điểm nổi bật của sản phẩm này là giọng nói được tạo ra dựa trên đoạn ghi âm dài 10 giây với chất lượng âm thanh bình thường nên người bệnh rất dễ có thể tìm kiếm âm thanh gốc từ một đoạn video ngắn, hay tin nhắn thoại….để cho AI nhân bản. Điểm đặc biệt thứ hai là phần mềm này độc lập với ngôn ngữ nên có thể tạo ra giọng nói của nhiều ngôn ngữ với các giọng điệu và phong cách khác nhau. Điểm đặc biệt thứ ba là giọng nói được tái tạo rất giống với giọng ban đầu nên rất có ý nghĩa với nhiều gia đình.Tiến sỹ Farzaneh Ahmadi, nhà sáng lập Laronix khẳng định công nghệ này có thể giúp đỡ những người bị cắt dây thanh quản, bị mắc ung thư vòm họng, bệnh nhân Parkinson và các bệnh lý thần kinh khác tìm lại giọng nói của mình.   Không chỉ dừng lại ở ứng dụng này, tiến sỹ Ahmadi cho biết, nhóm nghiên cứu cũng đang tìm cách sử dụng công nghệ này với các bệnh nhân tạm thời không nói được sau khi sử dụng máy thở cũng như những người bị rối loạn ngôn ngữ và hỗ trợ giáo viên, diễn viên, ca sỹ và những người sử dụng giọng nói chuyên nghiệp khác vào những ngày họ tạm thời mất giọng./.Việt Nga/VOV Australia Ảnh minh họa

掌握前瞻趨勢與科技脈動，立即訂閱 IC之音電子報：https://pse.is/8wpwwx ------------------------------AI不只改變產品，也正在改寫整個半導體產業的競爭版圖。今年COMPUTEX最值得關注的，不只是新晶片，而是晶片大廠紛紛「撈過界」：輝達重返PC市場，高通跨足資料中心，聯發科則攜手輝達、Google切入AI PC與AI ASIC。當GPU、CPU、AI PC、資料中心與邊緣AI的界線愈來愈模糊，一場全新的運算大戰已經展開。本集《科技領航家》，邀請今周刊財經顧問、資深科技產業觀察家林宏文，帶大家解析AI如何重塑晶片產業新格局，以及台灣供應鏈將迎來哪些新機會。------------------------------製作 | 李翊嘉

Triple H and Stephanie are finally reconciling backstage when there's a knock on the door. It's the police, responding to a spousal abuse complaint. Triple H gets put in the car. Mick Foley runs up asking what about his main event. Tommy Blacha and Rob Pasbani recap the August 31, 2000 edition of WWF SmackDown, live from the Crown Coliseum in Fayetteville, North Carolina.The show opens with Stephanie explaining her whereabouts since SummerSlam, but Angle immediately counters with footage of Chyna and Triple H's hug on Raw, then footage of Chyna tending to Triple H after their match. He accuses Triple H of domestic violence, Chyna comes to the ring to defend herself, and Angle gives her the Angle Slam. Tommy and Rob call it A+ soap opera from start to finish.With Triple H in a police car, the main event becomes Eddie vs Angle one-on-one, Chyna in Eddie's corner and Stephanie forced into Angle's. Angle cheats to win using the IC title belt. Chyna beats up Angle post-match, turns to face Stephanie, who tries to talk her way out. Chyna grabs her by the hair and DDTs her to one of the loudest pops of the night.Other major discussion points include:Edge and Christian mock Monday's mini Hardies in a promoAl Snow pins Saturn with a dragon sleeper submission to win the European Championship in a match involving Head, a stopped pinfall by Teddy Long, and a finish nobody saw comingUndertaker vs Benoit for the number one contendership ends by disqualification when Kane interferesKaientai get caught laughing at the Dudleys' TLC losses, leading to the APA costing the Dudleys the match before the Dudleys table Funaki as a receiptTrish Stratus shows up to Val Venis demanding respect, Val tears into her, telegraphing his turn to Right to CensorTommy covers Sunday Night Heat: Crash Holly, Dean Malenko, Kane's mask promo targeting The Rock, and what happened to T&A and the APAJust announced! Rob and Tommy announce the launch of their brand new Patreon featuring watch-alongs for classic matches. New watch along added: RVD/Sabu vs. Hayabusa/HakushiThe first watch along includes Rock vs Hogan and Buddy Rogers vs. Pat O'Connor.Please subscribe at https://patreon.com/talesfromtheattitudeeraChapters:00:00 Intro2:51 Crown Coliseum, Fayetteville, NC 5:18 Tommy's Story: Steiners at a WCW Clash of Champions in This Arena7:32 Back to SmackDown / Storyline Momentum vs Raw7:56 Recap Focuses on Raw, Not SummerSlam 8:15 Stephanie McMahon Opens the Show: The Alibi 9:36 Kurt Angle Interrupts Stephanie 12:54 Chyna Comes to the Ring14:34 Angle Slam on Chyna / Eddie Runs Out / Segment Analysis15:53 Triple H Arrives at the Arena / Stephanie Slaps Him16:43 Road Dog Promo at Fort Bragg / vs Bull Buchanan 18:58 Chyna and Eddie Approach Foley19:41 Edge and Christian Promo / Six-Person Tag vs Hardy Boyz and Lita23:11 Backstage: Angle Approaches Stephanie About the Kiss24:45 European Championship: Al Snow vs Saturn27:42 Triple H and Stephanie Reconcile Backstage29:04 Al Snow's Post-Match Promo: "Citizens of Europea"30:49 Number One Contender Match: Undertaker vs Benoit / Kane and Rock Arrive32:13 The DQ Finish Problem: "Only Pinfall or Submission" Goes Out the Window35:55 Tazz Promo / Jericho Cuts Him Off40:00 Tazz vs Chris Jericho / Lawler Interferes41:05 What Went Wrong with Tazz in WWF / Tommy's Take on Vince's Blind Spot44:06 Just Joe Tells Angle About the Loud Noises / Angle Calls the Cops44:34 Kaientai Mock the Dudleys / Dudleys vs Kaientai / APA Interfere46:16 Backstage: Kurt Finds Stephanie / Trish Confronts Val Venis47:20 Rikishi vs Val Venis / Right to Censor Interference47:54 Chyna and Eddie Backstage Before the Main Event48:25 Triple H and Stephanie Make Up / Cops Arrive with Spousal Abuse Complaint49:24 Foley Changes the Main Event / Stephanie Forced Into Angle's Corner50:43 Main Event: Eddie Guerrero vs Kurt Angle51:05 Angle Wins with the IC Belt / Chyna DDTs Stephanie Post-Match54:21 Sunday Night Heat Rundown56:21 Closing Thoughts and Sign-Off Hosted on Acast. See acast.com/privacy for more information.

Greg and Dane head on out to Nashville for WWF In Your House II as we hear Double J singing his hit song "With My Baby Tonight" before he defends the IC title against HBK. We also have a big main event with Diesel and Sid in a Lumberjack Match, along with Barry Didinsky hawking silly merch. Plus, we believe this is the only WWF PPV that has an appearance from Mantaur!

掌握前瞻趨勢與科技脈動，立即訂閱 IC之音電子報：https://pse.is/8wpwwx—當AI浪潮興起，大家都用過ChatGPT、Gemini，甚至使用自動幫我們工作的AI代理人。台灣家長、學生、上班族與每一個人，面對快速變化的科技語言，都需要更清楚的學習路徑。本集節目特別邀請中原大學智慧運算與量子資訊學院 系主任暨研究所所長 胡筱薇，她不但是AI教育專家、專業AI節目《大智若魚》主持人，是三個孩子的母親，也是IC之音本年度的品牌大使。透過她的觀點，帶我們認識AI時代的優質教育，在台灣有哪些機會與可能性。在AI教育與親子教養的交會點上，如何理解AI、如何陪伴孩子使用AI？跟AI「共學」，實際嗎？孩子想得到新知識，AI一查就有，我們還能讓孩子真正學到有用的能力嗎？聯合國永續發展目標SDGs的優質教育，不僅關注公平、高品質的教育，也提倡終身學習。AI的發達，尤其生成式AI的出現，是否為終身學習帶來更有利的條件？對於全齡的終身學習，AI工具帶來哪些好處，又有哪些我們值得注意的認知陷阱呢？歡迎收聽！—製作團隊製作人：李知昂企劃團隊：李知昂 / 莊俐心 

為日常注入質感與溫度，邀你訂閱 IC之音電子報：https://pse.is/8wpwy6--主持人曾陽晴本集分享，使徒保羅在凱撒利亞受審的關鍵轉折。《本集經文》使徒行傳24:22-25：19腓力斯本是詳細曉得這道，就支吾他們說：且等千夫長呂西亞下來，我要審斷你們的事。於是吩咐百夫長看守保羅，並且寬待他，也不攔阻他的親友來供給他。過了幾天，腓力斯和他夫人─猶太的女子土西拉─一同來到，就叫了保羅來，聽他講論信基督耶穌的道。保羅講論公義、節制，和將來的審判。腓力斯甚覺恐懼，說：你暫且去吧，等我得便再叫你來。腓力斯又指望保羅送他銀錢，所以屢次叫他來，和他談論。過了兩年，波求非斯都接了腓力斯的任；腓力斯要討猶太人的喜歡，就留保羅在監裡。非斯都到了任，過了三天，就從該撒利亞上耶路撒冷去。祭司長和猶太人的首領向他控告保羅，又央告他，求他的情，將保羅提到耶路撒冷來，他們要在路上埋伏殺害他。非斯都卻回答說：保羅押在該撒利亞，我自己快要往那裡去；又說：你們中間有權勢的人與我一同下去，那人若有什麼不是，就可以告他。非斯都在他們那裡住了不過十天八天，就下該撒利亞去；第二天坐堂，吩咐將保羅提上來。保羅來了，那些從耶路撒冷下來的猶太人周圍站著，將許多重大的事控告他，都是不能證實的。保羅分訴說：無論猶太人的律法，或是聖殿，或是該撒，我都沒有干犯。但非斯都要討猶太人的喜歡，就問保羅說：你願意上耶路撒冷去，在那裡聽我審斷這事嗎？保羅說：我站在該撒的堂前，這就是我應當受審的地方。我向猶太人並沒有行過什麼不義的事，這也是你明明知道的。我若行了不義的事，犯了什麼該死的罪，就是死，我也不辭。他們所告我的事若都不實，就沒有人可以把我交給他們。我要上告於該撒。非斯都和議會商量了，就說：你既上告於該撒，可以往該撒那裡去。過了些日子，亞基帕王和百尼基氏來到該撒利亞，問非斯都安。在那裡住了多日，非斯都將保羅的事告訴王，說：這裡有一個人，是腓力斯留在監裡的。我在耶路撒冷的時候，祭司長和猶太的長老將他的事稟報了我，求我定他的罪。我對他們說，無論什麼人，被告還沒有和原告對質，未得機會分訴所告他的事，就先定他的罪，這不是羅馬人的條例。及至他們都來到這裡，我就不耽延，第二天便坐堂，吩咐把那人提上來。告他的人站著告他；所告的，並沒有我所逆料的那等惡事。不過是有幾樣辯論，為他們自己敬鬼神的事，又為一個人名叫耶穌，是已經死了，保羅卻說他是活著的。

✉️掌握前瞻趨勢與科技脈動，立即訂閱 IC之音電子報：https://pse.is/8wpwwx應邀參與政府單位的專家會議，應該站在什麼角度發言？邀請人與被邀請人是否曾對齊雙方的目標，讓會議結論能夠符合彼此期待，提供真正有用的專業意見？的確，參與會議看起來是再平常也不過的日常，但如果對「專業」有一定的標準與期待，這些環節，都應該是更值得用心的關鍵。黃欽勇Facebook https://www.facebook.com/hwangchinyeong

Hello and Welcome to the DX Corner for your weekly Dose of DX. I'm Bill, AJ8B.The following DX information comes from Bernie, W3UR, editor of the DailyDX, the WeeklyDX, and the How's DXcolumn in QST. If you would like a free 2-week trial of the DailyDX, your only source of real-time DX information, just drop me a note at thedxmentor@gmail.com{Marathon Alert} OH0 – Aland Islands - OH0ERF willbe active from August 5 to August 12, operated holiday style by a group of German hams. The team plans to operate all bands from 160 m through 10 m, using CW, SSB, and FT8, with 6 m added if conditions allow. Equipment includes an IC-7610, two IC-7300 transceivers, and an ACOM 700S amplifier. Planned antennas include a 3-band delta loop, a 3-element 3-band Mosley beam, 40 m and 160 m verticals, and various wire antennas. {Marathon Alert} UZBEKISTAN – UK -  Darek, SP9DLM and his son Greg SN9GM will be operating as UK/calls from July 27 until August 15.  {Marathon Alert} 3B9 - Rodrigues Island – Sebastian, 3B8HR, is QRV from 3B9HR, Rodrigues Island, until July 28th. holidaystyle. Since it is a holiday/vacation trip with simple gear, he will only be on the air “from time to time.” He is running 100 watts to an end fed long wire on HF, SSB only. His operating times are towards the end of the local day, 03-07Z.Confirmation of QSOs is through LoTW only. 9A – Croatia - The Croatian Amateur Radio Association willoperate the special event station 9A170NT to celebrate the 170th anniversary of Nikola Tesla's birth, and the station will remain active until December 31, 2026. QSL via LoTW, Club Log or QRZ.com. VP5 - Turks and Caicos Islands - G0VJG is QRV from North Caicos (NA-002), until July 30, operating VP5G from a beach location for 14 days. He will be using an HF9V and an inverted V, a 6-element 6M Yagi, and a Juma PA 1000 amplifier. During the IOTA Contest he will operate single op. Activity will include SSB, CW, and FT4/FT8. QSL via M0OXO. {Marathon Alert} T2 – Tuvalu - The Rebel DX Group fired up T22TT Wednesday just before 1900Z. So far, they have been reported on 30, 20, 17, 15, 12 and 10 meters, all FT8. Listen for activity over the next 10 days in between work on the island. As always QSL only via Club Log, once they start uploading their QSOs. {Marathon Alert} T2 – Tuvalu - JK1JXZ, Masaaki “Aki” Iwasawa, will have the callsign T2JK from Funafuti July 30 to August 7, 80-6M. On weekdays he expects to be on the air after 5 PM local time, and on the weekend, he will be on all day long. It is possible his stay will be extended to August 14.  {Marathon Alert} TY5FR, Benin -  DL1BUG, Red, is QRV as TY5FR, operating from Cotonou until August 4. He will be using an ICOM IC-7300, running 100W into a G5RV (2 x 15M) antenna. His planned activity will be on 160-10M CW and SSB. QSL via Club Log (bureau or direct via OQRS), as well as bureau or direct via his home call, and LoTW.  {Marathon Alert} E5/S – South Cook Islands - ZL2KE,Steve, is QRV as E51KEE from the South Cook Islands until August 14th, mainly on CW with some SSB, likely focusing on 30 and 40 meters unless propagation improves. Another Steve, ZL4CZ, is QRV as E51CZZ until August 6th on SSB. Neither operator is using FT8 or other digital modes.  {Marathon Alert} OJ0 - Märket Reef - OH6BG says a DXpedition is coming up August 15-22, OJ0JR, Henri; and OJ0YL, Anne, operating. He points out that the reef is the most exposed location in the Baltic Sea, “more rock than island, more sea than shelter.” To be dealt with before the first QSO is made is the weather, landing conditions, antennas, power and constant wind and waves, he says. They plan80-10 CW, SSB and FT8.  Until next week, this is Bill, AJ8B saying 73 and thanks to my XYL Karen for her love and support. I Hope to hear you in the pileups! Have a great DX week! 

Originally aired January of 2024:Anthony Di Marco joins the show to discuss the possibilities of what happens to the World Title, potential men's and women's Royal Rumble winners, why Becky vs Rhea doesn't feel as big as it could be, and if Brock Lesnar vs Gunther should have the IC title on the line.Go AD-FREE and get this show plus hundreds more by heading to Patreon.com/WWEPodcastBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-wwe-podcast--2187791/support.

In this episode we talk about promotions. You want to reward your best people by promoting them to new roles, but when and how? How can you avoid the dangers of bad promotions?  We are here to help! In this episode we answer questions including:When should an individual contributor become a manager?How do you handle an employee that isn't growing with the business?What happens when two people vie for the same role?All of these questions were submitted by listeners just like you. You can submit questions for us to answer on our website TheStartupHelpdesk.com or on X/Twitter @thestartuphd - we'd love to hear from you!Your hosts:Sean Byrnes: General Partner, LucidFog www.lucidfog.comAsh Rust: Managing Partner, Sterling Road www.sterlingroad.comNic Meliones: Founder, Startup Coach https://meliones.substack.com/Reminder: this is not legal advice or investment advice.Q1: When should an individual contributor become a manager?"We have a great engineer who wants to become a manager. I don't think they'll be good at it or enjoy it, but we don't want to lose them. What can I do?"Promotions are bets on future performance, not rewards for past work. Wanting a great IC to keep producing is natural, but don't promote someone into a role you believe they'll fail at. Management is a different job, not a higher rung on the same ladder.Test the desire before you grant the title:Send them to a management training course. Five days of reports, emotion-management, and conflict-resolution role-play is a filter. It either kills the fantasy or proves they're serious. If they're serious, they'll learn the mechanics (1-on-1s, conflict resolution, the parts of the job nobody romanticizes) before they run a real team.Try a 50/50 split. Keep them contributing while they test-drive managing people. Low risk, high signal.Examine your own bias. Part of you wants the status quo: a great engineer shipping great work. But your job is also to serve their career. Ask what they actually want long-term. Often it isn't "manage people" – it's bigger scope, real decision-making authority, or more visibility. Answer that, and you frequently get more of what you want, too.Build a real IC path. Most people only chase management because they've been taught it's the only way forward. Show them ICs who've grown into the equivalent of VPs and the pressure to become a manager evaporates for the ones who never wanted it. Make being a senior IC as prestigious as being the boss.The trap: creatively forcing people into roles they don't fit. Architect real jobs that solve real company problems. Don't invent a "team lead" title for a team of one.Q2: How do you handle an employee that isn't growing with the business?"One of our earliest employees is great, but is quickly being left behind as the business grows. Their role is shrinking as the company grows. I'd hate to lose them — how can I help?"This happens at every fast-growing company. The job someone was hired for often doesn't exist six months later. Not everyone scales, and that's not a moral failing.Set expectations early and honestly, before it becomes a crisis. Tell the whole team, on a regular cadence: "The job you have today won't exist tomorrow. We'd love you to be first choice for the next one, but it's on you to show us." That conversation, held often, does more than any rescue attempt later.Invest, but keep a close eye on who is already pulling themselves forward. Coaching and training are worth trying. Importantly: people who scale are already training themselves, learning new skills, taking initiative. If you have to supply all the initiative to drag someone forward, it rarely works. You can only pull someone so far, and you shouldn't contort the whole org to meet one person where they are.Be honest that the odds are low. In most cases this ends with letting the person go. Give them a fair chance to find their place, but time is limited, and if they're not climbing this mountain, they're unlikely to climb the next one.The kindest move is often the exit. Help them find their next role. At a bigger company they may be iced out and miserable; as an early-stage specialist elsewhere, their zero-to-one strengths are a genuine asset.Watch where the pressure actually comes from. They may not feel it in the work, but they feel it interpersonally, early. The trigger is frequently the people underneath them: strong reports threatening to leave unless the blocker moves. That forces the hard conversation, and it's never comfortable to tell someone the team has outgrown them.Q3: What happens when two people vie for the same role?"I have two great salespeople who both want to become sales director. There's one opening, so I can promote one — but at the risk of losing the other. What should I do?"Don't assume your best AE becomes your best director. A great AE is a closer. A great sales director is a scaler: coaching, building process, designing the machine. Different job, different skill set. The percentage of great AEs who become great sales leaders is low. Hire (or promote) for the scaler.Retain both, explicitly. Tell each of them, independently, that you want to keep them. For whoever doesn't get the role, ask directly: "If it's not you this time, what do we need to do to keep you scaling with us?"Run a transparent process, and don't stall. Be open about how you're evaluating. Then decide. Moving slowly doesn't preserve the peace; it raises the odds you lose one of them.Don't let fear pick the winner. If you choose based on who might quit, you've handed the team control of the company. Promote the person you genuinely believe will do the job best. If the other leaves, that's unfortunate. You deal with it, the same way you do with engineers or anyone else.Create a second path so "director" isn't the only summit. Strategic account management, a senior IC track, uncapped commission – give a top closer a reason to keep closing instead of elbowing into a management seat they don't actually want. (Fast-growing teams often keep great salespeople selling by not capping commission.)Always be hiring. You may need to backfill at any moment. Plan for it before you're forced into it. And if you ever find an AE who's genuinely a great sales director, never let them go.---The through-line: Every one of these problems eases when there's more than one way to grow. Give people a real IC path and management stops being the only way to ascend. Set honest expectations early and "you're not scaling" stops being an ambush. Build a second summit and one open role stops threatening two great people. Promotions go wrong when the org offers exactly one ladder and everyone's forced to climb it whether it fits or not.

✉️精彩話題與深度觀點，訂閱 IC之音電子報一手掌握：https://pse.is/8wpx6m★★歡迎點此寫電子小紙條給主持人，分享您對節目的感想。當戲曲功法走進影視鏡頭，京劇碰撞當代表演，究竟該如何在傳統與創新之間取得平衡？透過國光劇團知名演員盛鑑的分享，帶您走進他的最新創作歷程與跨界表演心法！8月即將於中台世界博物館登場的新編京劇《天女散花》，他將挑戰飾演維摩詰居士，首度以突破傳統對稱美學的造型設計，展現一位「在家修行者」獨特而超然的精神氣質，也重新詮釋佛典故事深刻的人文意涵。剛結束《王有道休妻》演出，回想起排練期間，原班人馬暌違二十二年再度同台，演員們重新拆解角色、內化人物，在彼此激盪與交流中找回最純粹的創作熱情，也讓這部作品在歲月淬鍊後綻放全新的生命力。除了舞台演出，盛鑑更首度跨足歌仔戲導演，以《鼓震千秋－梁紅玉》融合京劇功法與歌仔戲表演美學，探索不同劇種之間的交流與激盪；同時也談及影視作品《我們與惡的距離II》中飾演的胡家威一角，究竟他如何在影視與戲曲之間轉換表演語彙呢？又如何在電影《龍門飛甲》中，將京劇功法化為貼近鏡頭的真摯演出？從劇場到螢光幕，從演員到導演，盛鑑始終在傳承與創新之間尋找嶄新的表演語彙。邀您立即走進他的藝術世界，看見一位戲曲工作者如何以深厚功底與不斷突破的創作精神，開拓表演藝術更多元的可能。————————————企劃︱王安祈、羅仕龍、周信宏製作︱周信宏

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

Gabriel Rymberg has been with us in previous episodes speaking about the Center of Universe projects.Gabriel is one of the founders of CFU, and we will discuss a new initiative about the Angelic Story of Jesus, an interactive immersive experience, taking the teachings of Jesus and bringing them to life. Plus, Gabriel has some exciting news to announce for Urantia Book artists and creators who want to share the Revelation.Bring us your FER-related projects: ⁠⁠https://www.fer-studios.com/Experience the Angelic Story of Jesus, 1-1 with Gabriel: ⁠⁠https://calendly.com/meet-cfu/asjAll The Center for unity Center of Unity ProjectsWe also have some great news to share about a recent IC'26 Conference announcement, and my personal endeavor - the Third Circle Global Prayer Project. Find out more at ⁠UrantiaRadio.org⁠

啟發多元思考與學習成長，歡迎訂閱 IC之音電子報：https://pse.is/8wpx2c攀登高峰，是多數人設定的人生發展曲線!阻礙進行的，往往不是外部變數，而是自我動能、向上提升。如何保持不衰竭的願意呢。　＊＊＊馬太福音 5:1-12耶穌看見這許多的人，就上了山，既已坐下，門徒到他跟前來，他就開口教訓他們，說：虛心的人有福了！因為天國是他們的。哀慟的人有福了！因為他們必得安慰。溫柔的人有福了！因為他們必承受地土。飢渴慕義的人有福了！因為他們必得飽足。憐恤人的人有福了！因為他們必蒙憐恤。清心的人有福了！因為他們必得見神。使人和睦的人有福了！因為他們必稱為神的兒子。為義受逼迫的人有福了！因為天國是他們的。人若因我辱罵你們，逼迫你們，捏造各樣壞話毀謗你們，你們就有福了！應當歡喜快樂，因為你們在天上的賞賜是大的。在你們以前的先知，人也是這樣逼迫他們。。 

Please enjoy this encore episode from our #EmpathytoImpact archive. Stay tuned for new episodes coming in August 2026.If you have enjoyed the podcast please take a moment to subscribe, and also please leave a review on your favorite podcast platform. The way the algorithm works, this helps our podcast reach more listeners. Thanks from IC for your support. New from Inspire Citizens: Inspired Coaching & Inspired Experiences Learn more about how Inspire Citizens co-designs whole-school service learning programsYou can book a discovery call with Inspire Citizens at this linkShare on social media using #EmpathytoImpactEpisode Summary Tim, a 10th grade student at ISBkk, recognized that the inequity he was witnessing in his home country of Thailand was largely due to inequity in the access that people have to quality education. He was inspired by a summer internship program that he attended and develop the project to collaborate with teachers at his school to provide training for aspiring educators entering the teaching profession in Thailand. Listen to learn about this amazing program and the reciprocal partnership that has developed between the Equitable Education Fund and ISBkk as a result of Tim's work.Discover a transformative podcast on education and learning from a student perspective and student voice, exploring media, media literacy, and media production to inspire citizens in schools through a media lab focused on 21st-century learning, empathy to impact, Global citizenship, collaboration, systems thinking, service learning, PBL, CAS, MYP, PYP, DP, Service as Action, futures thinking, project-based learning, sustainability, well-being, harmony with nature, community engagement, experiential learning, and the role of teachers and teaching in fostering well-being and a better future.

Send us Fan MailWhen 50,000 people are evacuated, the emergency is only half the fight, the other half is the information. We pick up our conversation with Orange County Fire Authority Division Chief Craig Covey, a veteran incident commander, to unpack what really happens when a major response collides with nonstop public demand, intense media pressure, and elected officials who want to lead the narrative for their communities. The result can be a messy communications environment where accuracy, timing, and trust are on the line. We talk candidly about the “joint, unified information pipeline” and why going off script creates confusion for residents and unnecessary friction for responders. Covey explains how cooperator meetings and direct conversations can reset expectations, protect city managers from surprises, and keep updates clear and consistent across multiple impacted jurisdictions. You'll also hear a practical leadership move that turns political attention into operational advantage: tasking elected leaders to push emergency declarations quickly, delivering relief for residents and opening the door to state and federal resources if conditions worsen. The episode goes deep on modern crisis communications tools and threats: the value of a single crisis website or pre-built “dark site,” the challenge of fragmented city messaging and social media, and how AI can help teams publish faster in multiple languages across diverse communities. Then we hit the unnerving part, a deepfake video that made Covey appear to speak fluent Vietnamese using his own briefing footage, and what that implies for misinformation during disasters. If you care about emergency management, public information officer best practices, incident command leadership, or crisis communication strategy, this conversation is loaded with field-tested lessons. Subscribe, share this with your favorite PIO or IC, and leave a review, then tell us: what's the hardest part of keeping one trusted message during a crisis?#emergencymanagement #crisismanagement #crisiscommunications #ocfa #publicinformation #PIOSupport the showWe'd love to hear from you.  Email the show at Tom@leadinginacrisis.com.

Chyna knocks on Triple H's door to check on him after SummerSlam. They share a platonic hug. Eddie Guerrero walks in, shoves Triple H, and says "That's my mama." Tommy Blacha and Rob Pasbani recap the August 28, 2000 edition of WWF Monday Night Raw, live from Greensboro, North Carolina, one night after SummerSlam 2000.Triple H opened the show stranded in the parking garage, waiting for Stephanie who hasn't been seen since he accidentally hit her the night before. Kurt Angle comes to the ring and accuses him of domestic violence, then casually mentions his concussion is coming back to him. He remembers being in a hotel lobby after SummerSlam. He wasn't alone. Room A14. Wait, that was Stephanie's room. His memory is a little fuzzy.After Angle goads Eddie into defending Chyna's honor, he ends up running into the match with a steel chair anyway. The annoucers openly wonder whose side Chyna is really on. Tommy and Rob did not see this coming and loved every second of it.Other major discussion points include:Edge and Christian bring out miniature versions of all three TLC teams for their 36-second pose, complete with tiny ladders, tiny tables, and a fight between the mini Hardies and mini DudleysLita defends the Women's Championship against Jackie in the highest-rated quarter hour of the night, then eats an Edge spear post-match in what Tommy and Rob call a real test of whether her SummerSlam rub translated on its ownChyna retains the IC title when Val Venis abandons his Money Shot to yell at Trish Stratus, who had been watching at ringside the entire matchJR grabs a trash can lid and clocks Tazz at ringside, letting Blackman retain the Hardcore title in a feud Tommy and Rob agree never justified itselfThe Rock keeps the WWF title over Kane, but the Undertaker rides in on his motorcycle and choke slams Kane first, teasing a three-way championship programNaked Midian debuts as a streaker in the Al Snow and Saturn mixed tag, wearing nothing but a fanny pack and a thong00:00 Intro and Cold Open: Eddie Guerrero Shoves Triple H Over Chyna00:33 Welcome to Tales from the Attitude Era00:52 Why Raw Aired at 11pm: US Open Tennis and the USA Network Problem1:07 Tommy's Backstage Story: Vince McMahon vs the Dog Show5:01 Post-SummerSlam Booking Philosophy and the Late Start Show6:06 The Chyna and Triple H Reunion: Kayfabe, Reality, and the Real-Life Timeline8:15 Mick Foley Opens the Show in Greensboro / Four Title Matches Announced10:31 Triple H Waits in the Parking Garage / X-Pac and Road Dog Weigh In10:54 Opening Match: Y2J and the Acolytes vs Benoit and T&A16:35 Kurt Angle Accuses Triple H of Abuse / Room A14 Revealed18:40 Triple H Cheap Shots Angle / Chyna and Eddie Watch from Backstage19:03 Intercontinental Championship: Chyna vs Val Venis23:00 Michael Cole Interviews Angle: "I Remember Stephanie's Room"23:42 Right to Censor Promo / Too Cool and Rikishi Dance Segment26:53 Chyna Enters Triple H's Dressing Room29:41 Eddie Guerrero Bursts In: "That's My Mama"31:53 Edge and Christian's Mini TLC Teams Segment36:24 Angle Talks Eddie Into Challenging Triple H36:53 Hardcore Championship: Tazz vs Steve Blackman / JR with the Trash Can Lid41:54 Just Joe Gets Kicked Out / Eddie Barges In and a Match Is Made45:14 Women's Championship: Lita vs Jackie / Edge Spears Lita Post-Match49:14 Kane Promo: No Voice Box, New Monster Heel Direction53:28 Patreon Plug55:38 Chyna and Eddie Backstage Before the Match56:03 Eddie Guerrero vs Triple H1:00:07 Frog Splash Miss / Pedigree Attempt / Angle Runs In with a Chair1:01:04 Chyna Saves Both Men / The Love Quadrangle Is Born1:03:05 Al Snow and Saturn vs Cat and Terry / Naked Midian Debuts1:05:25 Dudleys at WWF New York / The Rock Promo with Kevin Kelly1:06:47 WWF Championship: The Rock vs Kane / The Undertaker Rides In1:09:24 Ratings Breakdown: 4.93 Rating and 11.5 Share1:12:46 Closing Thoughts and Sign-Off Hosted on Acast. See acast.com/privacy for more information.

掌握前瞻趨勢與科技脈動，立即訂閱 IC之音電子報：https://pse.is/8wpwwx—最近大家在新聞上最關切的，莫過於國內大豆沙拉油原料被檢出一級致癌物「苯駢芘」超標的事件。隨著風波擴大，波及了全台數百項食品、知名連鎖餐廳和超商，讓許多人面對每天餐桌上的家常菜，心中免不了有些不安與恐慌。到底這個俗稱「苯駢芘」的化學物質是怎麼產生的？它對我們身體的傷害到底有多大？今天節目特別邀請臺大醫院腎臟內科主治醫師/臺大醫學院毒理學研究所副教授趙家德，透過客觀、科學的視角，帶領大家了解這個致癌物的真相，以及我們在日常生活中，該如何透過正確的烹調與飲食策略，幫自己與家人減少危害。歡迎收聽！—製作團隊製作人：李知昂企劃團隊：李知昂 / 莊俐心 

  為日常注入質感與溫度，邀你訂閱 IC之音電子報：https://pse.is/8wpwy6--主持人曾陽晴本集分享，使徒保羅在耶路撒冷遭遇的困境。《本集經文》使徒行傳23:24-24:21也要預備牲口叫保羅騎上，護送到巡撫腓力斯那裡去。千夫長又寫了文書，大略說：革老丟呂西亞，請巡撫腓力斯大人安。這人被猶太人拿住，將要殺害，我得知他是羅馬人，就帶兵丁下去救他出來。因要知道他們告他的緣故，我就帶他下到他們的公會去，便查知他被告是因他們律法的辯論，並沒有什麼該死該綁的罪名。後來有人把要害他的計謀告訴我，我就立時解他到你那裡去，又吩咐告他的人在你面前告他。（有古卷在此有：願你平安！）於是，兵丁照所吩咐他們的，將保羅夜裡帶到安提帕底。第二天，讓馬兵護送，他們就回營樓去。馬兵來到該撒利亞，把文書呈給巡撫，便叫保羅站在他面前。巡撫看了文書，問保羅是哪省的人，既曉得他是基利家人，就說：等告你的人來到，我要細聽你的事；便吩咐人把他看守在希律的衙門裡。過了五天，大祭司亞拿尼亞同幾個長老，和一個辯士帖土羅下來，向巡撫控告保羅。保羅被提了來，帖土羅就告他說：腓力斯大人，我們因你得以大享太平，並且這一國的弊病，因著你的先見得以更正了；我們隨時隨地滿心感謝不盡。惟恐多說，你嫌煩絮，只求你寬容聽我們說幾句話。我們看這個人，如同瘟疫一般，是鼓動普天下眾猶太人生亂的，又是拿撒勒教黨裡的一個頭目，連聖殿他也想要污穢；我們把他捉住了。（有古卷在此有：要按我們的律法審問，不料千夫長呂西亞前來，甚是強橫，從我們手中把他奪去，吩咐告他的人到你這裡來。）你自己究問他，就可以知道我們告他的一切事了。眾猶太人也隨著告他說：事情誠然是這樣。巡撫點頭叫保羅說話。他就說：我知道你在這國裡斷事多年，所以我樂意為自己分訴。你查問就可以知道，從我上耶路撒冷禮拜到今日不過有十二天。他們並沒有看見我在殿裡，或是在會堂裡，或是在城裡，和人辯論，聳動眾人。他們現在所告我的事並不能對你證實了。但有一件事，我向你承認，就是他們所稱為異端的道，我正按著那道事奉我祖宗的神，又信合乎律法的和先知書上一切所記載的，並且靠著神，盼望死人，無論善惡，都要復活，就是他們自己也有這個盼望。我因此自己勉勵，對神對人，常存無虧的良心。過了幾年，我帶著賙濟本國的捐項和供獻的物上去。正獻的時候，他們看見我在殿裡已經潔淨了，並沒有聚眾，也沒有吵嚷，惟有幾個從亞西亞來的猶太人。他們若有告我的事，就應當到你面前來告我。即或不然，這些人若看出我站在公會前，有妄為的地方，他們自己也可以說明。縱然有，也不過一句話，就是我站在他們中間大聲說：我今日在你們面前受審，是為死人復活的道理。 

This week the boys are all back together ready to discuss the week that was in professional wrestling. The 1 count is AEW. Hangman Page makes a shocking return back to Collision to discuss his present and future. Kenny Omega his his victory celebration interrupted by Kevin Knight and Will Ospreay. Mark Davis vs Mike Bailey for the National title. Andrade one step closer to a title. Kyle Fletcher and Okada seem to be gunning for the same goal. The 2 count is New Japan Pro Wrestling's opening night of its G1 Climax Tournament as JCB was in attendance. The boys breakdown all the matches from the night including an injury to one of its competitors. The 3 count is WWE. Nick Aldis repeatedly being attacked by Gunther to close SmackDown. CM Punk and Cody Rhode make it official for SummerSlam. Roman Reigns and Seth Rollins sign their contract for SummerSlam as well. Chad Gable wins a shot at the IC title and a shocking return from one Baron Corbin. Available on all audio podcast platforms. Listen Share Subscribe Repeat! Rate and review on Apple and Spotify! AEW NJPW 47:05 WWE 1:17:40

Hello and welcome to episode 97 of TheDX Mentor – a discussion of the recently completed POTA expedition to the Dry Totugas islands off the coast of Key West. My guests tonight are Casey, KJ0NES, Mollie, W3NY, Ron, KC0ZPS, Julius, AK9IT, Elizabeth, N7MEB, and Dave, N9VFR. Here is the introduction as it was listed on the DXWorld website:   Look out for N4T team to be active from Dry Tortugas Island, NA-079 during February 17-25, 2026. Mainaim is 6m activity from rare grid EL84 and to QRV from various POTA parks throughout Florida Keys. QSL via KC3YQL.If this is the first time you are joining us, Welcome! We have a back catalog covering many aspects of DX in both podcast and YouTube format. Please check us out.  If you like what you find, please subscribe, like, and share to always be notified about upcoming events!     Another way to keep in touch and to see what we are up to is via the DX Mentor Facebook page. I will be posting aboutupcoming podcasts as well as other DX events so please follow us.    Real Time DX Info (DailyDX https://www.dailydx.com/Southwest Ohio DX Assoc. https://www.swodxa.orgDaily DX https://www.dailydx.com/DX Engineering https://www.dxengineering.com/Icom https://www.icomamerica.com/ IC-905 https://www.icomamerica.com/lineup/products/IC-905/ IC-9700 https://www.icomamerica.com/lineup/products/IC-9700/ IC-7610 https://www.icomamerica.com/lineup/products/IC-7610/ IC-7300 https://www.icomamerica.com/lineup/products/IC-7300/ Lets hear more.           

On today's podcast, Executive Editor Natalie Orpett talks with Lawfare Senior Editor Mike Feinberg and Lawfare Public Service Fellow Julia Curlee about the Office of the Director of National Intelligence, or ODNI, which was created to oversee the intelligence community. But much like the IC itself, the ODNI is somewhat mysterious to the general public—which makes it difficult to tell when something is going wrong. They talk about what ODNI does, why it exists at all, and how recent developments are undermining its mission.Read more of Mike and Julia's analysis in their recent article in Lawfare, “Gradually, and Then Suddenly: The Decline and Fall of ODNI.”To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.