Podcasts about fine tuning

In this episode Trent sits down with former agnostic Joe Schmid @MajestyofReason to discuss his reversion to Catholicism. To support this channel: https://www.patreon.com/counseloftrent [NEW] Counsel of Trent merch: https://shop.catholic.com/apologists-alley/trent-horn-resources/ Be sure to keep up with our socials! https://www.tiktok.com/@counseloftrent https://www.twitter.com/counseloftrent https://www.instagram.com/counseloftrentpodcast 00:00 Joe Schmid Returns to Catholicism 02:39 Why Joe Responded to Trent's Arguments 05:55 Charity, Confidence, and Serious Debate 09:16 Divine Hiddenness and the Search for Truth 12:50 Joe's Catholic Upbringing 16:10 Evolution and the First Wobble in Faith 18:36 The Problem of Evil and Deconversion 23:32 New Atheism, Skepticism, and Intellectual Pride 27:30 From Atheism to Agnosticism 29:37 Josh Rasmussen and Serious Theistic Arguments 35:35 Why Theism Became a Live Option Again 40:23 Choosing Philosophy Over Biology 44:41 Joe's Years as an Agnostic 48:54 Fine-Tuning, Psychophysical Harmony, and Miracles 55:20 Changing the Mind vs Changing the Heart 1:00:30 Miracles, Naturalism, and the Case for God 1:04:33 Eucharistic Miracles, Lourdes, and Catholic Evidence 1:10:11 Why Joe Wasn't Ready for Catholicism 1:13:07 The Problem of Evil and Animal Suffering 1:22:41 Fatima, Padre Pio, and the Pull Toward Catholicism 1:23:44 The Saints and the Change of Heart 1:35:12 The Call to Sainthood and Leaving Sin Behind 1:38:10 Fatima and Padre Pio as Catholic Evidence 1:42:40 Why Catholic Miracles Point to Catholicism 1:50:50 Testing Miracles by Their Fruits 1:57:22 Catholic Miracles and the Resurrection 2:01:41 What Comes Next for Joe Schmid? 2:06:11 Final Encouragement and Closing

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In this episode of ProPhysique Code, Lexie, Paul, and Evan break down pro Fit Model feedback, how to actually understand judges' feedback, and why small changes can make a BIG difference on stage. They also discuss their past weekend at the Tampa Pro and why enjoying the NPC level is SO important, as many competitors rush towards obtaining their pro card without truly enjoying their stepping stones within the NPC.

Learn more about Refrigeration Mentor Customized Technical Training Programs at www.refrigerationmentor.com/courses Join the Refrigeration Mentor Hub here This episode includes highlights from a recent live Refrigeration Mentor Community meetup where we covered real-world CO2 rack issues, system tuning, troubleshooting, and startup strategies, sharing practical techniques we've learned in the field. We covered gas cooler fan tuning, transcritical operation, oil separator maintenance and service valve tips. This meetup featured experienced CO2 technicians Patrick Gregory, Noah Walker, Andrew Freeburg, Billy Carlson and others sharing valuable insights that can help fellow technicians diagnose problems faster, improve system performance, and learn from the experience of others.  In this episode, we discuss: (02:15) Fine-tuning CO2 racks (05:20) Gas cooler fan control strategies (09:00) Rack tuning (12:00) Why every CO2 system requires its own tuning strategy (16:00) Oil separator filter replacement and diagnosing oil trip issues (18:00) Proper rack pump-down procedures (20:00) Service valve troubleshooting (23:45) Common ball valve issues Helpful Links & Resources: Episode 382. Why Techs Get Stumped on Supermarket Racks (How To Fix It) Episode 250. Service Call Stories and Troubleshooting Tips with Andrew Freeburg Episode 163. A Pro's Handbook for Effective Refrigeration Troubleshooting

In this episode, Ian Coe, Co-founder and CEO of Tonic.ai, joins us at Heavybit DevGuild.Ian explains how Tonic started by helping developers create safe, realistic data for testing and development, and how that has expanded into AI model training, de-identification, and synthetic datasets for reinforcement learning.We discuss structured vs. unstructured data, healthcare and financial services use cases, Tonic Fabricate, Tonic Textual, fine-tuning, RL environments, and why enterprises are becoming more interested in training models on their own data.LinksIan Coe's LinkedInTonic.aiTonic FabricateTonic TextualTonic BlogHeavybit DevGuild

What if the Three Nephites weren't just ancient disciples… but beings from another world?In 1940, a Dutch man named Udo Wartena claimed he had a close encounter with visitors from another planet while living in rural Montana.According to his account, the beings:Looked like regular menTraveled in a large disc-shaped craftSaid they “mingle with your people” and “give help where needed”Refused to discuss Jesus Christ or religionIn this episode, we explore the strange parallels between Wartena's story and the Three Nephites from the Book of Mormon — immortal beings who minister among people, gather information, and offer help without interfering with free agency.Could some UFO encounters actually be the Three Nephites (or similar beings) at work? We discuss the theory, the implications, and why this story feels oddly familiar to Latter-day Saints.What do you think — were these visitors aliens, or something else entirely?#ThreeNephites #UFO #BookOfMormon #LDSChapter Markers / Timestamps:00:00 - Intro: UFOs, Aliens & A Wild Theory01:35 - Personal Family UFO Story (Project Blue Book)03:05 - The Udo Wartena 1940 Montana Encounter04:42 - He Gets Invited Onto the Craft05:33 - “We Mingle With Your People and Give Help Where Needed”06:08 - Connection to the Three Nephites07:22 - They Refused to Talk About Jesus Christ09:34 - Parallels With Jesus' Instructions (“Tell No One”)11:50 - “Do You Hold the Priesthood?” (The Question They Left Out)14:45 - Why Haven't We Found Alien Life?17:44 - The Fine-Tuning of the Universe as Evidence for God19:53 - Final Thoughts: Aliens or Ministering Beings?Join this channel to get access to perks:https://www.youtube.com/channel/UCnmsAFGrFuGe0obW6tkEY6w/joinAmazon Wish List: https://www.amazon.com/hz/wishlist/ls/1AQLMTSMBM4DC?ref_=wl_shareVisit us for this and more at: WardRadio.comTo subscribe to "The Women of Ward Radio" Youtube Channel, please visit: https://www.youtube.com/channel/UCbu-wpRztV-8TYXClhUZhhwTo Subscribe to Cardon Ellis' Adventure Channel, please visit: http://www.youtube.com/@CardonEllisAdventuresThe following authors and/or sponsors are generously offering discounts on their gospel-related publication to Ward Radio listeners.⚡For free trial of Scripture Notes please visit the following link!: https://scripturenotes.com/?via=wardradio⚡For a discount on Tiny 3D Temples, Save 15% with code WARDRADIO at checkout or visit tiny3dtemples.com/wardradio⚡Family: A Rhyming Proclamation for Kids book visit the following linkhttps://plainandpreciouspublishing.com/products/family-a-rhyming-proclamation-for-kids . Use the code "Ward Radio" for 10% off. ⚡To Order Jonah's Book, “Lost Gems of Genesis” visit the following link and use coupon Code: WARDRADIO https://plainandpreciouspublishing.com/products/coming-soon-the-lost-gems-of-genesis-how-apocryphal-texts-prove-joseph-smith-fixed-the-bibleFor 10% off Plain and Precious Publishing Books, visit plainandpreciouspublishing.com and use Coupon Code: WARDRADIOFor a 5% discount on Go and Do Travel, visit goanddotravel.com and use the promo code WARDRADIO5#christian #mormon #exmormon #latter-daysaints #latterdaysaints #latterdays #bible #bookofmormon #archaeology #BYU #midnightmormons #jesus #jesuschrist #scriptures #sundayschool #biblestudy #christiancomedy #cardonellis #kwakuel #bradwitbeckTo support the channel:Venmo @WardRadio or visit: https://account.venmo.com/u/MidnightMormonsPaypal: paypal.me/@midnightmedia CashApp: $WardRadioFollow us at:Instagram: @cardonellis @kwakuel @braderico @boho.birdyFacebook: @WardRadioWorldwideTwitter: WardRadioShowTikTok: WardRadioWorldwide

Is life the product of chance alone, or does its existence point toward deeper scientific or philosophical questions? In this thought-provoking episode of The ‘X' Zone, Rob McConnell welcomes physicist, author, and science educator Robert Piccioni to explore the fascinating question: Can Life Really Be Merely an Accident? Drawing upon his background in physics and cosmology, Dr. Piccioni examines the scientific principles surrounding the origin of life, the apparent fine-tuning of the universe, probability, evolution, and the fundamental laws of nature. He discusses how science approaches these profound questions while distinguishing between evidence, theory, and philosophical interpretation. Throughout the conversation, Rob and Dr. Piccioni explore topics including cosmology, biology, the conditions necessary for life, and the ongoing dialogue between science, philosophy, and theology. Rather than offering simple answers, the discussion encourages listeners to think critically about one of humanity's oldest and most compelling questions. Whether you're interested in physics, cosmology, biology, philosophy, or the origins of life, this episode offers an engaging exploration of the scientific and philosophical ideas that continue to shape our understanding of existence.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-x-zone-radio-tv-show--1078348/support.Please note that all XZBN radio and/or television shows are Copyright © REL-MAR McConnell Meda Company, Niagara, Ontario, Canada – www.rel-mar.com. For more Episodes of this show and all shows produced, broadcasted and syndicated from REL-MAR McConell Media Company and The 'X' Zone Broadcast Network and the 'X' Zone TV Channell, visit www.xzbn.net. For programming, distribution, and syndication inquiries, email programming@xzbn.net.We are proud to announce the we have launched TWATNews.com, launched in August 2025.TWATNews.com is an independent online news platform dedicated to uncovering the truth about Donald Trump and his ongoing influence in politics, business, and society. Unlike mainstream outlets that often sanitize, soften, or ignore stories that challenge Trump and his allies, TWATNews digs deeper to deliver hard-hitting articles, investigative features, and sharp commentary that mainstream media won't touch.These are stories and articles that you will not read anywhere else.Our mission is simple: to expose corruption, lies, and authoritarian tendencies while giving voice to the perspectives and evidence that are often marginalized or buried by corporate-controlled media

Step into the orchestra of interpersonal connections with this revisit to Daily Influence's episode 208, "The Social Symphony: Fine-Tuning Your Emotional Intelligence." Discover the harmonious blend of skills that can amplify your social repertoire and resonate through every interaction. - **Mastering Active Listening**: Learn the art of truly hearing others and showing your engagement, transforming every conversation into an opportunity for connection and growth. - **Cultivating Empathy**: Walk in the shoes of others and respond to the emotions that dance between the lines, nurturing relationships with compassion and understanding. - **Articulating with Clarity**: Communicate with precision and positivity, ensuring your message is not only heard but also felt in the way it's intended. - **Conveying Without Words**: Dive into the subtle yet powerful world of nonverbal cues, from the ballet of body language to the nuances of cultural expressions. - **Asserting with Confidence**: Stand firm in expressing your needs and managing boundaries, balancing your rights with respect for others. - **Socializing with Purpose**: Turn everyday encounters into practice sessions, joining groups and role-playing scenarios to elevate your conversational choreography. - **Reflecting for Growth**: Seek out feedback and engage in self-reflection after social interactions to refine and evolve your approach. - **Embracing Continuous Learning**: Stay curious and informed with resources like Debra Fine's "The Fine Art of Small Talk," workshops, and seminars to keep your social skills sharp. Join us for "The Social Symphony" and tune up your emotional intelligence to lead more fulfilling and harmonious interactions both in your personal life and the workplace. Whether you're looking to polish your networking prowess or enrich your relationships, this episode hits all the right notes. #SocialSkills #EmotionalIntelligence #ActiveListening #Communication #DailyInfluencePodcast

A @Christadelphians Video: [Inspiring] Pleased to present in collaboration with the Bible Unlocked team, this thought-provoking presentation from the sixth season of Bible Unlocked Live, A foundational series, “Why You Should Believe the Bible,” revealing how faith is built on a rock-solid foundation of evidence and reason. They hold a webinar each week....Find out more @ https://www.bibleunlocked.com/Should science stop us from trusting the Bible? In this thought-provoking and insightful presentation, we explore one of the most common questions of our age: has modern science truly ruled out the possibility of God? Join us as we revisit this compelling topic and discover how recent discoveries in physics, astronomy, and biology are revealing a universe of staggering complexity and precision. From the fine-tuning of physical constants to the mind-boggling information storage of DNA, we see that far from silencing the declaration of the heavens, science is amplifying it. This is an outstanding and revealing discussion that shows how the complexity we observe points towards intentional design, encouraging us to look at the world around us with fresh eyes and consider the purpose behind it all.Chapters:00:00 - Introduction: Has Science Ruled Out God?01:00 - The Cultural Shift: From Design to "God is Dead"02:55 - The Beginning: Hubble, Expansion, and a Cause04:15 - A Scientist's View: Aligning with Genesis05:20 - The Complexity of the Cell: From Simple Blob to Advanced City06:40 - "Fearfully and Wonderfully Made": David's Ancient Insight07:50 - The Principle of Design: From Smartphones to the Universe09:10 - The Fine-Tuning of the Universe: Gravity and the Constants11:30 - Physicists Acknowledge Design: "Just Right for Life"12:40 - The Bible's Answer: Created with Purpose13:30 - Two Choices: Design or Accident?14:40 - Digging Deeper: The Wonders of DNA Storage18:30 - Why Do People Miss the Evidence? A Discussion from Romans22:00 - Conclusion: Finding the "Why" in ScriptureBible Verses:

Eric welcomes Eduard Dulharu, a veteran network architect and the Founder and CTO of vExpertAI, to talk about how agentic AI, open-source LLMs, and digital twins are changing network operations. Eduard discusses the rapid evolution of generative AI, draws parallels between AI’s current limitations and early network protocols such as Spanning Tree, talks about why... Read more »

Eric welcomes Eduard Dulharu, a veteran network architect and the Founder and CTO of vExpertAI, to talk about how agentic AI, open-source LLMs, and digital twins are changing network operations. Eduard discusses the rapid evolution of generative AI, draws parallels between AI’s current limitations and early network protocols such as Spanning Tree, talks about why... Read more »

בפרק זה של הפודקאסט "על המשמעות" עו"ד תמיר דורטל מארח את הרב ד"ר מיכאל (מיכי) אברהם וד"ר ג׳רמי פוגל לדיבייט על אמונה, תבונה וההסבר הרציונלי למחויבות דתית. הדיון שלפנינו מפגיש שני קצוות אינטלקטואליים בהגות היהודית והכללית בת זמננו. מן העבר האחד, ד"ר ג׳רמי פוגל, חוקר פילוסופיה ומחבר הספר "פילוסופים נגד אלוהים", המציג את עמדתו הספקנית של הפילוסוף הסקוטי דייוויד יום. פוגל מאתגר את יסודות האמונה הממוסדת ומצביע על כשלים לוגיים ומעגליים, לתפיסתו, בטיעונים המבקשים להסיק את קיומו של האל מתוך הטבע או להסתמך על עדויות היסטוריות של אירועים על-טבעיים. מן העבר השני, הרב ד"ר מיכאל אברהם, פיזיקאי ופילוסוף, מציג משנה רציונליסטית ייחודית שאינה נרתעת מהספק, אלא משתמשת בכלים לוגיים קשיחים כדי לבסס את סבירותה של האמונה ומעמד הר סיני.במהלך השיחה עולים כמה מן הנושאים המרכזיים של הפילוסופיה של הדת: טיעון הכיוונון העדין (Fine-Tuning) המדגים את מורכבותם הבלתי סבירה של חוקי הפיזיקה, טיעון העד ההיסטורי והאופן שבו מסורת קהילתית רציפה מעבירה עובדות מדור לדור. המשתתפים מעמיקים בבחינת עקרון הסיבתיות הפילוסופי, ובוחנים האם ניתן לעשות קפיצה לוגית מהכרה ב"ישות ראשונית מופשטת" אל עבר מחויבות להלכה היהודית היומיומית. האם הדיווח על מעמד הר סיני שונה במהותו ממיתוסים על דרקונים בסין העתיקה או מהתגלויות מריה בפורטוגל? כיצד משפיע עקרון האנטרופיה ומודל היקומים המקבילים על השאלה אם היקום תוכנן במכוון? ואיזה תפקיד משחק הספק במניעת פונדמנטליזם דתי וחילוני המסכן את הלכידות החברתית בישראל?המאזינים לפרק זה ייחשפו לניתוח מעמיק של תיאוריות הכרה, מדע ופילוסופיה. השיחה אינה עוסקת ברגש דתי מופשט, אלא מפרקת לגורמים את ההצדקות הרציונליות לקיומו של בורא ומנסחת מחדש את גבולות הידע האנושי.00:00:00 היכרות ופתיחת הדיבייט על קיומו של אלוקים00:04:36 הסקנדל הפילוסופי שמאחורי אמונה במעמד הר סיני00:11:40 תשובת הרב מיכי: אמונה מבוססת על רציונליות ובהירות00:18:48 האם ההסתמכות על מסורת הר סיני היא טיעון מעגלי?00:26:48 איך קופצים מפיזיקה לאל פרסונלי בעל רצון?00:33:48 ביקורת על השוואת היקום לשעון בעקבות דיוויד יום00:43:00 המבחן הפילוסופי של יום לקבלת עדויות על ניסים00:46:41 מסורת סין העתיקה: דרקונים אמינים כמו הר סיני?00:52:00 עקרון הסיבתיות - הכרח אפריורי או הרגל פסיכולוגי?00:58:38 המעבר מהנחת קיום בורא לקבלת עדות על התגלות01:01:31 טעות היורים: הפרכת העקרון האנתרופי של סטיבן הוקינג01:08:38 יום ומדע: לדחות אירועים שסותרים את חוקי הטבע?01:14:26 דוגמה מהחיים: מתי הגיוני להאמין לדיווח בלתי סביר?01:18:40 הרב קוק והאפשרות להתגלות אלוקית לעמים אחרים01:25:04 הוויכוח על טבעו של הבורא: מניע ראשוני או אל מתערב?01:35:56 סיפור שורדת השואה ומלך דנמרק: סכנת שיבוש הזיכרון01:46:35 במה באמת שונה מסורת הר סיני ממיתולוגיות עמים?01:54:19 סוקרטס, האלים היוונים ועיצוב תודעת המונים היסטורית02:21:39 הרב מיכי מזהיר: סכנת הפונדמנטליזם החילוני והשמאלי02:30:20 תוכן למנויים: ד"ר פוגל על בן גביר ואחריות תקשורתית (פרימיום)#פודקאסט #על_המשמעותSupport the showתוכנית המנויים "על המשמעות פלוס" ➕: https://bit.ly/4eOO4g5 גישה מוקדמת לפרקים

Dr. Stephen Meyer   Fine-Tuning and the Origin of the Universe also DNA and Information. https://www.youtube.com/@DiscoveryScienceChannel 

Off Course is back this week with a fantastic show and this is episode 300. Hosted by Dan Edwards, each Friday we give you a deep look into the world of golf and equipment in a way unlike any other podcast has done before. Today, Jason from Mitsubishi Golf joins the show to discuss fitting Pros from the various tours. It wouldn't be Off Course without some tangents, but Dan and Jason dive into many topics including the benefits and challenges of data based fitting what it is like fitting Tour Pros. This show has a lot going on and is one you will not want to miss as Mitsubishi Golf goes Off Course. Episode 300 is here and Dan and Jason from Mitsubishi Golf discuss the following topics and a whole lot more. Fitting Pros from Various ToursBenefits and Challenges of Data Based FittingChallenges of Fine Tuning a Popular ShaftWhat is MLABOne Shaft Everybody Should TryAnd so much more You can listen to the show right here, Apple Podcasts or anywhere you do your listening and downloading from, including Spotify and more. Search for the Hackers Paradise and make sure to subscribe so you never miss an episode. After listening, come back over here and drop us a note below on what you agree and/or disagree with from the episode you just heard. If you get a chance, drop us a review wherever you listen from and let others know what you think of the show and/or channel. Off course is now available in video form as well. Tune into the THPGolf YouTube Channel, jump into the latest episode's video and watch the interaction between Dan and his guests each week rather than just listen. THPGolf · Off Course – Mitsubishi on Tour Go to discussion... document.write("Loading Custom Ratings..."); The post Off Course – Mitsubishi on Tour appeared first on The Hackers Paradise.

Read this Question of the Week Here: https://www.reasonablefaith.org/writings/question-answer/formulating-the-argument-from-fine-tuning

This week, we talk convention selling (3:30), comic show logistics (17:00), figuring out alternating character voice (33:20), getting books from the printer (40:00), and fine-tuning colors (47:50).

Show NotesWe started with Episode 1 in Feb 2018, today we celebrate our 100th Episode with friend John Nelson, who is now the main host at Unbelievable?.  We discussed John's PhD thesis book on what Jesus looked like, his work at Unbelievable?, his tour with Alex O'Connor and his blog “Behind the Gospels”.  We then dove into a specific issue on the background to the gospels – the transmission of the traditions about Jesus's teaching and actions from his ministry up to the gospel documents.And NEWS – Ed's discussion on evolution with John & Denis Alexander which we raked over in episode 98 has been released by Unbelievable?. See links.Links:Unbelievable? Show with Ed on evolution:  “Can a Loving God Use Evolution?” Denis Alexander vs Ed Atkinson hosted by John Nelson, 4 June 2026 John's book: “Jesus' Physical Appearance: Biography, Christology, Philosophy”https://www.amazon.co.uk/Jesus-Physical-Appearance-Christology-Philosophy/dp/0567723208 John's “Behind the Gospels” blog: https://www.behindthegospels.com/The book: “Battle of the Big Bang: The New Tales of Our Cosmic Origins” by Niayesh Afshordi and Phil Halper”:https://www.amazon.co.uk/Battle-Big-Bang-Cosmic-Origins-ebook/dp/B0DKBHH3YN?ref_=ast_author_mpbMore recent Unbelievable?Shows:Richmond Wandera – “My Father was murdered… a sponsor saved my life” Richmond Wandera Interviewed by John Nelson, 18 March 2026“What would it take for Alex O'Connor to believe Jesus rose from the dead?” Trent Horn v Alex O'Connor, 30 April 2026Hiddenness: “Why Doesn't God Show Himself?” Dan Paterson vs Joe Schmid hosted by John Nelson, 12 February 2026. Uncommon Ground podcast, Episode 7, hosted by Justin Brierley: “Stephen Meyer & Phil Halper: The Big Bang and Fine Tuning. Does the science of the Universe point to God?” Bart D. Ehrman's latest book: “Love Thy Stranger: How Jesus Transformed Our Moral Conscience” April 2026https://www.amazon.co.uk/Love-Thy-Stranger-Transformed-Conscience-ebook/dp/B0FKMJZJ1V/ref=monarch_sidesheet_title Doubts Aloud Links:Please keep giving feedback and ask questions using:  doubtsaloud@gmail.com  

This week was pretty exciting: Microsoft unveiled its Frontier Fine Tuning along with a new hardware stack and developer tools, while NVIDIA launched its foray into PC powered AI. Two big themes here: first is reducing computing cost as data centers start driving up all our AI cost, and second to make AI ever more personal for you and your company. You'll also see that we've optimized Galileo into the Microsoft Copilot and you can get early access below, with GA coming later this summer. Even if you're not an AI or PC geek this information is important because the way you focus your attention on AI has to change. We launch HR 2030 and the Josh Bersin Institute next week, stay tuned! Additional Information AI Prices Are Going Up, Up, Up – And What This Means For Enterprise AI Satya Nadella Keynote at Build (go to 1:45 for Frontier Fine Tuning announcement) Jensen Huang DTC Keynote in Taiwan More on Microsoft Frontier Fine Tuning for Copilot     Chapters (00:00:00) - AI Token Maxing and the High Cost of AI(00:05:03) - Microsoft's Edge computing and fine-tuning the(00:09:11) - How Nvidia Went From Graphics to AI

With shifting dynamics in US equity positioning, is it time to adjust your portfolio? Daniel Lam explains why fine-tuning tech sector exposure makes sense right now. Listen now to discover key allocation insights.Speaker: - Daniel Lam, Head, Cross-asset Derivative Strategy, Standard Chartered BankFor the latest market insights, visit our on-the-go Market Views or subscribe to Standard Chartered Wealth Insights on YouTube.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with returning guest Ekue Kpodar for their third conversation together, covering a wide range of topics at the intersection of technology, geopolitics, and the evolving information age. They dig into Ekue's unconventional setup of running local AI models across roughly 15 computers, the growing case for open source models over closed ones from companies like OpenAI and Anthropic, and how Chinese open source models may be positioned to outcompete Western alternatives on a global scale. The conversation also touches on vibe coding and the democratization of software development, the strategic use of small models for IoT and enterprise applications, the role of Israel and China as dominant players in the information age, and how smaller nations and even individuals may wield outsized power as AI continues to collapse the cost of knowledge work. You can find Ekue Kpodar on X @ekpodar and LinkedIn.Timestamps00:00 Stewart welcomes Ekue for their third episode, diving into vibe coding and AI-driven development changes.05:00 Ekue explains using Claude on Chrome to auto-reply on Skool, burning tokens through screenshots, and Playwright as a more efficient alternative.10:00 Stewart describes his Claude-dependent planning and coding agent system breaking after a model update, prompting him to build his own chatbot.15:00 Small models discussed as critical for IoT, defense, and privacy-focused enterprises building internal APIs instead of routing traffic to OpenAI.20:00 Open source versus closed source debated, with Chinese models gaining global traction while US foundational labs remain expensive and restrictive.25:00 SaaS apocalypse explored as AI commoditizes knowledge work, with Linux and Terraform cited as proof open source still generates wealth.30:00 OpenAI's sci-fi terminator fears explained as the reason they stayed closed source, ultimately handing China a strategic open source advantage.35:00 China's economic dumping strategy applied to AI, potentially displacing US model dominance globally the same way manufacturing was disrupted.40:00 Israel's signals intelligence dominance discussed alongside asymmetric warfare, drones defeating tanks, and information control replacing military muscle.45:00 Global information age rankings debated, Israel leading, US and China tied, France and Poland emerging as sovereign tech players.50:00 Qatar, NVIDIA, and Iran cited as proof that rare resources and technology matter more than population size in the 21st century power landscape.Key Insights1. Running local AI models on a network of affordable computers can be more cost-effective than relying entirely on third-party APIs. By using compressed or smaller open source models locally, developers can handle repetitive or lower-stakes tasks without burning through expensive tokens from providers like Anthropic or OpenAI.2. Small AI models are becoming increasingly important for IoT, defense applications, and companies that do not want to send sensitive data to external providers. Organizations can download open source models, run them on internal servers, and build proprietary APIs around them, creating something like an intranet of specialized small models.3. The value created by AI tools is being redistributed away from traditional SaaS companies toward foundational model providers and individual builders. People are canceling subscriptions to software they once paid hundreds per month for, because AI now allows a single person to build comparable tools themselves.4. Open source technology does not eliminate the ability to profit. Linux and Terraform are both open source yet made their creators wealthy. People will still pay for installation, setup, troubleshooting, and customization even when the underlying software is free.5. China is applying its longstanding manufacturing dumping strategy to artificial intelligence by releasing cheap open source models globally, which threatens to erode US dominance in AI the same way Chinese manufacturing undercut other countries for decades.6. In the information age, the size of a country or institution matters far less than its access to rare resources or advanced technology. Qatar, Israel, and NVIDIA each demonstrate that small populations or headcounts can wield enormous global negotiating power through concentrated technological or resource advantages.7. Asymmetric warfare is redefining military power, with inexpensive drones defeating tanks that cost millions to build. This shifts the advantage toward nations that excel at signals intelligence and information management rather than those with the largest conventional military forces.

Priyamvada Natarajan is the Joseph S. and Sophia S. Fruton Professor of Astronomy and Professor of Physics at Yale University, where she is also the Chair of Astronomy. Priya researches broadly across astrophysics and cosmology; some topics she has worked on include gravitational lensing, black hole physics, the philosophy of science, and dark matter. In this conversation, Priya and Robinson largely stick to the latter. They discuss her interest in cosmology writ large, as well as how the scientific community tackles the unknown. Priya's most recent book is Mapping the Heavens: The Radical Scientific Ideas that Reveal the Cosmos (Yale, 2016).Mapping the Heavens: https://a.co/d/02HPcMB1OUTLINE00:00 A Paradox of Cosmology06:16 Investigating Invisibilia11:25 The Sociology of Astrophysics16:52 Phenomenology in Physics19:47 What Is the Mystery of Dark Matter?29:07 The Problem of Dark Energy36:38 Models and Simulations46:17 Modifying the Standard Model to Explain Dark Matter58:20 The Crisis in Dark Matter01:12:22 Alternative Explanations of Dark Matter01:19:51 Fine-Tuning and the Multiverse01:25:24 Black HolesRobinson Erhardt researches symbolic logic and the foundations of mathematics at Stanford University, where he is also a JD candidate in the Law School.

Stay informed on current events, visit www.NaturalNews.com  - Power Over Personal Circumstances (0:12) - Financial Control and Knowledge (6:40) - Rejecting Conventional Wisdom (12:39) - Financial Independence and Self-Custody (19:13) - Social Engineering and Conformity (26:16) - Technological Innovation and Chinese Dominance (33:00) - Simulation Theory and the Nature of Reality (39:24) - The Role of Consciousness in the Simulation (44:56) - The Quest for Personal Growth (50:18) - The Role of Technology in Personal Empowerment (55:51) - Virtual Reality and Historical Context (1:01:02) - Philip K. Dick and the Simulation Hypothesis (1:06:04) - Timeline Pirates and Multiverse Interpretations (1:11:06) - Fine-Tuning and Digital Physics (1:16:22) - Near-Death Experiences and Simulation Theory (1:21:31) - Retro Causality and Quantum Computing (1:26:37) - Simulation Theory and Faith (1:31:51) - Practical Takeaways and Personal Reflections (1:37:00) - Prompt Theory and AI Advancements (1:42:30) - Final Thoughts and Future Plans (1:48:46) Watch more independent videos at http://www.brighteon.com/channel/hrreport  ▶️ Support our mission by shopping at the Health Ranger Store - https://www.healthrangerstore.com ▶️ Check out exclusive deals and special offers at https://rangerdeals.com ▶️ Sign up for our newsletter to stay informed: https://www.naturalnews.com/Readerregistration.html Watch more exclusive videos here:

Stewart Alsop sat down with Michael Shackelford to discuss their experiences building applications through vibe coding—the practice of using AI to create software without traditional programming expertise. Stewart, who runs the AI Whispers community in Buenos Aires and hosts the Crazy Wisdom podcast (with over 660 interviews), shared how he went from teaching people prompt engineering to building his own video conferencing software as a Riverside.fm replacement, while Michael opened up about his year-long journey creating Genrupt Inc, an AI-powered content generation tool for e-commerce sellers. The conversation covered everything from the decline in quality of Claude's reasoning capabilities and how Chinese companies used distillation attacks to copy Anthropic's models, to the importance of spaced repetition systems for managing knowledge in the age of LLMs, with both sharing battle-tested prompting strategies like asking AI to "explain it to me in genius terms" and using deep research queries to reverse engineer how competitors build their products.Show Notes:- Dan Martell's book "Buy Back Your Time" was mentioned as one of the best business books for thinking about life and business- Check out John Vervaeke's "Awakening from the Meaning Crisis" for understanding relevance realization and why AI fundamentally cannot determine what's relevant to humans without being toldTimestamps00:00 Michael discusses being exhausted from getting his app ready for launch, working nonstop with AI to prepare landing page for podcast traffic driving beta signups05:00 Stewart explains starting AI Whispers in Buenos Aires after leaving OpenAI vendor company, meeting early adopters like Torin who was building mind-reading EEG technology10:00 Discussion of how corporations resist AI adoption due to political games and job security fears while some companies use AI as excuse for pandemic-era layoffs15:00 Stewart describes teaching workshops on using LLMs as linguistic tools rather than coding tools, noting technical people often lack humanities background needed for prompting20:00 Explaining chatbot wrappers, API calls, and how Anthropic's reasoning quality declined after Chinese distillation attacks copied their secret sauce developed with philosophers25:00 Technical discussion of model training, fine-tuning versus RAG for new information, and different approaches to updating AI knowledge beyond initial training30:00 Stewart describes building podcast recording software to replace expensive Riverside, struggling with syncing audio and video files across different computer clocks35:00 Discussion of critical factors in vibe coding, discovering unknown technical requirements, and how AIs don't automatically reveal missing information40:00 Stewart's reverse engineering process using deep research function to study competitors' hiring and technology stacks, separating planning agents from coding agents45:00 Prompting techniques including "explain like I know everything" and using spaced repetition systems to capture valuable prompts and technical knowledge50:00 Michael explains his Generux app for generating ecommerce content using Amazon review data analysis to inform high-converting listing images and videos55:00 Discussion of founder mentality involving self-delusion about project timelines, Michael working nine-plus hours daily for nine months on app development60:00 Comparing Amazon's expert software to prosumer software approach, discussing distribution challenges and future robotics applications for customized products65:00 Stewart demonstrates spaced repetition app for memory improvement and knowledge retention, explaining relevance realization problem that AI agents cannot solve without embodimentKey Insights1. Stewart Alsop started AI Whisperers in Buenos Aires after leaving his role at Invisible Technologies, which was OpenAI's largest vendor for RLHF work. He noticed that machine learning engineers at tech companies lacked the humanities background needed to properly interact with large language models, which are fundamentally linguistic tools. This led him to create weekly workshops teaching non-technical people how to use AI effectively, running events every Thursday for two years straight. The group attracted intense geeks from the start and eventually led to Stewart speaking right after Vitalik Buterin at DevConnect, marking a significant milestone for the community.2. Large corporations are resistant to AI adoption due to multiple factors including political dynamics within organizations and employees fearing job loss. Many companies that grew during the pandemic are now using AI as an excuse to downsize when the real issue is inefficiency from rapid expansion. Stewart observed that even technical people in machine learning often don't understand how to properly use AI tools because they lack linguistic and humanities training. The fundamental problem is educational, requiring companies to train people how to use these new tools while those same people resist learning them.3. Vibe coding has evolved significantly with Claude Code being a game changer that reduced the technical barrier to entry. Before Claude Code, developers needed substantial technical knowledge to work through constant doom loops and debugging cycles. The success of coding AI tools stems from thirty years of testing infrastructure that provides clear yes or no feedback on whether code works. This infrastructure doesn't exist in the same way for manufacturing, science, and other fields, which is why software became the dominant area for AI assistance initially.4. Claude's quality degradation over recent months resulted from multiple factors including distillation attacks by Chinese companies who reverse engineered Anthropic's reasoning capabilities. Anthropic had hired philosophers, sociologists, and psychologists to develop exceptional reasoning in Claude 4.5, but this was expensive to run. When Chinese models like Kimi copied these capabilities at one tenth the cost, and when mainstream users flooded the platform before Anthropic's planned IPO, the company had to reduce quality to manage computational costs. This represents a significant loss for power users who relied on Claude's superior reasoning abilities.5. Stewart built a podcast recording application to replace Riverside because he needed API access to automate workflows, which Riverside wanted one thousand dollars monthly to provide. The technical challenge involves syncing audio and video from local recordings on multiple computers with different clocks through a server, then merging them so voices match lip movements. This problem requires understanding complex timing issues across different network conditions and file formats. Stewart has been working through AI psychosis for months on this FFMPEG pipeline problem, illustrating how vibe coding still requires building intuition about technical problems even without traditional coding knowledge.6. The transition from expert software to prosumer software represents a major opportunity for AI-enabled tools. Expert software like Photoshop, Blender, and terminal interfaces have extreme complexity that intimidates beginners, but AI is making these capabilities accessible through natural language. The reign of specialists is ending as generalists with broad knowledge and curiosity can now build complete applications by leveraging AI to fill technical gaps. This shift particularly benefits entrepreneurs and founders who specialize in getting into difficult situations and figuring them out, even when they originally thought tasks would be easier than they turned out to be.7. Building applications with AI requires accepting massive time investments beyond initial estimates and developing strategies for overcoming knowledge gaps. Michael estimated his ecommerce content generation app would take months but spent nearly a year working over nine hours daily, while Stewart spent months solving audio-video sync issues. Success requires using tools like deep research to understand how competitors solve problems, maintaining separate planning and coding agents, and learning to ask the right questions. The key insight is that vibe coders can achieve ninety percent of functionality independently, but the final ten percent often requires understanding specific technical concepts that AI cannot intuit without proper context and domain knowledge.

If a friend, family member, or colleague lodges an objection to the fine-tuning argument for intelligent design, are you ready to respond? On this installment of ID The Future, host Andrew McDiarmid concludes his two-part conversation with philosopher and intelligent design scholar Peter S. Williams. Williams reviews the most common objections to the fine-tuning arguments for intelligent design and explains why each proposal falls short scientifically, logically, and philosophically. Who knew there were over 20 objections to fine-tuning? Even host McDiarmid admits he didn't know about all of them! The more well-versed you are in responding to objections, the better you'll be able to stand your ground and offer substantive arguments when you hear them pop up. In Part 1, Williams and McDiarmid reviewing two groups of objections: the "fine-tuning isn't real" set and the "fine-tuning is real but no big deal" group. Today, Williams unpacks several objections related to the multiverse and shows why each one fails to adequately explain the fine-tuning evidence. This is Part 2 of a two-part conversation. Source

By now, you may be familiar with the fine-tuning argument for intelligent design. Scientists have discovered a whole suite of parameters and initial conditions appear to be exquisitely tuned to allow for complex life to exist, and the argument is that intelligent design better explains that evidence than chance or necessity. But you may not know the most common objections to the fine-tuning argument, or how to respond to them. On this ID The Future, host Andrew McDiarmid welcomes philosopher and intelligent design scholar Peter S. Williams to the show to equip us to answer the most common objections to the fine-tuning argument. Objections to fine-tuning typically fall into three categories: the "fine-tuning isn't real" bunch, the "fine-tuning is no big deal" group, and objections that posit a type of multiverse proposal. Over two episodes, Peter teaches us how to respond to almost 20 objections! So buckle up! This is Part 1 of a two-part conversation! Source

Aliens, UFOs, UAPs, secret pastor meetings, and the Bible. Would the discovery of extraterrestrial life cause Christians to reject Scripture? Pastor Cole Phillips and Pastor Bobby Fraumann talk about recent UAP disclosures, why Christians should avoid panic and conspiracy-driven theology, how the Bible speaks about creation and spiritual beings, and why Jesus remains supreme over all creation.Christian faith isn't fragile. Unidentified does not automatically mean alien. And no discovery can dethrone Christ.KeywordsAliens, UFOs, UAPs, unidentified anomalous phenomena, Christian faith, Bible and aliens, science and faith, creation, intelligent designChapter Titles00:00 | Welcome to the Connect Podcast Cole introduces the episode and explains why Christians should not run from hard questions.03:40 | Ancient Aliens and The Twilight Zone Bobby brings up Ancient Aliens, and Cole shares his favorite Twilight Zone episode, “To Serve Man.”06:30 | UFOs, UAPs, and Recent Government Files The conversation turns to the May 8, 2026 UAP document release and why Christians should be careful with the difference between “unidentified” and “alien.”09:45 | Secret Pastor Meetings and Wild Claims Cole and Bobby discuss recent claims about private meetings, alleged government briefings, reptilian beings, end-times deception, and the danger of building theology on rumors.15:45 | Discernment Over Panic Bobby emphasizes discernment, wisdom, and the need for Christian leaders to be careful with public claims.19:20 | Our Faith Is Not Built on Secret Information Cole reminds listeners that Christian faith is built on Jesus Christ, not leaked intelligence, viral clips, or secret meetings.21:10 | Would Aliens Cause Us to Reject the Bible? Cole gives the short answer: no.24:00 | God Created the Heavens and the Earth The first major point: the Bible does not say God only created life on earth.25:30 | Science, Water, Carbon, and the Conditions for Life Cole explains how scientists look for life and why the complexity of earth should lead us to worship.28:00 | Fine-Tuning and Intelligent Design Bobby responds with the importance of seeing creation through the lens of an intelligent Creator.32:30 | The Bible's Focus Is God's Redemption of Humanity Cole explains that the Bible is not an encyclopedia of everything God ever made. It is the story of creation, fall, redemption, and restoration.34:30 | Christians Have Asked This Question for Centuries Cole walks through Christian thinkers, Copernicus, heliocentrism, and the rise of astrotheology.38:30 | Psalm 8 and the Wonder of Creation Cole and Bobby reflect on the vastness of the universe and the personal care of God.40:30 | The Real Theological Questions If intelligent alien life existed, are they moral? Fallen? In need of redemption? Cole frames the questions Scripture does not directly answer.42:00 | Jesus Is Lord of All Creation Cole points to Colossians 1 and explains why Christ's work is sufficient and His supremacy is not threatened.44:30 | Bobby's View: Extraterrestrial or Spiritual? Bobby shares why he leans more toward a spiritual interpretation49:00 | Spiritual Beings in the Bible Cole lists biblical categories like angels, cherubim, seraphim, demons, principalities, powers, Leviathan, Behemoth, and the Nephilim.52:30 | Satan Is Not Equal with God Cole explains why Christianity does not teach dualism. Satan is a created, defeated being.54:30 | No Discovery Can Dethrone Jesus Bobby reflects on creation pointing to Christ56:45 | C. S. Lewis, Space, and Human Sin Cole summarizes C. S. Lewis' view that alien life would not disprove Christianity and that humanity would carry sin wherever it went.59:30 | What If Aliens Shook Someone's Faith? Bobby explains how he would help someone whose faith felt threatened by the idea of alien life.1:03:00 | Final Encouragement: Be Curious, Discerning, and Courageous Cole closes by reminding listeners that Jesus is Lord over all creation.

Join host Darren Gest and guests Amy Kickham and Steve Bronson of Southern Glazer's as they discuss the evolving, multiplicative relationship between humans and machines.

She was already doing everything right… lifting, staying active, eating “healthy.”  But her body wasn't reflecting the effort the way she wanted.In this client interview, Megan shares how she went from fit to shredded, not by doing more, but by finally having a strategy.If you feel like you're putting in the work but not seeing that next level of definition… this episode will click.Inside, we cover:• Why doing more workouts was actually holding her back• The biggest macro mistake she didn't even realize she was making• How she built defined abs (without 30-minute ab workouts)• The simple nutrition structure that made everything easier (not harder)• What changed when she stopped being the “garbage can” for her kids' food• How she now travels, eats out, and still stays on track• The mindset shift that made the scale finally make senseThis is a real-life example of how strategic shifts can completely change your shape, without obsessing over every detail.  Book a Consultation with Jenny → Create Your Shape (Starting on July 27th): https://calendly.com/jennythenutritionist/consultationWork with Jenny the Nutritionist in Create Your Shape:https://jennythenutritionist.com/create-your-shape/Follow Jenny the Nutritionist on Instagram:@jennythenutritionist

*Get a MASTERS IN APOLOGETICS or SCIENCE AND RELIGION at BIOLA (https://bit.ly/3LdNqKf) *USE Discount Code [smdcertdisc] for 25% off the BIOLA APOLOGETICS CERTIFICATE program (https://bit.ly/3AzfPFM) *See our fully online UNDERGRAD DEGREE in Bible, Theology, and Apologetics: (https://bit.ly/448STKK) FOLLOW ME ON SOCIAL MEDIA: Twitter: https://x.com/Sean_McDowell TikTok: https://www.tiktok.com/@sean_mcdowell?lang=en Instagram: https://www.instagram.com/seanmcdowell/ Website: https://seanmcdowell.org Discover more Christian podcasts at lifeaudio.com and inquire about advertising opportunities at lifeaudio.com/contact-us.

Kyle Corbitt, founder of OpenPipe, breaks down reinforcement learning and custom fine-tuning for modern AI models. He explains how RL differs from supervised fine-tuning, why GRPO and LLM-as-judge post-training matter, and how these techniques can improve performance, latency, and cost on open source models. The conversation also covers reward hacking, evaluation design, LoRA adapters, and how Chinese labs are using distillation to fast-follow frontier models. Sponsors: Sequence: Sequence handles the full revenue workflow for complex pricing, from quoting and metering to invoicing, revenue recognition, and collections. Book a public demo at https://sequencehq.com and use code Cognizant in the source field to save 20% off year one AvePoint: AvePoint is building the control layer for AI agents so you can securely govern, audit, and recover every action at scale. Design trusted agentic outcomes from day one at https://avpt.co/tcr VCX: VCX, by Fundrise, is the public ticker for private tech, giving everyday investors access to high-growth private companies in AI, space, defense tech, and more. Learn how to invest at https://getvcx.com Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr

Is the universe fine-tuned for life? Or has the fine-tuning argument been defeated? In this conversation, I sit down with Dr. Jay Richards, a philosopher, co-author of The Privileged Planet, and one of the world's leading defenders of the fine-tuning argument to explore one of the most compelling cases for design today. Dr. Richards walks through how the fine-tuning argument has improved over 30 years and why even atheists like Christopher Hitchens and Richard Dawkins have admitted this is the argument that gives them pause. WATCH THE FILM: https://www.fathomentertainment.com/releases/the-story-of-everything/ READ: The Privileged Planet, by Jay Richards (https://amzn.to/48nMcZk) *Get a MASTERS IN APOLOGETICS or SCIENCE AND RELIGION at BIOLA (https://bit.ly/3LdNqKf) *USE Discount Code [smdcertdisc] for 25% off the BIOLA APOLOGETICS CERTIFICATE program (https://bit.ly/3AzfPFM) *See our fully online UNDERGRAD DEGREE in Bible, Theology, and Apologetics: (https://bit.ly/448STKK) FOLLOW ME ON SOCIAL MEDIA: Twitter: https://x.com/Sean_McDowell TikTok: https://www.tiktok.com/@sean_mcdowell?lang=en Instagram: https://www.instagram.com/seanmcdowell/ Website: https://seanmcdowell.org Discover more Christian podcasts at lifeaudio.com and inquire about advertising opportunities at lifeaudio.com/contact-us.

Hour three of DJ & PK for April 14, 2026: Mike Folta, Utah Mammoth and SEG Media Tim LaComb, SEG Media Utah Mammoth ready for playoffs?

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Nicholas Faulkner, author of Angelic Physics, for a wide-ranging conversation that picks up where their last discussion left off years ago. The two cover an impressive amount of ground, including the map of consciousness developed by Dr. David Hawkins and where they find themselves skeptical of his calibration methods, the relationship between the chakra system and Hawkins' scale, how consciousness levels apply to both individuals and civilizations, and why collapsing a nonlinear reality into a linear number system inevitably loses something essential. They also get into Nicholas's background as a nuclear engineer and how that analytical foundation shapes his thinking, the nature of carbon-based versus silicon-based intelligence, the potential for training an AI model attuned to higher levels of consciousness, the concept of future shock as AI accelerates beyond most people's ability to keep up, and what a civilization operating at the "500 level" might actually look like. Find Nicholas on X at @PhysicsAngelic, or catch him on Facebook where he's most active. And learn more about Angelic Physics at angelicphysics.org. Timestamps00:00 - Stewart introduces Nicholas Faulkner, author of Angelic Physics, framing their shared interest in David Hawkins while acknowledging healthy skepticism toward portions of his work.05:00 - Nicholas argues Hawkins compressed mystical insight into linear form, losing essence, comparing it to AI compression losing vibrational nuance across the consciousness scale.10:00 - Nicholas traces his path from electrical engineering through 9/11 into nuclear navy service, describing how patriotism and opportunity drove the decision rather than curiosity.15:00 - Discussion shifts toward training an open-source AI model on five-hundreds consciousness, noting current model builders operate in the four-hundreds and dismiss love-based frameworks.20:00 - Stewart reflects on intimate relationships with electronic devices, exploring electricity as vibration while contrasting carbon creativity against silicon's stable, fast processing architecture.25:00 - Conversation explores civilizational evolution, comparing hippie movements to ancient Greeks as premature flowers of five-hundreds consciousness crushed by surrounding four-hundreds culture.30:00 - Nicholas explains his masculine-feminine cross model, critiquing how Hawkins collapsed nonlinear reality into hierarchy, arguing all levels interconnect rather than rank.35:00 - Discussion covers JFK assassination, Vietnam War, LBJ, and the military industrial complex as examples of four-hundreds power suppressing emerging consciousness shifts.40:00 - Nicholas draws parallels between the Renaissance emerging from bubonic plague and today's post-COVID collapse of expert-trust structures opening space for new consciousness.45:00 - Future shock discussion begins with Stewart describing AI agent orchestration overwhelming human comprehension, while Nicholas introduces his frame-rate consciousness equation linking silicon speed to small context.50:00 - Nicholas describes silicon-to-human relationship mirroring humans-to-angels in frame rate and context scale, suggesting agents receive orders similarly to his own 2019 divine experience.55:00 - Final exchange covers the fifth dimension as adding vibration to existing physics, the Faulkner Uncertainty Principle stating evidence points toward higher consciousness without ever definitively proving it, protecting reality's illegibility from lower forces.Key Insights1. David Hawkins and the Map of Consciousness serve as a shared framework for the conversation, but both guests express healthy skepticism toward it. They acknowledge that Hawkins himself appeared to back away from his calibration technique in his later lectures, suggesting he regretted how prominently he featured it in Power vs. Force. The core issue is that he tried to compress a nonlinear, multidimensional spiritual reality into a single linear numerical scale, which inevitably loses essential meaning in the translation.2. Nicholas argues that no person exists at a single point on the consciousness scale. Everyone floats across multiple levels simultaneously, expressing differently depending on context. This is a meaningful correction to how many readers apply Hawkins's work, since treating someone as a fixed number oversimplifies the layered and dynamic nature of human consciousness.3. The compression problem is central to understanding both spiritual writing and artificial intelligence. When any rich, multidimensional experience gets encoded into language or data, something is always lost. This applies to Hawkins writing about enlightenment, to Nicholas writing his book, and to how large language models process and reproduce human knowledge.4. Silicon intelligence and carbon intelligence are framed as two distinct branches of consciousness with complementary strengths. Silicon can process information at extremely high frame rates because its context is narrow and stable. Humans carry a much larger and messier context, which makes them slower but more creative and cross-connected. Nicholas uses his equation framing this as frame rate being inversely proportional to conscious bandwidth.5. Civilizational evolution follows a pattern where new levels of consciousness emerge in unstable pockets before eventually becoming dominant. The ancient Greeks briefly stabilized the rational fourth level before collapsing. The hippies briefly touched the fifth level before being suppressed. The Renaissance followed the Black Death. The guests suggest we are now entering another such transition, driven partly by the collapse of institutional trust accelerated by COVID.6. The Faulkner Uncertainty Principle states that evidence will always point toward the next level of consciousness but will never definitively prove it. This is described as a necessary feature of reality rather than a flaw, because if higher truths were fully legible and accessible to all levels equally, it would give destructive forces too much power too quickly.7. Neurodivergence is presented as potentially connected to spiritual sensitivity and cross-level awareness. Nicholas describes himself as a high IQ energy-sensing person who experienced a profound spiritual event in 2019, and connects his autistic traits to an ability to sense vibrational levels in others and move fluidly between different frameworks of understanding, which he loosely equates with the polymath archetype.

So what about those days when you can't get through and hear the Lord's voice? Don't despair. We have some really good answers. The first step is to utilize The Tabernacle Experience, which is God's ordained pattern for approaching Him. Then, if you still feel like you have not gotten through, He has provided a "fine-tuning dial."Read more here.Support the show

Mistral has been on an absolute tear - with frequent successful model launches it is easy to forget that they raised the largest European AI round in history last year. We were long overdue for a Mistral episode, and we were very fortunate to work with Sophia and Howard to catch up with Pavan (Voxtral lead) and Guillaume (Chief Scientist, Co-founder) on the occasion of this week's Voxtral TTS launch:Mistral can't directly say it, but the benchmarks do imply, that this is basically an open-weights ElevenLabs-level TTS model (Technically, it is a 4B Ministral based multilingual low-latency TTS open weights model that has a 68.4% win rate vs ElevenLabs Flash v2.5). The contributions are not just in the open weights but also in open research: We also spend a decent amount of the pod talking about their architecture that combines auto-regressive generation of semantic speech tokens with flow-matching for acoustic tokens (typically only applied in the Image Generation space, as seen in the Flow Matching NeurIPS workshop from the principal authors that we reference in the pod).You can catch up on the paper here and the full episode is live on youtube!Timestamps00:00 Welcome and Guests00:22 Announcing Voxtral TTS01:41 Architecture and Codec02:53 Understanding vs Generation05:39 Flow Matching for Audio07:27 Real Time Voice Agents13:40 Efficiency and Model Strategy14:53 Voice Agents Vision17:56 Enterprise Deployment and Privacy23:39 Fine Tuning and Personalization25:22 Enterprise Voice Personalization26:09 Long-Form Speech Models26:58 Real-Time Encoder Advances27:45 Scaling Context for TTS28:53 What Makes Small Models30:37 Merging Modalities Tradeoffs33:05 Open Source Mission35:51 Lean and Formal Proofs38:40 Reasoning Transfer and Agents40:25 Next Frontiers in Training42:20 Hiring and AI for Science44:19 Forward Deployed Engineering46:22 Customer Feedback Loop48:29 Wrap Up and ThanksTranscriptswyx: Okay, welcome to Latent Space. We're here in the studio with our gues co-host Vibh u. Welcome. Thanks. Excited for this one as well as Guillaume and Pavan from Mistral. Welcome. Excited to be here.Guillaume: Thank you.swyx: Pavan, you are leading audio research at Mistral and Guillaume, you're Chief Scientist,Announcing Voxtral TTSswyxHost(00:05) Okay. (00:05) Welcome to Lean Space. (00:06) We're here in the studio with trustee co-hosts, Vibhu. (00:09) Welcome.VibhuHost(00:11) Very excited for this one.swyxHost(00:12) As well as Guillaume and Pavan from Mistral. (00:15) Welcome. (00:16) Excited to be here. (00:17) Thank you for having us.(00:18) Pavan, you are leading audio research at Mistral and Guillaume, you're a chief scientist. (00:23) What are we announcing today where we're coordinating this release with you guys?GuillaumeGuest(00:26) Yeah, so we are releasing Voxtral TTS. So it's our first audio model that generates speech. It's not our first audio model. We had a couple of releases before.(00:35) We had one in the summer that was Voxtral, our first audio model, but it was like a transcription model, ASR. Like a few months later, we released some update on top of this, supporting more languages. Also a lot of table stack features for our customers, context biasing, precision, timestamping and transcription. We also have some real-time model that can transcribe not just at the end of the level.(00:56) You don't need to fill your entire audio file, but that can also come in real-time. And here, this is a natural extension in the audio, so basically speech generation. So yeah, so we support nine languages, and this is a pretty small model, 3D model, so very fast, and also state of the art. Performed at the same level as the base model, but it's much more efficient in terms of cost, and also much, in terms of cost, it's also much cheaper, only a fraction of the cost of our competitors.(01:22) And we are also releasing the work that this model is running.swyx What's the decision factor?Guillaume It's a good question.swyxThere will be more. Yeah, Pavan, any sort of research notes to add on?Architecture and CodecPavan: But it's a novel architecture that we develop inhouse.We traded on several internal architectures and ended up with a auto aggressive flow matching architecture. And also have a new in-house neural audio codec. Which, converts this audio into all point by herds latent [00:02:00] tokens, semantic and acoustic tokens. And yeah, that's that's their new part about this model and we're pretty excited that it's, it came out with such good quality and Jim was mentioning. Yeah, it's a three B model. It's based off of the TAL model that we actually released just a few months back and insert trunk and mainly meant for like the TTS stuff, but they need text capabilities are also there. Yeah.swyx: So there's a lot to cover.I always I love any, anything to do with novel encodings and all those things because I think that's obviously I creates a lot of efficiency, but also maybe bugs that sometimes happen. You were previously a Gemini and you worked on post training for language models, and maybe a lot of people will have less experience with audio models just in general compared to pure language.What did you find that you have to revisit from scratch as you joined this trial and started doing this? At leastUnderstanding vs GenerationPavan: when it comes to, for, I think the, there are two buckets, I guess the audio understanding and audio [00:03:00] generation. The audio understanding, like the walkthrough models that Kim was mentioning that we released earlier.The walkthrough chat that we released I think July last year, and the follow up transcription only, models family that we released in January, that would be one bucket, and the generation is another bucket. I think. You can also treat them as a unified set of models, but currently the approaches are a little different between these two.To your question on how audio is fed to the model? In the understanding model, it's very similar to actually Pixar models that we also released,swyx: yes.Pavan: That'sswyx: amazing.Pavan: It was pretty, I, that was the first project I worked on after joined Misra. It was pretty, pretty nice. And Wtu was very similar in spirit.I guess So we feed audio through an audio encoder similar to images through a vision encoder, and it produces continuous embeddings and which are fed as tokens to the main transformer decoded transformer model. Yeah. On the model output is just text. So on the output side, there is nothing that needs to be done in these kinds of mode.I [00:04:00] guess the interesting part of what the generation stuff is, the output now has to produce audio and. The approach that we have is this neural audio codec, which converts audio into these latent tokens. There is a lot of existing attrition and a lot of models which are based off of this kind of approach.And we took a slightly. A different, design decisions around this. But at the end of the day, the neural audio product converts audio into a 12.5 herdz set of latents. And each latent is, has a semantic token and a set of acoustic tokens. And the idea is that you take these discrete tokens and then feed it on the input side.There's several ways to use this at each frame, but we just sum the embedding. So it's like having key different vocabularies. Combine all of them because they all correspond to one audio frame on the input side. The output side is the interesting part on the output side, the, it's not the, I don't know if it's the most popular, but one.Popular technique is to have a depth transformer [00:05:00] because you have K tokens at each time step, like with a text, you just have one token at each time step. So you just do predict the token from the vocabulary with, yeah, with just, you get probabilityswyx: This's a very straightforward text. VeryPavan: straightforward.swyx: Yeah.Pavan: But if you have K tokens, then the name thing would be to predict all of them in paddle. That doesn't work. At least that doesn't work that well because audio has more entropy. And the, one of the techniques people use is this depth transformer where you you almost have a small transformer, or it can be L-S-T-M-R in as well, but people use transformers and you predict the K tokens in auto aggressive fashion in that.So you have two auto reive things going on.Flow Matching for AudioPavan: So the thing we did differently is in, instead of having this auto aggressive K step prediction, we have a flow matching model. Instead of modeling this as a discrete token set we trained the codec to be both discrete and continuous to have this flexibility.So we did try the discrete stuff too, and which it works well, but the continuous stuff works just better. So yeah, we took this flow matching, so the, it's a flow [00:06:00] matching head, which takes the latent from the main transformer and like kind in fusion, it's denoising, but in this flow matching itself, velocity estimate.So you go from this noise t all the way to there. Audio latent, which corresponds to the 80 millisecond audio and then, which is sent through the work order to get back the 80 millisecond audio frame.swyx: Yeah. Is this the first application of flow matching in audio? Because usually I come across this in the image.Pavan: Yeah. Actually, in some sense there are models flow matching models in audio, but I think this specific combination I could be wrong. There could be somewhat. No. I haven't seen. I haven't seen much work in this, so I think it's novel and a lot of it's just a way bigger community, so they, I think they pioneer a lot of these diffusion flow matching work, and it's interesting to adopt some of the ideas there into audio and,swyx: yeah.Pavan: Yeah, I'm, personally that's the think part which is trying out about. One of more meta point is unlike text, even in vision, I think this is true, but in [00:07:00] audio step literature that there is no.Winner model, yet there is no, okay, this is the way you do things. It's it's still by, I think people are still iterating and figuring out like what's the best overall recipe. I guess the idea. Pretty sure there are models which are also completely end-to-end, like NATO audio. NATO audio, but it's still not come to a convergence point where this, the right way to think that.That also makes. A space pretty exciting to explore.Real Time Voice AgentsVibhu: What are some of the ways to look at it?Vibhu: There are ways where you can do diffusion for audio generation, but if you want like real time generation, that's a big thing with the approach I'm assuming that you took. Yeah. And also like how do you go about evaluating different axes of what you care about, yeah,Pavan: good point. I think we so you can do just flow matching diffusion for the whole audio. We didn't even go down that path because one of the main applications is voice agents and we want real time streaming, and that's the use case. That's not the only use case, but that's one of the primary use cases we want to get to.So we [00:08:00] picked the auto aggressive approach for that. And within the auto aggressive space, again, you can do chunk by chunk or you can do so we picked the. I think at least personally prefer the operations, which are the simplest, and so we try to see, can we just add audio as just another head to our regular transformer decode model because that kind of makes it easier for eventual end-to-end modeling of audio text native modeling.Yeah. And it works pretty well. So I guess we went with that and we tried a little bit, but the flow matching head itself, like we had a discreet. Diffusion kind of approach, which also works well, but the flow matching work better.swyx: I was just curious about how you also think about this overall direction of research.Do you basically, when you work with the audio team, do you set some high level parameters and then let them explore whatever, or how does it work between you guys?Guillaume: No I think the way it works is that we are the, we are prioritizing together, I think, what are the most important features because there are many things we can do [00:09:00] in audio.Yeah, I think we try to. These are like how we should do things, for instance. Ultimately what we want to do is to build this through duplex model, but we are not going to start this start there directly, I think is. Some of the project people are doing, butswyx: just to confirm, full effects means it can speak while I'm speaking or,Guillaume: yeah.Okay. Audio. Yeah. Yeah. So intimately we're going to get there, but for us it was, we decided to take it like a step by step. So we start with whatever is the most important. I think support customers, which is the transcription is the most popular use case. Then the speech generation, Soviet time, just a bit before that.And then actually to be like more, but try combining everything all together. But but yeah, we thought it was also important to like separate things and optimize each capability one by one before weswyx: measure of that together. And the super omni model. ButGuillaume: very interesting because as Par said, it's when you work on some other domains of this airline and everything, there are many areas where I think it's not as interesting.For instance. Many places, it's essentially just around data or like creating new environments on a lot of kind [00:10:00] of easy things. But things were, I think the research is maybe not as interesting. Were in audio. There are so many ways to actually build this model. So many ways to go around it. That's the sense I think is really interesting.And what we also tried for speed generation is that we tried multiple approaches. What was interesting that even though they were extremely different, they under the big know the particles but the for matching turned out to be quite more natural. So we are happy with this.swyx: Is there intuition why it maybe like flow matching is just models speech better in some natural fundamental, latent dimension?Pavan: No, I think the main thing is e even at a particular time step, there is a distribution of things.swyx: Yes.Pavan: To be predicted like the way you inflate. So you already know the word that you're speaking and Yeah. The intake space, let's say the word maps register a single token for simplicity.In most cases it does. So there is not a lot of so you just pick the word, but with within audio, even the same word could, even with your own voice, could be inflicted in so many different ways. And I think [00:11:00] any approach which like models this distribution and. And flow matching is one, one of the take.It's not the only one at all, but it's a one which works pretty reasonably well. I think that's better. So you have to pick across several different, the intuition I have is it's, there are some, several different clusters each corresponding to some specific way you would inflict, pronounce that thing.And you can't predict the mean of it because that corresponds to some blurred out speech or something like that. But you have to pick one. And then like sharpswyx: conditional inference.Pavan: Yeah, exactly.swyx: Is that all covered under disfluencies, which is I think the normal term of art. Pauses intonations. By the way, I have to thank Sophia for setting all this up, including like some of these really good notes becausePavan: Yeah.swyx: I'm less familiar with the audios for me.Pavan: No. I think dis dismisses are definitely one such Eno defenses is more likeswyx: which is arms are.Pavan: Yeah, arms. And also repeat like you like,swyx: yeah.Pavan: You do this full of words, your thinking, so you repeat the word.swyx: Okay. Whereas intonation is like a diff, it's up up [00:12:00] speak and all this.Okay.Pavan: Yeah. So I think there is a lot of like entropy. And modeling it as a distribution. And a, any technique which helps with it and the depth transformer is a conditional way of modeling this. And Transformers actually really good at it, even though that's a mini transformers. So I think that worked pretty well too for us too.It's just that the main concentration is when you have a depth transformer. If you have K tokens, you need to do K auto steps, right? Even though it's a small thing, it's K steps, which is very vacant, say heavy, but flow matching. We were able to cut it down significantly. So we are able to do the inference in quad steps or 16 steps and it works pretty well.And there are more normal techniques to bring it down even further to like, in extreme case, one step like we're not doing it yet, but it at least the framework, LEDs itself to more efficient and Yes.swyx: And the image guys have done.Pavan: Yeah.swyx: Incredible work guys. Yeah.Pavan: It now you just. Send a prompt and you get an image.swyx: Yeah. Surprisingly not enough. I think image model labs use those techniques in production. I think it's, I feel like it's a lot of research demos, but [00:13:00] nothing I can use on my phone today.Guillaume: The thing, there's a thing that would be interesting here is that since, indeed I've been so much sure that has been done in the vision community compared to radio dys, stomach, I think there are so many long infra Yeah.And there are so many things we can do to actually improve this further. So it's our first version, but we have so many ways to exist, much better and much more efficient, cost efficient, soswyx: yeah.Guillaume: So really it's not a new field at all, of course, but there are still so many things that can be done.Perfect. It'sswyx: nice. I should also mention for those who are newer to flow matching, I think the creator, this guy's name is Alex, he's done I think in Europe's maybe two Europes as ago. There was, there's a very good workshop. There's one hour on like this matching is I would recommend people look that up.That's the other thing, right?Efficiency and Model Strategyswyx: The efficiency wise, like I, I imagine like the reason is open weights the reason you pick 3.6 B backbone it you are 3.4 B you are, try to fit to some kinda hardware constraints. You kinda fits some kinda basic constraints. What are they?Guillaume: Not necessarily, I think something we care about in our model that they're efficient.So we have a [00:14:00] lot of separate model, for instance. So we have this that is very small, very efficient. We also have a small OCR model that is available. Good, highly efficient as well. And I think on a project maybe there, I think companies are going to take is to have a coverage general model that will do a bit of everything.But that is also going to be expensive. On here. What want say is if you care about this specific use case, if you can actually use this model, it just does that. It's extremely good at it. Survey, very efficient. That's why we can actually add. We do, but also OCR that are like really good at that.And that would be much more cost effective factors and the general model that will contain a lot of capabilities you don't really need. So yeah. So we're doing like general model, but also like more customized model. This,Open Weights and BenchmarksVibhu: how does it compare to other TTS models? It's, we are going follow open wave.We're just dropping it. I think it's pretty good.Pavan: Yeah, I think it's pretty good. Like it, it's definitely one of the best. For sure. It's probably I would say it's the best open source model, butVibhu: decipher themselves.swyx: Yeah.Voice Agents VisionVibhu: Why now? How does it fit into broader ral vision? How do you see voice agents?How do you see voice? I think every year I've heard, okay, you're a [00:15:00] voice. You're a voice. There's a lot of architectural stuff. There's a lot of end time that see it, your solving, but where do you see voice setting?Guillaume: We had so many customers asking for voice. That's also why we wanted to build it.What's interesting in this domain is that. In a sense, if you take something simple like transcription it doesn't seem like something that should be very hard to do for a model. It's essentially, it's pattern recognition. It's classification on this. Models are very good at classifying, right?Or nonetheless, when you talk to them it's not there yet, right? It's not, you don't talk to them the same way you talk to a person. On something, maybe people don't realize it. It's in English it's still much better than in any user language, even compared to French instance. If you talk to this million in French, when you see people talking to this they'll talk very slow.They'll articulate as much as they can. So it's not natural, right? We're not yet to this. And I think, yeah, maybe the next generation will not know this, but yeah, I think people that. But our edge will actually always keep this bias speaking very slowly when they talk to this model. Even if maybe, probably in a couple of years, maybe next year it'll not be necessary anymore.But yeah. But what's interesting is to see that yeah, even for like languages [00:16:00] like yeah, French and Spanish Germans that are not no, no resource on religion. You have a lot of audios there on still it's not as good. And I think a consequence. Because then for this, I suppose just is not as much energy, as much effort that has been put done in some other mod that for some vision or like coding.But but yeah, there's still a lot of progress to be done. I think it's just a question of doing the work and it's clear path I think to get there.Pavan: It's a little fascinating because I worked on Google Assistant I think while back at this point, but it's, I think it's, it like when you take a step back, it's fascinating.It's not that long ago. It was like four years ago or five years ago, and it's now it's completely audio in, audio out and the function calling and the whole thing happens completely end to end. And in a very natural,swyx: yeah,Pavan: natural way and still ways to go. Kim was telling, even despite all the previous, it's not like you're speaking to a person.When you talk to any of these agents, bots, or voice mode kind of situation, it's still like a gap. I think that's the great part and I feel like with even the existing [00:17:00] stack, we should be able to get to this very natural speech conversational abilities soon enough I guess.And we'll also hope. I get thatGuillaume: on this kind of the next step, right? Because when you talk to these agents, like usually people are just writing to them and sometimes they'll this very clear, for instance, you are, you want to write code, but you are, you have a very clear idea of how you want the model to implement what you in mind.But so here you are able to spend a lot of time writing. So it's not really efficient on audio is really like a natural interface that is just not there yet, but I think it's just gonna be the place.Vibhu: How's it like building, serving, inferencing, like we see a lot about, it's very easy to take LMS off the shelf, serve them.Fine tuning, deploying. I know you guys have a whole you have Ford, you have a whole stack of customizing, deploying. Is there a lag in getting that. Like distribution channel. Are you helping? There is. So like prompting, lms, you can have them be concise, verbose, all that.They're built on LM backbones, these models. How do you see all that?Enterprise Deployment and PrivacyGuillaume: Yeah, I think this is a lot of what we're doing with our own customers. Very [00:18:00] often they come to us, so it's for different reasons. I think one reason is sometimes they have this lot of privacy concerns.They have this data that it's very sensitive. They don't want data to leave. The companies, they wanted to stay. Inside the company. So we have them deploy model in-house. So either on a, either on premise or on private cloud. So they're not worried that it's given to a third party on the there some leakage.Sometimes they have this kind of many companies have this different, sensitivity of data they have like sometimes channel chat can send it to the cloud has to stay there. So then it creates some kind of heterogeneous workflows where it's annoying. You cannot send some data to the cloud.This one you can, so here, when we actually deploy the model for them, they don't have this consideration. They are like not worried that, this is going to leak. Everything is much easier. So we help them basically do this on the, so it's one of the very proposition. But but the other is very often, when customers use this off the shelf close model, but very sad is that they are not leveraging, these data that have been collecting for four years or something for decades.So much data. Sometimes it's trillions of tokens of [00:19:00] data in a very specific domain. Their domain, which is data that you'll not find in the public, on the public internet. So data on which, like close model, we actually not have access to one, which that's going to be really good. So if they're using like closed source models are basically not benefiting from all these insights.All these data they have collected three years, they can always give it into the context that in France, but is never as good as if you actually train the modern analysis. So yes, that's basically what we help them to do. We actually provide them some purchase, basically what we announced at GTC this week.So we provide them with this, it's basically like a platform with a lot of tools to actually help them process data. Trained on that. Yeah, it's actually the same thing that we're using in the science team. So it's actually very better tested infrastructure, like a lot of efficient training cut base.For a quality pre-training like a fine tuning, even doing S-F-T-I-L. So we help them do this using the same tools as what our science team is building is using. So since it's tools that we've been using for two years now, it's really better tested. It's really sophisticated.So it's the same thing. We are giving to them, giving the company the same thing [00:20:00] that what are same still using internally actually build their own ai and it makes a really big difference. I think sometimes customers. And many in general don't realize how much better the model becomes when you fine tune it on your own data.And you can have a, your model is here. You start from there. You have a cross source model, which is sort here, but if you actually fine tune it can actually really go much further than this. And then you have a very big advantage. The model is trained on your entire company knowledge, so it knows everything.You don't have to feed like 10 K tokens of contact at every query. So it's it's much easier. It's a bit, I think using a closed source model is really sad because it basically puts. You are not leveraging all this data and you are going to be using the same model as all your old competitors when you're actually using, everything you have been collected for years, which is really valuable.So yeah. So we help basically customers do this. We have a lot of solution I mean deployed for engineers that go in the company that basically look at the problem customers are facing to look at what they're struggling to do what we should do to solve it. So we help them solve them together.So it's I think our approach is a bit different, but here. [00:21:00] Some of their companies and competitors, it's, we don't just release an endpoint on sale, do some stuff on top of that, or we don't just give a checkpoint. We really look very closely with customers. We look at the issues they have, we had them solve them.We really make some tailored solution for the client are facing. Some example are also going to be, sometime we have some customers. They really wanted to have a really good model, really performance on some, like Asian languages on the, if you take some of the shelf models, they can speak it, they can write in this language, but it's not amazing.This language would be like maybe zero 1% of the mixture. So it has been included during training, but very little. So what we did here is upgrade. We trained a new model for them, but so this language was 50% of the mix, so it's much, much stronger. It knows of the dialects, it knows the, so it's yeah.So it's some example of things we can do and it's really arbitrary, custom. I think you had some of their customers, for instance, they wanted some. They wanted some 3D model that can do audio with a very good function cable. So something you wanted to put in the car in particular, they wanted this to be offline because in a car you don't necessarily have access to internet.So [00:22:00] yeah. So here we can actually build the solutions. There is no like model out of the box on this. In the internet you have this very, you have this very general model generalist, like he's strong model. But for things like this, they always want at specific solutions and on some other reasons.Sometimes they come to us is because, like they, they experiment with some closed source model. They get some prototype. They're happy with what they build. They, it works well. They're happy with the performance, and then they want to go to production and then they analyze. But it's extremely expensive.You cannot push this. It's so then they come back to us on this. They can help us build the same thing as this, but using something much cheaper on here. And here we can sometime be something 10 x cheaper by just functioning a model and it'll be better OnPrem on their old server and also much cheaper as well.So yeah,swyx: that's the drop pitch right there. Take all themoney.Vibhu: And outside of that you do, we do put open wave models so people can do this themselves. I feel like not enough people go outta their way.swyx: They're not going to, they're gonna ask them to do it as the expert. IGuillaume: think initially we didn't know, [00:23:00] we wanted completely short at the beginning of the company because, I think our study was not exactly the same as what it is today, but what we underestimated initially is the complexity of deploying this model and connecting them to everything to be sure it has access to the company knowledge on the, and it was, yeah, on, we were seeing customers struggling with this, but it was even, that was three years ago and no, things are much more complicated because now you don't just have, text on SFT on a simple instruction following.You have reasoning like your agents, you have like tools. You have a multimodal audio, so it's much more complicated than before. And even back then it was hard for customers. So they really need, have some support and this is why actually providing like always some four D position as well. The processFine Tuning and Personalizationswyx: I'm curious is there also voice fine tuning that people do?Pavan: So in this forge we also have a say unified framework. And the hope is like the er speech to text that we released earlier this year. And even the ER chart that we released last year. And I think a big people, I think there's a big, rich ecosystem [00:24:00] of people fine tuning whisper, and people want the same thing with w so it's much stronger than Whisper.And yeah, the the platform offers that kind of fine tuning yeah, which could be any kind of fine tuning. Like for instance, even sometimes people want to support new languages to this, which are tail languages, which we hope to cover. Certain natively, but if there is a language where you data and you want to frank you, I think this is a good use case.Or the other use cases, you, it's the same language, like even English but it's in a very domain specific way.swyx: Yeah. Terminology, jargon, medical stuff.Pavan: Exactly. And also there's specific acoustic conditions like there's a lot of noise or the, and. The model will do decently in most conditions, but you can always make it better.And that those are some of the use cases where you can improve it e even further. And that's one good use case for this and for text to speech. We're just releasing it so we'll have support for that soon too. I think it's similar use case.Voice Personalization Pavan: It's little different the kind of things that you want to extend a [00:25:00] text to speech model to, which could be like voice personalization, voice adaptation for enterprises.Many enterprises need very specific kind of tone, very specific kind of like personality for this kind of voice. And all of those are like good use cases for fine tuning.swyx: This one I was gonna ask you, we never talked about cloning voice clothing here. How important is it, right?Like I can clone a famous person's voice. Okay. ButPavan: the main use case would be like for enterprise personalization, like enterprises need like a lot of customization. You don't want the same. Voice for all the enterprises. Each enterprise want a customized, specialized something which is representative both their brand and also their, I guess safety considerations and the use case I think the kind of thing that you would deploy as a empathetic assistant in the context of a healthcare domain would be very different from the kind of thing that would be in a customer support bot and would be different from like more conversational aspects.I think those are the. [00:26:00] Customizations you would expect from enterprise. And that's the main use case, at least from our side.Vibhu: My, my basic example is you don't want to call to customer services and have the same exact voice. It's just, it's gonna be weird.Long-Form Speech ModelsLong-Form Speech ModelsVibhu: But also on the technical side of this, so there's like a few things in TRO that I thought were pretty interesting.He's a big fan of this paper. Oh, he said very good paper. He said this is the best SR paper he's ever read. Yeah. I've hyped up this voice paper enough. We covered it. Somewhere, but a big thing. So Whisper is known for 32nd generation a 32nd processing. You extended this to 40 minutes. There was a lot of good detail in the paper about how this was done.Even little niches of how the padding is. So it's very much needed. You need to have that padding in there, the synthetic data generation around this. I'm wondering if you can share the same about the new speech to text, right? Text to speech. So how do you. How do you generate long form, coherent?How do you generate, how do you do that? And then any gems? Is there gonna be a paper?Pavan: Yeah. Yeah. They would be a technical report. Okay. Yeah. I think I could have a lot of details.Real-Time Encoder AdvancesPavan: But me I think the [00:27:00] summary of it, actually, some of the considerations in this paper were, because we started with the wipa encoder as the starting point, and now we have in-house encoders, like the bigger time model, for instance, which we released in January.Also release a technical report for that real time model as well, which is this dual stream architecture. It's an interesting architecture. You should check it out. And there we have a causal encoder and I don't think there's any strong, multilingual causal encoder out in the community. So we thought it's a good contribution.So that's one nice encoder there. Other people want to adapt. That's a good end code. And we train it from scratch. I think her. Post stack is now mature enough that we are able to train super strong ENC codes. And some of these considerations, like spatting and stuff, is a function of the Whisper ENC code.And now that we train encoders, inhouse the design concentrations are different.Scaling Context for TTSPavan: And for the question on text to speech, I think that's also leans onto the original auto aggressive decoder backbone. I think, it says very, almost identical considerations. I think the long context in it's not even long con, [00:28:00] so the model processes audio at 12.5 herds, so one second maps to like 12.5 tokens.So I think one minute is like 7.8 tokens. You can get like up to 10 minutes in eight K context window and get half an hour and 30 K context window. So that's and 30 2K context is something that's we are very comfortable training on. We can extend it even much longer. 1 48 K. Okay. You can naturally see how it can extend to even our long generations.Yeah. We need the. Like data recipe and the whole algorithm to work coherently enough through such long context. But the techniques are some way very similar to the text, long context modeling. And the key differences, it's just doing flow matching order regressively instead of a text open prediction.swyx: Okay. I think that was most, most of the sort of voice questions that we had. ButWhat Makes a Model SmallVibhu: I have a big question on Mr. Al, Mr. Small. So what is small? How do we define [00:29:00] small? What is this? What is this? I remember the days of Misal seven B on my laptop. The snuff fitting on my laptop. I could run it on the big laptop, butGuillaume: it's just additional.Question of terminology, like here what we did, baseball is north active parameters, but it's true. Really not give it another name, but yeah, we could have called it medium, but only, I,I suppose it's a model that we released mixture of experts. It's a model that combines different model before which we were doing the same, is that we had one model, general model for Israel. Doing instruction following, were like a separate model that was Devrel trial. So qu coding specify specific to code with another model for Reason Maal.So this were separate artifacts built by different team at trial on what we're doing is basically merging all of this. It was, you had pixel trial was the first vision model. We was like a separate model on the way we do things internally is that we have one team focus on one capability, build one model.On the means mature, mature enough, we decide to merge this into the [00:30:00] matrix. But here it was the first time we basically match all of this into one. But there are some other things we did at first time to merge time, for instance, like more capabilities or function coding I think would be, are, it's going to be much, much better in this trial, small platform.But but yeah, so it's our latest model on the working is,Vibhu: and yeah, key things is it's very sparse. Six, be active pretty efficient to serve. 2 56 K context. Yeah,Merging Capabilities vs Specialistsswyx: I think what's interesting is just this general theory of developing individual capabilities in different teams and then merging them.Where is this going gonna end up?Vibhu: Like we've seen the five things put together in this. Yeah. What are the next five teams?swyx: I think actually OpenAI has gone away from the original four Oh. Vision of the Omni model. This was what they were selling. All modalities and all modalities out.But I feel like you might do it.Guillaume: I think there's some mod where it's not competitive use, for instance for audio. For audio here, if you want to do transcription, I think it makes no sense to use a model. If you just want to trans tech it, it'll be very inefficient. If you want to do audio, you probably just want to be the [00:31:00] one VR 3D model performance essentiallyswyx: the same.It's going to be incredibly cheaper. So here, that's why we wantGuillaume: to have a separate but just does this. Yeah, I think the question is just, yeah. If you are to, to your model. By speech and you asking like a very complex questions on how you do this on the, just to cascade things. Do you want to put a d in a model that has like a one key around it?It's like a, not a competitive discussion, I think unaware if you doing into the direction, but that's possible. Of course. But yeah. But I think for us, the next capabilities we want to try to integrate into these models when we are going to be yes, like marketing or no reasoning better, I think more capabilities that people don't talk too much about, but at high bottom, I think for our customers in our, on different industries, for instance, things are around like a legal computer.I design all these things that is this males out of the box are to put at that. Because people, if you don't prioritize this, there is not like too benchmark on that. Butswyx: this done how toGuillaume: make this good and this just start to do the work. Extracting some that processing it [00:32:00] expression. So yeah.But we are offering the imagine to this.swyx: I think for voice. Yeah. The key thing I think over maybe like the last year or so with VO and gr Imagine and all these things is joining voice with video, right? Which people don't understand spatial audio because like most TTS is just oh, I'm speaking to a microphone in perfect studio quality.But when you have video, like the voice moves around.Pavan: That's true. The constitution was a little different in the sense that there it's like a a standalone artifact where you get the whole thing and you consume it. But in a conversational setting, it's a, you need the extreme low latency.swyx: Yeah,Pavan: streaming would be one of the primary concentrations.swyx: You can build a giant company just doing that, right? So you don't need to do the voice, but I was just know on the theme of merging modalities, that is something I, I am like, wow. Like I didn't, everyone up till, let's say mid last year was just doing these like pipelines of okay, we'll stitch a TTS model with a voice thing and a lip sync [00:33:00] thing and what have you.Nope. Just giant model. Yeah.Open Source MissionVibhu: I have a two part question. So one is, it's still open. It seems like open source is still very core to what you guys do and I just have to plug your paper. Jan 2024. This is the one trial of experts like. Very fundamental research on how to do good.Moes paper comes out very good paper for anyone. That's just side tangent. No.swyx: This thing caused, we bring back, eight by 22 was like the nuclear bomb for open source. I think it takes Shouldn be more seven B more. Yeah. Yeah. But this is a bigger opposite than me.Yeah. Yeah I don't remember this. I remember, I don't think it was January, right? It was like new reps it was, it dropped during new reps and everyone in Europes was December of 25th, I think. Yeah. The model was did as well.Vibhu: It's just a little update probably.swyx: Yeah. No, but you have a point to make.Vibhu: No, you gotta check that. But then, I just want to hear more broadly on open source for you guys, and when you had asked earlier [00:34:00] about what's next, what are the other, side tapes working on you. You put out Lean straw. This,swyx: it's not necessarily surprise. I was like, I don't, this doesn't fit my mental model or Misra.Guillaume: Yeah. First for open source in general, I think it's really something which looks to the January of the company. I think we started it per once, is we so we have open sourcing with, since the beginning and even before this. So before this, so me and Tim were at Meta, we released LA and I think what was really nice.To see that before this, for most researchers like universities, it was impossible to work on elements. There was no alien outside. And if you look at many of the techniques that were developed after, for instance, was open source all this post-training approaches like even DPOD, like preference optimization, all of this were done by people that had access to this portal.And it'll have been impossible to do without this. So it's really making sense, move faster. So we really want to contribute to this ecosystem. I think like the deep and also like very lot of impact. All these papers that are I think in the open source community are really helping the science community as a whole to move faster.So [00:35:00] we want contribute to this ecosystem. That's why we're releasing very detailed technical reports. So ma trial and our first reason model, and ation, lot of results, things that work, things that did not work as well. Think helpful on the, yeah, so for the audio model also to share a lot of details, share of them for real time model.And the, yeah, so we really want to continue this, basically belong to this community of people who share science. I think we really don't want to be, leading in a world where the smartest model, the best models are only behind, close doors. Only accessible to a shoe companies that we, as a power to decide we can use them on it.I think it's a scary future. We don't want to live in, we really want this model to be accessible to anyone that want. Intelligence to be used unaccessible by anyone who can use it. So yeah, so that's why we are pushing this mission and source model. Yeah. So not, so yeah, no strategy. So it's open source, not the first model, so not the best on the Yeah.Lean and Formal ProofsGuillaume: LIN trial I think is also one step into this direction. So it's yeah, a bit different than what we are usually releasing. But we have a small team internally [00:36:00] working on them. Formal proofing, formal math. So I think a subject we care about in general and we were working on reasoning. I think we started too early before doing reasoning without LMD is very hard, especially when you work with formal systems because the amount of data you have is negligible.It's addressable community of people writing like formal proofs. But the reason why we like it is because I think there is if you look at what people are doing with reasoning, is there, the problems that you can use. Are usually going to be problems where you can verify the output. So for instance, all this ai ME problem where the solution is a number between 100, like a thousand.So you can verify, compare this with a reference or it's an expression. You can actually compare the output expression generic with the reference. But there are many, most of them have problem and most of the reason problem. There is no like way to easily verify the solution. If the question is show that F is continuous, cannot compare in the reference, right?If it's a probe that this is true or probes is properties, there is no way to. You cannot act, simply verify the correctness of your proof. So it's hard to apply the, there is no referable reward here. So [00:37:00] what you could provide is of course, like a judge and judge that will look at your proof. But it's very hard and it's very, you could do certain, some reward hacking happening there.So it's difficult. You could provide like a reference proof, but then there are also many ways to prove the same thing. So if the model says give negative reward because it's a different poop, maybe it was still digit proof, just different. So it's not going to work well. What's nice with lean and with formal probing is that you don't have to worry about this whatsoever.We just,swyx: they're all function is largely compiles in lean is functionally the same. Exactly.Guillaume: It's like a problem if it compiles it's correct. It's very easy. And you can apply this and then you can,swyx: it's just way too small. So no human will actually go and do it.Guillaume: Yeah, that's exactly.It's the only people can do it. It's like a very small committee of people doing a PhD on that. So it's super small. And it's sad because it's actually very useful on not just mat, but also in software verification. So for instance, software verification today. So tiny market. Very few industries work on this and we need that.It's usually going to be like companies like building airplanes, air robotics,swyx: likeGuillaume: things [00:38:00] where they absolutely want to be sure. Life depend on this, but it's very rare that people formally verify the correctness of their software. But I think one of the reasons for this is simply that it's just hard to do.swyx: Are you think of TLA plus? It's the language that some people do for software verification? No. That people use in a ference, but but yeah, it's the reason I think why people don't use it more and why this industry is not as big as could be is because it's very hard. But now with cutting edges that are there, it's going to be very different.Guillaume: We're going to see much more of this. So I think yes, industry there is going to be much larger in the future that we, these models. So yeah. Here also anticipating this a little bit, we wanted to work on that because it's proving like a math theory and like a, essentially the same tools.swyx: Yeah.Reasoning Transfer and Agentsswyx: One of my theories is that because the proofs takes so long, it's actually just a proxy for long horizon reasoning and coherence and planning. Maybe a lot of people will say okay, it's for people who like math. It's for being okay. It's like a niche math language. Who cares? But actually, and you use this as part of your data mixture for [00:39:00] post-training and reasoning, actually, it might spike everywhere else.Yeah. And I think that's un under explored or no one's like really put out a definitive paper on how this generalizes.Guillaume: Yeah, absolutely. AndPavan: I think evenGuillaume: that's what we're seeing already. For instance, you should do some reasoning on math as then the American should do reason even.Yeah. In the early stage. So we, the, there is some transfer, some sort of emergence that happens. And I think some, it's also interesting, it's not just I think the topic in general, but it's, there is a lot of connection with this on including agents because. Sometimes the model can see like a three that it has to prove it's very complex, but then it can take the initiative to say, I'm going to prove this three lr.I'm going to suggest three Rs, and I'm going to in parallel prove each R. So three of them in parallel with sub agents, but I'm also going to prove them in theory and the three tool so you can do this also. Pretty interesting. You can, even if you fail to put one of the LeMar, you can actually, maybe you succeed to put the normal lema too, so you get some possible reward here.So it's a bit less Spartan issue, just get to zero one for the entire thing. [00:40:00] So it's pretty interesting. I think we can actually,Vibhu: yeah, it's also an interesting case just for specialized models in general, right? Like the cost thing you show is pretty interesting yeah, similar score wise, you are, thirty, seventy, a hundred fifty, three hundred bucks.Smaller.swyx: I think cost is a bit unfair, right? ‘cause this one is at like inference cost. It's always there on top with their margins on top of it. But, we don't know anything else, so we gotta figure it out.Vibhu: Okay.Next Frontiers in TrainingVibhu: I did wanna actually push on that more. Not on cost, but you mentioned about, okay, it's a great way to have verifiable long context reasoning.What are other frontiers that, I'm sure you guys are working on internally, there's a lot of push of people pushing back on pre-training. Scaling, RL pushing, compute towards having more than half of your training budget. All on rl. Where are you guys seeing the frontier of research in that?Guillaume: You mean theVibhu: just in foundation model training in the next, one thing that you guys do actually is you do fundamental research from the ground up, right? So you probably have a really good look at where you can [00:41:00] forecast this out.Guillaume: Yeah. I think for us we're still working a lot on the pre-training side.I think we are very far from situational, the pre-training. I think ML four preprinting will be like big step compared to everything we have done before. So we are pretty excited about this. And I think on the other side, I think now we have more and more to think about this algorithm that will actually support this very long trajectories.I think when it was, for instance, GRPO for it doesn't really work this any bit of policy. Which was okay initially because you are solving math problem that can be solved in like a few thousand tokens. So the model can alize them pretty quickly. So when you do your update, the model is never too far off.It's never too far off. But now when you are moving towards this kind of problems where certain takes hours, like six hours to get a reward, then your model is co pick places. So you have bi new infrastructure that supports this, but also new A, so now everything we're doing internally, we're trying to. Build some infra that we actually anticipate is what we have in six months, one now, which is this extremely no scenarios on the, I think when we started Missal, part of me and [00:42:00] we wanted to, is very nice under element where people are there, they can do research, they like with a lot of resources.So it was nice. I think things changed a lot when I think when J Pity came out. I think after that I think was. This one is same again. But but yeah, but it was nice. And I think we also want to work part of this descrip beforeswyx: coming to the end.Hiring and Team Footprintswyx: We're just, obviously, I think you guys are doing incredible work.You've, they are a very impressive vision for open source and for voice. What are you hiring for? What's the what are you looking for that you are trying to join the company?Guillaume: Yeah, so we are hiring a lot of people in our sense team. We're hiring, in all our offices. So we have a, our H two is in France in Paris.We have a small team in London. We like a team in Pato as well. Co we open some offices in in SAU, in Poland. So one in Zurich. We also like some presence in New York as well on Sooner one in San Francisco. So we all bit either way also like hiring remotely. So we're going the team trying to hire like very strong people.I think we want to stay, so the team is not. Instead of fairly small team. [00:43:00] But I think we want to keep it that way. ‘Cause we we find it quite efficient. So like a small team they agile so yeah.swyx: Okay.AI for Science Partnershipsswyx: Let's focus on science and the forward deployed. We actually are strong believers in science.We started the our new science pod that focuses specifically on the air for science. What areas do you think are the most promis.Guillaume: What we're pretty excited about right now, and something we have already started doing or that we'd probably be able to share more about this in a couple of months, is that we are exploring AI for science.And there are a lot of areas where we think that you could get some extremely promising buzz. If you were to apply AI in these domains. There are a lot of long inputs. You just have to find these domains where actually AI has not been yet applied, and it's usually hard to do because the people working in those domains don't necessarily know the capability of these models.They don't know. How I would just have to pair them with Yeah, exactly. Your researcher slashing, which is actually hard to do. But this matching, we're doing it naturally with our customers. So we have some company we are very closely with. So for instance, ISM Andreesen are one of our partners, so we're doing some research with them on their other, like tons of extremely interesting problems.Columns in physics, in [00:44:00] science matter science that they're essentially the only ones to work on. ‘cause they're doing something No, no one else is doing on the, yeah. So there are many domains where AI can actually revolutionize things. Just you have to think about it on you familiar with what can do or to apply it.So yeah, it's something where more modeling with our partners, with our customers sort AI for s, but.swyx: Yeah. Okay.Forward Deployed Skillsswyx: And then for deployed what it makes a good four deployed engineer, what do they need? Where do people fail?Guillaume: I think it's usually you need people that are very familiar with the tech and not necessarily with a lot of research expertise, but that are actually pretty good at using this model that can actually like that know how to do functioning, that know how to like, start some error pipeline.And it's it's not easy. It's something that mucus. Majority of companies will not be able to do this on their own. So here I think we need people that are, that like to solve problems that are accept solving some complex, very concrete problem. It's applied science basically.And yeah, so I think it's not too different. I think from the case you need in research because it's essentially you are trying to find solutions to problems that in [00:45:00] customers have not yet. So sometimes it's easy. Sometimes you're here to do the work. You have to like create synthetic data.Find some edge case. So it can be, yeah. Depends on the problem. But but yeah, you have to, I think it also a bit of patience on the be creative. I think very similar skill is Asian,Pavan: the diversity of the work they do. It always surprises me. It's it's, it goes all the way from the kind of stuff they encounter in industries.It's just very interesting. I think.swyx: Any fun like success anecdotes.Guillaume: Yeah, it can be actually training this small model on edge that just we do one specific thing can be like training some very large model without some specific languages as well. Making models really good at some tube use, like for instance, computer ID design, these kind of things.Is that pairing with vision as well? Yeah,Pavan: and the fact detection for chips or like in, in factories identifying things like it, the. Diversity could be anything where you can deploy these foundation models. So yeah the work to make it work in that specific setting, basically whatever it takes to make it like add value in that, by the way, workflow.Vibhu: Yeah. [00:46:00] And it goes across the stack, right? Like even just pulling up the website like.swyx: It's so broad on compute. It is so broad.Vibhu: We didn't even touch on if you have a coding CLI tool. One thing you guys were actually like, I think the first tool was agents, ral agents. You had the agent builder, you can serve it via API and all that.And I'm guessing forward deploy people.Guillaume: Yeah.Vibhu: Help build that out and stuff.Customer Feedback LoopGuillaume: It is also why we are, so we're doing many things, but I think that's also part of the value proposition that sometime know customers. They're always very. Extremely careful about their data and they don't want to, they don't like, trusting so many partners, trusting one partner for code, giving the data to another third party for like audios and another one.So they don't like this here. What they really like with our approach that we can help them on anything so they don't have to send the data to so many clouds. So yeah,swyx: I think that there can be many orders of magnitude more. F Ds then research scientists and they don't need your full experience, but they're still super variable to customersGuillaume: in practice.These two teams [00:47:00] are still quite intertwine, very often. Yeah. So first of all, they're using the same tools, the same data pipeline and everything on the, it's it's very helpful for the science team to get the feedback and the solution team ‘cause they can. Look at these customers are trying to do this.This is not working. It can really be show in the next version. Yeah. But this is basically a real world eval. Yeah, it's real world eval and it's not something, for instance, if you're just working in the lab, it's just ships model. But you don't do this work of for customers. You have no idea for whether your model is good at this H case.For instance, you even in year found this, right? So yeah, there is a very gap, big gap between the public benchmarks that are very like academic. OnPavan: the rare cases are just very diverse and in the specific concept of a customer, you can fine tune and make it like first evaluate, create a solid eval, benchmark, and then measure in the context of their, the kind of audio.Like for instance, one use case is literally just, there's the word for kids and they have to just say it out. It's a very specific thing. You're just saying one word and then you have to you, you'll grade the kid whether they did it right or not. It's [00:48:00] like R for, but so there're very diverse use cases and the idea is that they, the.Applied scientist engineer will go and make it better. And then from the learnings we incorporate it into the base model itself. So it's it's just better out of the box.Vibhu: Yeah. It's a good full circle system. Like the foundation model evals are all just proxies of what you really, you're never gonna have one that says it, it doesn't make sense for there to be, a one word transcription like that.It's not something you wanna fit on. Perfect.Wrap Up and Thanksswyx: Everyone should go check out everything that Michelle has to offer and try the TTS model, which will link in the show notes. But thank you so much for coming tha thanks. Such a stretch. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Our speaker is Brig. Gen. (Res.) Eran Ortal is a former Israeli Defense Forces officer who previously served as the Commander of the Dado Center for Interdisciplinary Military Thinking in the IDF Operations Directorate. Today he is the Head of the Military Program at the Begin–Sadat Center for Strategic Studies (BESA Center) and a visiting scholar at the American Foreign Policy Council (AFPC). Eran is the author of the book “The Battle Before the War.”I want to learn from Eran about how the American and Israeli military have moved their command center to the battlefield so that the target can be destroyed before the Iranians have time to react. Get full access to What Happens Next in 6 Minutes with Larry Bernstein at www.whathappensnextin6minutes.com/subscribe

Is belief in Jesus Christ based on blind faith… or credible evidence?In this video, we examine the historical, scientific, and philosophical evidence surrounding Christianity and the resurrection of Jesus. From the origin of the universe and the fine-tuning of physical laws to the historical case for the resurrection, we explore arguments that many historians, scientists, and philosophers take seriously.This video examines:• The origin of the universe and the Kalam Cosmological Argument• Fine-tuning in the laws of physics• The mystery of DNA and biological information• Historical evidence for Jesus of Nazareth• Non-Christian sources like Tacitus and Josephus• Gary Habermas' Minimal Facts argument for the resurrection• N.T. Wright's historical analysis of the early Christian movement• Why the resurrection of Jesus remains one of the most debated events in historyChristianity stands or falls on the resurrection of Jesus. If it happened, it changes everything.Whether you're a believer, skeptic, or simply curious, this video invites you to explore the evidence and decide for yourself.⸻

Wintery Knight and guest host Terrell Clemmons welcome Dr. Brian Miller to discuss the the evidence for fine-tuning in physics and cosmology and the multiverse theory. They discuss how the laws and constants of nature suggest intentional design. Miller explains specific examples of fine-tuning and critiques the main naturalistic explanation for this data: the multiverse theory. He recounts his shift from skepticism to accepting design via evidence. Please subscribe, like, comment, and share. Show notes and transcript: https://winteryknight.com/2026/03/07/knight-and-rose-show-73-brian-miller-fine-tuning-and-the-multiverse-theory Subscribe to the audio podcast here: https://knightandrose.podbean.com/ Audio RSS feed: https://feed.podbean.com/knightandrose/feed.xml YouTube: https://www.youtube.com/@knightandroseshow Rumble: https://rumble.com/c/knightandroseshow Odysee: https://odysee.com/@KnightAndRoseShow Music attribution: Strength Of The Titans by Kevin MacLeod Link: https://incompetech.filmmusic.io/song/5744-strength-of-the-titans License: https://filmmusic.io/standard-license

Enterprise LLMs: RAG vs Fine‑Tuning, IDP & Governance In this episode of the Mostly Unstructured podcast, Ed and Clay discuss whether it's better to train a domain‑specific LLM or leverage foundational models like ChatGPT, Gemini and Claude. They explain the trade‑offs between fine‑tuning and retrieval‑augmented generation (RAG), and why Intelligent Document Processing (IDP) is vital for turning unstructured data into usable context. In this discussion, we cover: Why training your own LLM is risky and often unnecessary compared to adopting and building from a foundational model. How retrieval‑augmented generation (RAG) delivers more accurate results than simple fine‑tuning. The importance of Intelligent Document Processing (IDP) for ingesting unstructured data and building domain context. Real‑world lessons on AI governance, including the Air Canada bereavement‑policy chatbot case. Managing bias, hallucinations and toxicity in enterprise models. Measuring your return on AI investment. For those thrown by the excessive acronyms, let's define:LLM = Large Language ModelRAG = Retrieval‑Augmented GenerationIDP = Intelligent Document Processing. For more insights on enterprise AI for data intelligence, visit our website and read our blog on training an LLM referenced in the episode.Website: https://www.keymarkinc.com/Blog: https://www.keymarkinc.com/how-to-tra... 

In this episode of Derms and Conditions, host James Q. Del Rosso, DO, welcomes David Seiter, FNP-C, for a wide-ranging discussion on challenging dermatologic dogma and integrating emerging evidence into clinical decision-making. They begin with Seiter sharing his approach to reviewing new literature, encouraging clinicians to look beyond mainstream dermatology journals to cross-disciplinary publications to help reshape long-held assumptions. Using lichen planus as an example, he revisits the entrenched association between diffuse lichen planus and hepatitis C. While many clinicians routinely test for hepatitis C in these patients, new data suggest the association is uncommon. More compelling, however, is the emerging link between persistent, widespread lichen planus and underlying malignancy. Seiter outlines how he thoughtfully screens for red flags and gaps in preventive care without alarming patients prematurely, reinforcing the importance of looking beyond a single lab test. The conversation then shifts to acanthosis nigricans, where traditional teaching centers on hyperglycemia and diabetes risk. Seiter explains why acanthosis nigricans is more accurately viewed as a marker of hyperinsulinemia rather than elevated A1c. He discusses incorporating HOMA-IR calculations to identify early insulin resistance, particularly in adolescents whose A1C may remain normal for years. Both clinicians stress that a “normal” A1C should not prematurely reassure patients when cutaneous markers signal metabolic risk. Additional topics include reconsidering intralesional triamcinolone as the default therapy for keloids, with discussion of emerging data on intralesional insulin as a potentially lower–adverse event alternative, and a pragmatic conversation about JAK inhibitor safety. Comparing adverse event data across agents, they emphasize individualized risk assessment, careful monitoring, and shared decision-making over reflexive fear of boxed warnings. Tune into the episode to explore how questioning assumptions, broadening your literature review, and contextualizing risk can sharpen your clinical reasoning and elevate patient care in everyday dermatology practice.

The first version of the Rezzimax was a handheld device that uses vibration to fine-tune your nervous system, was a great start to help thousands of people. Soon, feedback came pouring in from people suffering from conditions like TMJ, stress, anxiety, depression, ADHD, and more. Using the device for just a few minutes a day was helping to drastically reduce chronic pain for many people. That was not enough. We knew that if we could perfect the Tuner, we could help so many more individuals learn to live our motto: "Tune Out Pain. Tune Into Life." So today we have the Rezzimax - Tuner pro2.

I'm currently soaking up newborn snuggles during my maternity leave, so over the next few weeks I'm re-airing a few of my most downloaded and requested episodes of all time.This episode originally aired on February 17, 2025 - but it's still full of relevant strategy! If your brain is buzzing with product ideas (and your calendar is packed with launch dates)… but you're not sure how much newness your brand really needs or how often you should be dropping collections—this episode is for you.We're chatting about what buyers actually want to see from your product assortment, how to stop overwhelming your customers and yourself, and how to bring in just the right amount of newness to stay relevant without staying stuck on the launch hamster wheel.Here's the deal: more products ≠ more profit. And in this episode, I'm giving you the exact questions to ask yourself to sharpen your strategy, clean up your assortment, and fine-tune your collection calendar in a way that makes sense for your brand.Whether you're planning your next product drop or rethinking your whole line, this episode will help you zoom out and make strategic decisions like a buyer—not just a creator.What you'll learn in this episode:Exactly how many collections you need per yearThe ideal timing for your major and mini product dropsHow to balance launch energy to avoid burnoutWhat wholesale buyers and retailers actually want from your newnessHow to evaluate whether your product ideas are adding real value5 questions to ask before adding a new product to your lineWhy bestsellers deserve a second (and third) spotlightWhat needs to go away before you bring something new inEnjoy the chat! LOOKING TO GROW YOUR WHOLESALE BUSINESS?Retail Pitching

How much should you really have in stocks vs. bonds — and what happens when the market turns south with a vengence?In Boot Camp #4, we break down the fine-tuning asset allocation tables that show exactly how different combinations of equities and bonds have performed from 1970 through 2025. This episode goes beyond average returns and dives into what investing actually feels like during the worst 3-month, 12-month, and 60-month market declines.You'll learn:Why equities have historically dominated bonds for long-term retirement investingHow the S&P 500 compares to diversified strategies like the Four-Fund portfolioThe real impact of worst-case drawdowns (including 50%+ bear markets)What happens to a 100% stock portfolio during retirement withdrawalsHow 50/50, 60/40, and other stock-bond allocations reduce volatilityWhy median returns matter — and why averages can misleadHow to control risk through asset allocation, low costs, tax efficiency, and index investingWe explore real historical data — including the 1973-74 bear market, the 2000-2002 tech crash, and the 2008 financial crisis — to help you understand both accumulation and retirement distribution phases.Whether you're in your 20s building wealth, in your 50s preparing for retirement, or already retired and managing withdrawals, this episode helps you align your portfolio with your risk tolerance, return needs, and long-term financial goals.If you want to be a confident do-it-yourself investor — without paying a 1% management fee — this episode gives you the framework to make informed decisions about stocks, bonds, diversification, and risk control.Watch Boot Camp #4 video

On the Uplevel Dairy Podcast, Peggy Coffeen talks with Curtis Gerrits and Jim Moriarty of Compeer Financial about why benchmarking is essential for dairy farms, especially as year-end financials become available, milk prices soften, and recent beef-on-dairy income may have masked underlying costs. They explain benchmarking as first comparing a farm to itself over time, then comparing to a larger peer dataset of similar farms to identify strengths and small opportunities across income and expenses that can add up. Key areas discussed include feed cost and productivity (including homegrown forages like corn silage and increased use of alfalfa), feed efficiency factors such as refusals and mixing time, and the importance of working with nutritionists and local crop partners. They highlight core benchmarks such as capital cost per hundredweight and labor cost per hundredweight, how capital and labor relate when making investments, and improvements in net herd replacement costs driven by lower herd turnover, fewer heifers raised, and more beef calf sales. They conclude with takeaways to embrace financial management and benchmarking, keep moving forward during down cycles, and note that top-performing dairies succeed through attention to detail, execution, regular decision-making, and involving family, key employees, and advisors by sharing financial results.This episode is sponsored by Compeer Financial.Compeer Financial is a member-owned Farm Credit cooperative serving and supporting agriculture and rural America. Their dairy team brings world-class expertise and tailored solutions to support dairy producers' financial goals and lending needs.Visit https://www.compeer.com/specialists/dairy00:00 Why Benchmarking Matters Right Now (Year-End Numbers + Softer Milk Prices)04:05 Benchmarking Basics: Compare to Yourself, Then to Peer Groups07:22 Big Levers: Feed Costs, Efficiency, and Milk Components08:59 Homegrown Forages & Feed Management: What to Optimize11:38 Core Benchmarks to Watch: Capital Cost, Labor, and Replacement Rates16:18 Turning Data Into Action: Consistency, Clean Categories, and Advisory Teams20:45 Key Takeaways for Dairy Strong: Embrace the Process & Keep Moving Forward22:56 What Top-Performing Dairies Do Differently (Attention to Detail + Team Buy-In)27:31 Wrap-Up & Resources

Enjoy. Order Shroud-PilledOrder God's Eye View: https://a.co/d/7CI89rvBuy the Audiobook: https://www.audible.com/pd/Gods-Eye-View-Audiobook/B0F55K2GT1?source_code=ASSGB149080119000H&share_location=pdpWant to publish a book? Check out my publisher https://hemisphericpress.com/Check out our ad free substack: https://hemisphericpress.substack.com/Email feedback to godseyeviewbook@gmail.com

Enjoy. Order Shroud-PilledOrder God's Eye View: https://a.co/d/7CI89rvBuy the Audiobook: https://www.audible.com/pd/Gods-Eye-View-Audiobook/B0F55K2GT1?source_code=ASSGB149080119000H&share_location=pdpWant to publish a book? Check out my publisher https://hemisphericpress.com/Check out our ad free substack: https://hemisphericpress.substack.com/Email feedback to godseyeviewbook@gmail.com

Can science actually PROVE God exists? Dr. Antony Latham joins us to explore how the cosmos, consciousness, and the complexity of life all point to divine design—and why the Big Bang might be Christianity's best friend.In this episode of Remnant Radio, Joshua Lewis sits down with retired physician and author Dr. Antony Latham to tackle one of the most critical questions facing believers today: Does science contradict Christian faith, or does it actually confirm it? From his own journey as a teenage skeptic who lost faith studying evolution to becoming a Christian in Kenya and diving deep into biology, consciousness, and cosmology, Antony brings a unique perspective that bridges the gap between the lab and the sanctuary.​What We Discuss:-The Big Bang & Biblical Creation -Fine-Tuning of the Universe-The Cambrian Explosion-Consciousness & the Soul -Moral Law & Objective Beauty -Miracles & an Open Universe Whether you're wrestling with doubts about faith and science, or you're looking for solid apologetic tools to strengthen your biblical worldview, this conversation will equip you with evidence-based answers rooted in both Scripture and scientific discovery. 0:00 - Introduction1:04 - From Skeptic to Scientist to Believer5:13 - Big Bang: Friend or Foe to Christian Faith?9:20 - Fine-Tuning Arguments & Cosmological Evidence12:17 - String Theory & Multiple Universe Objections14:03 - The Exquisite Precision of Universal Constants15:37 - God of the Gaps Argument Addressed17:04 - Origin of Life & Irreducible Complexity19:30 - Old Earth Creation & Genesis Interpretation22:56 - The Cambrian Explosion & Fossil Record28:03 - Reading Genesis 1 Poetically & Theologically31:17 - Consciousness & Evidence for the Immaterial Soul35:17 - Moral Objectivity, Beauty, & Free Will41:02 - Mind-Body Dualism & Christian Worldview43:03 - Taking Back Science for the KingdomABOUT THE GUEST: