Podcasts about Formal

Steven Volpe's headquarters are in San Francisco, but his work has an international appeal—partially a product of the four years he spent in Paris as a young man, soaking up the city's architecture and design. Today he runs a team of twenty, taking on projects around the world. Volpe's serene, timeless work is widely published, and he's a regular presence on the AD100. On this episode of the podcast he speaks with host Dennis Scully about how youthful confidence helped him build his career, why he likes having owner's reps on the job site, and how formality has changed, but not disappeared from the home. This episode is sponsored by Ernesta and KohlerLINKSStudio VolpeDennis ScullyBusiness of Home

In this episode of The Sensitive and Neurodivergent Podcast, Julie Bjelland, LMFT, shares what she has learned from thousands of conversations with autistic women, her own late autism discovery, autism assessments, and her work writing Autistic Women: A Clinician's Guide to Neurodiversity-Affirming Identification and Support.Julie explores common themes many autistic women recognize in themselves, including feeling different, deep empathy, sensory sensitivity, masking, burnout, chronic overwhelm, perfectionism, social exhaustion, uneven capacity, self-blame, and the healing shift that can happen through autism discovery. She also discusses why so many highly sensitive people later discover they are autistic and why lived experience is essential to understanding autism in women.This conversation offers a compassionate, affirming lens for anyone exploring autism, high sensitivity, neurodivergence, or late discovery. Julie reminds listeners that what may have been interpreted as failure may actually have been extraordinary effort that went unseen for years.Resources Mentioned:Forthcoming book Autistic Women: A Clinician's Guide to Neurodiversity-Affirming Identification and Support Published by W. W. NortonYour website JulieBjelland.comFree autism quizExtensive resources and research about late-discovered autismThe Sensitive and Neurodivergent CommunityAdult self-assessments Formal autism assessments for womenAuthor BioJulie Bjelland, LMFT, is a psychotherapist, author, adult-discovered autistic woman, and founder of The Sensitive and Neurodivergent Community, Podcast, and Blog. She specializes in high sensitivity, autism assessments for late-discovered autistic women, and supporting sensitive and neurodivergent people in understanding their nervous systems with more self-compassion. Julie is the author of the forthcoming book Autistic Women: A Clinician's Guide to Neurodiversity-Affirming Identification and Support, published by W. W. Norton. Learn more at JulieBjelland.com.

Emily Pilbeam presents a mixtape of her personal selection of tracks from BBC Introducing, including tracks from: walt disco, Bloodworm, Spike, The Healing Power of Horses, Cusk, Tooth, Flying On The Ground, ovajoi, Aby Coulibaly, Konyikeh, Marsy, ffogg, Max Sloan, jo from school, Ping Pong 100 and a Track Of The Week from Formal Sppeedwear.Produced by BBC Audio for BBC Radio 6 Music.

Emisión del miércoles 27 de Mayo de 2026 La Encuesta Nacional de Ocupación y Empleo (ENOE), difundida este martes, plantea una pregunta incómoda: ¿puede revertirse el deterioro del mercado laboral? La respuesta es que sí, en principio, pero no con las políticas actuales ni a corto plazo. "Deja que tus oídos te abran los ojos." #RuizHealyTimes #AbriendoLaConversación www.ruizhealytimes.com

Edward J. Larson explains that the formal signing of the Declaration of Independence marked a permanent break with monarchy. New state constitutions prioritized popular sovereignty, establishing the rule of law as the foundation of the Republic. (16/16)1789 TRENTON BRIDGE

27 de mayo iniciarán diálogo formal sobre el T-MEC: Ebrard México rompe récord en inversión extranjera directa“Cátedras de la Diáspora” otorgará apoyos de 45 mil pesos anuales a doctores mexicanosMás información en nuestro podcast#grc

↩️ Can You Stop a Divorce After Filing? | Los Angeles Divorce ↩️ Filed for divorce—but now thinking about stopping the process? In California, you may be able to withdraw or dismiss your divorce case, but it has to be handled properly with the court.

The National Potato Council, and 13 state potato organizations, have formally requested the USDA immediately reinstate a previous ban on fresh potato imports from the Canadian province of Prince Edward Island.

Do your emails in English sound too formal… or unnatural?In this lesson, you'll learn how to write professional but natural emails in English using real Business English phrases like:✔ Get the ball rolling✔ Follow up✔ On the same page✔ Touch base✔ Look into✔ I'm swampedInstead of sounding robotic or textbook, you'll learn how native speakers actually write emails at work.

Descubre las cifras detrás de la pérdida de 47 mil registros patronales y cómo el aumento de los costos operativos está asfixiando a los pequeños negocios. En este episodio desglosamos la presión de una deuda pública que ya representa casi la mitad del PIB nacional y el millonario costo del contrabando de combustible que desangra a la nación

Formal methods are a branch of mathematics and computer science focused on proving the correctness of systems, and they have long promised a more rigorous foundation for software. However, their complexity has kept them confined to a small community of specialists. That is now changing as agentic AI systems take on increasingly autonomous roles. The The post Formal Methods as Agent Guardrails appeared first on Software Engineering Daily.

Formal vs. material cooperation with evil, Aquinas' Quinque Viae and the Christian God, prayer and more on today's Called to Communion with Dr. David Anders.

Formal methods are a branch of mathematics and computer science focused on proving the correctness of systems, and they have long promised a more rigorous foundation for software. However, their complexity has kept them confined to a small community of specialists. That is now changing as agentic AI systems take on increasingly autonomous roles. The The post Formal Methods as Agent Guardrails appeared first on Software Engineering Daily.

Mandy Wiener speaks to EWN Reporter, Mongezi Koko about Taxi Boss Joe Sibanyoni along three other men are returning to court for a formal bail application. The Midday Report with Mandy Wiener is 702 and CapeTalk’s flagship news show, your hour of essential news radio. The show is podcasted every weekday, allowing you to catch up with a 60-minute weekday wrap of the day's main news. It's packed with fast-paced interviews with the day’s newsmakers, as well as those who can make sense of the news and explain what's happening in your world. All the interviews are podcasted for you to catch up and listen to. Thank you for listening to this podcast of The Midday Report Listen live on weekdays between 12:00 and 13:00 (SA Time) to The Midday Report broadcast on 702 https://buff.ly/gk3y0Kj and on CapeTalk https://buff.ly/NnFM3Nk For more from The Midday Report, go to https://buff.ly/BTGmL9H and find all the catch-up podcasts here https://buff.ly/LcbDdFI Subscribe to the 702 and CapeTalk daily and weekly newsletters https://buff.ly/v5mfetc Follow us on social media: 702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio702 CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/CapeTalk CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567See omnystudio.com/listener for privacy information.

In EVN Report's news roundup for the week of May 15: Armenia and Turkey launch formal trade relations; Russian President Vladimir Putin demands clarity on Armenia's European ambitions; Brussels releases its first progress report on Armenia's visa liberalization process and more.

This show is sponsored by BetterHelp. Sign up and get 10% off at http://BetterHelp.com/optic Invincible VS is out now on PlayStation, Xbox, and PC. New users get $50 when they play their first $5 lineup on https://www.prizepicks.com using code: OpTic Full Leaderboard, Rules, Competitors, and more info on the AT&T Annihilator Cup can be found at https://att.com/annihilatorcup OpTic Gaming Merch: https://shop.opticgaming.com/ Check out the OpTic SCUF collection and use code “OpTic” for a discount: https://scuf.co/OpTic Check out the OpTic Podcast here: https://podcasts.apple.com/us/podcast/optic-podcast/id1542810047 https://open.spotify.com/show/25iPKftrl0akOZKqS0wHQG 00:00 - Intro 00:59 - FormaL's Move 01:49 - Casey Neistat + Scump Hates Camping 05:39 - Ice Fishing 08:10 - OpTic x Huntsmen Pro-Am Major 3 18:50 - DreamHack Atlanta 21:36 - Hantavirus 26:46 - Methodz's CDL Record Survives 29:12 - P7 vs Bush INSANE Ending 32:50 - Prize Picks 35:03 - Better Help 36:43 - Invincible VS 38:42- 13-1'd by Lightning McQueen 39:53 - HOW Was This 21 Years Ago?? 41:56 - AT&T Annihilator Cup Week 1 Recap 51:01 - Scump Still Won't Go to Faker 51:57 - AT&T Annihilator Cup Week 2 56:17 - CouRage is Engaged! 57:36 - Did We Take This for Granted? (Nostalgia Bait) 01:00:48 - ChilledChaos Retires 01:02:17 - Gaming Reddit Read (Insp. by SmoshPit) Learn more about your ad choices. Visit megaphone.fm/adchoices

Fuzzing and Dynamic Analysis for High-Integrity Software with Paul Butcher We sit down with Paul Butcher, Unit Director of Dynamic Analysis at AdaCore, to explore verification techniques beyond basic compliance in safety-critical software. Paul shares his experience from Eurofighter to automated trains, explaining how dynamic analysis—from unit testing to coverage analysis to fuzzing—helps find bugs that traditional testing misses. The conversation dives deep into fuzzing: how it works, why it's so effective at finding corner-case bugs (even in well-tested systems), and the challenges of applying it to embedded systems with timing constraints. Paul introduces an intriguing approach that combines static analysis with targeted fuzzing to automatically triage false positives and generate reproducers. We also touch on formal verification, the role of LLMs in verification workflows, and why the simplest software is often the safest. Whether you're working in aerospace, medical devices, or any safety-critical domain, this episode offers practical insights into building more robust systems. Key Topics [02:30] Paul's background in high-integrity embedded systems: Eurofighter, rail, drones, and AdaCore's dynamic analysis tools [05:00] Dynamic vs. static analysis: executing code to observe real behavior across different environments [08:15] How fuzzing works: mutation engines, anomaly detection, and finding bugs through negative testing [14:20] Challenges of fuzzing timing and concurrency bugs in embedded systems [17:45] Real-world success: fuzzing the NH90 avionics via the MIL bus uncovered numerous bugs [22:30] Safety standards (DO-178C, SIL levels) and objective-based approaches vs. checkbox compliance [28:00] Determining 'enough' fuzzing: coverage, input space complexity, and building certification arguments [32:15] Combining static analysis with targeted fuzzing to automatically triage false positives and generate reproducers [38:45] Symbolic execution and theorem provers: breaking through complex branch conditions in fuzzing campaigns [42:00] Shift-left philosophy: building verifiable software from the start with testing and analysis tools [47:30] Formal verification in practice: London Underground's Victoria line uses SPARK-proven emergency braking [51:00] LLMs in verification: cautious adoption for report analysis, but determinism remains critical for core tools [54:30] High Integrity Software Conference (HISC) in Birmingham, October 2026 Notable Quotes "Software testing is typically about, is it functionally correct? Fuzzing is like a negative testing technique. It's the inverse of that. It fires random inputs into your system with the intent of finding anomalies." — Paul Butcher "Every time I speak to someone who's tried fuzzing, even if it's a system that's considered high integrity with a high level of assurance, they always find something. It's really good at eking out those weird corner case scenarios." — Paul Butcher "With testing you would like to prove the absence of bugs, but unfortunately you can't. So you have to settle for a very distant second place of proving the presence of bugs." — Luca Ingianni Resources Mentioned Paul's paper on fuzzing in safety-critical contexts - Detailed discussion of how to argue 'enough' fuzzing for certification High Integrity Software Conference (HISC) - Annual conference in Birmingham, UK (October 2026) covering high-integrity software across industries AdaCore Dynamic Analysis Tools - Coverage, fuzzing, and unit testing solutions for high-integrity software SPARK formal verification - Formal proof technology used in London Underground's Victoria line emergency braking AFL++ - Successor to the discontinued AFL (American Fuzzy Lop): Fuzzing technology mentioned as capable of quickly finding the Heartbleed bug You can find Jeff at https://jeffgable.com.You can find Luca at https://luca.engineer.Want to join the agile Embedded Slack? Click hereAre you looking for embedded-focused trainings? Head to https://agileembedded.academy/Ryan Torvik and Luca have started the Embedded AI podcast, check it out at https://embeddedaipodcast.com/

There's a theorem being tested about how AI reaches general intelligence. Carina Hong's answer: through mathematics. Carina is the founder of Axiom, and in less than a year of building, her team's AI has scored a perfect 120/120 on the Putnam mathematical competition — a test where more than 50% of brilliant undergraduates score zero. More concretely, Axiom Prover has reached 98.93% on a Lean software verification benchmark that leading alternatives solve at 11–12%. In this conversation with Matt McIlwain, Carina explains her central thesis: that math and code are the two pillars of the digital world, and that any AI infrastructure missing a formal verification layer is structurally incomplete. She walks through the history of verified AI research at Google, DeepMind, OpenAI, and Meta, and explains why each effort stalled just as commercial pressure mounted. She describes what makes hardware and software verification the natural first commercial market, and what Axiom discovered when they tested their prover against circuits that industry-standard formal checkers could not verify. For founders and operators trying to understand what's actually changing in AI capability, and for anyone building in adjacent infrastructure spaces, this is a map of where the frontier is and where it's heading. Transcript: https://www.madrona.com/agi-needs-formal-reasoning-carina-hong-is-building-it-at-axiom Chapters:  (00:00) – Introduction (02:01) – How to Define AGI Right Now — and Why There Are Two Competing Definitions (04:17) – Math Is AGI (06:12) – Math Data Scarcity: Why a Disadvantaged Domain Accelerates Progress (08:13) – Formal vs. Informal Math: Why AI Researchers Treat This Like a Religion (13:44) – Google, OpenAI, DeepMind, and Meta All Abandoned Formal Math Research (21:20) – The Putnam Story: First AI Perfect Score (28:13) – Hardware Verification as the Commercial Frontier: What Axiom Found Testing Real Circuits (31:22) – 98.93% vs. 11–12%: What the Benchmark Gap Reveals About Formal Provers (34:22) – Math and Code as the Two Pillars of the Digital World (37:00) – Team Building Around a Shared Dream: Recruiting for Mathematical Superintelligence (38:03) – What Autonomous Proof Generation Looks Like

Catch Up on the latest leading news stories around the country with Mandy Wiener on Midday Report from 12:00 to 13:00. The Midday Report with Mandy Wiener is 702 and CapeTalk’s flagship news show, your hour of essential news radio. The show is podcasted every weekday, allowing you to catch up with a 60-minute weekday wrap of the day's main news. It's packed with fast-paced interviews with the day’s newsmakers, as well as those who can make sense of the news and explain what's happening in your world. All the interviews are podcasted for you to catch up and listen to. Thank you for listening to this podcast of The Midday Report Listen live on weekdays between 12:00 and 13:00 (SA Time) to The Midday Report broadcast on 702 https://buff.ly/gk3y0Kj and on CapeTalk https://buff.ly/NnFM3Nk For more from The Midday Report, go to https://buff.ly/BTGmL9H and find all the catch-up podcasts here https://buff.ly/LcbDdFI Subscribe to the 702 and CapeTalk daily and weekly newsletters https://buff.ly/v5mfetc Follow us on social media: 702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio702 CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/CapeTalk CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567See omnystudio.com/listener for privacy information.

Mandy Wiener speaks to ActionSA National Chairperson, Michael Beaumont about ActionSA laying formal criminal charges against President Ramaphosa over the Phala Phala matter. The Midday Report with Mandy Wiener is 702 and CapeTalk’s flagship news show, your hour of essential news radio. The show is podcasted every weekday, allowing you to catch up with a 60-minute weekday wrap of the day's main news. It's packed with fast-paced interviews with the day’s newsmakers, as well as those who can make sense of the news and explain what's happening in your world. All the interviews are podcasted for you to catch up and listen to. Thank you for listening to this podcast of The Midday Report Listen live on weekdays between 12:00 and 13:00 (SA Time) to The Midday Report broadcast on 702 https://buff.ly/gk3y0Kj and on CapeTalk https://buff.ly/NnFM3Nk For more from The Midday Report, go to https://buff.ly/BTGmL9H and find all the catch-up podcasts here https://buff.ly/LcbDdFI Subscribe to the 702 and CapeTalk daily and weekly newsletters https://buff.ly/v5mfetc Follow us on social media: 702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio702 CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/CapeTalk CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567See omnystudio.com/listener for privacy information.

Jose Mario De La Garza presidente y fundador de la asociación civil Perteneces justicia e igualdad

El deporte del miércoles con Jesús Gallego: la previa del Bayern - PSG, la resaca de la eliminación del Atlético, la previa del Estrasburgo - Rayo, Luis De la Fuente presenta su autobiografía, Euroliga...

Formal cross-border care in Europe accounts for less than 1% of health spending, but that number doesn't tell the whole story. For some 150 million European patients living near internal borders, the nearest care, the shortest wait, or the best specialist for their situation, may be in another country. Join HIMSS' Tom Leary and Health Connect Partners' Dr. Petra Wilson for Episode 1 of “HIMSSCast Presents: Healthcare Without Borders” as they discuss how AI and virtual care are accelerating healthcare's shift from a location-bound to a borderless activity.

El deporte del miércoles con Jesús Gallego: la previa del Bayern - PSG, la resaca de la eliminación del Atlético, la previa del Estrasburgo - Rayo, Luis De la Fuente presenta su autobiografía, Euroliga...

✅ Work directly with me: https://launchabrand.com

Where can I find a woman like...Dante's girl? On this episode of Young Heretics, we finally get the high-fallutin' invocation of the Muses we've come to expect from any epic poem. But it's not enough! Dante needs more woman than the Muses can be...more grace, more truth, more light. And it comes from on high--first from the Virgin Mary, then Sainty Lucy, then finally his beautiful, his famous, his beloved Beatrice. Today we introduce this central figure in the Comedy, inspiration of Dante's career and "lady of his mind." We'll talk about the Muses, memory, and the communion of saints. And hopefully by the end, we'll see how much courtly love meant to the poets of this age--and how much, perhaps, it can mean to us. Check out my book, Light of the Mind, Light of the World: https://amzn.to/4tKWACP Sign up for Hebrew, Greek, or Latin courses at the Ancient Language Institute: https://ancientlanguage.com/heretics/ Get the Anthony Esolen translation: https://amzn.to/4sgKLTj Get the Dorothy L. Sayers translation: https://amzn.to/4djdh2s Chapters 00:00 Introduction 02:59 Dante's Girl 06:11 Invocation of the Muses 21:09 Beatrice and Saint Lucy 34:15 Courtly Love 50:32 Formal and Final Corner 1:01:14 Closing Remarks

Gary Mason, National Affairs Columnist for The Globe and Mail Learn more about your ad choices. Visit megaphone.fm/adchoices

King Charles scheduled to address a joint session of Congress today with a formal state dinner afterwards. Learn more about your ad choices. Visit megaphone.fm/adchoices

This Day in Legal History: Lincoln Suspends Habeas CorpusOn April 27, 1861, President Abraham Lincoln authorized military officials to suspend the writ of habeas corpus along the rail lines between Philadelphia and Washington, D.C. The order came in the opening weeks of the Civil War, when Washington was vulnerable, Union troops were moving through hostile territory, and federal officials feared sabotage and rebellion along critical transportation routes.Habeas corpus is one of the oldest protections in Anglo-American law, allowing a detained person to demand that the government justify their imprisonment before a court. By suspending it, Lincoln allowed military authorities to detain certain people without immediately producing them for judicial review. The legal problem was that the Constitution says habeas corpus may be suspended “when in cases of rebellion or invasion the public safety may require it,” but it does not clearly say which branch of government may do the suspending.Lincoln argued that the rebellion created an emergency that required swift executive action. Critics argued that the suspension power belonged to Congress, not the president, because the Suspension Clause appears in Article I, the part of the Constitution dealing mostly with legislative powers. The conflict soon came to a head in Ex parte Merryman, after John Merryman, a Maryland secessionist, was arrested by military authorities and denied ordinary habeas review.Chief Justice Roger Taney, sitting as a circuit judge, ruled that Lincoln had exceeded his constitutional authority and that only Congress could suspend the writ. Lincoln did not comply with Taney's order, maintaining that the survival of the Union justified extraordinary action. Congress later gave statutory support for wartime habeas suspension, but the controversy over Lincoln's initial action has remained central to debates over presidential power, civil liberties, and constitutional government during crisis.The U.S. Supreme Court is set to hear a case involving Cisco Systems and the Alien Tort Statute, focusing on whether U.S. companies can face liability for allegedly helping foreign governments commit human rights abuses. The case comes from Falun Gong practitioners who claim Cisco built surveillance tools for China's “Golden Shield” program that helped officials identify, detain, torture, and persecute members of the religious movement. A federal district court dismissed the case, but the Ninth Circuit revived much of it in 2023, finding the plaintiffs had plausibly alleged that Cisco aided and abetted violations of international law. Cisco argues that the Ninth Circuit improperly expanded the Alien Tort Statute by recognizing aiding-and-abetting liability even though Congress did not expressly create that cause of action. The company says the ATS was originally meant to cover only a narrow set of claims, such as piracy, violations of safe conduct, and harms to ambassadors. Cisco also relies on Supreme Court precedent to argue that courts should not create secondary liability unless Congress clearly authorizes it.The Falun Gong plaintiffs respond that aiding-and-abetting liability has long been part of international law and is especially important when serious abuses require technology, infrastructure, or corporate support. They argue that torture, extrajudicial killing, disappearances, and prolonged arbitrary detention are already recognized as serious international-law violations that can support ATS claims. Business groups and the federal government warn that expanding ATS liability could chill foreign investment and interfere with U.S. foreign relations by forcing American courts to judge the conduct of foreign governments. Supporters of the plaintiffs argue that corporate accountability can discourage companies from profiting from foreign repression and can promote fair competition for businesses that follow human rights standards. The Supreme Court's ruling could shape how much legal risk U.S. companies face when selling technology or services to governments accused of human rights abuses.Justices To Focus On Alien Tort Statute In Cisco Spying CaseThe U.S. Supreme Court is hearing Bayer's attempt to limit or end a large wave of lawsuits over Roundup, the weedkiller Bayer acquired when it bought Monsanto in 2018. The case involves John Durnell, a Missouri man who won a $1.25 million jury verdict after claiming years of Roundup exposure contributed to his non-Hodgkin lymphoma. Bayer argues that federal pesticide law should block state-law failure-to-warn claims because the Environmental Protection Agency has approved Roundup labels without a cancer warning. The company says EPA approval shows the product was not legally “misbranded” and that Bayer could not substantially change the label without agency approval. Durnell's lawyers argue that EPA registration does not make the label immune from challenge and that Missouri warning law mirrors federal requirements rather than adding new ones.The dispute turns on the Federal Insecticide, Fungicide and Rodenticide Act, which regulates pesticide labeling and limits states from imposing requirements that differ from federal law. Bayer says more than 100,000 plaintiffs have brought Roundup-related cancer claims and that a Supreme Court win could largely end the litigation. The company has also proposed a $7.25 billion settlement to resolve many current and future claims, though some pending appeals and excluded claims would remain outside the deal. Agricultural and crop industry groups, along with the Trump administration, support Bayer, while environmental, farmworker, and public health groups support Durnell. Bayer warns that the lawsuits could threaten its ability to keep supplying glyphosate products to farmers. A decision is expected by the end of June.US Supreme Court hears Bayer's fight against Roundup lawsuits | ReutersElon Musk's lawsuit against OpenAI, Sam Altman, Greg Brockman, and Microsoft is headed to trial in federal court in Oakland, California. Musk claims OpenAI betrayed its original nonprofit mission by creating a for-profit structure after he left the board, while using his name and early financial support to build what he calls a profit-driven enterprise. He is reportedly seeking $150 billion in damages, with money going to OpenAI's charitable arm, and also wants OpenAI returned to nonprofit status. OpenAI denies wrongdoing and argues that Musk's real motive is to regain control and help his own AI company, xAI. Microsoft also denies collusion and says its partnership with OpenAI began after Musk had left.The trial is expected to feature testimony from major tech figures, including Musk, Altman, and Microsoft CEO Satya Nadella. Internal documents are likely to play a major role, including diary entries from Brockman that reveal tension inside OpenAI over Musk's influence and the organization's future. Musk's side points to those materials as evidence that OpenAI's leaders became focused on profit rather than the public-benefit mission. OpenAI's side says Musk knew about possible restructuring plans, wanted to be CEO, and later attacked the company after it became successful. The case comes as OpenAI faces heavy competition, major computing costs, and possible IPO plans, while Musk's xAI is also trying to compete in the AI market. The broader fight is not just about money, but about who controls one of the most influential companies in artificial intelligence.Elon Musk's trial against Sam Altman to reveal the ongoing power struggle for OpenAI | ReutersCole Tomas Allen, a 31-year-old California man, is expected to appear in Washington federal court after allegedly trying to breach security at the White House Correspondents' Association Dinner while President Donald Trump was present. Authorities say Allen shot at a U.S. Secret Service agent at a hotel checkpoint before being tackled and arrested. The agent was hit, but a tactical vest stopped the shot, and the agent was later released from the hospital. Formal charges had not yet been filed at the time of the report, but prosecutors said Allen is expected to face charges including assault on a federal officer and using a firearm during a crime of violence. Officials also said more serious charges, including attempted assassination, could still be considered as the investigation continues.Authorities say Allen traveled from California to Washington by train and booked a room at the Washington Hilton, where the dinner was held. They also say he left family members a manifesto referring to himself as the “Friendly Federal Assassin” and discussing plans to target senior Trump administration officials. Acting Attorney General Todd Blanche said Trump may have been among the intended targets. The shooting disrupted the high-profile dinner, forced attendees to take cover, and led security personnel to move senior officials out of the room. Monday's court hearing is expected to be brief, with a judge advising Allen of his rights and prosecutors likely asking that he remain detained. The incident has renewed concerns about security for Trump and other public officials.Suspect in Washington dinner shooting set to appear in court | Reuters This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.minimumcomp.com/subscribe

We may have just solved getting dressed. Because we accidentally created a fashion rule that fixes every outfit dilemma: casual, casual, formal. Formal, formal, casual. Also: Sarah bought a very spenny shirt that she now claims is only for 'special occasions.' Will the podcast ever make the cut? Plus, the very specific signs someone was rich when you were a kid Then after Lise watches the Manosphere documentary with her son, we dive into the world of “looksmaxxing”. Fascinating, unsettling, and maybe closer to home than you think The show phone is 0489 214 653 The Fine Print: You can now watch the show on Spotify and Youtube! Just search for Lise and Sarah Vote on Spotify if you want Sarah to wear her fancy pants shirt on the pod We record at Clearshot Digital in Brisbane Want to support the show and become a Goldie? Subscribe to Lise and Sarah GOLD here For Android users, you don't need the Apple Podcasts app - you can subscribe via your web browser. How does it work? Here's a step-by-step • Click here: http://apple.co/LiseandSarah • The link will open in a web browser • From there, click on sign in, log in/create an Apple Account - it's free to do this • You can now proceed to sign up for The Lise & Sarah Show subscription (it may look like a TRY FREE button) • We suggest you save/bookmark/create a shortcut for the link for easy access whenever you want to tune inSee omnystudio.com/listener for privacy information.

David Anthony Burke, twenty-one, professionally known as D4VD, has been charged by the Los Angeles County District Attorney's Office with first-degree murder with special circumstances, continuous sexual abuse of a child under fourteen years of age, and mutilation of human remains in the death of Celeste Rivas Hernandez. Burke has entered a plea of not guilty. The special circumstances attached to the murder charge — lying in wait, commission of a crime for financial gain, and killing a witness to a crime — render Burke eligible for life in prison without the possibility of parole or the death penalty. Prosecutors have not yet announced whether they will seek the death penalty.Celeste Rivas Hernandez was fourteen years old. She had been reported missing from Lake Elsinore, California, three separate times. She was last known alive arriving at Burke's Hollywood Hills residence in April 2025. Her remains were discovered in September 2025 inside the front trunk of an impounded Tesla registered to Burke at a Hollywood tow yard, after a worker reported a foul odor emanating from the vehicle.Burke was initially arrested on April 16 by LAPD Robbery-Homicide on a probable cause warrant — known as a Ramey warrant — secured directly from a judge prior to the filing of formal charges. A grand jury investigation had been underway for months, its existence publicly revealed only when Burke's family members challenged subpoenas in a Texas court. Formal charges were filed by the DA's office, and Burke was arraigned and entered his not guilty plea.Retired FBI Special Agent Jennifer Coffindaffer provides procedural and investigative analysis of the case. She examines the reported tracking data allegedly placing Burke in a remote area of Santa Barbara County during the relevant window, the age-concealment patterns described in reports, the electronics seizures, the burn cage incinerator found at Burke's rental property, the continued evidence retrieval on the night of the arrest, and the public dispute between the Los Angeles County Medical Examiner's office and LAPD over the sealed autopsy results.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1Instagram https://www.instagram.com/hiddenkillerspod/Facebook https://www.facebook.com/hiddenkillerspod/Tik-Tok https://www.tiktok.com/@hiddenkillerspodX Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#D4VD #CelesteRivasHernandez #DavidAnthonyBurke #LAPD #TrueCrimeToday #JusticeForCeleste #MurderCharges #LosAngeles #FBIAnalysis #SpecialCircumstances

Jim talks with Bonnitta Roy, interdisciplinary thinker and founder of the Pop-Up School and the Divinity School, about her worldview, the deep foundations of her work, and an upcoming conference in Cambridge. They discuss the phenomenology of waking up and recomposing, life as a stream of participation, being nested in place through horses, pigeons, bees, and gardens, covariant motions as her process-philosophy term for embeddedness, the limits of computational rationalism, the bench scientist versus the metatheoretical interpreter, Michael Levin's interpretive science and the standards it demands, McGilchrist's left-brain dominance in late-stage Game A, early complexity theory's assumption that enough compute could map all relations, the open future and retrofitted causal explanation, emergence and causality as co-resident trees, Bonnitta's critique that emergence does insufficient explanatory work, continuous gradients beneath emergent thresholds, the traffic jam as a case study in laminar flow breakdown and downward causality, a 55-gallon drum of Jim Rutt chemicals, modularity as a post-hoc feature of development rather than its driver, where the impulse to get a beer actually comes from, the Buddhist thought experiment of cells covarying above and below thresholds, the evolutionary stack from amoeba to eukaryote to bone, white blood cells as ancient life forms living inside the body as habitat, the importance of precise definitions of consciousness, levels of simulation from New Caledonian crows to humans simulating a simulation into other people, the introspective nervous system's first-person and always-running third-person modes, Anil Seth's hallucination framing and Bonnitta's belief that simulation is the better word, why calling biological visual adjustment a hallucination is irresponsible pedagogy, Kant and the grounded approximation of reality, cultural variation in color perception, complex potential states versus the adjacent possible, Elon Musk as an example of seeing past constraints to new potential states, Bonnitta's critique of stage theory as pipeline-shaped rather than genuinely developmental, the Agile Manifesto generation acting their way into results without the formation stage theory assumes, David Bays's mathematics book and culturally bound leaps in simulation capacity, egocentric versus allocentric modes in neurodynamics, the self-generative trap of inner development and parts work where parts have parts, the three-legged stool of self, other, and world, the egregore as a hugely powerful collective agent, the historical arc from Renaissance world-builders to postmodern distributed agency, the Divinity School's question of how to lead free and willing participants, post-formal actor superpower types with powerful action logics but insufficient character, and much more. Episode Transcript Divinity School Conference: Innovations in Biological Intelligence & Machine Agency JRS EP 17: Bonnitta Roy on Process Thinking and Complexity The Pop-Up School (Substack) GSNV (Substack) Bonnitta Roy is founder of Alderlore Insight Center, and academic director of The Divinity School. She describes herself as a gardener, horse whisperer, and insight guide. She has two Substack publications: The POP-UP School where she is currently building out her philosophy of The Global State Naturalized View, and GSNV, where she posts articles generated by her GPT-engine trained on that view.

Formal trade talks with the U.S. are at risk of derailing before they've even begun, as Radio-Canada learns the U.S. is demanding an 'entry fee' for Canada to even sit at the negotiating table. Power & Politics hears from Jean Charest, former Quebec premier and a member of the prime minister's new advisory committee on Canada-U.S. economic relations. Plus, the Liberals make a move to seize control of key House of Commons committees, and opposition parties are not impressed. P&P speaks with three party House leaders.

Mark “Murch” Erhardt, Gustavo Flores Echaiz, and Mike Schmidt are joined by Remix7531 and Luis Schwab to discuss Newsletter #401.News● Discussion of using nested MuSig2 in the Lightning Network (34:05) ● Formal verification of secp256k1 modular scalar multiplication (01:10) Changes to services and client software● Coldcard 6.5.0 adds MuSig2 and miniscript (40:56) ● Frigate 1.4.0 released (41:46) ● Bitcoin Backbone updates (47:10) ● Utreexod 0.5 released (16:18) ● Floresta 0.9.0 released (19:41) Releases and release candidates● Bitcoin Core 31.0rc4 (48:01) ● Core Lightning 26.04rc3 (49:00) Notable code and documentation changes● Bitcoin Core #34401 (50:01) ● Bitcoin Core #35032 (51:51) ● Core Lightning #9021 (55:33) ● Core Lightning #9046 (56:53) ● LDK #4515 (58:25) ● LDK #4558 (59:51) ● LND #9985 (1:01:53) ● BTCPay Server #7250 (1:04:07) ● BIPs #2089 (1:07:28)

On this Wednesday edition of 2 Pros & A Cup Of Joe, Jonas, Knox, Brady Quinn, & LaVar Arrington go into depth on Diana Russini's resignation from the Athletic. Plus, the guys react to the NBA Play-In games with the Hornets beating the Heat, LaMelo Ball tripping Bam Adebayo, an outburst edition of ICYMI, and more!See omnystudio.com/listener for privacy information.

"Do nothing ..do nothing...do nothing ..and then oh F**k." That's how a leading economist has charactersied the Government's four-part fuel plan. New Zealand's currently in phase one of the plan which means there's enough fuel but prices are rising. Formal rationing would kick in at phase four. Simplicity chief economist Shamubeel Eaqub spoke to Lisa Owen.

Bitcoin's Satoshi drama heats up again as a major journalistic “reveal” drops, just as the crypto industry gets rocked by a quantum computing breakthrough that pulls up security timelines—and AI-powered exploits are suddenly real. We break down Satoshi theories, Blockstream PR whispers, the new quantum risk landscape, Ethereum vs. Bitcoin migration pain, and why your favorite protocols might not be ready for North Korea or superintelligent bug finders. Welcome to The Chopping Block — where crypto insiders Haseeb Qureshi, Tom Schmidt, Tarun Chitra, and Robert Leshner chop it up about the latest in crypto. This week we're joined by Justin Drake, Ethereum Foundation researcher and the internet's favorite quantum attack alarm bell ringer. Things get spicy immediately: the eternal guessing game “Who is Satoshi?” gets a new round of attention as John Carreyrou (yeah, Theranos guy) drops a supposed expose pointing his finger at none other than Blockstream's Adam Back.  The crew debates whether this Satoshi story is tired PR, inside baseball, or a genuine existential turning point for Bitcoin culture. Then things escalate: Justin walks us through Google and Atomic's quantum computing breakthrough—a real, validated step forward that potentially pulls the “Q-day” clock up to as soon as 2029. The implications? Bitcoin and Ethereum's security models are suddenly under the gun, and community denial is in full effect. Who's better poised to survive a quantum apocalypse… and is coin burning on the menu for Satoshi's stash? Later, we break down the Drift hack—North Korea's latest state-level heist, featuring IRL social engineering that sounds like Mr. Robot meets Oceans Eleven. Finally, it's an AI arms race: Anthropic's Mythos model is reportedly the most dangerous security researcher ever coded, and it's already quietly hardening corporate fortresses.  Panic? Prepare? Both? One thing's for sure—there are no do-overs on the blockchain, so let's get into it. Listen to the episode on Apple Podcasts, Spotify, Pods, Fountain, Podcast Addict, Pocket Casts, Amazon Music, or on your favorite podcast platform. Show highlights

El deporte del jueves, con Jesús Gallego: el Barça clama por la polémica mano de Pubill, el Atlético, satisfecho con la ventaja, el Rayo Vallecano golea al AEK en casa, previa del Friburgo - Celta, resaca del empate del Betis en Portugal, triunfo de Alcaraz en Mónaco, inicio del Masters de Augusta y la jornada en la Euroliga...

No_Tuxedo_for_the_School_Formal

IMSS reporta más de 22.7 millones de empleos formales Rehabilitación de Línea 2 del Metro va más allá del MundialEfeméride: España intentó reconquistar México en 1829Más información en nuestro podcast#grc

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

Evan McClanahan and Matthew Tyson look at the question of the death penalty and ask if Christians should support it or not. It seems there was unanimity on this subject for much of Christian history, but now, many Christians oppose the death penalty. We will consider Old Testament Law, ethics, pragmatism, and more as we unpack this complex topic. This is the first of a regular offering of debates we hope to offer as a joint effort between First Lutheran and Theology by the Pint. For years, the First Word Debate series featured in-person debates on a robust number of topics. As the debate world has moved online, we thought we would try our hand at producing interesting debates for the online community as well. If there are topics you would like to see us take on, don't hesitate to reach out to us at flhouston.org or theologybythepint.org.

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What does it mean when you feel lost in life?In this episode, I speak with Dr. Adam Formal, a clinical psychologist who works with elite athletes and people dealing with burnout and addiction.We explore why so many people feel lost and how small daily decisions can slowly pull you away from who you really are.This episode will explain why you can feel lost in life and how to start changing it.Dr. Adam Formal is a clinical psychologist based in NYC.Timestamps:(00:00) Introduction To Feeling Lost(00:27) Emotions Are Like Signals(01:17) Going Through A Crisis(03:19) Micro Choices Drift(04:29) Money And Arrival Myth(06:44) Intrinsic Motivation Wins(11:34) The Tyranny Of Should(12:45) Your Early Training(14:42) Ego Versus Meaning(19:32) Helping One Person(22:36) Choice Over Reactivity(24:39) Boredom And Reflection(27:50) Is Life Meant To Be Hard?(29:51) Meaningful Stretch Goals(32:17) Expectations Versus Reality(33:36) Micro Goals Over Outcomes(35:10) Corporate Carrots And School(36:24) Curiosity Driven Learning(38:33) Workplace Intrinsic Fit(40:28) Sports Motivation And Money(43:03) Longevity Through Love(44:58) Peace In Giving Your All(48:21) First Steps When Lost(51:05) Reverse Engineering Regret(54:54) Closing QuestionsConnect with Nick:Instagram: https://instagram.com/nickbracksWebsite: http://nickbracks.comEmail: contact@nickbracks.comConnect with Adam:Website: https://www.formaltherapy.com/ Hosted on Acast. See acast.com/privacy for more information.

Send us a Positive Review!Series Title: Straight Talk on Patriarchy and Harm [Part II of II]In this episode Val & Nathan lay out a several reasons why they feel that the LDS church is not able to address the strong correlation between its patriarchal structure, s*xual abuse & s*xual abuse under-reporting. The foundation of this struggle is a refusal to question the divinity of patriarchy...which leads to the patterns outlined in this episode. Please note--episodes of this nature are intended to be "hard on system, soft on people." Val & Nathan maintain that no person is actively trying to harm others--but harm is the inevitable outcome of structures that will not confront their own shadows.Timestamps:00:00 Introduction and Welcome 00:16 Recap of Previous Episode 01:00 Nathan's Faith Journey and Institutional Critique 02:43 The Harm of Institutional Patriarchy 05:11 Centralized Authority and Its Consequences 07:38 Theological Justifications and Their Flaws 11:45 The "Ship vs. People" Metaphor 15:20 Patriarchy in the Book of Jacob 17:38 Patterns of Abuse and Institutional Response 21:15 Science vs. Religion: Reconciling Evidence 25:30 Handling Abuse Internally 30:41 Legal Counsel and Institutional Protection 35:32 Credibility Issues and Reporting Abuse 38:45 Women Reporting to Women: Limitations 41:24 Conclusion and Call for ChangeSupport the showSupport the showListen, Share, Rate & Review EPISODESFriday Episodes Annual Access $89Friday Episodes Monthly Access $10Valerie's Support & Processing GroupsGift a ScholarshipDownload Free ResourcesVisit our Website

This episode is sponsored by BetterHelp. Go to http://betterhelp.com/optic for 10% off your first month. OpTic Gaming Merch: https://shop.opticgaming.com/ Check out the OpTic SCUF collection and use code “OpTic” for a discount: https://scuf.co/OpTic Check out the OpTic Podcast here: https://podcasts.apple.com/us/podcast/optic-podcast/id1542810047 https://open.spotify.com/show/25iPKftrl0akOZKqS0wHQG 00:00 - Intro 01:42 - Things to Do in Dallas 03:25 - OpTic Texas Are 8-0 06:02 - Mercules on the Map Pool 07:40 - CDL Standings/Major 2 Scenarios 14:45 - FormaL's Watch Parties 16:59 - Dashy Sniper Clips 19:58 - FormaL on Watching the CDL 22:39 - Shotzzy's Insane SND Timing 23:10 - Nadeshot Rages off Stream 24:25 - Major 2 Schedule Released 28:25 - Huntsmen Update 30:36 - Better Help 32:18 - Blackout Gameplay Reveal 38:01 - Ranked Play Hacking Situation 42:48 - Gentle Mates Tease Major 4 44:07- Champs Vegas Plans 52:47 - Shotzzy is DIFFERENT in Vegas 53:52 - Will We EVER Get New Maps? 57:58 - Around the Internet 01:02:19 - Ultimate 1/1 Draft Learn more about your ad choices. Visit megaphone.fm/adchoices

You are cordially invited to attend a most distinguished yet eminently fake wake in honor of Trixie Mattel and Katya Zamolodchikova, the undisputed queens of chaos, podcasts, and mirth. We shall convene in a chapel that smells faintly of Red Bull, stale makeup, and air conditioner refrigerant. Guests will enjoy a program of refined absurdities including a dramatic reading of Whitney Houston's 2001 BET Awards acceptance speech, a panel discussion on Taco Bell entitled, "Beans, Cheese, and Consequences," and a ceremonial burning of Katya's old hip. Attendees will encounter a tasteful lounge where retired wigs dispense movie reviews and gallon-jugs of iced coffee. The service will feature a eulogy by a Dunkin' Donuts cashier from Back Bay, culminating in the release of several doves that will immediately relieve themselves on Trixie and Katya's pink and crimson coffins. Formal mourning attire is required, though guests are strongly encouraged to incorporate elements of high camp, delusion, and inappropriate accessories that suggest you are in dire need of therapy. To get simple, online access to personalized, affordable care for ED, Hair Loss, Weight Loss, and more, visit: https://Hims.com/BALD This episode is sponsored by BetterHelp. Sign up and get 10% off at: https://BetterHelp.com/BALD Join Rakuten to start saving money today! Join for free by downloading the app or going to: ⁠https://Rakuten.com⁠ Follow Trixie: @TrixieMattel Follow Katya: @Katya_Zamo To watch the podcast on YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://bit.ly/TrixieKatyaYT⁠⁠⁠⁠⁠⁠⁠⁠⁠ To check out our official YouTube Clips Channel: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/TrixieAndKatyaClipYT⁠⁠⁠⁠⁠⁠⁠⁠⁠ Don't forget to follow the podcast for free wherever you're listening or by using this link: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/thebaldandthebeautifulpodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠ If you want to support the show, and get all the episodes ad-free go to: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thebaldandthebeautiful.supercast.com⁠⁠⁠⁠⁠⁠⁠⁠⁠ To check out future Live Podcast Shows, go to: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://trixieandkatya.com/#tour⁠⁠⁠⁠⁠⁠⁠⁠⁠ To check out the Trixie Motel in Palm Springs, CA: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.trixiemotel.com⁠⁠⁠⁠⁠⁠⁠⁠⁠ Listen and Watch Anywhere! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://bit.ly/thebaldandthebeautifulpodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow Trixie: Official Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.trixiemattel.com⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.tiktok.com/@trixie⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/trixiemattel⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/trixiemattel⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter (X): ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/trixiemattel⁠⁠⁠⁠⁠⁠⁠⁠⁠   Follow Katya: Official Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.welovekatya.com⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.tiktok.com/@katya_zamo⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/welovekatya⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/katya_zamo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter (X): ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/katya_zamo⁠⁠⁠⁠⁠⁠⁠⁠⁠   #TrixieMattel #KatyaZamo #BaldBeautiful Learn more about your ad choices. Visit podcastchoices.com/adchoices

Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 3927: Brian Tracy explains how your “mental diet” shapes your character, confidence, and ultimate success, outlining seven practical keys to building a truly positive personality. From affirmations and visualization to health habits and lifelong learning, he shows how deliberate mental conditioning can transform your self-esteem and results. Listen to discover how small, consistent shifts in thinking can elevate every area of your life. Read along with the original article(s) here: https://www.briantracy.com/blog/personal-success/be-the-best-you-7-keys-to-a-positive-personality/ Quotes to ponder: "Formal education will make you a living; self-education will make you a fortune." "We believe that fully 95% of your emotions are determined by the way you talk to yourself as you go throughout your day." "Your expectations become your own self-fulfilling prophesies." Episode references: Vince Lombardi Biography: https://www.biography.com/sports/vince-lombardi Learn more about your ad choices. Visit megaphone.fm/adchoices

Professor Evan Ellis reports that the US allows Venezuelan oil resale to Cuba's private sector to empower citizens, while Nicolas Maduro faces criminal proceedings in a formal New York courtroom. 11.1900 MEXICO