Podcasts about Pato

O que eu faria se fosse começar a divulgar a minha marca hoje? Sobre EFEITO ORNA Somos uma instituição de ensino dedicada a formar profissionais e empresas na criação e gestão de marcas icônicas. Oferecemos cursos livres e multidisciplinares que combinam teoria e prática para ensinar estratégias de branding eficazes, preparando nossos alunos para deixar uma marca duradoura no mundo. Desde 2016, com mais de 30 mil alunos, somos a Escola de Marcas.Instagram: @efeitoorna Sobre DÉBORA ALCÂNTARADébora Alcântara é uma comunicadora premiada, líder em inovação no mercado digital e branding. Reconhecida como Profissional do Ano pela ABRADI em 2020 e como Top Voice no LinkedIn, Débora é uma referência em estratégias de marca. Graduada em Comunicação Social pela PUCPR, ela começou sua carreira em 2010 ao fundar o blog Tudo Orna, que se tornou um case de sucesso digital. Além disso, é autora dos livros Instagram Skills, Deixe sua Marca e Marketing de Influência, e continua impactando o mercado por meio de palestras e mentorias. Instagram: @deboralcantara

O Pulo do Pato dessa semana refere-se a série da Netflix "Bebê Rena" e seus aspectos analisados pela Débora Alcantara. O que você achou dessa série? Nos deixe saber nos comentários!Sobre EFEITO ORNA Somos uma instituição de ensino dedicada a formar profissionais e empresas na criação e gestão de marcas icônicas. Oferecemos cursos livres e multidisciplinares que combinam teoria e prática para ensinar estratégias de branding eficazes, preparando nossos alunos para deixar uma marca duradoura no mundo. Desde 2016, com mais de 30 mil alunos, somos a Escola de Marcas. Instagram: @efeitoorna Sobre DÉBORA ALCÂNTARA Débora Alcântara é uma comunicadora premiada, líder em inovação no mercado digital e branding. Reconhecida como Profissional do Ano pela ABRADI em 2020 e como Top Voice no LinkedIn, Débora é uma referência em estratégias de marca. Graduada em Comunicação Social pela PUCPR, ela começou sua carreira em 2010 ao fundar o blog Tudo Orna, que se tornou um case de sucesso digital. Além disso, é autora dos livros Instagram Skills, Deixe sua Marca, Marketing de Influência e Seja Um Pato e continua impactando o mercado por meio de palestras e mentorias. Instagram: @deboralcantara

Thank you to my sponsor: BlueChew BlueChew - Get 10% off your first month of BlueChew Gold with code DAVID at https://bluechew.com More Pat O'Neill IG: https://www.instagram.com/patoneillcomedy Facebook: https://www.facebook.com/pat.o.neill.534798/# YouTube: https://www.youtube.com/@patoneillcomedy David Lucas Tour Dates: https://www.davidlucascomedy.com/tour 0:00 Fishing with Pat O'Neill 7:30 Mark Normand 9:00 Kill Tony then vs now 20:40 David's beginnings 30:43 Comedy Cities 35:40 Looking back at The Roast of Kevin Hart 45:33 Good-looking comics 48:13 Roast Battle 53:53 Starting out in Open Mics 1:01:18 Acting in movies 1:03:13 Tony Hinchcliffe 1:06:33 Aspirations in your comedy career NEW MERCH AVAILABLE https://shopdavidlucas.com/ Connect with David Lucas Website: https://www.davidlucascomedy.com Merch: https://shopdavidlucas.com/ Instagram: https://www.instagram.com/davidlucasfunny Twitter: https://twitter.com/funnydavidlucas Youtube: ​⁠@DavidLucasComedian David Lucas was born in Macon, GA. He started acting an early age, performing in numerous stage plays at the Macon Little Theatre. He relocated to Hollywood where he was a contestant on, “MTV Yo Momma”. He has since written for several television shows and continues to perform stand up all over the country (for such comedians as Louis CK, Erik Griffin, Joe Rogan, Brendan Schaub, Tony Hinchcliffe, Bert Kreisher, DL Hughley and many more). David is a Kill Tony Hall of Famer and currently headlining his own tour! A 7EQUIS Network Show https://www.instagram.com/7equis https://www.7equis.com Learn more about your ad choices. Visit megaphone.fm/adchoices

“Tęsknię za Ballmerem na scenie.” Łukasz po keynote'cie Build 2026, na którym Satya wymuszał z widowni klaskanie - “nie było wow” - a po osobowościach pokroju Guthriego i Russinovicha został korporacyjny autopilot. Bo to pierwszy od lat Build, gdzie zamiast Azure'owych fajerwerków dostajemy Windows, Windows, Windows.

Poslechněte si další epizodu podcastu Dopravní 6, který se zaměřuje na dopravu v městské části Praha 6.Témata:

TITULO: La adicción a prepararnosRESEÑA:¿Qué pasa cuando aprender se convierte en una forma de evitar actuar?Una conversación sobre conocimiento, incomodidad, transformación y el desafío de dejar de buscar respuestas para empezar a vivirlas.

Archie is trying to throw a party for the Duffy's wedding anniversary, but where is Duffy?Originally aired on March 16, 1945. This is episode 163 of Duffy's Tavern.Become a supporter of this podcast: https://www.spreaker.com/podcast/classic-comedy-of-old-time-radio--5818299/support.Please email questions and comments to host@classiccomedyotr.com.Like us on Facebook at facebook.com/classiccomedyotr. Please share this podcast with your friends and family.You can also subscribe to our podcast on Spreaker.com, Spotify, iTunes, Stitcher, TuneIn, iHeartRadio, and Google podcasts.This show is supported by Spreaker Prime.Become a supporter of this podcast: https://www.spreaker.com/podcast/classic-comedy-of-old-time-radio--5818299/support.

Pat O'Grady joins the show to discuss his publication 'Bee Gees, Process and Latent Elements in Music Production', breaking down his process of constructing the text and how he has interpreted different elements of the Gibb's music, such as examining the use of space and reverb in 'New York Mining Disaster 1941', or competent layering in 'You Win Again'. To find out more about the book and Pat O'Grady's work and career: Research Publications – Dr Pat O'Grady – Music Researcher, Educator and ArtistInstagram: Pat O'Grady (@pogmusic) • Instagram photos and videosFind us on social medias @wordsbeegeespodcast. Email us: wordsbeegeespodcast@gmail.com Hosted on Acast. See acast.com/privacy for more information.

En este nuevo capítulo de Espiral conversé con la poeta gallega Chus Pato, una de las voces más relevantes de la poesía contemporánea gallega. Hablamos sobre la huella que dejó el franquismo en su vida y en su generación, de la memoria heredada y de los distintos despojos que han marcado su escritura.  También tuvimos una profunda reflexión sobre la diferencia entre poesía y poema, la creación, el lenguaje y el oficio poético. Además, conversamos sobre los premios literarios, la experiencia de ser abuela y la necesidad de poder asegurar el tiempo y silencio que exige la escritura. Gracias por escuchar Espiral, el podcast de Karen Codner.  Si disfrutas de estas conversaciones, tu apoyo hace una gran diferencia: deja tu comentario, valora Espiral en tu plataforma favorita y comparte el podcast con quienes también disfrutan de las buenas historias. Y para seguir conectados, visita karencodner.com y suscríbete a Oda, la newsletter donde cada semana encontrarás inspiración, lecturas y nuevas ideas para alimentar tu propia espiral creativa. Learn more about your ad choices. Visit megaphone.fm/adchoices

TITULO: Cada vez más informados y menos conectados. Más información, menos claridad.RESEÑA:Sabemos qué pasa en el mundo, pero muchas veces no sabemos qué nos está pasando a nosotros.En este encuentro exploramos cómo recuperar claridad y registro en una época que nos invita permanentemente a mirar hacia afuera.

“Najgorsze co można zrobić, to zerknąć.” Piotr Karwatka o pętlach kodujących, w których agent pracuje 4 dni non-stop, a Ty masz siedzieć na rękach. Bo jak zerkniesz, trafisz na głupotę, przerwiesz mu tok myślenia i wszystko się posypie. Witamy w świecie, gdzie spec-driven development zastępuje ping-pong z Claude'em.

"Sobre la obra Hordas de escritura, que le valió a Chus Pato el Premio de la Crítica en 2009, se sitúa en un territorio híbrido entre el ensayo, la narración, la exploración poética y la reflexión crítica. La obra examina la práctica de la escritura no como una técnica disciplinada, sino como un impulso, un exceso y una comunidad. A través de sus páginas, la autora se interroga sobre el origen de la necesidad de escribir, evitando tonos académicos para ofrecer una prosa que piensa mientras avanza."La literatura contemporánea en Chile recibe una de las voces más transgresoras y relevantes de las letras europeas actuales. Conversamos en el #TraficantesDeCultura con Chus Pato, recientemente galardonada con el Premio Nacional de Poesía 2024 en España, autora de Hordas de escritura, una obra que desafía las convenciones literarias y propone una reflexión radical sobre el acto de escribir. Libro editado en Chile por FALSO AZUFRE.Conduce: Humberto Fuentes

TITULO:Como construir organizaciones donde las personas quieran quedarse?RESEÑA:Conversamos sobre el nuevo desafío del liderazgo: construir organizacionesdonde las pers onas quieran quedarse, crecer y comprometerse. Una reflexión profunda sobre desgaste, bienestar, cultura laboral y la necesidad de humanizar la manera en la que trabajamos.

“Jak ktoś to słyszy z branży IT, to już w tym momencie pewnie wyłączy podcast: gość gada głupoty i tyle.” Jakub Mrugalski aka Unknown o postawieniu hostingu na IPv6 i dzieleniu jednego IPv4 między setki użytkowników. Witamy w świecie, gdzie WordPress staje na 18 MB RAM-u, a najtańszy VPS kosztuje 35 zł rocznie.

Oh god where do we begin? Rossi's on crutches, emma had a vision of Armstrong winning only for his teammate to do so, Hannah's keeping track of the cautions like her life depends on it and sadly this was not Pato's year (again). In F1 news, George Russell needs to get over himself and just go play catch with his little brother (Kimi) and then all will be right in the world.

Produtores franceses pediram intervenção diplomática urgente.Esse conteúdo é uma parceria entre RW Cast e RFI.

Onde está o “pulo do pato”? Enquanto “pulo do gato” refere-se a um truque ou segredo especial para o sucesso, nesta série brinco com a ideia do “pulo do pato”. Trata-se de sinais e mensagens sutis que, embora pareçam pequenos, têm um impacto profundo na percepção das pessoas. Meu objetivo é despertar aquela sensação de “Como não percebi isso antes?” ou “Esse é o pulo do pato!”. O “pato” é uma referência ao símbolo da nossa comunidade. Escute o primeiro episódio e descubra questões sobre comunicação, semiótica e posicionamento de marcas pessoais e institucionais. Deixe seus comentários, críticas e sugestões! Sobre EFEITO ORNA Somos uma instituição de ensino dedicada a formar profissionais e empresas na criação e gestão de marcas icônicas. Oferecemos cursos livres e multidisciplinares que combinam teoria e prática para ensinar estratégias de branding eficazes, preparando nossos alunos para deixar uma marca duradoura no mundo. Desde 2016, com mais de 30 mil alunos, somos a Escola de Marcas.Instagram: @efeitoorna Sobre DÉBORA ALCÂNTARADébora é uma comunicadora premiada, líder em inovação no mercado digital e branding. Reconhecida como Profissional do Ano pela ABRADI em 2020 e como Top Voice no LinkedIn, Débora é uma referência em estratégias de marca. Graduada em Comunicação Social pela PUCPR, ela começou sua carreira em 2010 ao fundar o blog Tudo Orna, que se tornou um case de sucesso digital. Além disso, é autora dos livros Instagram Skills, Deixe sua Marca e Marketing de Influência, e continua impactando o mercado por meio de palestras e mentorias. Instagram: @deboralcantara

Victorias mexicanas en el extranjero, gran trabajo de Pato en Indy y un GP de Canadá encendido con los Mercedes. Esto y más tendremos en este capítulo.

ON TODAYS PROGRAM… GEORGE'S MERC GOES KABLAMO WHILE IN THE LEAD GIFTING ANTONELLI THE WIN! MCLAREN MAKES ONE. BLUNDER AFTER ANOTHER… GOOD FIGHT BETWEEN MAX AND LCH FOR P2 AND… FELIX ROSENQVIST WINS THE INDY 500 BY A NOSE!!! SUPER SAD NEWS…KYLE BUSCH GONE AT 41… THIS WEEK'S NASIR HAMEED CORNER WE HAVE: A MOMENT IN MOTORSPORTS HISTORY WITH FRANCOIS CASTAIN!… Rosenqvist Earns Epic Victory in Closest-Ever Indianapolis 500 Finish INDIANAPOLIS (Sunday, May 24, 2026) – Felix Rosenqvist capped his magical May by edging David Malukas in a last-lap drag race to the Yard of Bricks with the highest stakes, winning the 110th Indianapolis 500 presented by Gainbridge on Sunday at Indianapolis Motor Speedway in the closest finish in the century-plus history of “The Greatest Spectacle in Racing.” Rosenqvist rode the high line against the concrete wall exiting Turn 4 on Lap 200 in the No. 60 SiriusXM Honda of Meyer Shank Racing w/Curb Agajanian and powered past the No. 12 Verizon Team Penske Chevrolet of David Malukas to prevail by .0233 of a second. The previous closest finish in “500” history came in 1992, when Al Unser Jr. held off a charging Scott Goodyear by .043 of a second. SEE: Race Results “Unreal; I still don't believe it,” Rosenqvist said. “It kind of worked out the right way when I got back to third, and then I just had to flat-out lap on the high line, and it stuck,” Rosenqvist said. “It was just the coolest way you can finish and win an Indy 500.” The breathtaking race featured an event-record 70 lead changes over its 200 scintillating laps, breaking the previous mark of 68 set in 2013. With his second career NTT INDYCAR SERIES victory, Rosenqvist became the third Swedish driver to win “The Greatest Spectacle in Racing,” joining Kenny Brack (1999) and Marcus Ericsson (2022). Meyer Shank Racing also earned its second NTT INDYCAR SERIES victory – both coming in the most prestigious race in the world. Helio Castroneves captured his record-tying fourth Indianapolis 500 victory in 2021 for the Ohio-based team. The victory capped a remarkable month for Rosenqvist. He and his wife, Emille, welcomed their first child, a daughter named Stella, on May 4. “I really miss my wife and my newborn child, Stella,” Rosenqvist said. “I wish they were here with me. This whole month, becoming a dad and winning the ‘500' … We joked about it in the beginning: ‘Maybe you'll win the ‘500' and have a baby.' It's just unreal.” Scott McLaughlin finished third in the No. 3 Pennzoil Team Penske Chevrolet, as the fabled team placed two drivers in the top three but fell just short of a record-extending 21st Indy 500 victory. Pato O'Ward placed fourth in the No. 5 Arrow McLaren Chevrolet, his fifth career top-four finish in seven “500” starts without a victory. Marcus Armstrong rounded out the top five in the No. 66 Acura Honda of Meyer Shank Racing w/Curb Agajanian despite taking the green flag in the lead on a one-lap shootout for the victory after a late caution. An incredible .4360 of a second separated the top-five finishers. Rosenqvist's average speed was 162.021 mph. The one-lap dash to the checkered flag and immortality was set up when rookie Mick Schumacher brushed the SAFER Barrier in Turn 2 in his No. 47 Rahal Letterman Lanigan Honda on Lap 197. Racing resumed at the end of Lap 199, with Armstrong leading to the flag stand with the white flag in the air and one lap remaining, with Malukas in second and Rosenqvist third. Malukas powered to the lead entering Turn 1 and started to pull away on the backstretch of the 2.5-mile oval with teammates Armstrong and Rosenqvist running side by side in a joust for second. Rosenqvist, running the high line around the oval, nosed ahead of Armstrong in Turn 4 and set his sights on Malukas. With the checkered flag in the air ahead, Malukas drove his car toward the pit wall to try and break Rosenqvist's aerodynamic tow. Malukas then moved toward the center of the track, and Rosenqvist quickly swung his machine back toward the top of the racetrack, just barely avoiding contact. The two cars were side by side yards from the finish line when Rosenqvist nosed ahead and crossed the Yard of Bricks first by about a half-car length, the capacity crowd of 350,000 pulsating in delight. It was the most important of the 629 on-track passes in the race, including 567 for position. “I don't know what else we could have done,” Malukas said as he choked back tears in his pit box. “We were the fastest car that whole race. I gave it 150 percent. I mean, I almost crashed this damn car every lap, and we still ended up with a P2. “I just can't believe it. I don't know what else I can give. So close. This place, we're going to come back and bring it everything. We're going to give it 160 percent the next time.” Said Rosenqvist: “Good job to Marcus and David at the end. They raced really cleanly. It's because of drivers like that you get really good racing. Unbelievable.” McLaughlin, O'Ward and Armstrong then crossed the Yard of Bricks three-wide in the sprint for third, capping a race for the ages. The spellbinding finish was the final act of a dual-strategy drama that unfolded over the closing laps. O'Ward, Armstrong and Rosenqvist made their final pit stops on Laps 164, 165 and 166, respectively, right at the edge of the fuel window to finish the race without another stop under green-flag racing. Meanwhile, Malukas, McLaughlin and pole sitter Alex Palou in the No. 10 DHL Chip Ganassi Racing Honda were among a group of cars that were on a different sequence and had to make their final stops on Laps 175 (Malukas) and 176 (Palou and McLaughlin). Malukas took control of that chasing group, but they were more than 20 seconds behind O'Ward, Rosenqvist and Armstrong with less than 25 laps to go. Rosenqvist, with two more laps of fuel than O'Ward, was content to ride in the draft of the Mexican and save even more fuel as both lapped nearly 10 mph slower than the chasing pack to ensure they could make it to the finish. Rosenqvist finally pounced past O'Ward for the lead on Lap 185 and was headed toward Easy Street. The chasing trio of Malukas, McLaughlin and Palou appeared to be running out of laps to catch O'Ward, Rosenqvist and Armstrong, but the field was bunched on Lap 192 when rookie Caio Collet slammed the SAFER Barrier in Turn 2 in the No. 4 Combitrans Amazonia Chevrolet of A.J. Foyt Enterprises, triggering the sixth of seven caution periods in the race. Race officials immediately red-flagged the event for accident cleanup, with all cars pulling into the pits. “It was the perfect situation for us before that,” Rosenqvist said. “We kind of had everything lined up. Pato was struggling with fuel, and we were pretty rich (on fuel) to the end. I was like: ‘This is going to be great. At some point you're just going to pass him and hopefully cruise to the win.' But then in the end, everything flipped upside-down. “But you just have to reload. I was a little negative at first. I was like, ‘Of course, this happened.' But then you just had to think forward. It actually was good when I got back to third because it felt like I was hunting instead of being hunted.” Rosenqvist led the field to green flag on the Lap 196 restart after the 10-minute red flag period, with O'Ward second and Armstrong third. But Armstrong powered to the front in the four-wide restart with a bold outside move in Turn 1, with Malukas riding his aerodynamic coattails to second. But then Schumacher made contact with the SAFER Barrier to bring out the final caution on Lap 197, setting up the one-lap dash for glory. NTT P1 Award winner Palou led a race-high 59 laps but finished seventh. Adding his 12 bonus points for earning the Indy 500 pole, Palou leads the series standings by 42 points over Malukas entering the next event, the Chevrolet Detroit Grand Prix presented by Lear on Sunday, May 31 on the streets of Detroit. Kimi Antonelli First of all, massive commiserations to George. I feel very sorry for him as he was leading the race and was super strong. We were having a great battle in that first stint and very close on pace. I am sure it would have gone right until the end of the Grand Prix, and I am disappointed we didn't get the chance to continue that. It was not an easy race for us. The wind was very tricky and with the low temperatures, it was hard to get the tyres working. We had several lock-ups, particularly in the early stages, but fortunately were able to keep it on the track and get to the chequered flag first. It is of course not how we want to win but we will take it. We now get ready for the European portion of the season and six races in eight weekends leading up to shutdown. It will be an intense period, but we are looking forward to it. George Russell I am proud of my weekend, no matter that it ended in a retirement today. I took pole for the Sprint, won that race, took pole for the Grand Prix and was leading before we had the Power Unit issue that finished our race. I know there is nothing more I could have done this weekend to perform and that fills me with confidence moving forward into the rest of the season. It is of course a painful way to finish our Canadian Grand Prix weekend, but I will leave here satisfied that I did my best. Up until lap 30, I was thoroughly enjoying the race. I loved the battle with Kimi, and I am sure he did too. It was like going back to karting days where you are racing wheel-to-wheel, swapping the lead multiple times. I hope everyone enjoyed watching it as much as I enjoyed being in it. I just wish we could have continued it until the end of the Grand Prix. MAX... We made the right calls and didn't leave anything on the table! Finish Position: 3, Start Position: 6 "It's great to be back on the podium. It was a little bit of a surprise, but we made the right calls and didn't leave anything on the table. We had a very good first stint on the Soft tyre, and that gave us the gap we needed. The Medium tyre was more difficult because managing the temperatures, combined with going in and out of Virtual Safety Cars, made things more challenging. I enjoyed the last few laps battling with Lewis, and I pushed hard to take the position back. Over the last two weekends, we've been a lot closer, and there have been positive steps forward. It's also our first podium with our own powertrain, which is a great milestone for the Team, so credit to everyone for getting us here.”  

“The POC must deliver a fully functional production capable MVP.” Autentyczny cytat od klienta, który Łukasz wyciąga z Teamsów jak dowód rzeczowy w sprawie o zbiorowe pomieszanie pojęć. Brzmi znajomo?

(00:00-29:25) – Jake is back after spending yesterday at the rookie celebration and opens by wanting to embrace the rain happening right now and hope it goes away. He then explains why Buddy Rice is perfect to have on the program today due to the weather. Then, Jake reflects on the rookie luncheon yesterday and the milking of the cow tradition. He touches on Caitlin Clark having a “Beatles-like” feel, and how Pato O’Ward has the same type of crowd that follows him. Jake tries to make sense of why people feel the connection to Pato. (29:26-37:58) – Jake gives away some more numbers in his Indy 500 numbers game! (37:58-45:30) – Jake gives his take on where the Fever stand through four games and why the perimeter defense is still a question mark before their game tonight. (45:31-1:12:59) – 2004 Indianapolis 500 Winner Buddy Rice joins to talk about his win he had in the rain back in the day and what strategies he had going into it with all of the weather questions. He said in that moment that his life wouldn’t change that much. Jake asks him how much it has changed if at all. He talks about managing his kids racing careers, how much he keeps up with racing and if he misses it. Also, what is the hardest turn at IMS? He answers some funny rapid fire questions from Jake. Fill-in Producer Caleb is going to the race for the first time and Jake explains what it means to him to see younger generations experience the 500 for the first time. (1:12:59-1:23:45) – Sports radio crutches and Jake’s confusion as to who is Stephen A Smith’s audience is. (1:23:46-1:31:34) – Jake gives away another round of numbers for his numbers game! (1:31:35-1:56:11) – Of Ed Carpenter racing, Christian Rasmussen joins to talk about how he’s adjusting to the flexible race schedule, keeping an eye on the rain and how he’s preparing for the race. Jake then asks him a question on who his favorite Denmark rappers are, the languages he used, his favorite Danish dish and more. Then Jake tells Caleb to play some of Christian’s favorite music artists. (1:56:11-2:08:47) – Jake gives some breaking news on Eddie Garrison’s travels, an update on their baseball game and why this is an important stretch for the fever (2:08:47-2:13:31) – The show ends with JMV joining live from Pivot Bar to preview his show! Support the show: https://1075thefan.com/query-and-company/See omnystudio.com/listener for privacy information.

Pato O'Ward es nuestro invitado en Fórmula Latina previo a un gran fin de semana de carreras, con un domingo que inicia con Indy 500 y remata con el GP de Canadá. El piloto mexicano habla con detalle sobre la previa de la carrera más importante de la temporada de IndyCar y con sinceridad sobre su presente y su futuro. Hosted on Acast. See acast.com/privacy for more information.

Qualifying weekend was...something? one and done for these drivers which sucked if you were in a Penske car that wasn't the 12 orr if you were an Andretti...lord pray for race day. Shoutout Conor Daly and our boy lil Dave and if we don't see Rossi winning the 500 next weekend then it BETTER be Pato.

00:00 – 20:16 – The latest on Alexander Rossi after yesterday’s incident, a wild Game 1 between the Spurs and Thunder last night, Pato’s involvement, the plan heading into Sunday with cars needing to be fixed or replaced 20:17 – 29:32 – Colts OTAs start next week, will Daniel Jones participate in any of it?, what are the checkpoints for Daniel Jones’ health heading into the season?, if he’s 70% is it worth starting him in Week 1? 29:33 – 40:58 – Trackside’s Kevin Lee joins us and discusses yesterday’s incident with Alexander Rossi and the fallout from it, Pato O’Ward’s involvement and the importance of Friday for both of them, what if Rossi can’t go on Sunday?, the Burger Bash 40:59 – 52:30 - Meyer Shank Racing owner Michael Shank joins us and discusses how he feels about his team heading into the 500, Helio’s Drive For Five, the first time they ran multiple cars at the 500, his thoughts on Alexander Rossi/Pato O’Ward, does he have to have a backup list of drivers ready?, mindset heading into SundaySupport the show: https://1075thefan.com/the-wake-up-call-1075-the-fan/See omnystudio.com/listener for privacy information.

00:00 – 14:10 – An epic Game 1 between the Spurs and Thunder last night, rain overnight and how it looks the rest of the week, Alexander Rossi’s accident yesterday and the latest from the track 14:11 – 21:44 – Morning Checkdown 21:45 – 41:55– The latest on Alexander Rossi after yesterday’s incident, a wild Game 1 between the Spurs and Thunder last night, Pato’s involvement, the plan heading into Sunday with cars needing to be fixed or replaced 41:56 – 1:07:19 – Roller rink jams, Rossi update after minor surgery, Pato’s vehicle, Colts OTAs start next week, will Daniel Jones participate in any of it?, what are the checkpoints for Daniel Jones’ health heading into the season?, if he’s 70% is it worth starting him in Week 1?, Morning Checkdown 1:07:20 – 1:20:52 – Trackside’s Kevin Lee joins us and discusses yesterday’s incident with Alexander Rossi and the fallout from it, Pato O’Ward’s involvement and the importance of Friday for both of them, what if Rossi can’t go on Sunday?, the Burger Bash 1:20:53 – 1:24:59 – Roller rink jams part 2, Alexander Rossi posts an update this morning, the shot last night that made Kevin audibly gasp during Spurs/Thunder last night 1:25:00 – 1:54:15 – Meyer Shank Racing owner Michael Shank joins us and discusses how he feels about his team heading into the 500, Helio’s Drive For Five, the first time they ran multiple cars at the 500, his thoughts on Alexander Rossi/Pato O’Ward, does he have to have a backup list of drivers ready?, mindset heading into Sunday, we react to our conversation with Michael Shank, Morning Checkdown 1:54:16 – 1:59:57 – Our first round of the Indy 500 draft 1:59:58 – 2:08:17 – A new Purdue commit, Garrick Higgo fires his caddie after 2-stroke penalty buffoonery, Mike Singletary’s all-time rantSupport the show: https://1075thefan.com/the-wake-up-call-1075-the-fan/See omnystudio.com/listener for privacy information.

En este episodio #218 tenemos como gran invitado a Pato quien nos cuenta sobre su marca Ekei y los productos que tiene disponible, nos cuenta sobre el video que están grabando con patinadores para la marca, armar eventos para la comunidad... Eso y mas X Efecto Ollie

En este episodio #218 tenemos como gran invitado a Pato quien nos cuenta sobre su marca Ekei y los productos que tiene disponible, nos cuenta sobre el video que están grabando con patinadores para la marca, armar eventos para la comunidad... Eso y mas X Efecto Ollie

Reinventarte es ampliar tu M2 de verdad RESEÑA: ¿Querés cambiar algo en tu vida o convertirte en alguien capaz de sostener ese cambio? Hoy la propuesta es sobrereinvención, liderazgo consciente y el sostén interno que toda transformación personal y profesional necesita

“Google Cloud Next - nudno jak nie wiem co.” Szymon o 260 ogłoszeniach, w których słowo agent pada częściej niż litera D. Rebranding Vertexa w Gemini Enterprise Agent Platform brzmi jak generator nazw na pełnych obrotach, ale pod marketingowym szumem są konkrety: TPU Gen 8 z natywnym PyTorch bez przerabiania kodu (“po latach zrozumieli, że nie każdy jest Googlem”) i Apache Iceberg do query'owania danych z innych cloudów.

L'oro è per i dilettanti. A Canyon City si ruba il futuro. Ken Parker 13 "La città calda", scritto da Giancarlo Berardi, disegnato da Sergio Trevisan, edito dalla Sergio Bonelli Editore.

This interview with Pat O'Neill was filmed on March 18th 2026 just a little under 4 weeks before Pat was named the newest full time regular on Kill Tony during episode 766 featuring Sam Tallent. Thanks for listening! Watch the full episode here: https://www.youtube.com/watch?v=qANtyBRjkscPlease Subscribe & Follow Joke WRLD On:Patreon - https://www.patreon.com/jokewrldInstagram - https://www.instagram.com/joke.wrld/Tik Tok - https://vm.tiktok.com/ZMdMus6EWe'd love to see you this August 14 & 15 at Joke WRLD Fest in St Pete, FL.https://jokewrldfest.com

This week on the show Maine and Corey recap the 2026 IBJJF Brasileiro Championship.In the recap section of the show, we run through both the men's and the women's adult black belt divisions, discussing select finals match as well as the open class for both the men's and women's divisions.In our recap of the men's divisions, We talk about Rerisson Gabriel's win over Diego "Pato" Oliveira Batista in the light-featherweight division by 2 advantages. We talk about Meyram Maquiné Alves 5-2 points win over Cleison Gabriel Santos at featherweight. We discuss Andy Murasaki's armbar submission against Luis Felipe da Silva Ribas in the lightweight division. We break down Tainan Dalpra's toe hold victory over José Steve Nduazulu Ndilu in the middleweight finals. We talk about Alex Munis and his 2-0 points win over João Gabriel Galvão at medium-heavyweight. As well as Leonardo Silveira Ferreira securing a win on advantages over Rider Zuchi in the heavy division. We discuss Vinicius Liberati's advantage win over Erich Munis at super-heavyweight, and Gutemberg Pereira's referee decision victory over Pedro Alex in the ultra-heavyweight finals. Finally, we break down Erich Munis vs Gabriel Ribeiro in the absolute division and talk about Munis triangle armbar sub to win the open class title.In our recaps of the women's divisions we talk about Mayssa Bastos vs Jessica Caroline and how Mayssa was up 17-0 before her mounted cross choke or breadcutter choke win. We talk about Sarah Galvao's 4-0 advantages win over Vitoria Vieira at lightweight.We discuss Elisabeth Clay's toe hold win against Lillian Marchand in the middleweight finals. We break down Gabi Pessanha vs Mikaela Lima and talk about her arm triangle choke finish. We talk about Gabi Pessanha's absolute final rematch with Sarah Galvao and her commanding 12-0 points victory to secure double gold.Recorded 5-11-2026

FROM THE DKIT CLASSROOMS TO THE DOME IN TRALEE: JOIN LOUTH ROSE CHLOE MORAN AS SHE DISCUSSES HER SELECTION AND THE PRIDE OF REPRESENTING THE WEE COUNTY. 1,200 KILOMETERS, 11 AMBULANCES, AND ONE MISSION OF MERCY: JOIN MEATH'S ELAINE DIXON AS SHE DISCUSSES THE HOPE ON WHEELS CONVOY TO UGANDA. FROM LOCKS TO LUXURY SPINS: CUCHULAINN CYCLING CLUB'S PAT O'SHAUGHNESSY JOINS US TO DISCUSS HOW TO OVERCOME SAFETY FEARS AND EMBRACE BIKE WEEK 2026. Hosted on Acast. See acast.com/privacy for more information.

Município registrou temperaturas entre 0 e 4 °C neste final de semana

Pat O'Connell didn't touch saltwater until he was twelve and somehow became one of surfing's most beloved figures. A natural competitor, he represented the U.S. at the 1990 World Amateur Championships before landing a starring role in Bruce Brown's Endless Summer II — a film that introduced surfing's joy to a whole new generation. He went on to compete on the ASP World Tour for over a decade, peaking at 11th in the world, before transitioning into the surf industry with Hurley, the WSL, and Florence Marine X.  John Brooks and Kevin Miller lock Pat in the hot seat for some quality stories.  Enjoy.  

Jill Upton and Simon Nash chat with Nic Paterson who owns Chateau Pato and makes the wines for Elbourne Wines in the Hunter.@thewineshowaustralia @chateaupatowineelbournewines

Čtení s Respektem v Knihovně Václava Havla s Tomášem Halíkem, Vojtěchem Sedláčkem, Václavem Sokolem a Marií Pětovou o životě a díle univerzitního profesora, programátora, vyučeného zlatníka, chartisty i politika, který se málem stal prezidentem. Jan Sokol (18. 4. 1936 - 16. 2. 2021) byl výraznou ale přitom velice skromnou a uměřenou osobností vyčnívající z české polistopadové společnosti, která se těšila velké autoritě napříč různými proudy. Za svůj dlouhý a vrstevnatý život byl opravdu mnohým a vystřídal řadu povolání a rolí. Mimo jiné byl filosofem, učitelem, překladatelem, matematikem, programátorem, vyučeným zlatníkem, univerzitním profesorem, děkanem, jedním z prvních chartistů, poslancem, ministrem školství, kandidátem na prezidenta, ale také manželem Františky Patočkové, otcem tří dětí, dědečkem nebo wikipedistou, publicistou, autorem desítek knih a v neposlední řadě křesťanským intelektuálem, který se podílel na ekumenickém překladu Bible a občas pronášel zamyšlení během nedělní bohoslužby v kostele sv. Markéty na Břevnově.„Svět, ve kterém žijeme, život každého z nás a všechno, na čem nám v tomto životě nejvíc záleží – štěstí, zdraví, láska, děti, přátelství – nic z toho nemáme v rukou, nic z toho si nemůžeme ani koupit, ani zasloužit, ani vydělat. Všechno je dar.“ To Jan Sokol často připomínal. A vděčnost za tento dar provázená důvěrou a nadějí stály v základech jeho bohatého a naplněného života. Čeho si na něm nejvíc vážili jeho blízcí? Co vyzdvihují z jeho myšlenkového díla a co chystá vznikající Knihovna Jana Sokola? Nejen o tom debatovali jeho bratr a výtvarník Václav Sokol, katolický kněz a sociolog Tomáš Halík, podnikatel a programárot Vojtěch Sedláček a filosofka Marie Pětová. Večerem v Knihovně Václava Havla provázel Štěpán Sedláček.

Javier Trejo Garay, Adal Franco, Alex Pombo y José Antonio Cortés analizan el arranque de temporada de la IndyCar Racing, donde el mexicano Patricio O'Ward no ha encontrado el triunfo ante un Alex Palou dominante y su motor Honda en 2026. Además, la FIA escucha las quejas de los equipos y realiza modificaciones en su reglamento, rumbo a la reanudación del calendario de Formula 1 en el GP de Miami. Learn more about your ad choices. Visit podcastchoices.com/adchoices

If you think it feels like things are speeding up and change is coming faster than ever, well, you’re right. It took us hundreds of thousands of years to get to a worldwide population of 3 billion. We got there in 1927. From there, it only took about 70 more years – one lifetime – to get to a population of 6 billion. Back at the 3 billion mark, fewer Americans lived in urban areas. When they needed to shop for necessary supplies they’d talk about “going to town.” “Town” was often a single street. A commercial corridor. A “Main Street.” Here in New Orleans, over the 300 years of our existence we’ve had a number of main streets. Canal Street. Dryades Street. Magazine Street. Recently we’ve added Freret Street. The current incarnation of Freret street as a commercial district began with the re-birth of New Orleans after Hurricane Katrina. One of the first outposts of resurrection was a then audaciously high-end, hip, cocktail bar and restaurant, called Cure. Cure would go on to win all kinds of awards, including the Oscar of hospitality - a James Beard Award for “Outstanding Bar Program” - and the company behind it, CureCo Bar & Restaurant Group would go on to open Val’s, also on Freret Street, and other establishments, including Cane & Table on Decatur Street. Neal Bodenheimer is a Co-Founder and the Managing Partner of these ventures, as well as a partner in Dauphine’s in Washington DC, Co-Chair of the Board of Directors of Tales of the Cocktail Foundation, and author of the book, Cure, New Orleans Drinks and How to Mix ‘em. Tourists who visit New Orleans will often check out Freret Street and Magazine Street, but mostly they want to see The French Quarter. Typically, they’ll stay in a hotel in the Quarter, or at an Air B’nB in a neighborhood. There’s another local accommodation option too. It’s just west of the city. It sits on 7 and a half acres. It’s staffed by 12 employees, 24 hours a day 7 days a week, and has a full concierge desk. There’s a shuttle service to the French Quarter, and to special events like Saints games and Jazz Fest. The concierge will arrange any tour you want to go on. And they have a souvenir shop where everything is intentionally priced lower than downtown. So, what is this place? It’s the KOA campground in River Ridge. There are 100 RV sites, 4 tent sites, and 3 deluxe lodges. The owners of KOA Campgrounds New Orleans are husband and wife team, Mike and Deborah Dunn. Yes, we have Big Ass Beers and Pat O’Briens, but we also have craft cocktails and Cure. And, yes, we have hotels on Bourbon Street and Air BnB’s Uptown, but we also have KOA Campgrounds in River Ridge. There is, as they say, more than one way to skin a cat. According to AI, the origin of that saying is unknown, but it’s thought to refer to the various solutions to the tricky business of cleaning and preparing catfish. Which is entirely appropriate for this part of the world, and for today’s conversation, referring, as it does, to various ways of achieving the desired result of enjoying a long life or a short stay in New Orleans by taking the road less traveled. Whether its locals who have turned Cure into an institution or visitors who have discovered the benefits of urban camping, all of us appreciate the unique ways both Deborah Sunn and Neal Bodenheimer are helping retain New Orleans’ reputation as a city that defies easy definition. Out to Lunch was recorded live over lunch at Columns in Uptown New Orleans. You can find photos from this show by Jill Lafleur at itsneworleans.com.See omnystudio.com/listener for privacy information.

A jam-packed new episode of the “From the Fabricator” podcast is now ready for you.  It's a rare “3 sets of guests” edition, and each one brings something cool to the table.  I start out with Scott Kennett of AMS/NACC/AGMT & Nicolas Esquivel of CDC, and we talk about certification/licensing and the role it's playing now and, more importantly, in the future.  Then Pat O'Connor joins, and we talk about the upcoming Glass Symposium, which is a very cool and unique event.  Last, I end with Abbie Legara of Aluminum & Glazing Lines.  Just a very impressive person and operator, and it was great to dive into her business, AI, and being a woman-led company.  Really neat insights.  THANK YOU for listeningAnd thank you to FHC- Framless Hardware Company for the support!  A lot of new and exciting things are happening at FHC.  You now have a choice- see it all at www.fhc-usa.com. From the Fabricator- #Glass and #Glazing hosted by Max Perilstein, Managing Partner of Sole Source Consultants.  Connect with Max on LinkedIn at https://www.linkedin.com/in/max-perilstein-409ba111/

En este episodio de Hermana Hermana, Clau y Andie invitaron a Pato y a Tefy a platicar sobre su compromiso.

Episode Notes In this episode, Matteo sits down with Patricio (Pato), a Madrid-based compliance expert and vocal remote work advocate. Despite a career spent at legacy tech giants like Microsoft and a background in the often-rigid world of compliance, Pato shares how his “lightbulb moment” during the pandemic shifted his perspective on professional output and personal well-being. Our Guest: Patricio Roffo Operations leader shaped by both big-tech complexity and scale-up velocity. Thrives where ambition meets ambiguity, building clarity, accountability, and performance into organizations navigating growth across time zones. Remote-first by design, not by trend. Designs teams that execute across borders with ownership, and measurable impact. Recognized for turning strategy into sustained results. References: Patricio Roffo Linkedin profile Listen to the next Episode All Podcast Episodes

TalkLPnews Host Amber Bradley is back with Pat O'Leary, Head of Sales of North America at SAI Group, for the next round of their AI rapid-fire chat. Spoiler alert: AI is no longer a buzzword, it's a must have. The question isn't if anymore, it's how without drowning in hardware costs and getting stuck in a pilot that goes nowhere.Pat breaks down why two-thirds of retailers can't get past the pilot phase). Plus, vendor consolidation is having its moment, because nobody wants five different vendors doing five different things while their IT department silently plots revenge.If you're still shopping for one solution for shoplifting, another for self-checkout, and yet another for slip and falls… this episode is your wake-up call. Take a listen!

PIT PASS INDY PRESENTED BY PENSKE TRUCK RENTAL – SEASON 6, EPISODE 12 – Alex Palou wins at Barber Motorsports Park Second Year In a Row. Also, Catching up with Pato O'Ward March 31, 2026 Show host Bruce Martin and Pit Pass Indy Presented By Penske Truck Rental have another big show after the Children's of Alabama Indy Grand Prix, on March 29. Martin's guests include race winner Alex Palou of Chip Ganassi Racing, Christian Rasmussen of ECR, Kyle Kirkwood of Andretti Global, Scott McLaughlin of Team Penske and a feature interview with the most popular driver in IndyCar, Pato O'Ward of Arrow McLaren. For more INDYCAR coverage, follow Bruce Martin at X, previously known as Twitter, at @BruceMartin_500PIT PASS INDY PRESENTED BY PENSKE TRUCK RENTAL – SEASON 6, EPISODE 12 – Alex Palou wins at Barber Motorsports Park Second Year In a Row. Also, Catching up with Pato O'Ward March 31, 2026 Show host Bruce Martin and Pit Pass Indy Presented By Penske Truck Rental have another big show after the Children's of Alabama Indy Grand Prix, on March 29. Martin's guests include race winner Alex Palou of Chip Ganassi Racing, Christian Rasmussen of ECR, Kyle Kirkwood of Andretti Global, Scott McLaughlin of Team Penske and a feature interview with the most popular driver in IndyCar, Pato O'Ward of Arrow McLaren. For more INDYCAR coverage, follow Bruce Martin at X, previously known as Twitter, at @BruceMartin_500

Send the show a text message!This episode features Pat O'Brien from upstate New York, sharing his journey as a passionate Dave Matthews Band fan. We explore his first concert experience, the development of his fan community, and innovative ways he's engaging with fellow fans through a custom app and website.Check out So Much To Play here:https://somuchtoplay.org/Send your show feedback! renae@thespacebetweenpodcastDMB.com Support the showTo share your DMB fan journey, email Renae:renae@thespacebetweenpodcastDMB.com

This week on the show, Maine and Miranda recap the 2026 IBJJF pan championships. We recap every single black belt, adult finals match, including the open class for both the men's and women's divisions. We kick off the recap with the men's Roosterweight Division with Marcos Gomes vs Bebeto Oliveira. Discussing Marcos Gomes belly down ankle lock win.In the Light-Featherweight Division Diego "Pato" Oliveira vs Shoya Ishiguro we discussed Diego "Pato" Oliveira win on Points 8-2, vs the fantastic guard work of Shoya.In the Featherweight Division Cole Abate vs Samuel Nagai we talk about the final sequence that allowed Cole Abate to win by Points 6-4In the Lightweight Division Jackson Nagai vs Will Wilson we talked about Jackson Nagai points win after some fantastic offense in Will's deep half, and an out of bounds scoring call. In the Middleweight Division Tainan Dalpra vs Elijah Dorsey we talked about Tainan Dalpra pull and sweep and passing that has him take the match on Points 9-0In the Medium-Heavyweight Division Enderson Dias vs Alex Munis, we talked about Enderson Dias winning by 1 advantage on a double pull come up.In the Heavyweight Division Felipinho Assis vs Rider Zuchi we talked about Felipinho Assis winning by one stalling penalty against Rider. In the Super-Heavyweight Division Nolan Stuart vs Vinicius Liberati we talked about Nolan Stuart winning by Points 6-4 after two single leg guard pulls to give up 4 points. In the Ultra-Heavyweight Division Seif-Eddine Houmine vs Anderson Kauan we talked about Seif-Eddine Houmine wining by North-South Lapel ChokeIn the Absolute Division Gabriel Veloso vs Gabriel Caboja we talked about how Gabriel Veloso was able to counter De La Worm guard and win by Points 2-0In the recap of the womens divisions we start with the Roosterweight Division Ana Lima vs Mariana Rolszt with Ana Lima winning by Advantages 1-0In the Light-Featherweight Division Mayssa Bastos vs Ashlee Funegra we say Mayssa Bastos takes the CloseoutIn the Featherweight Division Margot Ciccarelli vs Larissa Campos we talked about Margot Ciccarelli activity and sweep setups and how this allows her tin win by Referee DecisionIn the Lightweight Division Sarah Galvão vs Janaina Lebre we talk about Sarah Galvão winning by Referee DecisionIn the Middleweight Division Lilian Marchand vs Elisabeth Clay we talk about Clay leg injury and Lilian Marchand fantastic pass to Armbar winIn Medium-Heavyweight Division Elizabeth Mitrovic vs Maca Vicentini we talk about the stack guard defense from Elizabeth Mitrovic and her Points 4-2 winIn the Heavyweight Division Larissa Dias vs Maria Malyjasiak we talk about the rubber match that saw Larissa Dias win by Points 3-0In the Super-Heavyweight Division Gabi Pessanha vs Raniele Alencar we talk about Gabi Pessanha Smother choke winIn the Absolute Division Sarah Galvão vs Gabi Pessanha we talk about Sarah Galvão strategy in the Euros rematch and single leg to back take to win by Points 6-0We also briefly preview, WNO 32 and CFFC BJJ 17. Recorded 3-30-2026

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

Condemnation comes easily when we quietly assume we earned where we stand.In this episode, John explores two truths that change everything about how we see other people: we do not know their full story — and we do not know their future.Drawing on Psalm 103 and the classic film Angels with Dirty Faces, John tells the story of two boys who run from the police. One clears a fence. One doesn't. That single moment sends their lives in opposite directions. Years later, one is a priest. One is a criminal.How much of what we call character was shaped by circumstances we did not choose?You'll discover:- Why condemnation assumes too much- What Psalm 103 means when it says “we are dust”- How curiosity disarms contempt- Why only God sees the whole arc of a life- How to bless someone you're tempted to judgeJohn closes by inviting you to bring to mind the person you're most tempted to hold in contempt — and to pray for them instead.Because there is now no condemnation.

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