Podcasts about Pavan

Je vous réunirai entre mes mains – Prophétesse Carine Pavan-Gentile Dans ce message inspiré d'Ézéchiel 37:15-28, nous découvrons le cœur de Dieu qui rassemble, restaure et réconcilie. À travers l'image des deux morceaux de bois réunis en un seul dans la main du prophète, le Seigneur révèle Son désir de mettre fin aux divisions et d'établir l'unité parmi Son peuple. Dieu n'est pas un Dieu de dispersion mais de rassemblement. Là où les blessures, les séparations et les ruptures ont laissé des traces, Il promet d'agir avec puissance pour réunir ce qui était dispersé, guérir ce qui était brisé et restaurer ce qui semblait perdu. Ce message nous rappelle également la fidélité de Dieu envers Son alliance. Il désire être notre Dieu, faire de nous Son peuple et établir Sa paix au milieu de nous. Son projet n'est pas seulement de nous sauver individuellement, mais aussi de nous rassembler sous Son autorité afin que nous marchions dans l'unité de l'Esprit et dans l'accomplissement de Ses promesses. Que cette prédication fortifie votre foi et vous encourage à faire confiance au Seigneur qui est capable de réunir ce que l'homme a séparé et de restaurer ce qui semblait irréparable. Verset clé : « Je ferai d'eux une seule nation dans le pays, dans les montagnes d'Israël; ils auront tous un même roi, ils ne formeront plus deux nations, et ne seront plus divisés en deux royaumes. » (Ézéchiel 37:22)

"Cose di Calcio" con Antonio Paolino. Ospiti: Attila Malfatti (Al-Ittifaq), Alessandra Perotta (Counseleur), Massimo Pavan (Tuttojuve)

"Massimo Pavan (TuttoJuve)" a Cose di Calcio.

Pulled from the Hiring Excellence vault — Johnny's conversation with Pavan Kumar, recorded nine months before agentic AI became the dominant talking point in TA. At the time, Pavan was leading global talent acquisition at Eightfold, rolling out their AI Recruiter to 600+ candidates across 35 roles. The argument that only got more relevant: interviewing is more automatable than scheduling, top-talent sourcing still belongs to humans, and it all falls apart without human-in-the-loop guardrails. Worth a listen if you're piloting agentic AI now.

In this episode of the Capital Raiser Show, Richard Wilson sits down with billionaire entrepreneur and AI investor Pavan Agarwal for a fireside chat on mindset, artificial intelligence, mortgage innovation, and building a long-term technology platform. Pavan shares how his family built SunWest Mortgage into a major national lender, why trust and integrity helped the business survive the financial crisis, and how Angel AI is being developed to simplify financial services, lending, credit, insurance, taxes, and long-term wealth planning. This conversation explores the mindset behind building and protecting a massive AI portfolio — and why the biggest opportunities may come from combining deep industry experience with technical execution. Topics covered include: The mindset shift required to scale a national business Why integrity matters when markets turn against you How AI is changing mortgage lending and financial services Why AI is an "ocean," not just a wave The value of patents, proprietary technology, and long-term vision Why founders should trust their own instincts earlier How Angel AI aims to become a personal financial companion Real estate, fixed income, and technology investing insights Why patient capital can win in AI and startups The biggest mistakes founders make when chasing trends To meet investors in person and learn directly from decamillionaires, family offices, and ultra-wealthy investors, visit FamilyOffices.com

Har Har Patit Pavan, ਹਰਿ ਹਰਿ ਪਤਿਤ ਪਾਵਨ (Sri Guru Granth Sahib Ang 717 Sabad 1931)

I have a rotten phlegmy cold so no news and clips today but I do have a great first time guest!  Subscribe and Watch Interviews LIVE : On YOUTUBE.com/StandUpWithPete ON SubstackStandUpWithPete Stand Up is a daily podcast. I book,host,edit, post and promote new episodes with brilliant guests every day. This show is Ad free and fully supported by listeners like you! Please subscribe now for as little as 5$ and gain access to a community of over 750 awesome, curious, kind, funny, brilliant, generous souls Shannon Minter is the Vice President of Legal (Legal Director)  Over his more than 30 years at NCLR, Shannon Minter has led impact litigation, legislative, and public policy efforts. He has filed multiple lawsuits challenging a range of Trump administration anti-transgender executive orders. He is one of the nation's foremost experts on conversion therapy, helping to draft and pass legislation in states to protect LGBTQ youth and support survivors. He served as lead counsel in the landmark California marriage equality case, and he led NCLR's contributions to multiple Supreme Court cases, such as Pavan v. Smith, Obergefell v. Hodges, and Christian Legal Society v. Martinez. An appointee to President Obama's Commission on White House Fellowships, Shannon was one of the most senior transgender appointees in the Obama administration. He has taught law at UCLA, Stanford, Golden Gate University, and Santa Clara University. Shannon is currently counsel in six cases challenging the Trump administration's anti-transgender policies, including Talbott v. Trump, which seeks to restore the right of transgender Americans to serve openly in the armed forces. His work challenging anti-transgender military policies spans nearly a decade — he previously challenged the 2017 transgender military ban under the first Trump administration, and co-chaired the Planning Commission on Transgender Military Service, which produced a comprehensive study demonstrating that inclusive service policies are both administratively feasible and militarily beneficial. Shannon has been at the forefront of efforts to protect LGBTQ+ youth from conversion therapy. He founded NCLR's Born Perfect project, a national campaign to end conversion therapy through legislation, litigation, and public education. He has helped draft laws protecting LGBTQ youth from conversion therapy across the country and continues to advocate for legal remedies to hold practitioners accountable for the harm they cause, including through malpractice, consumer fraud claims, and professional licensing sanctions. Shannon was lead counsel for same-sex couples in the landmark California marriage equality case, which was the first state supreme court decision to hold that same-sex couples have a fundamental right to marry and that laws discriminating based on sexual orientation are subject to the highest level of constitutional scrutiny. He was also counsel for married same-sex couples from Tennessee in Obergefell v. Hodges, the landmark 2015 U.S. Supreme Court decision establishing marriage equality nationwide, and NCLR's lead attorney in Pavan v. Smith, a 2017 Supreme Court decision requiring equal treatment of same-sex parents, and in Christian Legal Society v. Martinez, a U.S. Supreme Court decision upholding nondiscrimination policies based on sexual orientation and gender identity. In 2015, President Obama appointed Shannon to the President's Commission on White House Fellowships, making him one of the most senior transgender appointees in the Obama administration. Shannon called the appointment a reflection of the President's commitment to building a government that reflects the full diversity of the American people. He is co-editor of Transgender Rights (2006), the first comprehensive book on the transgender civil rights movement. Among his many honors, Shannon has received the ABA's Stonewall Award, the Ford Foundation's Leadership for a Changing World Award, the Cornell Law School Exemplary Public Service Award, the Dan Bradley Award from the National LGBTQ Bar Association, and the California Lawyer of the Year designation from California Lawyer magazine. He received his B.A. from the University of Texas at Austin and his J.D. from Cornell Law School. On YOUTUBE.com/StandUpWithPete ON SubstackStandUpWithPete Pete on Blue Sky Pete on Threads Pete on Tik Tok Pete on YouTube  Pete on Twitter Pete On Instagram Pete Personal FB page Stand Up with Pete FB page All things Jon Carroll  Follow and Support Pete Coe Buy Ava's Art  Hire DJ Monzyk to build your website or help you with Marketing

I have a rotten phlegmy cold so no news and clips today but I do have a great first time guest!  Subscribe and Watch Interviews LIVE : On YOUTUBE.com/StandUpWithPete ON SubstackStandUpWithPete Stand Up is a daily podcast. I book,host,edit, post and promote new episodes with brilliant guests every day. This show is Ad free and fully supported by listeners like you! Please subscribe now for as little as 5$ and gain access to a community of over 750 awesome, curious, kind, funny, brilliant, generous souls Shannon Minter is the Vice President of Legal (Legal Director)  Over his more than 30 years at NCLR, Shannon Minter has led impact litigation, legislative, and public policy efforts. He has filed multiple lawsuits challenging a range of Trump administration anti-transgender executive orders. He is one of the nation's foremost experts on conversion therapy, helping to draft and pass legislation in states to protect LGBTQ youth and support survivors. He served as lead counsel in the landmark California marriage equality case, and he led NCLR's contributions to multiple Supreme Court cases, such as Pavan v. Smith, Obergefell v. Hodges, and Christian Legal Society v. Martinez. An appointee to President Obama's Commission on White House Fellowships, Shannon was one of the most senior transgender appointees in the Obama administration. He has taught law at UCLA, Stanford, Golden Gate University, and Santa Clara University. Shannon is currently counsel in six cases challenging the Trump administration's anti-transgender policies, including Talbott v. Trump, which seeks to restore the right of transgender Americans to serve openly in the armed forces. His work challenging anti-transgender military policies spans nearly a decade — he previously challenged the 2017 transgender military ban under the first Trump administration, and co-chaired the Planning Commission on Transgender Military Service, which produced a comprehensive study demonstrating that inclusive service policies are both administratively feasible and militarily beneficial. Shannon has been at the forefront of efforts to protect LGBTQ+ youth from conversion therapy. He founded NCLR's Born Perfect project, a national campaign to end conversion therapy through legislation, litigation, and public education. He has helped draft laws protecting LGBTQ youth from conversion therapy across the country and continues to advocate for legal remedies to hold practitioners accountable for the harm they cause, including through malpractice, consumer fraud claims, and professional licensing sanctions. Shannon was lead counsel for same-sex couples in the landmark California marriage equality case, which was the first state supreme court decision to hold that same-sex couples have a fundamental right to marry and that laws discriminating based on sexual orientation are subject to the highest level of constitutional scrutiny. He was also counsel for married same-sex couples from Tennessee in Obergefell v. Hodges, the landmark 2015 U.S. Supreme Court decision establishing marriage equality nationwide, and NCLR's lead attorney in Pavan v. Smith, a 2017 Supreme Court decision requiring equal treatment of same-sex parents, and in Christian Legal Society v. Martinez, a U.S. Supreme Court decision upholding nondiscrimination policies based on sexual orientation and gender identity. In 2015, President Obama appointed Shannon to the President's Commission on White House Fellowships, making him one of the most senior transgender appointees in the Obama administration. Shannon called the appointment a reflection of the President's commitment to building a government that reflects the full diversity of the American people. He is co-editor of Transgender Rights (2006), the first comprehensive book on the transgender civil rights movement. Among his many honors, Shannon has received the ABA's Stonewall Award, the Ford Foundation's Leadership for a Changing World Award, the Cornell Law School Exemplary Public Service Award, the Dan Bradley Award from the National LGBTQ Bar Association, and the California Lawyer of the Year designation from California Lawyer magazine. He received his B.A. from the University of Texas at Austin and his J.D. from Cornell Law School. On YOUTUBE.com/StandUpWithPete ON SubstackStandUpWithPete Pete on Blue Sky Pete on Threads Pete on Tik Tok Pete on YouTube  Pete on Twitter Pete On Instagram Pete Personal FB page Stand Up with Pete FB page All things Jon Carroll  Follow and Support Pete Coe Buy Ava's Art  Hire DJ Monzyk to build your website or help you with Marketing

Chiara Pavan al BSMT ci ha raccontato come è passata dagli studi filosofici alla cucina d'autore, trasformando il pensiero in pratica, idee in piatti. Un percorso che dice molto anche sul suo modo di intendere la cucina: non solo tecnica, ma visione. Episodio completo qui: https://open.spotify.com/episode/5pUtVdQWnBpxRyTvZEOx54?si=00Nd6bJJSo2ldNEYxn-35Q Learn more about your ad choices. Visit megaphone.fm/adchoices

Buffet pieni, avanzi all'All you can eat, piatti lasciati a metà. È qui che, secondo Chiara Pavan, il problema dello spreco si manifesta con maggiore evidenza… Episodio completo qui: https://open.spotify.com/episode/5pUtVdQWnBpxRyTvZEOx54?si=00Nd6bJJSo2ldNEYxn-35Q Learn more about your ad choices. Visit megaphone.fm/adchoices

Non sempre la cucina è solo tecnica. A volte è pensiero, visione, responsabilità. Chiara Pavan è una di quelle chef che hanno ridefinito cosa significa cucinare oggi. ✨ Alla guida del ristorante Venissa, una stella Michelin, ha costruito un percorso unico, in cui il cibo diventa racconto del territorio, sostenibilità e ricerca continua. Al BSMT abbiamo parlato del suo modo di vivere la cucina: non solo ingredienti e piatti, ma scelte, impatto e consapevolezza. Di cosa significa lavorare con la natura, rispettarla, e trasformarla in qualcosa che abbia senso, prima ancora che sapore. Ma anche del suo percorso personale, delle difficoltà, delle decisioni e del coraggio di seguire una strada diversa, fuori dagli schemi della ristorazione tradizionale. Una chiacchierata delicata, profonda, che apre uno sguardo nuovo su quello che mettiamo nel piatto… e su come scegliamo di farlo. Buona visione! _______________________ 00:00 INTRO 9:36 L'AVVENTURA A MASTERCHEF 27:06 L'ORIGINE DELLA PASSIONE PER LA CUCINA 35:28 LE REGOLE BASE DELLA CUCINA AMBIENTALE 41:28 IL PROBLEMA DELL'INDUSTRIA AGROALIMENTARE 44:26 COMBATTERE L'INVASIONE DEI GRANCHI BLU 50:20 LA SCELTA DI DIVENTARE VEGETARIANA 53:06 LA FILOSOFIA ANTISPRECO 1:00:59 PRO E CONTRO DEL LAVORARE IN UN FINE DINING 1:04:00 UN'AMORE NATO IN CUCINA 1:06:28 IL FUTURO DELLE DONNE NELLE CUCINE 1:22:56 IL PROBLEMA NELLE CUCINE DEL FINE DINING 1:27:25 I PIATTI FORTI DI CHIARA PAVAN 1:28:40 CORSA E INCONTRI SPECIALI 1:31:46 SALUTI FINALI Learn more about your ad choices. Visit megaphone.fm/adchoices

Entrevista com a Mari Pavan, do Agiliza Lab, que leva autonomia doméstica pra mulheres como ferramenta de transformação pessoal.

Han var en av de mest originella artisterna i sin tid. Bohannon skalade bort låten för att få fram all funk, förändrade populärmusikens puls och förebådade disco och house. Lyssna på alla avsnitt i Sveriges Radios app. I den första delen följde vi Bohannon från småstaden i ett segregerat Georgia på 40- och 50-talet, till den stora chansen som Stevie Wonders trummis och bandledare på Motown.När bolaget flyttade till Los Angeles stannade Bohannon kvar i Detroit och drömde fram musik som ingen hade hört förut. Han spelade in album på löpande band under 70-talet med ett obevekligt, kompromisslöst groove. Rytm och ljudimpulser satte igång skeenden i hjärnan, och vidare i hela kroppen. Eller tvärtom. En väg till världen värd att leva i, signerad mannen som redan som barn vägrade att bli fråntagen sitt mänskliga värde.Programmet innehåller även möten med George Cllinton, Earl Young (The Trammps, MFSB), Leroy Sugar Bonner (Ohio Players), Sly Stone, James Brown, Pavan och Farley Jackmaster Funk.

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

Det var ingen som tog groove så seriöst och bokstavligt som Hamilton Bohannon. Trummisen och bandledaren som startade dansen, och fick människor i trans med ett beat som blev modellen för disco, house, techno och andra stilar. Lyssna på alla avsnitt i Sveriges Radios app. Bohannon levererade skev rytmisk briljans och äventyrslysten synkopering i hypnotiska milstolpar mellan 1973 och 1983 (”Foot stompin music”, ”South african man”, ”Let's start the dance”).Sedan valde Bohannon att stoppa beatet, drog sig tillbaka till huset utanför Atlanta, och levde på sin musik som samplades och omtolkades av nya generationer. Han blev något av en eremit. Ryktena började att cirkulera om ett av funkens olösta mysterium. När Mats Nileskär stod vid uppfarten till Bohannons villa i skogen i College Park var trollkarlen redo att prata för första gången på decennier. Men var han villig att avslöja hemligheten?Programmet innehåller även möten med George Clinton, Earl Young (The Trammps, MFSB), Leroy Sugar Bonner (Ohio Players), Sly Stone, James Brown, Pavan och Farley Jackmaster Funk.

"Cose di Calcio" con Paolo Rossi. Ospiti: Massimo Pavan, Alessandra Bacchetta.

Mastering Meetings with Preparation and InsightPavan Bachwal, VP, Head of Financial Services, Ericsson, explains that success in meetings comes from preparation and understanding your audience. Whether addressing a technology team or a marketing group, tailoring your message matters.He emphasizes: know the customer insights, do internal dry runs, and approach every meeting ready to deliver your best."Preparation and context are the keys to making every meeting count," Pavan says.Listen to the full podcast now- https://premade.outgrow.us/interview-with-Pavan-Bachwal #PavanBachwal #BusinessTips #CustomerInsights #MeetingPrep #Leadership #ProfessionalGrowth #CommunicationSkills #TechAndBusiness

On Sep. 6, 2018, India's Supreme Court ruled that Section 377, a law that criminalized consensual homosexual activity, was unconstitutional, reversing an earlier decision from 2013. Both news headlines and LGBT activists hailed the decision as a major step forward for same-sex rights in India. But in Mahesh Rao's new novel Half Light (Penguin Random House India, 2025), the court's deliberations sit in the background behind the budding relationship between Pavan, a hotel worker in Darjeeling, and Neville, a young, confident student. They meet first in Pavan's hotel in Darjeeling in 2014; after a tragic incident, they meet again four years later, in Mumbai in 2018. We're joined again by Prarthana Prakash as a guest host. Mahesh Rao grew up in Nairobi, Kenya. He has worked as a lawyer, academic researcher and bookseller in the UK. His debut novel The Smoke is Rising won the Tata First Book Award for fiction. His short fiction has been shortlisted for numerous awards. One Point Two Billion, his collection of short stories set across 13 Indian states, and Polite Society, a Delhi-set reimagining of Jane Austen's Emma, have both been published to critical acclaim. Mahesh has written for the New York Times, The Baffler, Prospect and Elle. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of Half Light. Follow on Twitter at @BookReviewsAsia. Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at@nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

On Sep. 6, 2018, India's Supreme Court ruled that Section 377, a law that criminalized consensual homosexual activity, was unconstitutional, reversing an earlier decision from 2013. Both news headlines and LGBT activists hailed the decision as a major step forward for same-sex rights in India. But in Mahesh Rao's new novel Half Light (Penguin Random House India, 2025), the court's deliberations sit in the background behind the budding relationship between Pavan, a hotel worker in Darjeeling, and Neville, a young, confident student. They meet first in Pavan's hotel in Darjeeling in 2014; after a tragic incident, they meet again four years later, in Mumbai in 2018. We're joined again by Prarthana Prakash as a guest host. Mahesh Rao grew up in Nairobi, Kenya. He has worked as a lawyer, academic researcher and bookseller in the UK. His debut novel The Smoke is Rising won the Tata First Book Award for fiction. His short fiction has been shortlisted for numerous awards. One Point Two Billion, his collection of short stories set across 13 Indian states, and Polite Society, a Delhi-set reimagining of Jane Austen's Emma, have both been published to critical acclaim. Mahesh has written for the New York Times, The Baffler, Prospect and Elle. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of Half Light. Follow on Twitter at @BookReviewsAsia. Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at@nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/literature

On Sep. 6, 2018, India's Supreme Court ruled that Section 377, a law that criminalized consensual homosexual activity, was unconstitutional, reversing an earlier decision from 2013. Both news headlines and LGBT activists hailed the decision as a major step forward for same-sex rights in India. But in Mahesh Rao's new novel Half Light (Penguin Random House India, 2025), the court's deliberations sit in the background behind the budding relationship between Pavan, a hotel worker in Darjeeling, and Neville, a young, confident student. They meet first in Pavan's hotel in Darjeeling in 2014; after a tragic incident, they meet again four years later, in Mumbai in 2018. We're joined again by Prarthana Prakash as a guest host. Mahesh Rao grew up in Nairobi, Kenya. He has worked as a lawyer, academic researcher and bookseller in the UK. His debut novel The Smoke is Rising won the Tata First Book Award for fiction. His short fiction has been shortlisted for numerous awards. One Point Two Billion, his collection of short stories set across 13 Indian states, and Polite Society, a Delhi-set reimagining of Jane Austen's Emma, have both been published to critical acclaim. Mahesh has written for the New York Times, The Baffler, Prospect and Elle. You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of Half Light. Follow on Twitter at @BookReviewsAsia. Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at@nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/asian-review

Que veux-tu que je fasse pour toi ? – Prophétesse Carine Pavan-Gentile Dans ce message puissant et inspiré, la Prophétesse Carine Pavan-Gentile nous conduit au cœur d'une question divine qui peut transformer toute une destinée : « Que veux-tu que je fasse pour toi ? » À travers la Parole de Dieu, nous découvrons que le Seigneur ne répond pas seulement aux besoins, mais qu'Il attend une foi claire, une attente précise et un cœur disposé. Comme l'aveugle Bartimée, il ne suffit pas de crier, il faut aussi savoir ce que l'on attend réellement de Dieu.

  What if you could predict which Meta Ad was going to work best and maybe even more important, which one is going to bomb! Well, that is what Pavan Pant with Extuitive is doing for brands and retailers. With an incredible behavioral database mixed with incredible AI powered tech, they are making brands and retailers money. Huge treat, Brent Peterson with Talk Commerce and Issac Morey from Content Cucumber join the show.  Enjoy Always Off Brand is always a Laugh & Learn!    FEEDSPOT TOP 10 Retail Podcast! https://podcast.feedspot.com/retail_podcasts/?feedid=5770554&_src=f2_featured_email   Guest: Pavan Pant  LinkedIn: https://www.linkedin.com/in/pavan-pant-92939a1/ Extuitive - https://extuitive.com/   Guest: Brent Peterson  LinkedIn: https://www.linkedin.com/in/brentwpeterson/ Podcast: https://podcasts.apple.com/us/podcast/talk-commerce/id1561204656   Guest: Isaac Morey  LinkedIn: https://www.linkedin.com/in/isaac-morey/ Content Cucumber- https://contentcucumber.com/    eTail: https://etailwest.wbresearch.com/ QUICKFIRE Info:   Website: https://www.quickfirenow.com/ Email the Show: info@quickfirenow.com  Talk to us on Social: Facebook: https://www.facebook.com/quickfireproductions Instagram: https://www.instagram.com/quickfire__/ TikTok: https://www.tiktok.com/@quickfiremarketing LinkedIn : https://www.linkedin.com/company/quickfire-productions-llc/about/ Sports podcast Scott has been doing since 2017, Scott & Tim Sports Show part of Somethin About Nothin:  https://podcasts.apple.com/us/podcast/somethin-about-nothin/id1306950451 HOSTS: Summer Jubelirer has been in digital commerce and marketing for over 17 years. After spending many years working for digital and ecommerce agencies working with multi-million dollar brands and running teams of Account Managers, she is now the Amazon Manager at OLLY PBC.   LinkedIn https://www.linkedin.com/in/summerjubelirer/   Scott Ohsman has been working with brands for over 30 years in retail, online and has launched over 200 brands on Amazon. Mr. Ohsman has been managing brands on Amazon for 19yrs. Owning his own sales and marketing agency in the Pacific NW, is now VP of Digital Commerce for Quickfire LLC. Producer and Co-Host for the top 5 retail podcast, Always Off Brand. He also produces the Brain Driven Brands Podcast featuring leading Consumer Behaviorist Sarah Levinger. Scott has been a featured speaker at national trade shows and has developed distribution strategies for many top brands. LinkedIn https://www.linkedin.com/in/scott-ohsman-861196a6/   Hayley Brucker has been working in retail and with Amazon for years. Hayley has extensive experience in digital advertising, both seller and vendor central on Amazon. Hayley lives in North Carolina.  LinkedIn -https://www.linkedin.com/in/hayley-brucker-1945bb229/   Huge thanks to Cytrus our show theme music "Office Party" available wherever you get your music. Check them out here: Facebook https://www.facebook.com/cytrusmusic Instagram https://www.instagram.com/cytrusmusic/ Twitter https://twitter.com/cytrusmusic SPOTIFY: https://open.spotify.com/artist/6VrNLN6Thj1iUMsiL4Yt5q?si=MeRsjqYfQiafl0f021kHwg APPLE MUSIC https://music.apple.com/us/artist/cytrus/1462321449   "Always Off Brand" is part of the Quickfire Podcast Network and produced by Quickfire LLC.  

We zijn een weekje in de Algarve geweest. Omdat Peter er vandaag niet is bespreken we alleen even de banen waar Peter niet mee was, Espiche, Monte Rei en Riba Golf Oaks. Espiche was niet makkelijk inkomen, de 'goodnight' op Monte Rei claimde 31 ballen van ons en Ribagolf was vergane glorie (maar wel met potentie) waar Martijn de ronde van de week liep.Na deze geweldige training speelde we op Amelisweerd de clubkampioenschappen foursome. Martijn en Paul werden knap 2e!Na de choke van Lowry vorige week stond deze week de Arnold Palmer op het programma. Scheffler had het niet, maar Berger wel. Van begin tot eind had Berger het onder controle totdat Bhatia met zijn broomstick ineens langszij kwam en in de playoff het toernooi won.In Zuid-Afrika was een andere broomstick de winnaar: Casey Jarvis kon in het rijtje van Seve en Faldo komen door voor een derde keer te winnen, maar dat lukte net niet. Broomstick Bradbury maakte de een na de andere put en pakte zijn 2e Joburg Open.Op LIV verdedigde Sergio Garcia zijn titel in HongKong, hij eindigde T8. Een andere Spanjaard wist eindelijk te winnen: Rahmbo! De 4Aces waren het beste team, met name door de Belgen Detry en Pieters die achter Rahm 2e en 3e werden. Ricky kralenketting Castillo wint het alternate event op de PGA Tour in Puerto Rico.In ons jaarspel pakken wij zelf punten met Morikawa en Henley en we voorspellen The Players volgende week. Paul gaat voor een sensatie: Jordan Spieth. Si-Woo Morikawa en Henley zijn onze andere favorieten.In de korte ronde: McClaren Golf, Pavan, kaartjes te koop voor Adare to Dream en Luke Donald die zijn 3e Ryder Cup daar gaat winnen, Romy Meekers, John Daly junior, quizje over back to back winnaars op de DP World Tour, Wesley Sneijder, afscheid van Bert Maalderink en Sablikova, FORE roepen en onze voorbereiding naar de competitie. Raad de Speler iemand die in zijn eerste Players na een tap in birdie op de bekende eiland green aan de leiding ging in de eerste ronde.0:00 - 20:22 Eigen golf20:22 - 44:21 Professioneel golf44:21 - 46:52 Jaarspel46:52 - 1:01:52 Korte Ronde1:01:52 - 1:02:29 Raad de Speler

This episode explores the evolving landscape of family law, focusing on parentage, jurisdiction, assisted reproduction, surrogacy, and the future of legal parenthood. It provides a comprehensive guide for students and practitioners to navigate complex legal scenarios.Unlock the secrets of modern family law and discover how the definition of "parent" is changing faster than ever. From the ancient presumption of legitimacy to cutting-edge issues like surrogacy, assisted reproduction, and multi-parent arrangements, this episode takes you deep into the legal transformation shaping families today. If you're a law student, legal professional, or simply curious about how society's evolving notions of parenthood are written into law, this is your essential guide.Imagine navigating a maze of complex statutes, constitutional rights, and interstate jurisdictional conflicts—without getting lost. We break down the key frameworks like the Uniform Parentage Act, the UCCJEA, and the nuances of biological versus intent-based parentage. You'll learn how courts determine legal parenthood through a mix of traditional presumptions, voluntary acknowledgements, and emerging concepts like de facto parenting, where intention trumps biology. We explore landmark cases like Michael H. v. Gerald D., Pavan v. Smith, and Santosky v. Kramer, revealing how courts balance biological facts with social stability and constitutional protections.This episode clarifies the critical distinctions between parentage and custody, explains the often-misunderstood jurisdictional rules—home state vs. significant connection—and highlights what every legal practitioner and student must know to master the topic. You'll discover practical checklists to analyze ART (assisted reproductive technology) agreements, surrogacy contracts, and rights of unwed or non-traditional parents. Whether tackling hypothetical exam questions or real-life dilemmas, you'll leave with a clear methodology to identify, rebut, and litigate parentage issues confidently.Why does this matter? Because the law is shifting toward recognizing a broader spectrum of familial bonds, challenging long-held assumptions about biology. Families are no longer just biological units—they are constructed through intent, support, and evolving social roles. Missing these nuances risks legal errors, missed opportunities for justice, and a failure to protect the best interests of children in complex cases.Perfect for law students prepping for exams, legal practitioners handling family disputes, or anyone interested in society's shifting view of parenthood—this episode arms you with the knowledge, case law, and frameworks to navigate the future of family law. Dive in now and see how society's definition of “family” is being rewritten—one case, one statute, and one decision at a time.TakeawaysParentage law is moving from a focus on biology to one on intent and support.Jurisdictional rules like the UCCJEA are crucial to prevent forum shopping and ensure stability.Surrogacy laws vary widely by state, with gestational surrogacy generally more enforceable than traditional.The marital presumption is strong but has specific time limits and exceptions.Termination of parental rights requires clear and convincing evidence, with high constitutional protections.Key TopicsThe shift from biological to intent-based parentageJurisdictional rules under the UCCJEA and their importanceLegal considerations in surrogacy and assisted reproductionThe significance of the marital presumption and its limitationsProcedural rules for termination of parental rights and adoptionfamily law, parentage, jurisdiction, surrogacy, adoption, UCCJEA, intent-based parentage, assisted reproduction, legal parents, custody

Money in the bank doesn't always mean security… and today's conversation may completely change how you see your financial future. Today on Her Best Version, I'm joined by the inspiring Carmelle Pavan — General Manager at leading Australian bullion company As Good As Gold Australia, and co-founder of As Good As Gold Women, the first precious metals company in the world dedicated entirely to empowering women toward financial freedom. Carmelle brings her deep knowledge of the financial system into this conversation, as we explore a topic that feels more important than ever: the truth about money, inflation, and how we can create greater financial security in uncertain times. Together, we unpack how money began, how the modern banking system works behind the scenes, why money sitting in the bank can quietly lose value over time, and why precious metals like gold and silver have remained trusted stores of wealth for centuries. This is such an empowering episode, especially for women who want to feel more informed, more confident, and more in control of their financial future. ............... PODCAST SHOWNOTES: https://www.kaseywillson.com/blog/episode139 More From Carmelle & Good As Gold Australia: (03) 8375 9674 or 1300 295 833 (ask for Carmelle)  Low Tox- Sustainable Activewear: https://www.zonebylydia.com/KASEY10 (use code KASEY10 for a special community discount) Say hi on Insta: @KaseyWillson.Naturopath  

Patit Pavan Madhau, ਪਤਿਤ ਪਾਵਨ ਮਾਧਉ (Sri Guru Granth Sahib Ang 694 Sabad 1847)

James Caton hosts Pavan Ganugapati, EMEA Leader at Accenture for the Microsoft Alliance, discusses the historic 20th consecutive Partner of the Year award, and the impact strategic partnership with Microsoft. The episode explores AI transformation, sovereign cloud, and leveraging sovereignty as a strategic advantage. Pavan emphasizes that sovereignty is not just about compliance but also a driver for value creation, with Microsoft technology supporting a full-stack sovereign architecture to adapt to evolving regulations.

Send us a text"When you are weak, do not bluff. Overselling your position can burn years of trust."In this exclusive Billionaires.com interview, technologist, banker and long term operator Pavan Agarwal shares how he has spent decades at the intersection of artificial intelligence, real estate and financial services.Pavan grew up helping his family build Sun West Mortgage, which started with less than $100,000 and became a multi billion dollar national mortgage lender and servicer. As a first generation immigrant who wrote his first AI program in 1985, he understands both the emotional side of homeownership and the deep technical side of lending.In this conversation with Richard C. Wilson (Billionaires.com and Family Office Club), Pavan talks about:How Sun West survived the 2008 crisis with only $50,000 left and why long term partners stepped in to helpWhy AI is more like an ocean than a short term wave and why most people are chasing hype instead of fundamentalsHow AngelAi, a fintech AI companion, works to reduce bias, build trust and simplify life changing financial decisionsHow an AI patent portfolio received a valuation of more than $100 billionWhat gets his attention when a founder does not have a big track record yetThe biggest mistakes he sees investors and founders make and how to think in cycles instead of headlinesIf you are interested in AI, real estate, mortgages, wealth building or family office investing, this interview is packed with practical insights and real stories.Subscribe for more billionaire and decamillionaire interviews:Billionaires.com | FamilyOfficeClub.comhttps://familyoffices.com/

What does sustainability look like in the outdoor classroom—without the pressure to be perfect?In this episode, Victoria talks with sustainability advocate and content creator Alexa Pavan about weaving environmental and sustainability literacy into outdoor classrooms in meaningful, accessible ways. Together, they explore how outdoor learning can help children understand their connection to the planet while modeling curiosity, care, and progress over perfection.

What's Really Driving the Next Wave of Fintech?In this snippet, Pavan Bachwal, VP & Head of Financial Services at Ericsson, breaks down what's fueling the momentum in fintech today.From single APIs and lower transaction costs to real-time payments and cross-border transfers, the industry is moving fast. Pavan connects these shifts to the growing impact of AI and digital assets, which together are reshaping how financial services are built and delivered.The outcome?

Confira no Morning Show desta terça-feira (20): O presidente dos Estados Unidos, Donald Trump, afirmou que concordou em se reunir com líderes europeus para discutir a Groenlândia, em meio à sua investida para anexar a ilha estratégica no Ártico. Segundo Trump, a decisão veio após uma “muito boa” conversa telefônica com o secretário-geral da Otan, Mark Rutte. O encontro está previsto para acontecer em Davos, na Suíça, durante o Fórum Econômico Mundial. A Polícia Civil de São Paulo resgatou, na manhã desta terça-feira (20), o juiz Samuel de Oliveira Magro, auditor fiscal e integrante do Tribunal de Impostos e Taxas (TIT) do Estado, que havia sido vítima de um sequestro relâmpago no último domingo (18). O magistrado era mantido em um cativeiro localizado em Osasco, na Grande São Paulo, e foi libertado durante uma operação que resultou na prisão de cinco suspeitos. Para falar sobre o assunto, o Morning Show entrevista o secretário de Segurança de SP, Oswaldo Nico Gonçalves. A Polícia Civil do Distrito Federal prendeu três técnicos de enfermagem suspeitos de envolvimento na morte de ao menos três pacientes internados na UTI do Hospital Anchieta, em Taguatinga. De acordo com as investigações, os profissionais teriam aplicado medicamentos de forma irregular, diretamente na veia das vítimas, sem indicação médica adequada. A oposição no Senado atingiu, nesta segunda-feira (19), 42 assinaturas para a instalação de uma Comissão Parlamentar de Inquérito (CPI) com a finalidade de investigar o caso do Banco Master. O requerimento, apresentado pelo senador Eduardo Girão (Novo-CE), tem o apoio de mais da metade do Senado. O prefeito de Camboriú (SC), Leonel Pavan (PSD), criticou duramente a decisão do PL de lançar Carlos Bolsonaro como candidato ao Senado por Santa Catarina. Em entrevista, Pavan classificou a estratégia como uma “loucura” e afirmou que o partido age como se o estado fosse um “balcão de negócios”. Carlos renunciou ao cargo de vereador no Rio de Janeiro e transferiu seu domicílio eleitoral para Santa Catarina em dezembro. O presidente dos Estados Unidos, Donald Trump, enviou convites a lideranças de cerca de 60 países para a criação de um “Conselho da Paz”. Há, no entanto, receio na comunidade internacional de que o grupo enfraqueça o papel da Organização das Nações Unidas (ONU). A bancada do Morning Show opinou sobre o assunto. Essas e outras notícias você confere no Morning Show.

Financial Services Is an Ecosystem, Not a Solo Act!In this snippet, Pavan Bachwal, VP & Head of Financial Services at Ericsson, explains why building in financial services is never a one-play show.Success requires aligning multiple moving parts:

So what does Windows want for Christmas? Paul Thurrott is back to talk about everything that happened to Windows in 2025, and what that might hold for 2026. Paul talks about Pavan Davuluri being promoted to President of Windows & Devices - and reunifying the Windows Client and Windows Core teams. That sets up an opportunity to make some significant moves from Windows, which, of course, will involve AI - although the reaction to the public when Pavan said as much was not all that positive. Windows has plenty of problems to address, but one of the brighter notes is Windows on ARM and the Snapdragon processor. And maybe, just maybe, someone's wish will come true, and we'll see Windows 12!LinksPavan's Famous AI TweetM365 CopilotMicrosoft LoopMicrosoft RecallRecorded December 16, 2025

Building a Rock-Solid GTM in Financial ServicesIn this snippet, Pavan Bachwal, VP & Head of Financial Services at Ericsson, breaks down what it really takes to build a successful go-to-market strategy in the complex financial services ecosystem.According to him, the foundation rests on four core pillars:Technology- getting it right from day one.Organization- aligning legal, regulatory, operational, technical, marketing, and consumer-facing teams.People- motivating and bringing together like-minded talent.Licensing- ensuring every piece of compliance is locked in.Once these are in place, the GTM plan becomes clear:

In this episode, we sit down with Pavan Davuluri, Corporate Vice President of Microsoft's Windows + Devices business, to explore how Windows is evolving into an AI-native platform. Pavan leads the team responsible for strategy, design, and delivery of Windows products across the full stack - from silicon and devices to platform, OS, apps, experiences, security, and cloud. With 23 years at Microsoft, he's driven the creation of the Surface line and now oversees how hardware and software fuse together with AI at the center. We explore how Copilot is being deeply integrated into Windows, the engineering shifts required to make Windows a more proactive and intelligent platform, and how Microsoft balances powerful automation with user control. From Surface design standards influencing the broader ecosystem to supporting OEM partners in the AI PC era, Pavan reveals the principles guiding Windows' transformation and what the computing experience will look like in the next five years.Subscribe to The Neuron newsletter: https://theneuron.aiMicrosoft Surface: https://www.microsoft.com/surfaceWindows AI features: https://www.microsoft.com/windows/ai-features

Most people run from government bureaucracy. Pavan Parikh ran toward it—and decided to rewrite the system from the inside.He believes public service should move like a startup: fast, transparent, and built around people, not process.But when tradition, power, and red tape pushed back, he didn't fold—he went to the statehouse to fight for reform.So how do you disrupt a 200-year-old system that was never built for speed or equity?In Episode 188 of the Disruption Now Podcast, Pavan breaks down how he's modernizing Hamilton County's court systems, digitizing paper-heavy workflows, and using AI and automation to reduce barriers to justice rather than create new ones. Whether you work in government, policy, law, or tech, you'll see how startup tools and mindsets can create real impact, not just buzzwords.Pavan Parikh is the elected Hamilton County Clerk of Courts in Ohio, focused on increasing access to justice, improving customer service, and modernizing one of the county's most important institutions. In this episode, we talk about what happens when a startup mindset collides with decades-old court processes, why culture eats technology for breakfast, and how AI can help everyday people navigate civil cases, evictions, and protection orders more effectively.You'll also hear Pavan's personal journey—from planning a career in medicine to 9/11 shifting him toward law and public service to ultimately leading one of the most prominent offices in Hamilton County. We get into fear of AI, job-loss anxiety within government, and how he's reframing AI as a teammate that frees staff for higher-value work rather than replacing them.If you've ever looked at the justice system and thought “there has to be a better way,” this deep dive into startup thinking for government will show you what that better way can look like—and what it takes to build it from the inside.What you'll learn in this episode:How startup thinking for government can reduce friction and errors in court processesWhy is Pavan obsessed with access to justice and end-user experience for everyday residents?How Hamilton County is digitizing records, streamlining evictions, and modernizing civil protection order filingWhere AI and automation can safely support court staff and help-center attorneysWhy change management is the real challenge—not the technologyHow local government can be a faster “lab” for responsible AI than federal agenciesWhat it really looks like to design systems around people, not paperworkChapters:00:00 Why the government needs startup thinking03:15 Pavan's path from medicine to law and 9/11's impact10:45 Modernizing Hamilton County courts and killing paper workflows22:10 AI, access to justice, and reimagining the Help Center35:30 Careers, values, and becoming a disruptor in public serviceQuick Q&A (for searchers):Q: What does “startup thinking for government” mean in this episode?A: Treating residents as end users, iterating on systems, and using tech and AI to automate low-value tasks so staff can focus on service and justice outcomes.Q: How is Hamilton County using technology to improve access to justice?A: By digitizing records, expanding the Help Center, improving online access to cases, limiting or removing outdated eviction records, and building easier online processes for civil protection orders.Q: Will AI replace court jobs?A: Pavan argues AI should handle repetitive questions and data lookups so humans can spend more time problem-solving, doing quality control, and helping people with complex issues.Connect with Pavan Parikh (verified/public handles):Website: PavanParikh.comX (Twitter): @KeepPavanClerkFacebook: Pavan Parikh for Clerk of Courts / @KeepPavanClerkInstagram: @KeepPavanClerkOffice channel: Hamilton County Clerk of Courts – @HamCoClerk on YouTubeDisruption Now resources:Subscribe to YouTube for more conversations at the intersection of AI, policy, government, and impact.Join the newsletter for weekly trends in AI and emerging tech for people who want to change systems, not just complain about them. bit.ly/newsletterDN#StartupThinking #GovTech #AccessToJusticeDisruption Now: Disrupting the status quo, making emerging tech human-centric and Accessible to all. Website https://disruptionnow.com/podcast Apply to get on the Podcast https://form.typeform.com/to/Ir6Agmzr?typeform-source=disruptionnow.comMusic credit:Embrace - Evgeny Bardyuzha

Building a Compliant and Scalable FinTech Ecosystem

At the CIO100 Symposium, Department of Transportation CIO Pavan Pidugu discusses modernizing the DOT through AI and digital transformation. From combating fraud to improving safety and efficiency, Pidugu details how data-driven innovation and cultural change have reshaped federal technology, earning the agency three consecutive CIO100 Awards for excellence in modernization. Guest Pavan Pidugu: https://www.linkedin.com/in/pidugupavan/ linkedin.comlinkedin.com

⚙️ Optimizing Data for Better ROI and Efficiency

A prefeita Balneário Camboriú, Juliana Pavan (PSD), disse que “não consegue entender” o que o vereador Jair Renan (PL) diz. Questionada pelo jornalista Upiara Boschi sobre a atuação política do filho 04 de Jair Bolsonaro, Pavan disse o seguinte, há cerca de um mês: “Ele quase não fala, não aparece. Quando fala, fala pouco. Mas, assim, respeito também ele. Ele é um vereador, foi eleito, assim como eu também fui vereadora, fui eleita. Eu respeito o Legislativo, eu vejo que a Câmara de Vereadores é atuante, e cada um ali tem o seu mandato independente. “Eles são funcionários do povo, assim como eu, e foram eleitos para trabalhar pelo povo. Então, é importante. Assim, quando me perguntam sobre ele, eu, muitas vezes eu… Respeito o posicionamento dele, apesar de ele quase não se posicionar, e, quando se posiciona, eu não consigo entender o que ele fala”,Madeleine Lacsko, Duda Teixeira e Ricardo Kertzman comentam:Papo Antagonista é o programa que explica e debate os principais acontecimentos do   dia com análises críticas e aprofundadas sobre a política brasileira e seus bastidores.     Apresentado por Madeleine Lacsko, o programa traz contexto e opinião sobre os temas mais quentes da atualidade.     Com foco em jornalismo, eleições e debate, é um espaço essencial para quem busca informação de qualidade.     Ao vivo de segunda a sexta-feira às 18h.    Apoie o jornalismo Vigilante: 10% de desconto para audiência do Papo Antagonista  https://bit.ly/papoantagonista  Siga O Antagonista no X:  https://x.com/o_antagonista   Acompanhe O Antagonista no canal do WhatsApp. Boletins diários, conteúdos exclusivos em vídeo e muito mais.  https://whatsapp.com/channel/0029Va2SurQHLHQbI5yJN344  Leia mais em www.oantagonista.com.br | www.crusoe.com.br 

I promised not to break your brain—but too late. Angel AI creator Pavan Agarwal and I go full Westworld meets Wall Street: robots, reality, family values, and why Silicon Valley sold its soul to the algorithm. From Hollywood's dystopian brainwashing to how the West lost its dignity chasing clicks, to the wild possibility that reality itself might just be a projection. Everyone's terrified of robots taking over—but what if the real threat isn't AI itself, it's who's controlling it?Follow me:IG: @talktometaylorX: @TaylorFerberTikTok: @TalkToMeTaylor

Originally loaded October 13th, reloaded October 31st. Jeffrey Mosher welcomes back Pavan Muzumdar, Chief Operating Officer for Automation Alley & Project DIAMOnD CEO, Troy, MI, for a Project DIAMOnD Marketplace discussion. There were several questions Jeffrey wanted to find out from Pavan in this conversation: 1. The big picture: Pavan, for those who may not be familiar, in a few sentences can you explain what Project DIAMOnD is and how it's changing the way manufacturing happens here in Michigan? 2. From network to marketplace: Automation Alley recently launched the Project DIAMOnD Marketplace. Tell us what this new peer-to-peer platform does and why it's such a major milestone for distributed manufacturing. 3. Impact so far: The Project DIAMOnD network has already completed more than 50,000 3D print jobs. What kinds of parts are being produced, and what does that volume tell us about the potential of this model? 4. Empowering small manufacturers: A big part of Project DIAMOnD's mission is giving small and medium-sized manufacturers access to digital transformation technology. How does the marketplace make it easier for those smaller companies to compete, even if they don't own a 3D printer themselves? 5. Security and collaboration: You've emphasized that the marketplace protects intellectual property while enabling collaboration across hundreds of Michigan manufacturers. How does that balance work in practice? 6. Michigan's leadership role: Governor Whitmer and multiple county executives have endorsed Project DIAMOnD's statewide expansion. Why is Michigan uniquely positioned to lead the country in distributed manufacturing? 7. Call to action – getting involved: For the businesses listening, whether they want to have parts printed or join the network – how can they get involved with the Project DIAMOnD Marketplace, and what kind of opportunities does it open up for them? Automation Alley's Project DIAMOnD Launches Peer-to-Peer Marketplace for Distributed 3D Printing The marketplace builds on the success of 50,000+ 3D print jobs, showcasing the power of distributed manufacturing in Michigan TROY, Mich. – Sept. 25, 2025 – Project DIAMOnD, the nation's largest distributed 3D printing network led by Automation Alley and funded by Oakland County, has launched its new additive manufacturing marketplace – a secure, peer-to-peer platform where companies can submit 3D printing jobs at scale, and participating small manufacturers across Michigan can fulfill those jobs collaboratively. How it works: Companies that want to print at scale via the Project DIAMOnD Marketplace can reach out to contact@projectdiamond.org. Manufacturers interested in joining the network to receive a free 3D printer and training can apply through the “Join” form at www.projectdiamond.org. The marketplace is designed to protect the intellectual property of designers while giving them access to the full power of the distributed network. Jobs can be produced at higher volumes and faster speeds without requiring companies to purchase their own equipment. In fact, designers don't need to own a 3D printer at all to benefit from the network. They can simply submit their designs and have them securely manufactured at scale by participating Project DIAMOnD members. About Project DIAMOnD Project DIAMOnD (Distributed Independent and Agile Manufacturing on Demand) is creating the nation's largest connected 3D printing network, enabling small and medium-sized manufacturers and tech companies to access Industry 4.0 tools, diversify production capabilities, and respond rapidly to market demands. Funded by Oakland County in Phase 2 and powered by Automation Alley, the program provides participating businesses with grant-funded 3D printers, training, and access to a secure digital marketplace for on-demand production.

Today Justin sits down with Laken Pavan. Laken is an 18-year-old student from Vancouver, Canada. In April, 2024, he left Vancouver and traveled to Eastern Ukraine to join a volunteer group there called InterBrigade. Shortly after arriving, he was confronted by agents from the Russian FSB Federal Security Service, who recruited him to work on their behalf gathering information elsewhere in Europe. He first traveled to Denmark and later to Poland, communicating with his handlers online. Laken was arrested not long afterwards in Warsaw and sentenced to 20 months in prison for espionage. He's only recently been released and returned home to Vancouver and is telling his story here now for the first time.Connect with Laken:IG: @laken.caConnect with Spycraft 101:Get Justin's latest book, Murder, Intrigue, and Conspiracy: Stories from the Cold War and Beyond, here.spycraft101.comIG: @spycraft101Shop: shop.spycraft101.comPatreon: Spycraft 101Subtack: spycraft101.substack.comFind Justin's first book, Spyshots: Volume One, here.Check out Justin's second book, Covert Arms, here.Download the free eBook, The Clandestine Operative's Sidearm of Choice, here.Support the show

Interviewee: Bassel Shanab, BS is a fourth-year medical student at the Yale School of Medicine.  Interviewer:  Lisa Meeks, PhD, MA, Guest Editor, Academic Medicine Supplement on Disability Inclusion in UME. Description: This episode of Stories Behind the Science sits down with Bassel Shanab (Yale School of Medicine), co-first author of “The Intersection of Disability, Race, Ethnicity, and Financial Background on Food Insecurity Among Medical Students,” part of the Academic Medicine supplement on Disability Inclusion in UME. We move beyond prevalence rates to the lived realities behind them—and why hunger so often hides in plain sight in elite training environments. Bassel shares the personal experiences that shaped his questions, the multi-institutional data that sharpened the answers, and the practical moves any school can make now: screen routinely, get cost-of-living estimates right, normalize help-seeking, and invest in evidence-based campus supports. Along the way, we talk flourishing (not just “fixing”), student-led research networks, and why transparency beats stigma every time. Whether you're a dean, DRP, faculty member, or student, this conversation offers a humane roadmap from surviving to thriving. Links to the open-access article, and related tools are in the show notes. Transcript:  https://docs.google.com/document/d/184LJqvcAgHGmpHyOcaxOxRw4yetR7qrGPPin0HDX7i4/edit?usp=sharing   Bios:   Bassel Shanab, BS is a fourth-year medical student at the Yale School of Medicine. He holds a Bachelor of Arts in Biological Sciences and Global Health Studies from Northwestern University, graduating with distinction. His academic interests include medical education, cardiovascular health, social determinants of health, and health policy. Key Words:   Food insecurity Medical students Disability Race and ethnicity Underrepresented in medicine (URiM) Low-income background Intersectionality Student well-being Academic performance   Resources:  Article from Today's Talk The Intersection of Disability, Race, Ethnicity, and Financial Background on Food Insecurity Among Medical Students   Nguyen, Mytien MS; Shanab, Bassel M.; Khosla, Pavan; Boatright, Dowin MD, MBA, MHS; Chaudhry, Sarwat I. MD; Brandt, Eric J. MD, MHS; Hammad, Nour M. MS; Grob, Karri L. EdD, MA; Brinker, Morgan; Cannon, Caden; Cermack, Katherine; Fathali, Maha; Kincaid, John W.R. MS, MPhil; Ma, Yuxing Emily; Ohno, Yuu MS; Pradeep, Aishwarya; Quintero, Anitza MBA; Raja, Neelufar; Rooney, Brendan L.; Stogniy, Sasha; Smith, Kiara K.; Sun, George; Sunkara, Jahnavi; Tang, Belinda; Rubick, Gabriella VanAken MD; Wang, JiCi MD; Bhagwagar, Sanaea Z.; Luzum, Nathan; Liu, Frank MS; Francis, John S. MD, PhD; Meeks, Lisa M. PhD, MA; Leung, Cindy W. PhD. The Intersection of Disability, Race, Ethnicity, and Financial Background on Food Insecurity Among Medical Students. Academic Medicine 100(10S):p S113-S118, October 2025. | DOI: 10.1097/ACM.0000000000006156   https://journals.lww.com/academicmedicine/fulltext/2025/10001/the_intersection_of_disability,_race,_ethnicity,.12.aspx The Docs With Disabilities Podcast https://www.docswithdisabilities.org/docswithpodcast

Todays Film Rage at CIFF session is with Pavan Moondi, the director of the feature Middle Life which plays this week at The Calgary International Film Festival. You can catch his film on Sept. 20th with an encore presentation Sept. 24th.  More information at CIFF (ciffcalgary.ca)     Rage On!   https://www.pavanmoondi.com/ https://www.instagram.com/pavanmoondi ----------------------------------- CIFF (ciffcalgary.ca) --------------------- https://nerdyphotographer.com/social/ https://www.filmrageyyc.com/ https://filmrage.podbean.com/ https://www.facebook.com/filmrageyyc

In this episode of the Real Estate Pro Show, host Erika interviews Pavan Maddi, a successful real estate investor who shares his journey in the industry, investment strategies, the importance of building a strong network, and the role of education in real estate. Pavan discusses his approach to analyzing deals, managing risks, and the significance of tax strategies in maximizing profits. He also shares insights on future goals and fundraising strategies for larger projects.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true ‘white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a “mini-mastermind” with Mike and his private clients on an upcoming “Retreat”, either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas “Big H Ranch”? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

Carne cultivada é o novo paradigma na ciência de alimentos que estamos lidando nesta década. Empresas de diversos portes, tanto startups quanto grandes conglomerados alimentícios, estão investindo milhões de dólares nas primeiras unidades de cultivo de carne ao redor do mundo. O produto, também chamado de carne artificial, in vitro ou sintética, tende a resolver muitos problemas e riscos na cadeia da proteína animal ou poderia trazer novos problemas? Mas afinal, o que é esse produto? Tem algum risco de consumirmos essa carne? Patronato do SciCast: 1. Patreon SciCast 2. Apoia.se/Scicast 3. Nos ajude via Pix também, chave: contato@scicast.com.br ou acesse o QRcode: Sua pequena contribuição ajuda o Portal Deviante a continuar divulgando Ciência! Contatos: contato@scicast.com.br https://twitter.com/scicastpodcast https://www.facebook.com/scicastpodcast https://instagram.com/scicastpodcast Fale conosco! E não esqueça de deixar o seu comentário na postagem desse episódio! Expediente: Produção Geral: Tarik Fernandes e André Trapani Equipe de Gravação: Tarik Fernandes, Marcelo de Matos, Gustavo Rebello, Yasmin Pussente, Lenin Machado, Celina Decol Citação ABNT: Scicast #652: Carne Cultivada. Locução: Tarik Fernandes, Marcelo de Matos, Gustavo Rebello, Yasmin Pussente, Lenin Machado, Celina Decol. [S.l.] Portal Deviante, 01/08/2025. Podcast. Disponível em: https://www.deviante.com.br/podcasts/scicast-652 Imagem de capa: Liudmila Chernetska/Getty Images. Leia mais em: https://forbes.com.br/colunas/2022/04/helen-jacintho-o-que-e-carne-cultivada-em-laboratorio/ Para apoiar o Pirulla, use o Pix abaixo: pirula1408@gmail.com Em nome de Marcos Siqueira (primo do Pirulla) [caption id="attachment_65160" align="aligncenter" width="300"] QR code PIX[/caption] https://www.youtube.com/watch?v=BecoooBM7ME&t=2170s Site: https://www.pirulla.com.br/ Referências e Indicações Sugestões de filmes: Meat of the Future (2020) da Liz Marhall que acompanha o trabalho do Dr. Uma Valeti, cofundador da Memphis Meats (hoje Upside Foods), explorando o surgimento da agricultura celular como uma solução sustentável para a produção de alimentos. Disponível em algumas plataformas de streaming como o Tubi. Sugestões de links: Florida e Alabama proibem carne cultivada: https://apnews.com/article/florida-lab-grown-meat-ban-1613765b1750119ff265fb3c5c56e2aa Itália proibiu carne cultivada a despeito da UE ainda não decidir: https://www.bbc.com/news/world-europe-67448116 Sugestões de games: Cyberpunk 2077 (2020), não é o tema principal do jogo, mas o jogo se passa em um futuro onde a comida sintética e cultivada é comum devido à escassez de recursos naturais. Em Night City, há menções a "carne sintética" e alimentos artificiais vendidos por corporações, refletindo um mundo onde a carne cultivada poderia ser a norma. A série animada derivada, Cyberpunk: Edgerunners (Netflix), também toca nesse tema indiretamente. REFERÊNCIAS: [1]: PAVAN, Bruno. Com produção recorde e queda de preço, consumo de carne bovina volta a crescer no Brasil. Portal Dinheiro Rural, 08 jul. 2024. Disponível em: . Acesso em 06 fev. 2025. [2]: SARTORELLO, Silvio. Estudo da demanda de carnes bovina, suína e de frango pela população brasileira durante o período da pandemia de Covid-19. 2024. Tese de Doutorado. Universidade de São Paulo. [3]: AGROBAND. Abate de bovinos chegou 38,6 milhões em 2024, aponta relatório de Safras. Portal Band.com.br, 10 jan. 2025. Disponível em: . Acesso em 06 fev. 2025. [4]: FICHER, Alison. Carne de laboratório! JBS abala indústria mundial ao anunciar tecnologia capaz de produzir carne real sem precisar abater animais. Portal CPG, Click Petróleo e Gás, 14 ago. 2024. Disponível em: . Acesso em 10 fev. 2025. [5]: BUENO, Maria Eduarda Campa; ISSAKOWICZ, Juliano. Estratégias de mitigação de metano entérico em ruminantes: uma revisão. Desenvolvimento Rural Sustentável: Novas Perspectivas, v. 1, p. 66-86, 2024. [6]: REYNOL, Fabio. Pesquisa avalia emissão de metano por bovinos. Portal Agência Fapesp, 13 fev. 2015. Disponível em: . Acesso em 10 fev. 2025. [7]:GALILEU, Redação. Emissão de metano por gado é medida do espaço pela primeira vez. Portal Galileu, 03 mai. 2022. Disponível em: . Acesso em 10 fev. 2025. [8]: DE OLIVEIRA, Carlos André Dantas; DE MENDONÇA, Lidiane Pinto. Carne cultivada: uma alternativa sustentável. Revista Meio Ambiente e Sustentabilidade, v. 12, n. 24, p. 42-56, 2023. [9]: WIENER-BRONNER, Danielle. Carne de laboratório não envolve abate e abre debate se pode ser considerada halal ou kasher; entenda. Portal CNN Brasil, 28 jun. 2023. Disponível em: . Acesso em 26 fev. 2025. [10]: DOS SANTOS, Ane Iara Machado et al. Um estudo sobre a Prasada: o alimento como um fenômeno cultural, o elo entre o mundo material ao espiritual. Latitude, v. 14, n. 1, p. 162-185, 2020. [11]: [5]: TECNOCARNE, Redação. Regulamentação da carne cultivada: qual é o cenário atual?. Portal Food Connection, 16 ago. 2024. Disponível em: . Acesso em 14 mar. 2025. [12]: BRASIL. Resolução - RDC Nº 839, de 14 de dezembro de 2023. Dispõe sobre a comprovação de segurança e a autorização de uso de novos alimentos e novos ingredientes. Agência Nacional de Vigilância Sanitária, Brasília, 14 dez. 2023. Disponível em: . Acesso em 14 mar. 2025. [13]: SEIBERT, Gabrielle Antunes et al. Carne Cultivada: Tendências na Utilização de Scaffolds Vegetais para Estruturação. Trabalho de Conclusão de Curso do curso apresentado ao Departamento de Engenharia Química e Engenharia de Alimentos, vol. 1. 80 p. Universidade Federal de Santa Catarina. 2023. [14]: CNN. China suspende importação de carne bovina de três frigoríficos brasileiros. Portal CNN Money, 03 mar. 2025. Disponível aqui: . Acesso em 17 mar. 2025. [15]: OFFICE, Governor’s. Governo Gianfort bans lab-grown meat in Montana. State of Montana Newsroom, 13 mai. 2025. Disponível aqui: . Acesso em 19 mai. 2025. [16]: https://www.yahoo.com/lifestyle/articles/lab-grown-salmon-got-fda-090300636.htmlSee omnystudio.com/listener for privacy information.

How comfortable are you recommending high end products to your patients? Whether its contact lenses, boutique frames, or ophthalmic lenses, recommending the best-in-class to patients has long been a challenge for many ECPs. But why? And, more importantly, how can we overcome this hurdle?In this episode, I chat with Dr. Pavan Avinashi who is the owner of Hollyburn Eye Clinic in North Vancouver. Over the last 22 years, Dr. Avinashi has built Hollyburn into the type of clinic many ODs aspire to run: a 6-lane, modern practice with 7 ODs, a dedicated aesthetics spa, and a reputation for offering the best options in eyewear.So, how did Dr. Avinashi build his practice to this level? Today, he shares his top three recommendations for business owners who aspire to offer the best to their patients.Big thanks to Hoya Vision Care Canada for their support of this episode.Learn more about Hoya and their premium lens offering:https://www.hoyavision.com/en-ca/vision-products/Love the show? Subscribe, rate, review & share! http://www.aboutmyeyes.com/podcast/