Podcasts about Misra

Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ https://youtu.be/j0TuosYDQe4?si=7mzUwBe4PrQ-eB2E In this insightful session from the Ultimate Partner Live event in Bellevue, Washington, Vince Menzione sits down with Stephen Boyle, Corporate Vice President for Enterprise Partners at Microsoft, to pull back the curtain on the tectonic shifts redefining the tech ecosystem. Boyle details Microsoft's massive organizational pivot into enterprise and SME/channel divisions , explaining how artificial intelligence acts as the foundational thread unifying systems integrators, software vendors, and digital natives. Moving past market noise surrounding competing foundational models , he highlights Microsoft's strategy to become the ultimate “platform of platforms” by prioritizing user choice, security, and trust. Emphasizing a shift away from infrastructure technicalities and toward practical business outcomes , Boyle delivers an urgent mandate for partners to scale technical talent, eliminate traditional operational silos, and brace for the incoming consumption-driven, agent-based future of enterprise computing. Key Takeaways Microsoft has restructured its global sales divisions into distinct Enterprise and SME/Channel organizations to better target its massive total addressable markets. Artificial intelligence is fundamentally altering the partner ecosystem by dismantling traditional software and systems integrator silos to build interconnected, multi-party solutions. Rather than forcing alignment to a singular model, Microsoft aims to be the definitive platform of platforms by offering extensive choice across over 1,100 language models. The enterprise landscape is rapidly moving past experimental AI pilot phases and entering production setups completely focused on transforming core business outcomes. Tomorrow's service organizations are aggressively evolving into software-minded operations that deploy repeatable, highly specialized internal autonomous agents. Managing tokens and monitoring usage metrics represents the emerging operational baseline for balancing efficiency against the scaling expenses of large language models. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags AI frontier, platform of platforms, enterprise partners, global systems integrators, digital natives, language models, token consumption, agent sprawl, citizen developers, shadow IT, business outcomes, technical enablement, marketplace growth, hyper-scalers, processing fluency, sovereign AI, industry ecosystems, data governance. Transcript [00:00:00] Stephen Boyle: This is the biggest, most transformative, iterative change in technology we’ve ever seen, where, if you wanna call it a paradigm shift or whatever word comes after paradigm shift. [00:00:12] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. Uh, I am thrilled to invite our next guest up on stage. I’ve known this gentleman for several years back in my days at Microsoft, and, um, we’ve been friends, actually Microsoft, and then we both went and did different things, came he’s come back to Microsoft in a big way. [00:00:46] Vince Menzione: Uh, Steven Boyle, for those of you don’t know, is recently a named the C. We will talk about it in a second, but I, I need to announce you properly. Is the corporate vice president, which by the way in Microsoft is a big deal for enterprise partners. He and Nicole De and I would say are the two Microsoft leaders in the organization. [00:01:06] Vince Menzione: Nicole is the channel chief. Steven has a, a big remit and we’ll talk about that up on stage. But I’m just so delightful for his support and for making the time in a very busy week at Microsoft ’cause this is CEO summit this week to make some time to come with us and be on stage with me. Please welcome my good friend Steven Boyle. [00:01:29] Vince Menzione: Good to see you, sir. To see. So I’m gonna put you on this side. [00:01:33] Stephen Boyle: Okay. [00:01:35] Vince Menzione: The hot seat. So I’m gonna, I, I didn’t do a justice and I, I wanted you to explain your role. I, I think I know, but I think for the, for the people in the room, uh, talk to us what Enterprise Partners means at Microsoft and what that role remit and remit looks like. [00:01:50] Stephen Boyle: Um, CVPs may or may not be important, but one thing they don’t do is get invites to the CEO summit. So I’m super pleased to be here with you guys. No, no, it’s totally cool. It’s totally cool if that phone rings. No, I’m kidding. Doesn’t. So what does it mean? So I’d like quickly, um. January last year, uh, we split the sales organization into enterprise and small to medium enterprise and channel. [00:02:15] Stephen Boyle: You guys probably familiar with that? Nicole is the, uh, chief partner officer lives in the SMA and C world and drives the channel, um, drives our marketplace business and, and a lot of other things. Um, for that 60 billion, um, you know, total addressable market that we have. Down there in SME and C. Um, at the same time, we established enterprise partner as part of Nick Parker’s overall organization. [00:02:40] Stephen Boyle: Um, but for most of 2025 we ran it as global systems integrators and advisories, ISVs and digital natives. So three separate footprints all focused entirely on, on, on enterprise. Um, in December, January, we talked about establishing an enterprise partner leader that would. You know, aggregate all of this stuff. [00:03:00] Stephen Boyle: Um, I was fortunate to come through, um, some frankly, pretty hairy, uh, experiences, I bet with some of our senior leaders. Um, I, I’ve loved to [00:03:08] Vince Menzione: been in the room for that [00:03:09] Stephen Boyle: questions like, why Steven Boyle and things like that, right? And really have to dig deep to, uh, to justify. Anyway, uh, I’m blessed and honored, uh, to run that entire portfolio of partners, uh, for the entirety of the enterprise partner world, which now from a chief revenue officer perspective, belongs to Deb. [00:03:25] Stephen Boyle: Deb Co. So Deb is the enterprise leader for all of our sales that we do into that space. Awesome. Um, I have three regional leaders, Nina Harding here in the United States, Ehab Ra in in Europe, and Heather Gordon in Asia that mirror and replicate and flow down the things that we decide to do from a strategy perspective for the, uh, for the core. [00:03:45] Vince Menzione: And we love Nina. She’s been, she was at our last event, [00:03:47] Stephen Boyle: super, super lady. And, uh, you know, the US is still 50% of our overall business. [00:03:53] Vince Menzione: Yeah. [00:03:53] Stephen Boyle: Too big to fabric. Every time I talk to Nina, I’m like, Nina, you’re too big to fail. We can’t cover you anywhere else. So you know, you’ve gotta be successful here in the Americas. [00:04:01] Vince Menzione: So I think just for breaking it up, I, ’cause I do want to like, it’ll lead to the next question, right? So you have the global systems integrators, all these systems integrators. Essentially you have all of the software companies we used to call ISVs, we now call SDCs or software development corporations. [00:04:17] Vince Menzione: And then you also have the AI stack, I’ll call it. Right? So under Jason Grafe. Yeah. Many, many might know. Jason’s been a guest on the podcast and was Satya’s chief of staff at one time, eight years. Eight years. Wow. I didn’t realize there was that many. [00:04:31] Stephen Boyle: Carry carried a lot of bags for Satya over the years. [00:04:34] Vince Menzione: Unbelievable. Well, let’s, I mean, so AI is an important component, right? And you saw Jay’s, Jay talking, just talking about AI and all these things. I would love to start here, right? Because, uh, you’re, you’re, I wanna get your perspective as Microsoft, your perspective as Microsoft on the biggest shifts you’re seeing in defining this we’ll call AI Frontier. [00:04:54] Vince Menzione: We’re seeing right now, how should partners translate that into how they position and go to market externally? How, how do we need to think about this time? [00:05:02] Stephen Boyle: Yeah, that is, uh, that is a huge question and I’m not sure we’ve got enough time to go into the, into all of the detail. Um, so let me sort of up level it a little bit for you. [00:05:10] Stephen Boyle: And I think, look, the move that we meet at made a couple of months ago and pulling together those three aspects. Nicole had already done it in SME and C. Right. One partner organization across the world with a very common set of goals. We were working closely together, Sandy Gupta, on ISV, Jason on ai, and myself on on si. [00:05:29] Stephen Boyle: But we were still working closely together across silos. So the opportunity for me, 60 days into this role is AI just allows you to wire the partner ecosystem together differently. Right? And even if you look at how we’re going to market an AI today, um. You know, with, with, with chat GPT, with Claude, with Anthropic, um, I think there’s something like 1100 different, you know, language models on Microsoft today. [00:05:55] Stephen Boyle: So the way I think about AI is we are absolutely gonna be the ultimate platform of platforms. Yeah, choice is incredibly important. Um. It’s, it’s, you know, turn the clock back 12 months, everybody was chat gpt five point x, you know, and then six months ago it was Gemini and now it seems to be clawed. And honestly I don’t know what it’s gonna be next quarter. [00:06:15] Stephen Boyle: So the only thing I can do is offer you choice. [00:06:18] Vince Menzione: Yeah. [00:06:18] Stephen Boyle: And from a partner perspective, I think that minimizes or reduces the risk that you have betting on the Microsoft platform because you can go in a multitude of different directions. I know we’re not in Europe, but if you were in Europe and you were worried about G-G-D-P-R and Jay mentioned sovereignty, you’d probably be like lining up really closely to Misra. [00:06:37] Stephen Boyle: Yeah. And a bunch of other Europe, European partners. So wherever you are in the globe, I wanna be that platform choice. Um, and we will lead with our own first party solutions. I hope they’re not coming for me. Um. I parked safely in the hotel. It can’t be me. Um, but you weren’t vibe coding in the room. Um, but you know, wherever you are in the world, in whichever industry you are in, um, it is our intent to, to offer that platform of platforms and to give the broadest set of partners the opportunity to engage with us. [00:07:07] Vince Menzione: I think that’s really important because I, I have found, especially in the last month or two, people are, it’s almost like a knee jerk. Don’t you feel like people don’t know what to do? There’s been so much noise in the press and the media and, and the markets around open AI and anthropic especially. Where do I go? [00:07:26] Vince Menzione: Seems to be like when I, when I sit, I watch everybody in the room here. I think they’re, they’ve all been thinking that as well. So you can, [00:07:31] Stephen Boyle: there’s a, a little bit of a deer in the headlights moment. Yes. And even I like, I get that. Yeah. Um, you know, I saw, uh, Jay slides. Jay, love the presentation. Love the slides, man. [00:07:40] Stephen Boyle: I’m gonna steal several of them. Um, we’ll talk about that later. We, we [00:07:43] Vince Menzione: have the deck, [00:07:45] Stephen Boyle: but, but in all seriousness, you know, this, this is like. It’s a new paradigm. I will date myself a little bit. Some of you might heard me say this. I sold many computers in the 1980s. Mini computers. Some of you in the room are going, what’s a mini computer? [00:07:59] Stephen Boyle: Um, I sold client server for Sun Microsystems in the nineties. I sold an awful lot of Oracle databases in the Auts, I think they’re called, and I’ve done two stints with Microsoft. This is the biggest, most transformative. Iterative change in technology we’ve ever seen. What, if you wanna call it a paradigm shift or whatever word comes after paradigm shift. [00:08:18] Stephen Boyle: Um, and we are building intelligent systems at scale faster than we’ve ever seen. Scalable, mission critical solutions being implemented today inside of Microsoft and with our most important customers. So, and we can’t do it without partners, right? There is absolutely nothing we can do in this industry. I will, I will put the, you know, the elephant in the room out there. [00:08:40] Stephen Boyle: Our ISD organization has between five and 7,000 people. Our forward deployed engineering organization is about a thousand people. [00:08:47] Vince Menzione: Yeah. [00:08:48] Stephen Boyle: So when you look at the scale of the total addressable market that Jay just talked about. We are gonna service directly like this much [00:08:55] Vince Menzione: used to be 5%. Was it even, is it even that high? [00:08:58] Stephen Boyle: I doubt it’s, I doubt it’s even that. And the billions of dollars that we spend every year helping our customers transform to what we’re now calling frontier firms is gonna be, have to be driven with every single person in this room in some way, shape, or form. Judson is not asking Marla to significantly increase ISD. [00:09:15] Stephen Boyle: Not asking John to significantly increase FDE, although we probably will hire in that area just because of the, the newness and the, you know, bright shiny object that everybody’s like, oh, FDE, I’ve gotta have those. We’ve got a thousand already today that have been around in John’s organization for 10 plus years doing the things that we are doing today. [00:09:32] Stephen Boyle: But we are gonna build out that muscle. But the real way we’re gonna build out that muscle is with all of you in this room. That’s like categorical. That is my like, probably number one goal for the next one to three years is make sure that, that story that Jay just told about Microsoft not being involved in AstraZeneca. [00:09:48] Stephen Boyle: I probably won’t tell Judson that Jay, but I love the story. Um, like if you could all do that for me, like win, um, that is so, you know, from our worldwide learning, through our skilling enablement through our cloud solution architects that I personally own. We are pivoting aggressively towards making sure that the partners understand our platforms better than any other job, number one for me right now, if you don’t understand what I’m selling, like I’m kind of dead in the water obviously. [00:10:15] Stephen Boyle: Well, [00:10:15] Vince Menzione: I was gonna ask you why now? Why Microsoft? Why now? Right? Because there is a lot of noise. You know, Google just announced, you all announced your results on the same day, which was astounding. That was freaky, wasn’t it? It was. It was the first time. And the, the total commitment, customer commitment is over a trillion dollars now, I think 1.2 trillion is what I counted up. [00:10:33] Stephen Boyle: Yeah. [00:10:34] Vince Menzione: But it’s saying a lot about like, what do I do now, like as these partners in the room. Um, how, I think you kind of already, and you’ve talked about this, about differentiating where Microsoft is, I think J Slide does a lot of justice there. It says how, uh, Microsoft Partners came into the room, surrounded the customer. [00:10:52] Vince Menzione: It feels like Microsoft has always leaned in big time on partners. Uh, more so I would say than any other organization out there. What would [00:10:59] Stephen Boyle: you say Joe Roses, my chief of staff, business manager and so many other things was telling me last night that, you know, we used to say 500,000 partners. [00:11:05] Vince Menzione: Yeah, [00:11:06] Stephen Boyle: it’s a, it’s a significantly higher number than that as well. [00:11:09] Stephen Boyle: So there’s an element of, you know, back to the deer in the headlights, which partners are, are more important. One of my other phrases that I say on a regular basis, the winners and losers are yet to be decided in this next wave. Like, I want all of us to on the right side of that argument. Right? But, but it’s gonna be a challenge and, and companies are going through shifts. [00:11:28] Stephen Boyle: You know, Accenture, maybe, possibly doesn’t need 750,000 employees in the not too distant future. Maybe TCS at 600,000 doesn’t need 600,000 human employees. So we’re going through this dramatic shift of, you know, what’s the right balance going forward. What I would say about Microsoft is notwithstanding the fact that we’ve figured this out for 51 years, which is a little bit mind blowing, um, that you know, all the way back in the seventies we’ve gone through so many iterative changes. [00:11:56] Stephen Boyle: People have questioned just like they’ve questions. A lot of other technology companies, are you gonna be around for the long haul? I think we’ve proven time and time again, and I love Jay’s story. I’ve used that myself about how many companies disappear on a, on a decade to decade, you know, business. 10 years ago I had the opportunity to listen to Craig Clayton Christensen, who’s sadly no longer with us. [00:12:15] Stephen Boyle: Yeah. But you know, the books that he wrote and the story that he told to Microsoft 2014, we were nowhere in cloud. [00:12:21] Vince Menzione: Yeah. [00:12:22] Stephen Boyle: AWS was so far ahead of us, it was crazy. And he came in and he’s like. You know what? You guys need to be successful. You need to figure out how to cross this chasm again, and we’ve done it time and time again. [00:12:32] Stephen Boyle: You can go back. You know, Microsoft used to be known as a fast follower in ai. I don’t think we’re a fast follower. I think we’re right up there. We’re right at the front, but that race is still being run and the winners are losers are yet to be decided. [00:12:44] Vince Menzione: I was in that room with Clayton Christensen with you, by the way. [00:12:46] Vince Menzione: I remember, I remember that. That was at a Prism conference. [00:12:49] Stephen Boyle: Yeah. Yeah. [00:12:50] Vince Menzione: You men, you touched on this with the GSIs a little bit. How do you see the roles evolving? You know, we, we, we bucketed all, we’ve always been. Fantastic about bucketing ISVs or SDCs and sis and digital natives. Yeah. How does it, how does that all come together? [00:13:06] Vince Menzione: Does it come together any differently in this new AI platform era, or is it the same? [00:13:11] Stephen Boyle: I look, I, I’ve said this for a long time, like if you go into AstraZeneca, the six plus, you know, frontline partners, there’s probably a whole board of second, third tier that, that we don’t know about doing, you know, things across the AstraZeneca group. [00:13:25] Stephen Boyle: It takes several villages and sometimes a small town, especially in my world, in the enterprise world, strategic five hundreds. Yeah. Um, you know, we, we ran some reports a few years ago and it is shocking how many global systems integrators have a footprint in Shell or Exxon or, you know, bank of America or whatever else. [00:13:44] Stephen Boyle: So I’ve always believed that partner to partner is critical. Yeah. I think it became even more critical in the, in the AI world, and I’ll take my new friends at Anthropic. So I went to the first Anthropic partner Summit. Some of you might have been down there in, in San Diego, um, just a couple of months ago. [00:13:59] Stephen Boyle: Same partners, same people from the same partners. In the room, you know, talking about what they’re gonna do together with Anthropic. Um, and I’m looking out across this audience going, okay, well I know him and I know her and I know those guys, and like, I need to figure out how I’m gonna weave this together. [00:14:14] Stephen Boyle: So it’s not just an Accenture and Anthropic or an NTT data and anthropic, but it’s an NTT data plus anthropic plus Microsoft. Story going forward. And then who’s best at delivering those services capabilities? So it’s it at every juncture that I see in the, in the partner community, and this is the, the reason why I argued vehemently with Nick, that it has to be one organization I’m gonna create maybe given a little bit away. [00:14:40] Stephen Boyle: So if you’re recording, stop now. Um, I’m gonna create an enablement organization that is partner agnostic. I don’t necessarily care. I do care about the digital natives, but I don’t care about how I train them. Right. What I’m more important of is how do I train the digital natives in what the sis are doing, and how do I train the sis and what the ISVs Plus digital Natives are doing. [00:15:01] Vince Menzione: Yeah. [00:15:01] Stephen Boyle: That is my, that’s my game plan. If I fail there, then I think we fail to raise the bar and be differentiated in an AI world, and I’m not set up like that today. [00:15:12] Vince Menzione: I wanna, I wanna ask you, uh, uh, because I was looking at Jay’s slide and the, the managed piece is. And we have a lot of managed service providers in this room today. [00:15:20] Vince Menzione: A lot of them, by the way, come from the old school of managed services. The managed piece seems to be like, if I’m doing something today with ai, we’re gonna talk about security next, uh, up on stage here. It seems like there’s a new set of skills or a different approach to the customer, don’t you? Don’t you agree? [00:15:37] Stephen Boyle: I I [00:15:37] Vince Menzione: think you need to keep your hands on the steering wheel at all [00:15:39] Stephen Boyle: times. I think what it boils down to is you can’t do AI unless you do certain other things. [00:15:44] Vince Menzione: Yeah. [00:15:44] Stephen Boyle: Right. You could be a modern work specialist and you could make a lot of money being a modern work specialist, or you could be a, a dynamic specialist. [00:15:52] Stephen Boyle: We just held our, uh, inner A in a circle conference last last week, which I was disappointed to miss for the first time in a few years. Those, those days are, are, are fast becoming over. [00:16:03] Vince Menzione: Yeah. [00:16:04] Stephen Boyle: Um, why? Because everything that I’ve just said is tied together by ai. Yes. And in order to do good ai, you need good data. [00:16:12] Stephen Boyle: And in order to trust everything that you’re getting, as Judson talks about trust and intelligence, you need to wrap that in a really secure [00:16:19] Vince Menzione: Yes. [00:16:19] Stephen Boyle: You know, en en environment. Now we will do our best to provide levels of security into how we deliver ai. But that’s not the end of the game, right? You have to take it all, all the way to the edge. [00:16:30] Stephen Boyle: So that’s why a siloed partner or a singular commercial solution area partner in Microsoft’s terms, has got to transform its business. ’cause if you’re gonna do ai, you’ve gotta do those other things as well. [00:16:41] Vince Menzione: Agreed. I must see the model changing, and in fact, I see like bigger organizations becoming managed service providers in many respects. [00:16:48] Stephen Boyle: Yeah. Yeah. I mean, look, there’s still, there’s still a role for all the old terminology you mentioned is SV to sdc. Yeah. I’m like, I’m been around long enough. Look, it’s ANB still anv, it’s still an isv. Thank you. Independent software vendor. Um, and it’s, you know, where, where AI is allowing software to be, you know, frankly developed in a number of different places. [00:17:07] Stephen Boyle: We are all citizen developers. Um, you know, I was on a call with our internal leadership yesterday, um, and you guys might have heard this story ’cause I think it came out at Ignite. When we turn the agent 365, around and on ourselves. We found 130,000 agents running across Microsoft that had been developed and deployed internally with, I mean, you could call it shadow it. [00:17:28] Stephen Boyle: I guess that would be one phrase that you would use for it, but the reality is if you, if you haven’t got something to do your job today, you have the tools. To build it really, really fast. Um, and that, you know, that’s, that’s a great opportunity for people to be able to do their work, you know, in a better and in a different way. [00:17:45] Stephen Boyle: But it’s also a huge opportunity to make sure that data governance and security and all the other things that we need to deliver are there out of, out of the gate and out of the platform that we deliver. So security’s absolutely critical. Not saying that managed services won’t grow, um, at, at some level as well, but only if they transform into this multifaceted way. [00:18:04] Stephen Boyle: Yeah. Thinking [00:18:05] Vince Menzione: about, well, that’s what I was, I was gonna lead to here with innovating. It’s happening across, I mean, we’re talking about chips, we’re talking about foundational models, LLMs, we’re talking about applications, we’re talking about agents. How should we think about where to play and how to differentiate as partners in this room? [00:18:22] Stephen Boyle: I think. [00:18:25] Stephen Boyle: So look, I mean, one, one of the ways that Judson talks about it is I think silicon’s gonna change over time. Yes. NVIDIA’s definitely the 800 pound gorilla, maybe the 8,000 pound gorilla. Yeah. Uh, but you know, if you read the press, there’s, there’s things happening in, in different places as first party silicon, which we clearly are, are developing, um, in a quantum direction for sure. [00:18:45] Stephen Boyle: Um, there’s lots of different language models that haven’t even been launched on, on, on the marketplace yet, so. You know, Judson’s trying to uplevel our conversations. You’ll hear us talking about conversations more and more as we go into FY 27, um, that obviate all of those layers. Just like even when I was selling Sun Microsystems, it was about the business outcome and the business solution that we were solving for not necessarily the fastest piece of hardware or the best client service solution on, on the market. [00:19:17] Stephen Boyle: So I think what’s gonna happen over the next 12 to 24 months is we’ll have so many different models to choose from. We’ll have more silicon to choose from, but those won’t be the real buying decisions. The real buying decisions of what? How am I trying to transform my finance organization, my HR organization, and my supply chain? [00:19:36] Stephen Boyle: Because the underlying technology, Judson says commodity I, I guess I can go with that. It will be commoditized and we’ll really start to focus back on what the important things are. We’re moving a lot from pilot to production. You guys have probably seen that. The numbers that Jay just showed about how many. [00:19:52] Stephen Boyle: Projects are failing, is getting less and less because we’re getting smarter and smarter about what it takes to actually drive the business outcome. And I need all of us to be talking that same language. Yeah. Having conversations with head of HR about how we’re gonna transform human capital management in the, in the age of agents, if you like, like the underlying platform. [00:20:14] Stephen Boyle: It’s not, don’t worry about it. You wanna be on a secure platform. Don’t get me wrong. But at the same time, I don’t think we, we spent too much time worrying about that. [00:20:21] Vince Menzione: Yeah. We’re not, what you’re saying is we’re not spending enough time on outcomes. On the business outcomes. Right. And that’s where we need to focus. [00:20:27] Vince Menzione: We’re, we’re focusing on, I, I feel like we’re, it’s a signal to, to noise ratio that we’re living through right now. There’s too much noise. [00:20:33] Stephen Boyle: Yeah. [00:20:34] Vince Menzione: And we’re not focusing on the signal. I think that’s what you’re saying. [00:20:36] Stephen Boyle: I, it’s got to be, I mean, to be honest with you, it’s always been, you know, even when I sold what I would perceive, you know, sun in the nineties was a rockman ship to the stars and, you know, kind of sad what happened to that company. [00:20:47] Stephen Boyle: Um, but we, we were, we were fixated on, we had the best client server. But, but nobody was buying, you know, a piece of Sun hardware as a room heater, which is all it did, you know, like for the longest. But if you had SAP, if you had Cybase, if you had Bond, remember Bond, I mean all of those applications that drove the business outcomes, we’ve gotta get back to that kind of mentality. [00:21:09] Stephen Boyle: Yes. And worrying a little bit less about the underlying architecture. Yeah. It needs to be, it needs to be part of the conversation. ’cause it needs to deliver trust and security and intelligence and everything else. Then you need to rapidly move to what are you trying to achieve and how can we ensure the, the, the success of, of your business outcome. [00:21:27] Stephen Boyle: And look, I mean, Palantir pri you know, sort of came out and said, well, the way we do that is through forward deployed engineering. Um, and they stole the show. And, and, you know, they’re, they’re doing very well as a result of doing that. Uh, but if you go and talk to, um, Tom Siebel’s organization at C3 ai. [00:21:43] Stephen Boyle: They’ve had FDS for quite a while. You know, I told you about John Chuchu 10 years ago. John Chu, Chuck’s job was to go and get all the applications that we needed on the Microsoft phone. Remember that? [00:21:54] Vince Menzione: Yes. Um, [00:21:55] Stephen Boyle: you know, so we’ve pivoted John o over the years to doing what he’s doing now, which is to go sometimes in partnership with, with partners into the customer and say, what is it you’re trying to achieve? [00:22:05] Stephen Boyle: Let me show you how I can build that for you in three weeks or three months. That might have taken you three years. We literally just did a hackathon with one partner last, last, last week with, uh, with our ISE organization, the, the, the forward deployed, uh, group that John runs. Um, and one of the big customers said, I’ve just done in three days what would’ve taken me three months. [00:22:26] Stephen Boyle: Now he hasn’t productized it and rolled it out and blah, blah, blah. But the reality is that is how fast things are changing. And this was not a small company. This was a very, very large oil company, and they were like blown away by how much we can achieve. We’ve gotta do that at scale. [00:22:41] Vince Menzione: Yeah. [00:22:42] Stephen Boyle: You know, we, we have a commitment to scale our FDE community through partnerships to touch all of the S 500 in a very personalized way. [00:22:51] Stephen Boyle: And then, you know, at a slightly, you know, lower ratios down through the, through the majors and into, into Nicole’s SME and C world as well. [00:22:59] Vince Menzione: Jay talks about the decade of the ecosystem. He coined that term back, back on a podcast way back in nine, in, uh, in 2020. Microsoft has been at the, for, we used to call partner to partner back, back in the day. [00:23:10] Vince Menzione: Mm-hmm. Do you remember those days? How do you think about this ecosystem evolving and what steps are you taking to help bring these organizations together? Because I, I, again, we look at the seven seats or 6.3 seats at the table. The customer has the power now that they didn’t have before. ’cause they have the commitment with like with Microsoft and they can buy off of the marketplace and pull together multiple organizations to go, go do that. [00:23:34] Vince Menzione: How do you think about helping to orchestrate that as the leader of the enterprise partner business? [00:23:39] Stephen Boyle: So I’ll start with a really big example, and I’ll try and sort of scale it down a little bit. But my friends at Accenture, with the Accenture, Microsoft Business Group, we spend an awful lot of time, you know, in, in each other’s pockets, in each other’s deals. [00:23:51] Stephen Boyle: We know everything that’s going on in the Accenture, Microsoft Business Group. And a couple of weeks, or maybe a month or so ago, I was told that the Microsoft Business Group is now larger than the SAP Business group. It probably flip flops. [00:24:03] Vince Menzione: Yeah, [00:24:04] Stephen Boyle: it won’t be too long before the Anthropic Business Group is bigger than both of those. [00:24:08] Stephen Boyle: So what I need my Microsoft team to do is to not spend all of their lives in the. A MBG, the Azure, the Accenture, Microsoft Business group, but to go make friends in the Anthropic Accenture Business group and frankly still to make friends in the SAP business group and maybe in the Oracle Business Group and the list goes on. [00:24:27] Stephen Boyle: So at a macro 11, in the very largest accounts where we haven multiple practices, where we haven’t spent time before, I’m gonna. Push my people into uncomfortable zones and I’m gonna push them to go into those other areas and I’m gonna load them up with technical talent and cloud solution architects and ai, you know, forward deployed engineers. [00:24:45] Stephen Boyle: And I’m gonna force different people to talk together that haven’t talked together. So I can do that in TCS. I can do that, Capgemini, I can do that. Um, you know, in Europe with Capgemini and Misra is a classic example. Um, with the, with the Indian sis, Indian based sis, they’re all big enough where I know all the practices exist. [00:25:04] Stephen Boyle: I just need to do a better job of, of talking to them. Now, when you downsize that into, you know, into a, a company that doesn’t have all of that scale, this the same truth still holds. I need to talk to people who aren’t necessarily motivated every single day to do something with Microsoft. I need to talk to people who are motivated to do something with an AI partner or even a traditional SaaS partner. [00:25:27] Stephen Boyle: I noticed yesterday, actually no, this morning I got a notification that we just passed, um, a billion dollars in revenue on the marketplace with ServiceNow. [00:25:35] Vince Menzione: Nice. [00:25:36] Stephen Boyle: Um, and I think AWS announced the same thing, by the way this month as well. Um, so thank you to the ServiceNow people. Yeah. Um, you know, that is that there’s a tremendous demonstration of how far we’ve come in marketplace. [00:25:48] Stephen Boyle: ’cause that’s another one where we trailed AWS quite significantly. But with the right partnerships. And driving the right motions, we can, you know, we can definitely catch up and we will continue to pass, uh, some of, some of the other hyperscalers in, in, in that way. So really the bottom line to your question is partner to partner is still real. [00:26:08] Vince Menzione: Yeah, [00:26:08] Stephen Boyle: how we do it and what we use to tie things together. And I know that compensation drives behavior and we’re not gonna get into a compensation about like how we get compensated and everything else, but the reality is I’ve gotta break down those barriers and those silos and I’ve gotta deliver real meaningful enablement and practice development so that, so that the people who sit in the Anthropic business group and the people who sit in the Microsoft Business Group are spending as much time together as they are with me. [00:26:34] Stephen Boyle: That makes sense. Simply put, that’s what I, I need to achieve at scale rapidly. [00:26:40] Vince Menzione: So to, we’re getting close to time here, but as you look forward, what would define the most successful partnerships in this ecosystem? Is it, is it what you described, the opening up the aperture or for the, for the leaders in the room here today, what should they go do better and differently? [00:26:58] Stephen Boyle: Um, so obviously we’re closing out this fiscal, we’ve got Microsoft start and Microsoft start for partners coming up in July. Um, I mentioned the fact that we’re, we’re driving. Cu customer engagement through the lens of conversations and how do we achieve business outcomes? I would encourage you to, to gravitate, if you like, above the commercial solution areas where you might have understood, this is how I interact with Microsoft today. [00:27:23] Stephen Boyle: Um, and abstract it up to that AI layer. You know, think about trust, think about intelligence, think about business outcomes, and how do I potentially weave together a story? If I’m in the dynamic space, how do I get better in data? If I’m in the data space, how do I get better in. In that modern work environment, but really use AI as the overlay to, to help tie that together. [00:27:44] Stephen Boyle: That’s one thing. The second thing is if we’re not training you in the right direction, it’s stevenBoyle@microsoft.com. Let me know. Awesome. Um, we’ve got programmatic stuff, um, you know, and we’ve got high touch stuff as well. So I think this is, this is another time where Microsoft is gonna over pivot on all of the training and enablement that we need to do to make sure that you’re, you know, you’re grounded in our platform. [00:28:07] Stephen Boyle: Um, I think there’s a huge opportunity with this agenda future to become more of a software partner. You know, even the deepest services organizations are going to need agents, and the more successful ones will be the ones that can turn on those agents in a repeatable way. So. Our agents, the new SaaS. I’m not exactly saying that, but I think that the agen future is one where even the more services oriented companies will, will have teams of agents that they’re deploying. [00:28:35] Stephen Boyle: In fact, I had a very, very large systems integrator, um, in, in the EBC just about a month ago, three weeks ago. Um, and I was sat next to their head of consulting and he showed me what he called his God dashboard. Uh, and right in the middle of his God dashboard there are like 450 accounts. All of whom I recognized, ’cause they were all in the enterprise, right in the middle of his dashboard was, how many tokens am I spending? [00:29:00] Vince Menzione: Yeah. [00:29:01] Stephen Boyle: Like, not like what’s my daily runway? You know, not am I making a profit on that account or anything else like that is like, how many tokens have I consumed? Yeah. Because there is an awful lot of, that is the new juice, if you like. That’s, that’s driving the success. You can have the smartest people on the planet, but you’ve got to still arm them with all the best tools that are available out there. [00:29:22] Stephen Boyle: So it’s fascinating to listen to him, how he had gone through that thing of, you know, agent sprawl, how many are really working, how many are not working? How can we prove that? You can prove it through, you know, managing your tokens. There’s a new version of. Finops for tokens, for want of a better phrase, that’s gonna be critical for us all to understand. [00:29:40] Stephen Boyle: ’cause they’re not cheap, they’re not free, that’s for sure. And, and they might not be cheap if you’re not, if you’re not managing them and using them effectively. Yeah. So that’s the other thing that I would really get on top of. And, you know, we’re gonna make some announcements in the not too distant future about the consumption driven future. [00:29:56] Stephen Boyle: Um, that, that we will, that we will deliver with our first party and third party platforms going forward. So that’s another. Another critical thing [00:30:03] Vince Menzione: sounds like some exciting announcements. Pretty soon. [00:30:06] Stephen Boyle: Yeah, could look close. Quarter four, help me close. Quarter four. Yes. That’s priority number one, two, and three right now. [00:30:12] Stephen Boyle: Uh, but get ready for some, you know, for some new announcements in July. Um, look, the future is incredibly bright with Microsoft. It’s incredibly bright in the industry as a whole, right? I mean, let, let’s be honest, the, the growth targets that we will have for ne next year are astronomical, and we will not make them without the partner community that we have, without training and enabling the partner community that we need for tomorrow. [00:30:34] Stephen Boyle: So like, stay close, you know, stay engaged. Talk to your partner development managers, talk to the talk to field reps, talk to the accounts that that, that you are in, and stay as close as you possibly can to our emerging strategy. And, um, you know, look, I, I think if I had fivefold or tenfold the people I have today, I still wouldn’t be able to touch everybody that I would like to touch in the partner community. [00:30:58] Stephen Boyle: So I’ll apologize in advance. Um, but we’re gonna have some, you know, some really cool ways of learning. Um, and we’re gonna make sure that they’re available to the widest possible audience. [00:31:07] Vince Menzione: Well, we bring the practitioners and the experts in the room to help with that as well. Right? Yeah. Because you can’t always have a partner development manager tied to everybody in the room. [00:31:14] Stephen Boyle: I, I would do hackathons on AI every week with every partner and every part of the world, but I can’t. [00:31:19] Vince Menzione: Yeah, exactly. Well, so good to have you today. Thank you. So good to see you again. I don’t know what your schedule is like. I, we didn’t, we don’t have enough time for questions. [00:31:28] Stephen Boyle: That’s cool. [00:31:28] Vince Menzione: From the audience. [00:31:29] Stephen Boyle: I’m gonna stay around for a little [00:31:30] Vince Menzione: while this [00:31:30] Stephen Boyle: morning and I’m coming back [00:31:31] Vince Menzione: for cocktails. Alright, terrific. So. Stephen Boyle will be here for cocktail hour. Thank you. Four 30 and uh, I wanna thank you, sir. So good to have you. Thank you. Good to see you. Absolutely. [00:31:42] Stephen Boyle: So much. Absolutely. Hey, thanks everybody. [00:31:43] Stephen Boyle: Thanks for what you do today, and hopefully thank you for what you do tomorrow as well. [00:31:46] Vince Menzione: Thank you. An incredible leader. [00:31:49] Stephen Boyle: Don’t forget, ultimate [00:31:51] Vince Menzione: partner Alive is coming soon, June 18th at our executive breakfast in New York. I hope to see you there.Description The Future of Tech is Here. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ I

JPMorgan Asset Management sees a resilient economy facing multiple supply shocks, with inflation still largely supply-led and the Federal Reserve likely to remain on hold for now. Priya Misra, fixed-income portfolio manager at the firm and a manager of the JPMorgan Core Plus Bond ETF (JCPB Equity), joins Ira Jersey, Bloomberg Intelligence chief US interest-rate strategist, on this Macro Matters edition of the FICC Focus podcast to explain how she defines the “plus” in core-plus investing, from macro duration and curve views to allocations across securitized credit, high yield and mortgage convexity management. She also discusses what Kevin Warsh's arrival as Fed chair could mean for communication policy and the dot plot, arguing that investors still need enough Fed transparency to understand each official's reaction function. The two examine where she sees value across fixed income, including high-quality spread product, duration in the five- to 10-year sector as a hedge and select structured-credit opportunities such as agency CMBS and non-agency mortgage exposure over parts of the agency RMBS market. The Macro Matters podcast is part of BI's FICC Focus series.

Was Pinocchio's Pleasure Island merely a children's story—or was it inspired by deeper currents flowing through the hidden history of Italy?In the final Part 4 of the Occult Mafia series, Joel Thomas follows the trail from Giuseppe Mazzini's revolutionary networks into the world of Egyptian Freemasonry, the Memphis-Misraïm Rite, and the rise of the criminal brotherhoods that would eventually establish themselves in the United States.This episode explores the connections between Memphis-Misraïm Masonry, Giuseppe Garibaldi, Young Italy, New Orleans, Tammany Hall, the Palermo Brotherhood, and the emergence of the American Mafia. Along the way, Joel examines the symbolism of Pleasure Island, the transformation of boys into donkeys within the Pinocchio story, and how these themes may mirror systems of initiation, manipulation, and social engineering hidden beneath the surface of modern history. Merchandise: https://freetherabbits.myshopify.comBuy Me A Coffee: DonateFollow: Website | Instagram | X | FacebookWatch: YouTube | RumbleMusic: YouTube | Spotify | Apple Music Films: https://merkelfilms.com Email: freetherabbitspodcast@gmail.comDistributed by: merkel.mediaIntro Music:Joel Thomas – Free The RabbitsYouTube | Spotify | Apple MusicOutro Music:Joel Thomas – GreyYouTube | Spotify | Apple MusicTopics Discussed:Occult Mafia, Pinocchio's Pleasure Island, Memphis Misraim, Egyptian Freemasonry, Giuseppe Mazzini, Giuseppe Garibaldi, Young Italy, Carbonari, American Mafia, Sicilian Mafia, New Orleans Mafia, Joseph Macheca, Tammany Hall, Ndrangheta, Secret Societies, Hermeticism, Hermes Trismegistus, Aleister Crowley, Hidden History, Conspiracy History, Free The Rabbits

In this episode of The Daily Lawyer, we sit down with Dr. Santrupt Misra, a rare polymath who has seamlessly navigated the high-levels of academia, business and Indian politics. Dr. Misra, who holds three PhDs and a D.Sc., shares his incredible journey from growing up in Odisha to leading major businesses for the Aditya Birla Group and now serving as a Member of Parliament. We dive deep into the philosophy of leadership, the "moral compact" between employers and employees, and why he believes statesmanship is about more than just electioneering. Dr. Misra also provides a nuanced look at the future of Artificial Intelligence in education and healthcare, and the importance of maintaining human empathy in an increasingly digital world. Special thanks to our partner Zoho Sign, a digital e-signature platform helping Indian businesses streamline workflows and digitize paperwork. If your business deals with a high volume of contracts and legal documentation, Zoho Sign is built to simplify and accelerate the process. If you enjoy conversations with top business leaders, law-makers, in-house counsels, law firm partners, legal entrepreneurs, and others from the legal ecosystem, subscribe to The Daily LawyerDrop your thoughts, questions, and takeaways in the comments. #SantruptMisra #TheDailyLawyer #Leadership #HRLeadership #CorporateCulture #NationBuilding #Odisha #DigitalTransformation #PublicService #CivicDuty #CorporateGovernance #RajyaSabha #Parliament

Part 2 for Members - www.parallelmike.com Substack & Newsletter – https://substack.com/@parallelmike Consult with Mike 1-2-1 - www.parallelmike.com/consultation Guest Links: Website: https://www.bibhudevmisra.com/ Book: Yuga Shift by Bibhu Dev Misra

Join us for an inspiring episode of The Brand Called You featuring Sandeep Misra, Co-Founder and Chief Growth Officer of Heald. In this deep-dive conversation, Sandeep shares powerful insights on revolutionizing diabetes management through a unique blend of technology and human-led support. Discover the misconceptions around diabetes, the importance of behavioral change, and how Heald stands apart with its bold claim to reverse diabetes — or your money back. Learn about the role of AI and predictive analytics in modern healthcare and gain actionable habits to help prevent or reverse diabetes.

Toni Sant presents the 771st in a series of podcasts featuring music by performers in or from Malta. Artists featured in this podcast: PART 1Sean Borg - Sabiħ UkollTara Formosa - Sky's the LimitMotherknives - OthersideAndre Camilleri - I Wish I Was Stoned TodayMegan May - HomePART 2Carlo Gerada feat. Kristina Casolani - Never Let Go (2013)Funk Initiative - The Furthest From Home That I've Ever Been (2016)Chris Tanti & Cheryl Balzan - Sweet SisterStefanos - Navigate (2012)Mistura - Misraħ il-ĦelsienKantilena -X'ubidú?Fredu Abela l-Bamboċċu - Tal-Life (1973)PART 3Featured album: That's About It by Carlo Muscat >> Details about this podcast [in Maltese] See also: - MMI Podcast: YouTube playlist - MMI Podcast: Facebook Page - MMI Archive on Mixcloud | @tonisant on Twitter - M3P: Malta Music Memory Project - Mużika Mod Ieħor ma' Toni Sant on Facebook (MP3)

In this episode of Best in Fest, host Leslie LaPage sits down with Aarti Misra — producer, business strategist, and global dealmaker working at the intersection of film, technology, and finance.With over 20 years of experience spanning Silicon Valley startups, film production, and international co-productions, Aarti breaks down how the entertainment industry is evolving—and what filmmakers must understand to stay competitive in an AI-driven, globally distributed marketplace.

According to the International Diabetes Federation (IDF) 2024 data, India has 89.8 million adults living with diabetes. More than any other country in the world. Yet most people still do not understand what diabetes actually is, how it develops, or what they can do to prevent or manage it.This week on Run with Fitpage, Vikas sits down with one of India's foremost authorities on the subject.Dr. Anoop Misra is the Chairman of Fortis C-DOC Centre of Excellence for Diabetes, Metabolic Diseases and Endocrinology in New Delhi, and a former Honorary Physician to the Prime Minister of India. A Padma Shri awardee and recipient of the Dr. B.C. Roy Award — India's highest medical honour — he has spent over 45 years studying what diabetes does to the Indian body and why Indians are uniquely vulnerable to it at lower body weights than the rest of the world.In this episode, the conversation starts from the very beginning — what blood sugar actually is, how insulin works, and why the body moves from healthy to pre-diabetic to diabetic over years without sending obvious signals. Dr. Misra explains why Indians develop diabetes at a BMI of 23 to 25 when Western guidelines only flag risk at 30, and why being slim does not mean being safe.Read more from his research here: Google ScholarDr. Anoop Misra's Books:Diabetes with Delight (English): AmazonDiabetes Ke Saath Bhi Khushaal Jeevan (Hindi): AmazonIn this episode we covered :→ What blood sugar actually is and why the body needs it→ How insulin works — and what goes wrong when it stops working→ The difference between Type 1 and Type 2 diabetes, and why insulin is not the enemy→ Why weight is the thick tree and blood sugar is just one of its branches→ The real cost of ignoring diabetes — from vision loss to kidney failure→ Why grip strength is as important as blood pressure and should be treated as a vital sign→ How to start managing diabetes or pre-diabetes from today — diet, exercise, and disciplineAbout Vikas Singh:Vikas Singh, an MBA from Chicago Booth, worked at Goldman Sachs, Morgan Stanley, APGlobale, and Reliance before coming up with the idea of democratizing fitness knowledge and helping beginners get on a fitness journey. Vikas is an avid long-distance runner, building fitpage to help people learn, train, and move better.For more information on Vikas, or to leave any feedback and requests, you can reach out to him via the channels below:Instagram: @vikas_singhhLinkedIn: Vikas SinghTwitter: @vikashsingh101Subscribe To Our Newsletter For Weekly Nuggets of Knowledge!

Joining me once again today is Bibhu Dev Misra, author of Yuga Shift, here to follow up on the conversation from our last interview and discuss what has happened since with the Trump administration and whether these tumultuous times are just coincidental or exactly what Bibhu predicted would be taking place. We discuss the general idea of the Yuga Cycle and what this means in terms of scientifically-documented periodic global cataclysms, as well as how the simple belief/knowledge that such events could take place can lead to actions exactly as we are seeing today. We also discuss where this may lead, both positive (the inevitable times of peace that may follow) and negative (the very technocratic future many are currently warning about) and what we can do to influence that change.  !function(r,u,m,b,l,e){r._Rumble=b,r[b]||(r[b]=function(){(r[b]._=r[b]._||[]).push(arguments);if(r[b]._.length==1){l=u.createElement(m),e=u.getElementsByTagName(m)[0],l.async=1,l.src="https://rumble.com/embedJS/u2q643"+(arguments[1].video?'.'+arguments[1].video:'')+"/?url="+encodeURIComponent(location.href)+"&args="+encodeURIComponent(JSON.stringify([].slice.apply(arguments))),e.parentNode.insertBefore(l,e)}})}(window, document, "script", "Rumble");   Rumble("play", {"video":"v76xyds","div":"rumble_v76xyds"}); Source Links: Bibhu Dev Misra Interview - The End Of The Kali Yuga (March 21st) & The Revolution Of Consciousness One year of Ekpyrosis, and already the World is on Fire! What comes next? - Ancient Inquiries Four Signs that the Yuga Shift is already underway - Ancient Inquiries YUGA SHIFT: THE END OF THE KALI YUGA & THE IMPENDING PLANETARY TRANSFORMATION by Bibhu Dev Misra | Goodreads DARPA's "Generative Optogenetics" Program Is All That We've Feared & Held Hostage By Geoengineering The Technocratic Tiptoe - The Last American Vagabond Pronomos Capital & The Rapid Transition To A Techno-Feudal State The Network State Coup And The Engineered Transition To "Tech Zionism" End of the World – Eleusis TV The Impending Future Of AI-Government - But Who Controls The AI? Trump's Leaked AI Government Plans, Trump Mobile & The Expected False Flag To Justify Iran War   Bitcoin Donations Are Appreciated: www.thelastamericanvagabond.com/bitcoin-donation (3FSozj9gQ1UniHvEiRmkPnXzHSVMc68U9f)

En el episodio 103 del podcast de Entre Dev y Ops hablaremos con Esteve Fernández de robótica y software libre. Blog Entre Dev y Ops - https://www.entredevyops.es Telegram Entre Dev y Ops - https://t.me/entredevyops Twitter Entre Dev y Ops - https://twitter.com/entredevyops LinkedIn Entre Dev y Ops - https://www.linkedin.com/company/entredevyops/ Patreon Entre Dev y Ops - https://www.patreon.com/edyo Amazon Entre Dev y Ops - https://amzn.to/2HrlmRw Enlaces comentados: TIER IV - https://tier4.jp Autoware - https://autoware.org/ Autoware en GitHub - https://github.com/autowarefoundation/autoware Open source robotics foundation - https://www.openrobotics.org/ ROS (Robot Operating System) - https://ros.org The Boost C++ Libraries - https://www.boost.org/ Google Summer of Code - https://summerofcode.withgoogle.com/ Charla Fosdem de Esteve - https://fosdem.org/2026/schedule/event/J8ZLKG-introducing_rclrs_the_official_ros_2_client_library_for_rust/  The ASF (Apache Software Foundation) - https://apache.org/ Apache Thrift - https://thrift.apache.org/ TurtleBot - https://www.turtlebot.com/ The Construct - https://www.theconstruct.ai/ OpenCode - https://opencode.ai/ GitHub Copilot - https://github.com/features/copilot Gazebo - https://gazebosim.org/ Carla - https://carla.org/ Unreal Engine - https://www.unrealengine.com/ Bullet - https://pybullet.org/ MISRA - https://misra.org.uk/ MISRA y Rust - https://github.com/PolySync/misra-rust Perfil de Esteve en LinkedIn - https://www.linkedin.com/in/estevefernandez/ Perfil de Esteve en GitHub - https://github.com/esteve Email de la ASF de Esteve - esteve@apache.org

Bernhard speaks with researcher and author Bibhu Dev Misra, author of Yuga Shift: The End of the Kali Yuga and the Impending Planetary Transformation, about the Yuga Cycle, the end of the Kali Yuga, and the larger planetary transformation now underway.

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

Tell us your story - who are you, what makes you different, and what sets your journey apart? We want to hear from the people who make our community what it is. Whether it's your favourite songs, meaningful milestones, or the moments that shaped you, come and share them live on air. Today, we're excited to welcome Shyam Misra into the studio to share his journey and the special stories that have shaped his path. If you'd like to be part of this inspiring new segment, give us a shout at info@lifeandstyle.fm and let your voice be heard! Radio Life & Style on Facebook

Vishal Misra returns to explain his latest research on how LLMs actually work under the hood. He walks through experiments showing that transformers update their predictions in a precise, mathematically predictable way as they process new information, explains why this still doesn't mean they're conscious, and describes what's actually required for AGI: the ability to keep learning after training and the move from pattern matching to understanding cause and effect.   Resources: Follow Vishal Misra on X: https://x.com/vishalmisra  Follow Martin Casado on X: https://x.com/martin_casado   Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Vishal Misra returns to explain his latest research on how LLMs actually work under the hood. He walks through experiments showing that transformers update their predictions in a precise, mathematically predictable way as they process new information, explains why this still doesn't mean they're conscious, and describes what's actually required for AGI: the ability to keep learning after training and the move from pattern matching to understanding cause and effect.   Resources: Follow Vishal Misra on X: https://x.com/vishalmisra  Follow Martin Casado on X: https://x.com/martin_casado   Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Discover how to transform stress into strength with insights from neuroscience and AI. This episode explores how resilience can be developed and measured, offering practical strategies for enhancing mental health and productivity. WONE's Reeva Misra joins Jess and Jason as to discuss the science of neuroplasticity and the role of AI coaches in revolutionizing workplace well-being.

In this episode of Taking Healthcare by Storm, Quality Insights Medical Director Dr. Jean Storm speaks with Anuruddh Kumar Misra, MD, FACP, FAMSSM, QME, DIP ABLM, a triple board-certified physician and non-operative sports medicine specialist, and Anuruddh Mishra, founder of August AI.Dr. Misra and Anuruddh share their journeys into medicine and technology while exploring the evolving role of AI in healthcare, its benefits, limitations, and future impact, as well as the importance of balancing innovation with human empathy and clinical judgment in patient care. If you have any topics or guests you'd like to see on future episodes, reach out to us on our website.The views and opinions expressed by the host and guests are their own and do not necessarily reflect the views, positions, or policies of Quality Insights. Publication number QI-020626-GK

Speed isn't just a nice-to-have - it affects user experience, cloud costs, and how fast teams can move. In this episode, we chat with Saurabh Misra about making Python performance a continuous habit rather than a last-minute clean-up. He introduces Codeflash, a tool that profiles real code paths, explores optimisation options with LLMs, and only suggests changes that preserve behaviour and deliver measurable speedups.We delve into how this works, from tracing and line-level profiling to coverage-guided inputs and concolic testing. Saurabh shares real examples, including smarter NumPy usage, avoiding unnecessary global sorts, and using Numba to speed up numeric hotspots. We also talk about fitting performance checks into everyday workflows via the CLI, VS Code, and GitHub Actions.The big takeaway: performance doesn't have to slow teams down — with the right tooling, it can be part of shipping well from day one.Connect with Saurabh at https://www.linkedin.com/in/saurabh-misra/ and find out more about Codeflash via the website https://www.codeflash.ai/.___

Subscribe to Dostcast Clips:https://www.youtube.com/@dostcastclips?sub_confirmation=1Listen to Dostcast on Spotify: https://open.spotify.com/show/70vrbHeSvrcXyOeISTyBSy?si=be05dbdd564245d9Join the Dostcast Janta Party on WhatsApp for regular updates: https://whatsapp.com/channel/0029VbAZwo5D8SDs5kf94N3TWant to suggest a guest?Fill this form: https://docs.google.com/forms/d/1ft_-1QDs7XpsSWnaPOeF21yUlhk9bzKvwHSyh4hHfBU/edit?usp=drivesdk====================================================================Timestamps 0:00 The Mounjaro Experience15:50 How Mounjaro Works23:55 What Are Peptides?28:32 Protein Response in Black Populations31:25 Side Effects of Mouth Breathing37:13 Average Protein Intake in India48:50 Why Eggs Are Great52:00 What Determines Penis Size?1:03:00 Will We Relish Food Less?1:09:15 Indians Becoming Immune to Antibiotics1:14:50 Why Nutritionists Are Celebrated1:17:00 We Need Stories to Believe1:24:00 Exercise is the Best Medicine1:33:40 Red Light Therapy1:35:00 Saunas & Nakedness1:46:00 Shame & Repression2:05:20 The "Red-Pilled" Society2:11:50 The #ProudR*ndi Movement2:13:31 India vs. America: Key Differences2:23:00 Conclusion====================================================================Vinamre Kasanaa is a writer at heart, podcaster and entrepreneur by craft.He spends a significant part of his time reading and researching.With over 500 podcasts under his belt, he's interviewed everyone—from HNIs and industry leaders to everyday superheroes.Follow Vinamre:LinkedIn: https://www.linkedin.com/in/vinamre-kasanaa-b8524496/Instagram: https://www.instagram.com/vinamrekasanaa/Twitter: https://twitter.com/VinamreKasanaaDostcast: Instagram: https://www.instagram.com/dostcast/Twitter: https://twitter.com/dostcast====================================================================Contact Us:For business inquiries: dostcast@egiplay.com

Bibhu Dev Misra's website: https://www.bibhudevmisra.com/ His article on the Galactic Center is: "Supermassive Black Hole or Galactic Consciousness?" can be accessed through this link: https://www.academia.edu/145102888/Su... Bibhu's book: Yuga Shift - https://www.amazon.com/YUGA-SHIFT-IMP... Heather's website: https://www.risingmoonhealingcenter.com/ To become a patron of Heather:   / heatherensworth  

Rohini Bajekal describes how she had years of receiving inappropriate lifestyle advice before Glucokinase MODY was diagnosed.  Dr Shivani Misra's research has shown that failure to diagnose monogenic diabetes is even common in South Asians than in the white European population.Send us a text

From GPT-1 to GPT-5, LLMs have made tremendous progress in modeling human language. But can they go beyond that to make new discoveries and move the needle on scientific progress?We sat down with distinguished Columbia CS professor Vishal Misra to discuss this, plus why chain-of-thought reasoning works so well, what real AGI would look like, and what actually causes hallucinations. Resources:Follow Dr. Misra on X: https://x.com/vishalmisraFollow Martin on X: https://x.com/martin_casado Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Building Platform Products That Scale — Without Drowning in Stakeholders Platform products are some of the most complex beasts in product management. They have to serve multiple teams, stay flexible, and scale across the org—all while keeping technical and business needs in sync. In this episode, Rina Alexin chats with Aindra Misra, Director of Product Management for AI, Data, and Developer Experience at BILL (and former Twitter PM), about how to bring structure to platform chaos, build for scale, and win over even the loudest stakeholders.Key Topics Discussed in This EpisodeWhat Makes a Great Platform PMWhy platform product management is the sweet spot for technically curious PMs—and how mindset matters more than your coding background. The Art (and Science) of PrioritizationAindra's framework for balancing competing use cases, weighing business impact, and keeping the long-term platform vision intact. Stakeholder Alignment Without the DramaHow to turn “fight for priority” meetings into data-driven discussions that build stronger teams and better platforms. Why Listen to This Episode?What will you get out of this discussion? In this thought-provoking conversation, you'll gain: A framework for prioritizing platform features by impact, effort, and strategic value Real talk on managing competing stakeholders (and surviving to tell the tale) Insight into “horizontal thinking” and why it's key to scalable platforms Lessons from Twitter and BILL on how to balance speed, flexibility, and tech debt If you've ever tried to scale a platform product (or want to move into this space) this is your playbook. Related ResourcesCheck out these additional tools and resources to add to your PM belt:Productside Resource Library More Productside Stories Podcast Episodes Explore Productside Courses 

Learn of the aspirations and healing power of eco-folk art and poetry through a conversation with Neha Misra. Her words will take you to another dimension of love and artistry. 

Artist, poet, and climate justice advocate Neha Misra joins The Truth In This Art podcast. Misra shares how growing up in India, studying physics, and her cultural roots shape her creative work. Misra's art blends visual art, poetry, and climate justice activism. Misra discusses how reclaiming creativity helped her navigate periods of loss and anxiety. Misra shares her belief in art as an essential force for healing, resilience, and connection. She talks about finding inspiration in nature and using art bring people together and inspire hope. Misra shares her experience sharing vulnerable work and why creativity matters for everyone. Topics Covered:How Indian heritage, physics, and environmental activism converge in Misra's artThe role of creativity and imagination in healing from loss and building communityUsing poetry, painting, and storytelling as tools for advocacy and connectionArt as medicine—for the self and society—in confronting climate and social crisesThe power of circles, collective stories, and shared vulnerabilityReclaiming and redefining creativity against systems of extraction and oppressionThe ongoing journey toward hope, resilience, and a more connected worldReady to experience Neha Misra's creative world? Visit www.nehamisrastudio.com or follow @nehamisrastudio on Instagram. Host: Rob LeeMusic: Original music by Daniel Alexis Music with additional music from Chipzard and TeTresSeis. Production:Produced by Rob Lee & Daniel AlexisEdited by Daniel AlexisShow Notes courtesy of Rob Lee and TransistorPhotos:Rob Lee photos by Vicente Martin for The Truth In This Art and Contrarian Aquarian Media.Guest photos courtesy of the guest, unless otherwise noted.Support the podcast The Truth In This Art Podcast Fractured Atlas (Fundraising): https://www.fracturedatlas.orgThe Truth In This Art Podcast Bluesky: https://bsky.app/profile/thetruthinthisart.bsky.socialThe Truth In This Art Podcast Instagram: https://www.instagram.com/truthinthisart/?hl=enThe Truth In This Art Podcast Website: https://www.thetruthinthisart.com/The Truth In This Art Podcast Shop: Merch from Redbubble ★ Support this podcast ★

Send us a textIn this episode of the Inside Data Centre Podcast, Andy Davis is joined by Ash Gupta and Dave Misra, Co-Founders of Galaxy Data Centres.They share their journeys into the sector, the founding story of Galaxy, and how the data centre landscape is shifting in response to AI and growing power demands.The conversation dives deep into energy efficiency, microgrids, and the unprecedented capital investment now flowing into digital infrastructure.Key Topics:The origins of Galaxy Data Centres and their energy-first approach since 2018How the focus on power has shifted from traditional industrial users to data centres.The rapid impact of AI on infrastructure design, cooling, and energy needs.Why the Redhill Data Centre marks a new chapter for Galaxy in ownership and operations.The challenge of meeting soaring power demand and the role of microgrids.How investors are treating data centres as critical national infrastructure.The future of the sector: integrating energy generation with data centre operations.This is a forward-looking conversation about where data centres are heading and why energy and AI will define the next chapter of the industry.Support the showThe Inside Data Centre Podcast is recorded in partnership with DataX Connect, a specialist data centre recruitment company based in the UK. They operate on a global scale to place passionate individuals at the heart of leading data centre companies. To learn more about Andy Davis and the rest of the DataX team, click here: DataX Connect

Spirit Switchboard Episode #112 Date: June 13th, 2025 Quantum Healing: Healing without Hurting Guest: Neha Misra In this enlightening episode of Spirit Switchboard, we welcome intuitive healer and quantum energy expert Neha Misra for a transformative conversation on healing without hurting. Discover the power of Quantum Healing—a gentle, yet profound approach that taps into the energetic blueprint of your being. Neha shares insights on how to release trauma, restore balance, and reclaim your inner peace through the subtle science of vibrational medicine. Tune in for heart-centered wisdom, practical healing tools, and a new perspective on what true wellness really means. Guest Bio: Quantum Energy Practitioner Trauma Recovery Expert Founder of Unlocked Bliss CIC Creator of the 3Rs Process of Quantum Guest Links: WEBSITE: http://www.unlockedbliss.com Socials: @unlockedbliss Host links: http://www.kerrilynnshellhorn.com https://linktr.ee/kerrilynn.shellhorn Message from Kerrilynn: I want to hear from you! I want to hear about your ghost stories, paranormal adventures and occurrences. I would also love your show suggestions to cover in the future. Email me at kerrilynn.shellhorn@gmail.com. If you enjoy the content on the channel please live, subscribe and share. My deepest gratitude to you all! A formal disclaimer: The opinions and information presented or expressed by guests on Spirit Switchboard are not necessarily those of the Host or the United Public Radio Network/UFO Paranormal Radio Network and its producers. As always Spirit Switchboard strives to hold space for open, respectful dialogue with show guests and listeners.

Part 2 for Members: www.parallelmike.com Mike's Investing Community and Financial Newsletter – www.patreon.com/parallelsystems Consult with Mike 1-2-1: www.parallelmike.com/consultation Guest Links: Youtube: https://www.bibhudevmisra.com/ Website: Yuga Shift by Bibhu Dev Misra

Bibhu's website: https://www.bibhudevmisra.com/ Bibhu's book: Yuga Shift To order Yuga Shift on Amazon: https://www.amazon.com/YUGA-SHIFT-IMP... Bibhu Dev Misra has been researching and writing on ancient civilizations and ancient mysteries for over a decade. He is a contributor to many international magazines and websites such as New Dawn, Nexus, Mysterious Universe and Graham Hancock. He has appeared on numerous podcasts and online conferences including the Society for Scientific Exploration (SSE), Earth Ancients and Portal to Ascension. He is an engineer and worked as an Information Technology Consultant for two decades for various global organizations. He lives in Kolkata, India, with his family. Heather's website: https://www.risingmoonhealingcenter.com To become a patron of Heather:   / heatherensworth  

Dr. Avishkar Misra shares his expertise on implementing AI effectively in businesses and explains how his company Berrijam is making AI accessible, explainable, and useful for organizations of all sizes.• AI is not just one solution but a set of tools, techniques, and frameworks that allows for intelligent decision-making at various scales• Berrijam offers three main services: AI strategy development, custom AI implementation, and their proprietary BerryJam AI focused on analytical applications• The Berrijam AI5C framework breaks down AI capabilities into five areas: Find, Make, Connect, Predict, and Optimize• Organizations need to start with their specific pain points and business requirements before selecting AI tools or implementation strategies• AI will transform jobs rather than simply eliminate them, creating opportunities for those who learn to work alongside AI• Leadership plays a critical role in successful AI adoption by fostering the right cultural framework and strategic approach• The future workplace will see AI handling mundane tasks, allowing professionals to focus on higher-value creative work• The IPX-hosted AI Readiness Workshops will help participants understand AI fundamentals, build business cases, and develop implementation strategiesStay in touch with us! Follow us on social: LinkedIn, Twitter, Facebook Contact us for info on IpX or for interest in being a podcast guest: info@ipxhq.com All podcasts produced by Elevate Media Group.

Almost every ancient culture believed that human civilization and consciousness has progressively declined since an erstwhile Golden Age or Satya Yuga till the current age of greed and lies, discord and strife, called the Iron Age or Kali Yuga. Unfortunately, during our long passage through the darkness of the Kali Yuga, the original formulation of the Yuga Cycle was lost.In this extensively researched book, Bibhu Dev Misra has delineated the common threads that run through the Yuga Cycle doctrines of multiple ancient cultures, taking the aid of scientific discoveries from various disciplines. His reconstruction of the original Yuga Cycle framework indicates that the end of the Kali Yuga is just around the corner - in 2025!Within a span of just 15 years, by the year 2040, the Kali Yuga civilization is likely to collapse due to a combination of global wars, environmental catastrophes and comet impacts. We are living in the end-times that the ancient prophecies had warned us about. The survivors of the impending cataclysms will inherit a renewed earth, bathed in the divine light of the Central Sun.There is compelling evidence from many sources that the Yuga Cycle is a valid scientific doctrine, and is perfectly aligned with the earth's precession cycle. It explains the periodic collapse and re-emergence of civilizations across the world every 3000-odd years, and the progressive decline in our physical size and cranial volume over the past 11,700 years of the descending Yuga Cycle.Why does our consciousness fluctuate in a sinusoidal manner over the course of the Yuga Cycle? What are the triggers for the cataclysmic obliteration of civilization during the periods of transition between the Yuga? What is the significance of the end-time prophecies which tell of a Savior or Avatar returning at the end of the Kali Yuga? How can we navigate through the upheavals and chaos of the Yuga-ending period?These are some of the key questions addressed in this book. This riveting and thought-provoking work contains one of the most important messages of our time.Bibhu Dev Misra has been actively researching and writing on ancient civilizations and ancient mysteries for more than a decade. He is a regular contributor to many international magazines and websites such as New Dawn, Nexus, Mysterious Universe, GrahamHancock Forum, Science to Sage etc., and has appeared on podcasts and online conferences on Earth Ancients, Portal to Ascension, OSOM, Watcher's Talk and more.Bibhu lives in Kolkata, India, with his family. When he is not researching, writing, or traveling to ancient sites, Bibhu plays cricket with his son, strikes up a tune on his keyboard, reads books on ancient mysteries and esoteric subjects, goes for long walks and practices yoga.By profession, he is an Engineer from IIT Kharagpur and a MBA from IIM Calcutta, and worked as an Information Technology Consultant for nearly two decades, providing technology solutions to various global organizations, including the World Bank and the United Nations. A few years back, he gave up his full-time job as a Technology Consultant in order to devote more time to his research and writing interests.This is Bibhu's debut book, based on one of his earliest articles. You can find out more about him and his research interests from his website "Ancient Inquiries".Become a supporter of this podcast: https://www.spreaker.com/podcast/earth-ancients--2790919/support.

Gaurav Misra is the co-founder and CEO of Captions, an AI-powered video creation company and one of the most successful consumer AI products in the world today. Previously he was a product leader at Snap, where he created the design engineering function and spent years helping develop features used by hundreds of millions of users worldwide. With a background in both engineering and design, Gaurav brings a unique cross-functional perspective to product development.What you'll learn:1. Why the “ship a marketable feature every week” approach helps his team stay focused and the product stay top of mind for users amid constant AI breakthroughs2. How to balance rapid shipping with maintaining quality by cutting scope rather than compromising on timelines3. The “secret roadmap” strategy that helps Captions develop breakthrough features competitors never see coming4. Why taking on strategic technical debt is essential for startups to outpace larger companies5. How Captions accidentally ignored their most successful product for 1.5 years (and why it still grew to 500K users with no updates or support)6. How Snap's unique product development approach—with designers functioning as PMs—enabled their success as the last major social network to break through7. Why AI video will transform marketing before other industries—Brought to you by:• Brex — The banking solution for startups• Paragon—Ship every SaaS integration your customers want• Coda—The all-in-one collaborative workspace—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-win-in-the-ai-era-gaurav-misra—Where to find Gaurav Misra:• X: https://x.com/gmharhar• LinkedIn: https://www.linkedin.com/in/gamisra1/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Gaurav's background(04:47) The exciting era of AI and startups(09:30) Staying top of mind(11:26) Tips for staying focused(13:14) Shipping marketable features weekly(19:03) Managing technical debt in startups(25:31) Snap's unique product development approach(32:09) Brainstorming with AI(35:09) What Snap got right(41:06) Scaling with a small, agile team(49:33) The shift toward prototyping in product management(51:47) The product manager role(55:40) Snap's mission and product decisions(01:02:13) The future of AI-generated video(01:10:20) Leveraging AI for marketing(01:14:37) Failure corner(01:20:21) Lightning round and closing thoughts—Referenced:• Snap: https://www.snap.com/• Captions: https://www.captions.ai/• Iron Man on Disney+: https://www.disneyplus.com/movies/iron-man/6aM2a8mZATiu• J.A.R.V.I.S.: https://en.wikipedia.org/wiki/J.A.R.V.I.S.• Cursor: https://www.cursor.com/• Devin: https://devin.ai/• Eye contact: https://www.captions.ai/eye-contact• Nvidia: https://www.nvidia.com• Descript: https://www.descript.com• Evan Spiegel on LinkedIn: https://www.linkedin.com/in/evan-spiegel-8ab74034a/• TikTok: https://www.tiktok.com/• Spotlight: https://www.snapchat.com/spotlight/• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein• Patrick Collison on X: https://x.com/patrickc• DeepSeek: https://www.deepseek.com/• ByteDance Goku: New video generation AI model, better than OpenAI Sora: https://medium.com/data-science-in-your-pocket/bytedance-goku-new-video-generation-ai-model-better-than-openai-sora-56c017a320a5• Will Smith eating spaghetti and other weird AI benchmarks that took off in 2024: https://techcrunch.com/2024/12/31/will-smith-eating-spaghetti-and-other-weird-ai-benchmarks-that-took-off-in-2024/• Silo on AppleTV+: https://tv.apple.com/us/show/silo/umc.cmc.3yksgc857px0k0rqe5zd4jice• Severance on AppleTV+: https://tv.apple.com/us/show/severance/umc.cmc.1srk2goyh2q2zdxcx605w8vtx• Linear: https://linear.app/• Superhuman: https://superhuman.com/• Notion: https://www.notion.com• Perplexity: https://www.perplexity.ai/• OmniHuman-1 AI Video Generation Looks Too Real: https://www.youtube.com/watch?v=fY0KB516m-E—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Equinox Greetings!Yesterday I gathered with founding subscribers for our quarterly livestream to celebrate the Equinox. I started the livestream with an interpretation of the Equinox chart and I want to share the audio with you because this Equinox is highly unusual and a Very Big Deal. The chart of the Equinox can be read as a blueprint for the new season, and as the “birth chart” for the new astrological year.Why is this Equinox so special? For the first and only time in our lives, the Equinox Sun - at the very first degree of the zodiac (0 Aries) - is conjunct Neptune at the very last degree of the zodiac (29 Pisces). AND, according to Bibhu dev Misra, author of Yuga Shift: The End of the Kali Yuga & the Impending Planetary Transformation, this Equinox marks the end of the Kali Yuga (may it be so!).This Equinox also occurs between two powerful Eclipses, and features both Mercury and Venus retrograde.Listen to the above audio (23 minutes) for all the Virgoan details! The Equinox chart is below. At the start of the livestream, I played Shine, by Fia, which feels like an appropriate Equinox anthem.If you're already a founding subscriber, you can find the full video replay here. If you would like to join me and our amazing community for future livestreams, you can become a founding subscriber below.Also! Re-Ignite Your Money Magic - A Venus Retrograde Reset & Upgrade starts tomorrow, and you're welcome to jump in! I'm collaborating with Soul Sherpa Anné Klint and we're excited to support you to make the most of this Venus retro and come into a more conscious, joyful and abundant relationship with Money. Wishing you a miraculous Equinox rebirth! love, Emily This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit embodiedaquarian.substack.com/subscribe

In this episode, we dive into how AI is transforming video editing with Gaurav Misra, the CEO of Captions. Launched in New York in 2021, Captions already empowers over 10 million creators worldwide, leveraging AI to make video production as simple as clicking a button.Discover the strategic framework that led to the inception of Captions, and learn how the founders identified societal changes and technological advancements to build a groundbreaking company. We explore the challenges and opportunities of building an AI product for video editing, including how Captions is outpacing traditional content production workflows.Gaurav shares insights into the future of video editing, the role of AI in democratizing video production, and the unique approach Captions takes to differentiate itself from industry giants like Adobe and Capcut. CaptionsWebsite - https://www.captions.aiX/Twitter - https://x.com/getcaptionsappGaurav MisraLinkedIn - https://www.linkedin.com/in/gamisra1X/Twitter - https://x.com/gmharharFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro(01:30) What is Captions?(03:43) How did Captions start?(08:25) The strategy behind launching Captions(12:32) How is Captions different from other editing tools?(14:13) How does it compare to CapCut?(18:22) Who is the typical Captions user?(20:13) Why ‘Captions'?(23:47) Captions' product suite for production and editing(26:37) AI models powering Captions(36:22) AI lipsync(38:49) Personalized fine-tuned models for creators?(39:38) Building models vs. building wrappers(43:09) Cloud AI vs. Local AI(45:19) Optimizing for low latency(48:07) AI/ML stack at Captions(51:10) “Hallucinations are a feature, not a bug”(53:19) Prompt engineering(54:12) Have we passed the uncanny valley for AI avatars?(01:01:47) The impact of deepfakes(01:04:33) CapCut ban and its effects(01:05:05) Evolving from paid to freemium(01:07:42) Building a company on foundation models(01:09:01) Running an AI company in New York

Hai wahii surma is jag me, Jo apni raah banata hai…koi chalta hai padchinho par, koi khud pad chinhh banata hai.Bahut kam aise mauke milte hai... joh logo ki kahaaniyan aap tak pahuchatey hai, unki kahaani aap tak lau. Aaj ka episode isiliye bahut special hai....Today, on the podcast 'The Billion Dreams' I get the privilege to have a dialogue with Neelesh Misra, a master storyteller whose evocative narratives have touched countless hearts. From humble beginnings in the heartland of India, Neelesh's path has been one of relentless dedication and passion, capturing the essence of India through his words and voice.Neelesh's storytelling journey began as a journalist, honing his craft by capturing the human experience in every piece he wrote. His transition to a celebrated storyteller was marked by the creation of "Yaadon Ka Idiot Box," a radio show on BIG FM 92.7 that became a phenomenon, enchanting audiences with nostalgic and poignant tales. Beyond radio, Neelesh is the Founder of Goan Connection Rural Media Enterprise and The Slow Movement, he has also made significant contributions as an author; lyricist having penned songs for more than 30 films like Bajrangi Bhaijaan, Barfi, etc.; screenwriter; and mentor, each endeavour marked by authenticity and empathy.His work isn't just about telling stories; it's about giving a voice to the voiceless and shining a light on the unseen. Neelesh's commitment to social causes and efforts to empower rural storytellers through initiatives like the Gaon Connection, ItsYourMic have had a profound impact. By nurturing new talent and promoting rural journalism, he ensures that stories from every corner of India are heard and cherished.But what makes Neelesh truly special is not just his professional achievements. Known for his humility and kindness, he remains deeply connected to his roots, always ready to lend a helping hand. His home in Gadahila Village is a haven for creativity and community, often hosting gatherings where artists, writers, and musicians come together to share ideas and inspire each other.In this episode, we celebrate Neelesh Misra – a storyteller, visionary, and true inspiration. Tune in to hear about his journey, his impact, and the lesser-known aspects of his life that make him a remarkable human being. This conversation promises to be a value add to your life.Do share your thoughts with us in the comments below.If you like what you have heard, please give a like.Remember to engage with our content by sharing and subscribing to our channel for more inspiring tales. With this podcast, 'The Billion Dreams' we aim to bring you stories that ignite and inspire your life.Until next time, let's keep surprising ourselves and embracing the infinite possibilities within us.Alshukran Bandhu,Alshukran Zindagi.-------------------Topics:1:31 Meeting Neelesh Misra's father 2:06 The inspiring story of his parents - Shiv Balak Misra & Nirmala Misra 9:31 Breaking bread together 9:54 Neelesh Misra, a true Pioneer 10:26 BTS of The Slow Interview 11:03 From Journalism to Writing Books 11:56 Shocking Story of 1999 Hijack of Air India Flight 14:48 Life-threatening experience of Odisha Cyclone 15:24 Meeting Mahesh Bhatt & Writing Songs for Jism Film20:47 Story of Sahir Ludhianvi 22:14 The Inception of The "Slow" Movement24:00 Regrets fueling Passion & being Relentlessly Restless 25:25 "I don't do it for others..." - Neelesh Misra 27.00 Superpowers of Neelesh Misra 28:07 Impacting on My Own Terms28:54 The Wrong Parameters of Success 30:35 Ashish Vidyarthi as the Hope Influencer 31:20 Starting the "Vidyarthi Samman" Awards 33:12 Ecstatic to being recognised as a Vlogger - Ashish Vidyarthi 33:33 "Decency is not incentivised in our Country..." - Neelesh Misra 35:49 This Mindset Needs To Change!!! 38:13 Jodhpur mein dekha ek kissa 39:16 Using Social Media Responsibly 39:40 Choosing Integrity & Pride over Money 41:22 Change of Scenes42:10 Does the village life upset Neelesh Misra?43:37 Bihar to Civil Services - Changing one's social status through education 44:52 Enjoying Chai the local way 45:16 The thought behind starting "Gaon Connection"47:27 Change of Scenes 48:05 Everyday is a New Chance to Change Yourself 49:37 Ageing without limitations51:56 What's inspiring about Neelesh Misra?---------Follow ‪@NeeleshMisra‬ on YT & Insta:   / neeleshmisra  ----------SIT DOWN ASHISH: 25th May 2024 - The Royal Opera House, Mumbai | 7:30 PM 7th June - Phoenix Marketcity, Pune | 7:30 PM 28th June - Underground Comedy Club, Bangalore | 7:00 PMCOMING SOON TO YOUR CITY TOO....! Follow me on Instagram for all the latest updates.   / ashishvidyarthi1  For all the upcoming show links, Click Here: https://linktr.ee/Ashishvidyarthi

Robert Seacord, the Standardization Lead at Woven by Toyota, the convenor of the C standards committee, and author of The CERT® C Coding Standard, Effective C, and Secure Coding in C and C++, speaks with SE Radio host Gavin Henry about What's New in the C Programming Language. They start with a review of the history of C and why it has a standard, and then they discuss what C23 brings and how programmers can take advantage of it. They consider the sectors in which C is most used and whether you should use C to start a brand new project in 2025. Seacord discusses 8 new things that C23 brings, use case examples, must haves, floating point numbers, how automotive systems use C, why C is used there, Rust vs C, compile time checks vs static analysis, all the various safety standards they can use, why you should use the right tool for the job and never trust user input no matter the language.  Brought to you by IEEE Computer Society and IEEE Software magazine.

My guests today are Dwight Churchill and Gaurav Misra, co-founders of Captions, which uses AI to generate and edit talking videos and has grown to significant scale at remarkable speed. We explore a key distinction in AI: tackling bounded problems like video generation versus unbounded problems like general intelligence and what this means for building sustainable businesses. We also explore their unique data flywheel, why video generation could reach Hollywood quality within 18 months, and why building advanced AI products doesn't require huge teams. Please enjoy this discussion with Dwight and Gaurav. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Ramp. Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Ramp is the fastest-growing FinTech company in history, and it's backed by more of my favorite past guests (at least 16 of them!) than probably any other company I'm aware of. Go to Ramp.com/invest to sign up for free and get a $250 welcome bonus. – This episode is brought to you by AlphaSense. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Imagine completing your research five to ten times faster with search that delivers the most relevant results, helping you make high-conviction decisions with confidence. Invest Like the Best listeners can get a free trial now at Alpha-Sense.com/Invest and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. –  This episode is brought to you by Ridgeline. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. I think this platform will become the standard for investment managers, and if you run an investing firm, I highly recommend you find time to speak with them. Head to ridgelineapps.com to learn more about the platform. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Follow us on Twitter: @patrick_oshag | @JoinColossus Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Welcome to Invest Like the Best (00:07:49) The Evolution and Impact of AI (00:09:14) Challenges in Video Data and AI (00:10:36) AI in Media Generation (00:12:07) Building a Sustainable AI Business (00:14:56) The Journey of a Video AI Company (00:25:41) AI Video Editing and Creation Tools (00:29:58) Future of AI in Video and Business (00:37:51) The Future of Likeness in Video (00:39:25) Training Models on Human Data (00:41:15) Competitive Landscape and Copycats (00:44:01) The Role of Research Talent (00:46:25) Pricing AI Software (00:51:51) Investor Perspectives on AI (01:02:44) Lessons from Snap (01:07:04) The Kindest Thing Anyone Has Done for Dwight & Gaurav

Ingredients for Success: Transforming Dog Grooming with Jane Misra In this episode of the Stay on Course podcast, Julie Riga chats with Jane Misra, a serial entrepreneur and owner of Dog People, a mobile dog grooming business. Jane shares her 3 essential ingredients for success in the dog grooming industry: agility, persistence, and not taking things personally. From navigating the challenges of an unregulated industry to building strong relationships with clients, Jane offers valuable insights and advice for entrepreneurs and small business owners.Learn more about Jane: linkedin.com/in/jane-misra-ab60375Key Takeaways:Agility is key

Our final house guest for 2024 is Shalini Misra, a globally-renowned interior architect, designer and property developer. She chats to Carole Annett about her incredible work, which prioritises sustainability and wellbeing, and discusses how her Indian heritage has shaped her work in interior design.

Drs. Karl and Spencer chat with Dr. Shivani Misra, a metabolic specialist physician researcher from the UK, all about the various forms of diabetes mellitus.Learn:The types of diabetes mellitusThe monogenic forms (some what refer to as MODY)The differences within type 2 diabetesWhether CGMs (continuous glucose monitors) are usefulIf low carb could help someFollow Dr. Misra on X/Twitter 

We welcomed our guest to discuss his paper, "A model for economic freedom on Mars" plus his recent book "Sovereign Mars: Transforming Our Values through Space Settlement" We talked quite a bit about a possible Mars economic system, settlement, do's and don'ts, capital usage on Marx, permissions for this or that from Earth and lots more. Toward the end of our hour, Jacob spoke to the UAP matter we have heard so much about. He was also clear that both his paper and his book were really thought experiments to get people talking about these issues soon rather than later. We talked Earth governance issues, possible timelines, Musk and how to target a larger audience for both Mars and settlement. Please read the full summary of this program at www.thespaceshow.com for this date, Friday, Nov. 22, 2024.

In my last episode, I shared an essay titled "The Kali Yuga Reaches Its Grand Finale", which I wrote as the introduction to my forthcoming new book "The Coming World Nation". As I was doing research for it, I came across the work of scholar Bibhu Dev Misra, who recently published a groundbreaking study of the Hindu Yuga system called "Yuga Shift".  In his book, Bibhu offers an innovative new approach to dating the timeline of the Yuga cycle and we discuss this in detail in a recent interview I conducted with him. In our chat, we focus in particular on dissecting the timeline and themes of the Kali Yuga, the "dark age" of the Yuga Cycle, which mankind has been trapped in for millennia but which Bibhu calculates is soon to be reaching its final end. For more on Bibhu's work, see his website: https://www.bibhudevmisra.com/To check out my essay on the topic, see: https://thewisdomtradition.substack.com Table of Contents00:00 - Introduction and BackgroundIntroduction to Bibhu Dev Misra and his journey from technology to ancient wisdom.05:00 - Understanding Yuga CyclesOverview of the Yuga cycles, different interpretations, and the significance of the Kali Yuga in various cultures.20:00 - Astronomical and Cultural AlignmentsAlignments of Yuga cycles with astronomical cycles and comparisons across cultural traditions.40:00 - Evidence of Lost CivilizationsDiscussion of ancient civilizations, the Younger Dryas impact, and cataclysmic resets in human history.1:00:00 - Materialism and the Kali Yuga's InfluenceHow the Kali Yuga influences materialism and spiritual decline in civilization.1:21:00 - Astrological Calculations and MidpointsInsights into astrological influences on Yuga cycles and the significance of historical midpoints.1:52:00 - Predictions for the End of the Kali YugaExamination of potential signs of the Kali Yuga's end, including comet impacts and global renewal.2:04:00 - Concluding Thoughts on Spiritual TransformationReflections on the importance of spiritual growth and personal transformation in times of global change.Send us a text

In the AI industry, where innovation evolves at lightning speed, some founders set the pace by combining a unique cultural perspective with a relentless drive to create groundbreaking technology. Gaurav Misra, founder and CEO of Captions, is one of these visionaries. Captions has attracted funding from top-tier investors like Adobe Ventures, HubSpot Ventures, Kleiner Perkins, Sequoia Capital, and Jared Leto.

Get the full 2 hour interviews with THC+: Subscribe via our website and get the Plus show on your usual podcast apps. Subscribe via Patreon, including the full Plus archive, a dedicated RSS feed, Spotify, & payment through Paypal. Subscribe via check, cash, money order, or crypto with the information at the bottom of the page. […] The post Bibhu Dev Misra | Yuga Shift, Underground Beings, & The Cosmic Rock Bottom appeared first on The Higherside Chats.

Bibhu Dev Misra joins us to discus his research into the ancient belief that civilization is progressively declining from a Golden Age to the current age of discord, known as the Kali Yuga. He predicts the end of the Kali Yuga in March 2025, which could trigger a global collapse by 2040 through wars, environmental disasters, and comet impacts, fulfilling ancient prophecies of Earth's renewal. He also elaborates on the scientific validation of the Yuga Cycle's alignment with Earth's precession cycle and its implications for understanding the cyclical nature of civilizations, the fluctuation of human consciousness, and the prophesied return of a savior at the cycle's conclusion. Then for our Plus+ members, we share stories from the incredible life of Dixie Yeterian, who became so proficient as a psychic detective that a hit was put on her. We discuss her encounters with angry spirits, backside psychometry, and more. Links Yuga Shift: The End Of The Kali Yuga & The Impending Planetary Transformation The End Of The Kali Yuga In 2025: Unraveling The Mysteries Of The Yuga Cycle Ancient Inquiries Bibhu's Website Plus+ Extension The extension of the show is EXCLUSIVE to Plus+ Members. To join, click HERE. Casebook Of A Psychic Detective Strange But True - Psychic Detective Dixie Yeterian Website Dixie Yeterian - 18 Apr 1986 - Daily Sitka Sentinel Newspaper Learn more about your ad choices. Visit megaphone.fm/adchoices

As we wrap up the celebration of World Doula Week, explore the transformative journey of pregnancy and childbirth in the Indian context with Divya Deswal and Neha Misra, founders of The Doula Collective©️. In this interview, Divya and Neha shed light on the unique challenges and triumphs faced by expectant families in India. You'll learn how The Doula Collective©️, with its culturally sensitive and trauma-informed approach, is reshaping the landscape of birth support!  Also... have you ever heard of a year-long doula training program? Divya and Neha share the intricacies of their in-depth doula training program and how it goes beyond traditional methods to create a profound impact on both birth practitioners and families. Join us as we confront the barriers that doulas face in a system where their role is often misunderstood. We end with an exploration of the soulful aspects of birth trauma, physical challenges of birth and birth work, and the power of resilience. Learn more about The Doula Collective here: https://thedoulacollective.in/ Follow their work on Instagram: @thedoulacollective.in EBB Resources: Watch the video of this podcast episode on the EBB YouTube channel here!  Join the EBB Pro Membership and get access to contact hours, a doula mentorship, live trainings, and a PDF Library with exclusive handouts (including a 2-page handout on breech) by joining here. Learn advocacy techniques through the EBB Childbirth Class. Follow Evidence Based Birth: Facebook Instagram X

Almost every ancient culture believed that human civilization and consciousness has progressively declined since an erstwhile Golden Age or Satya Yuga till the current age of greed and lies, discord and strife, called the Iron Age or Kali Yuga. Unfortunately, during our long passage through the darkness of the Kali Yuga, the original formulation of the Yuga Cycle was lost.In this extensively researched book, Bibhu Dev Misra has delineated the common threads that run through the Yuga Cycle doctrines of multiple ancient cultures, taking the aid of scientific discoveries from various disciplines. His reconstruction of the original Yuga Cycle framework indicates that the end of the Kali Yuga is just around the corner - in 2025!Within a span of just 15 years, by the year 2040, the Kali Yuga civilization is likely to collapse due to a combination of global wars, environmental catastrophes and comet impacts. We are living in the end-times that the ancient prophecies had warned us about. The survivors of the impending cataclysms will inherit a renewed earth, bathed in the divine light of the Central Sun.There is compelling evidence from many sources that the Yuga Cycle is a valid scientific doctrine, and is perfectly aligned with the earth's precession cycle. It explains the periodic collapse and re-emergence of civilizations across the world every 3000-odd years, and the progressive decline in our physical size and cranial volume over the past 11,700 years of the descending Yuga Cycle.Why does our consciousness fluctuate in a sinusoidal manner over the course of the Yuga Cycle? What are the triggers for the cataclysmic obliteration of civilization during the periods of transition between the Yuga? What is the significance of the end-time prophecies which tell of a Savior or Avatar returning at the end of the Kali Yuga? How can we navigate through the upheavals and chaos of the Yuga-ending period?These are some of the key questions addressed in this book. This riveting and thought-provoking work contains one of the most important messages of our time.Bibhu Dev Misra has been actively researching and writing on ancient civilizations and ancient mysteries for more than a decade. He is a regular contributor to many international magazines and websites such as New Dawn, Nexus, Mysterious Universe, GrahamHancock Forum, Science to Sage etc., and has appeared on podcasts and online conferences on Earth Ancients, Portal to Ascension, OSOM, Watcher's Talk and more.Bibhu lives in Kolkata, India, with his family. When he is not researching, writing, or traveling to ancient sites, Bibhu plays cricket with his son, strikes up a tune on his keyboard, reads books on ancient mysteries and esoteric subjects, goes for long walks and practices yoga.By profession, he is an Engineer from IIT Kharagpur and a MBA from IIM Calcutta, and worked as an Information Technology Consultant for nearly two decades, providing technology solutions to various global organizations, including the World Bank and the United Nations. A few years back, he gave up his full-time job as a Technology Consultant in order to devote more time to his research and writing interests.This is Bibhu's debut book, based on one of his earliest articles. You can find out more about him and his research interests from his website "Ancient Inquiries".https://www.bibhudevmisra.com/Become a supporter of this podcast: https://www.spreaker.com/podcast/earth-ancients--2790919/support.