Podcast appearances and mentions of Monte Carlo

Are you looking to save time, make money, and start winning with less risk? Then head to https://www.ovtlyr.com.Options traders know the Greeks are the backbone of smart decision making, and theta is one of the most misunderstood of them all. In this video we dive deep into theta—what it really means, how it impacts option prices, and most importantly, how you can use it to your advantage without falling into the traps that sink so many traders.You'll learn why theta represents time decay and why every passing day eats away at an option's extrinsic value. We'll break down the difference between intrinsic vs. extrinsic value, how deep in-the-money options compare to out-of-the-money “lotto tickets,” and why selling options might look attractive on paper but can quietly destroy an account in real life.The discussion explores real-world numbers, showing why an 84% win rate isn't enough if your losers are six to ten times larger than your winners. We'll walk through expectancy, Monte Carlo scenarios, and why relying on theta decay alone often ends in disaster. You'll see how sellers collect profits drip by drip, while disciplined buyers who focus on trend, delta, and leverage can capture gains in floods.We'll also cover:➡️ How theta accelerates as expiration approaches, especially for at-the-money contracts➡️ Why implied volatility can juice up extrinsic value and how to handle it without falling for “juicy” traps➡️ The critical role of selecting strike prices that limit extrinsic cost to 20–30% of the option's price➡️ Why deep in-the-money calls can serve as powerful stock replacement tools with far less theta risk➡️ How to spot the “break over” point where higher delta stops being worth the premiumInstead of just repeating theory, this breakdown ties the math back to real trading experience. You'll see the dangers of blindly selling options, the mistakes that cost traders hundreds of thousands of dollars, and the mindset shift needed to avoid them. By focusing on expectancy, risk management, and leveraging data the right way, you can start trading smarter and build consistency without gambling.This video is part of the OVTLYR mission to help traders save time, make money, start winning, and take on less risk. Theta is a cost of doing business in the markets, but when you understand it as a “business expense,” you can position yourself for success. Whether you're new to options or refining your edge, this deep dive will give you the clarity to trade with confidence.Don't fall for the seductive promises of easy income through option selling. Learn how to use theta to your advantage, protect your capital, and trade with the odds truly in your favor.Watch now and discover why theta isn't your enemy when you understand how to control it.Gain instant access to the AI-powered tools and behavioral insights top traders use to spot big moves before the crowd. Start trading smarter today

La question revient souvent : manger cru est-il forcément meilleur pour la santé ? Salades colorées, fruits frais, smoothies… Le cru évoque légèreté, vitalité et naturalité. Mais attention : si ses atouts sont réels, il n'est pas adapté à tout le monde.Les bienfaits du cruLes aliments crus conservent des vitamines et enzymes fragiles à la cuisson, comme la vitamine C ou certaines polyphénol-oxydases. Ils sont riches en fibres solubles, hydratants, faibles en calories et idéals pour ceux qui veulent "manger léger". Ils apportent aussi antioxydants et densité nutritionnelle, particulièrement intéressants pour une digestion robuste et un microbiote équilibré.Quand le cru devient difficile à digérerLe revers de la médaille ? Les fibres insolubles contenues dans certains légumes crus (chou, carotte, céleri) peuvent irriter la muqueuse digestive et provoquer ballonnements, fermentation et inconfort chez les personnes sensibles. Une étude publiée dans le World Journal of Gastroenterology (2014) a d'ailleurs montré que ces fibres aggravent les symptômes du côlon irritable.En médecine traditionnelle chinoise, on considère aussi que le cru "affaiblit le yang de la rate", c'est-à-dire la capacité du corps à transformer et assimiler les aliments. Résultat : fatigue après le repas, transit accéléré ou sensation de froid interne peuvent apparaître en cas d'excès.Trouver le bon équilibreLe cru n'est donc ni un miracle, ni un ennemi. Tout dépend du profil de chacun. Pour les personnes jeunes, toniques et sans troubles digestifs, il reste un atout nutritionnel. Pour les terrains plus fragiles, mieux vaut privilégier la cuisson douce (vapeur ou étouffée) qui assouplit les fibres, améliore l'assimilation des minéraux tout en préservant une partie des vitamines.En pratique, la meilleure approche est la modération : alterner crudités et légumes cuits, surtout en été, pour profiter des bienfaits du cru sans en subir les inconvénients.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Toutes les explications avec Laura Magrino, numérologue à retrouver chaque mardi à 10H30 sur Radio Monaco Feel Good Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Vous souvenez-vous du jeu "Cap ou pas cap ?" qu'on lançait enfants dans la cour de récré ? Un défi, un sourire malicieux, et sans réfléchir, on se jetait à l'eau… parfois littéralement. Avec les années, on devient adultes, sérieuses, organisées. Et on finit par oublier qu'oser peut aussi être synonyme de légèreté et de plaisir.Quand la peur de l'échec nous bloqueÀ force de vouloir bien faire, beaucoup se retrouvent piégées dans un excès de contrôle. On attend toujours "le bon moment", on veut être prête à 100 %, et bien souvent… on ne fait rien. Résultat : on reste coincée dans une zone de confort rassurante, mais qui finit par étouffer. Pourtant, la clé pour retrouver l'énergie et la confiance, c'est parfois de transformer le risque en jeu.Oser comme un jeu, pas comme un examenJouer à "Cap ou pas cap ?", même à l'âge adulte, c'est se donner la permission d'essayer sans se juger. Plus besoin d'être parfaite, plus besoin d'avoir un plan précis : il suffit de dire "cap !" et de voir ce qui se passe. Un petit défi peut suffire à tout changer. Monter sur scène au karaoké, tester un cours de salsa, oser dire non pour la première fois. Ces expériences simples créent une adrénaline positive, un sentiment de liberté et surtout, elles font grandir la confiance en soi.Prendre un risque ne veut pas dire se mettre en danger. Cela peut être juste une manière de s'offrir un souvenir inattendu, un moment de fun ou une nouvelle passion. Et plus on ose, plus il devient facile de recommencer.Cette semaine, choisissez un défi qui vous amuse, partagez-le avec quelqu'un pour qu'il vous encourage, et lancez-vous. Vous verrez : la vie reprend des couleurs dès qu'on y met un peu de jeu.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Are you looking to save time, make money, and start winning with less risk? Then head to https://www.ovtlyr.com.The market has been throwing off warning signs, and if you've been blindly buying dips, you already know how painful that can be. This video is all about flipping that script and building a plan that actually works. We break down why patience, discipline, and simple rule-following often beat complex systems and flashy strategies. Trading isn't about hope—it's about execution.In this session, we dig into how OVTLYR 4.0 takes proven trading principles and turbocharges them with data. From position sizing rules tied to the 10 EMA to expectancy testing that's over 5X stronger than before, the results show why math-driven decisions give traders the edge. You'll also learn how small tweaks, like avoiding overhead order blocks within 2%, improve outcomes and keep you out of traps that crush most retail accounts.Here's what you'll discover in this breakdown:➡️ Why winners average winners and losers average losers➡️ How to use the 10 EMA as a trailing stop instead of ATR for better results➡️ The simple filters that raise expectancy without overcomplicating entries➡️ What the “Big Trend” (145) and “Batman's Favorite Trade” (137) really mean➡️ Why trading cheap stocks can actually improve signals➡️ How OVTLYR Nine confirms setups across market, sector, and stock levels➡️ The importance of stock, sector, and SPY heatmaps all rising together➡️ Why exiting before earnings often delivers easy profitsWe also explore advanced backtesting insights. Running simulations showed expectancy jumping from 0.20 to over 1.00 in key strategies, with average wins higher and average losses lower. These aren't just academic numbers—they reflect real trading improvements when discipline meets data. Monte Carlo testing confirmed that protecting against big losers while letting winners run produces consistent, repeatable results.OVTLYR 4.0 makes applying all of this easier than ever. From built-in heatmaps to channel scores and instant screener updates, traders can now see the strongest setups without digging through endless charts. Whether it's confirming 137/145 setups, catching earnings run-ups, or filtering sectors above their 10 EMA, the process is now faster, cleaner, and more confidence-building.The biggest takeaway is simple: success comes from having rules and following them, not chasing every signal. By combining proven principles with OVTLYR's powerful 4.0 update, traders can finally cut through noise, avoid emotional mistakes, and focus on what works.If you're serious about saving time, making money, and trading with less risk, this breakdown is for you. Watch now to see how to build a strategy that lasts—and why OVTLYR 4.0 is helping traders everywhere turn market chaos into opportunity.Gain instant access to the AI-powered tools and behavioral insights top traders use to spot big moves before the crowd. Start trading smarter today

Tornano le stelle, torna la Champions League. Dall'urna di Montecarlo è nata la Coppa Campioni edizione 2025-2026.

Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Retirement is about so much more than money—it's a complete life transition. In this episode, I'm sharing my recent conversation with Adam Cmejla, where we explore the often-overlooked aspects of retirement planning. We discuss: ‣ Why retirement can feel like a grieving process—and how to navigate it ‣ The surprising role responsibility and community play in long-term happiness ‣ Smarter ways to approach Monte Carlo simulations, taxes, and cash management We also share our favorite retirement and investing charts that reinforce the principles essential to keeping financial plans grounded. Whether you're 10 years out from retirement or already there, this conversation challenges common assumptions and offers a helpful perspective on this stage of life. *** SCHEDULE YOUR FREE DISCOVERY MEETING: My team and I have guided hundreds of families across the U.S. through retirement's biggest challenges over the last two decades. The result? Smarter tax strategies, better investment decisions, and a more confident retirement. If you're seeking clarity and a proven retirement planning process, we'd be honored to help.

Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

During the last Euroleague Head Coaches Board in Oaka, we've the chance to interview ex Monaco coach Sasa Obradovic, talking about his tenure in Montecarlo, his particular relationship with Mike James, the growth of Alpha Diallo and many other interesting topic.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/backdoor-podcast--4175169/support.

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One of the many tools used during the financial planning process is the Monte Carlo simulation, which uses historical and personal financial data to predict the probability of certain outcomes in retirement. Nathan discusses how advisors use statistical analysis to help clients plan for retirement, and how the quality of the data you put in can mean the difference between success and failure. Also, on our MoneyTalk Moment in Financial History, Nathan and Daniel cover the birth of communism during the Russian Revolution, and the economic collapse that followed. Host: Nathan Beauvais CFP®, CIMA®, CPWA®; Special Guest: Daniel Sowa; Air Date: 8/20/2025. Have a question for the hosts? Visit sowafinancial.com/moneytalk to join the conversation!See omnystudio.com/listener for privacy information.

Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Prudence de rigueur pour les marchés, avant les grands rendez-vous de la semaineLes Bourses européennes ont démarré la semaine dans le rouge : à Paris, le CAC 40 perdait 0,55% hier, suivi par Francfort et Londres, elles aussi en légère baisse. L'EuroStoxx 50 était aussi en recul de 0,42%, preuve que la prudence domine chez les investisseurs.Même tendance outre Atlantique à la clôture des marchés européens. Quels sont ces grands rendez-vous de la semaine ?En toile de fond, l'actualité géopolitique retient toute l'attention : le président américain Donald Trump a reçu hier son homologue ukrainien Volodimir Zelensky, ainsi que plusieurs dirigeants européens, dont Emmanuel Macron, pour discuter d'un possible accord de paix.Côté économie, tous les regards sont tournés vers le symposium de Jackson Hole, qui se tiendra cette semaine. Le président de la Réserve fédérale américaine, Jérôme Powell, devrait y préciser la trajectoire des taux d'intérêt. Les marchés anticipent d'ailleurs à 85% une baisse des taux lors de la prochaine réunion de la Fed en septembre.Une semaine donc placée sous le signe de la prudence et de l'attente, tant sur le plan géopolitique qu'économique.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Exclusive interview with Monaco Small Forward Alpha Diallo talking about his tenure in Montecarlo, the influence Obradovic and Spanoulis had to his game, his idol and much more.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/backdoor-podcast--4175169/support.

Listener Q&A covering early retirement feasibility, VT vs. SPGM ETF comparison, tax-efficient liquidation of a legacy mutual fund, recommended financial planning resources and Monte Carlo tools, and the pros and cons of laddering target-date funds. 1:36 Can $120K a year work with two pensions and a 7% return? 4:57 VT vs. SPGM — same global reach or hidden differences? 8:58 Selling Grandma's mutual fund without gifting Uncle Sam 11:44 Best deep-dive planning books and free Monte Carlo tools 15:56 Target-date laddering — smart risk tweak or needless fuss? Learn more about your ad choices. Visit megaphone.fm/adchoices

Summer rewind: Greg Lindsay is an urban tech expert and a Senior Fellow at MIT. He's also a two-time Jeopardy champion and the only human to go undefeated against IBM's Watson. Greg joins thinkenergy to talk about how artificial intelligence (AI) is reshaping how we manage, consume, and produce energy—from personal devices to provincial grids, its rapid growth to the rising energy demand from AI itself. Listen in to learn how AI impacts our energy systems and what it means individually and industry-wide. Related links: ●       Greg Lindsay website: https://greglindsay.org/ ●       Greg Lindsay on LinkedIn: https://www.linkedin.com/in/greg-lindsay-8b16952/ ●       International Energy Agency (IEA): https://www.iea.org/ ●       Trevor Freeman on LinkedIn: https://www.linkedin.com/in/trevor-freeman-p-eng-cem-leed-ap-8b612114/ ●       Hydro Ottawa: https://hydroottawa.com/en    To subscribe using Apple Podcasts: https://podcasts.apple.com/us/podcast/thinkenergy/id1465129405   To subscribe using Spotify: https://open.spotify.com/show/7wFz7rdR8Gq3f2WOafjxpl   To subscribe on Libsyn: http://thinkenergy.libsyn.com/ --- Subscribe so you don't miss a video: https://www.youtube.com/user/hydroottawalimited   Follow along on Instagram: https://www.instagram.com/hydroottawa   Stay in the know on Facebook: https://www.facebook.com/HydroOttawa   Keep up with the posts on X: https://twitter.com/thinkenergypod --- Transcript: Trevor Freeman  00:00 Hi everyone. Well, summer is here, and the think energy team is stepping back a bit to recharge and plan out some content for the next season. We hope all of you get some much needed downtime as well, but we aren't planning on leaving you hanging over the next few months, we will be re releasing some of our favorite episodes from the past year that we think really highlight innovation, sustainability and community. These episodes highlight the changing nature of how we use and manage energy, and the investments needed to expand, modernize and strengthen our grid in response to that. All of this driven by people and our changing needs and relationship to energy as we move forward into a cleaner, more electrified future, the energy transition, as we talk about many times on this show. Thanks so much for listening, and we'll be back with all new content in September. Until then, happy listening.   Trevor Freeman  00:55 Welcome to think energy, a podcast that dives into the fast changing world of energy through conversations with industry leaders, innovators and people on the front lines of the energy transition. Join me, Trevor Freeman, as I explore the traditional, unconventional and up and coming facets of the energy industry. If you have any thoughts feedback or ideas for topics we should cover, please reach out to us at think energy at hydro ottawa.com, Hi everyone. Welcome back. Artificial intelligence, or AI, is a term that you're likely seeing and hearing everywhere today, and with good reason, the effectiveness and efficiency of today's AI, along with the ever increasing applications and use cases mean that in just the past few years, AI went from being a little bit fringe, maybe a little bit theoretical to very real and likely touching everyone's day to day lives in ways that we don't even notice, and we're just at the beginning of what looks to be a wave of many different ways that AI will shape and influence our society and our lives in the years to come. And the world of energy is no different. AI has the potential to change how we manage energy at all levels, from our individual devices and homes and businesses all the way up to our grids at the local, provincial and even national and international levels. At the same time, AI is also a massive consumer of energy, and the proliferation of AI data centers is putting pressure on utilities for more and more power at an unprecedented pace. But before we dive into all that, I also think it will be helpful to define what AI is. After all, the term isn't new. Like me, many of our listeners may have grown up hearing about Skynet from Terminator, or how from 2001 A Space Odyssey, but those malignant, almost sentient versions of AI aren't really what we're talking about here today. And to help shed some light on both what AI is as well as what it can do and how it might influence the world of energy, my guest today is Greg Lindsay, to put it in technical jargon, Greg's bio is super neat, so I do want to take time to run through it properly. Greg is a non resident Senior Fellow of MIT's future urban collectives lab Arizona State University's threat casting lab and the Atlantic Council's Scowcroft center for strategy and security. Most recently, he was a 2022-2023 urban tech Fellow at Cornell Tech's Jacobs Institute, where he explored the implications of AI and augmented reality at an urban scale. Previously, he was an urbanist in resident, which is a pretty cool title, at BMW minis urban tech accelerator, urban X, as well as the director of Applied Research at Montreal's new cities and Founding Director of Strategy at its mobility focused offshoot, co motion. He's advised such firms as Intel, Samsung, Audi, Hyundai, IKEA and Starbucks, along with numerous government entities such as 10 Downing Street, us, Department of Energy and NATO. And finally, and maybe coolest of all, Greg is also a two time Jeopardy champion and the only human to go undefeated against IBM's Watson. So on that note, Greg Lindsey, welcome to the show.   Greg Lindsay  04:14 Great to be here. Thanks for having me. Trevor,   Trevor Freeman  04:16 So Greg, we're here to talk about AI and the impacts that AI is going to have on energy, but AI is a bit of one of those buzzwords that we hear out there in a number of different spheres today. So let's start by setting the stage of what exactly we're talking about. So what do we mean when we say AI or artificial intelligence?   Speaker 1  04:37 Well, I'd say the first thing to keep in mind is that it is neither artificial nor intelligence. It's actually composites of many human hands making it. And of course, it's not truly intelligent either. I think there's at least two definitions for the layman's purposes. One is statistical machine learning. You know that is the previous generation of AI, we could say, doing deep, deep statistical analysis, looking for patterns fitting to. Patterns doing prediction. There's a great book, actually, by some ut professors at monk called prediction machines, which that was a great way of thinking about machine learning and sense of being able to do large scale prediction at scale. And that's how I imagine hydro, Ottawa and others are using this to model out network efficiencies and predictive maintenance and all these great uses. And then the newer, trendier version, of course, is large language models, your quads, your chat gpts, your others, which are based on transformer models, which is a whole series of work that many Canadians worked on, including Geoffrey Hinton and others. And this is what has produced the seemingly magical abilities to produce text and images on demand and large scale analysis. And that is the real power hungry beast that we think of as AI today.   Trevor Freeman  05:42 Right! So different types of AI. I just want to pick those apart a little bit. When you say machine learning, it's kind of being able to repetitively look at something or a set of data over and over and over again. And because it's a computer, it can do it, you know, 1000s or millions of times a second, and learn what, learn how to make decisions based on that. Is that fair to say?   Greg Lindsay  06:06 That's fair to say. And the thing about that is, is like you can train it on an output that you already know, large language models are just vomiting up large parts of pattern recognition, which, again, can feel like magic because of our own human brains doing it. But yeah, machine learning, you can, you know, you can train it to achieve outcomes. You can overfit the models where it like it's trained too much in the past, but, yeah, it's a large scale probabilistic prediction of things, which makes it so powerful for certain uses.   Trevor Freeman  06:26 Yeah, one of the neatest explanations or examples I've seen is, you know, you've got these language models where it seems like this AI, whether it's chat, DBT or whatever, is writing really well, like, you know, it's improving our writing. It's making things sound better. And it seems like it's got a brain behind it, but really, what it's doing is it's going out there saying, What have millions or billions of other people written like this? And how can I take the best things of that? And it can just do that really quickly, and it's learned that that model, so that's super helpful to understand what we're talking about here. So obviously, in your work, you look at the impact of AI on a number of different aspects of our world, our society. What we're talking about here today is particularly the impact of AI when it comes to energy. And I'd like to kind of bucketize our conversation a little bit today, and the first area I want to look at is, what will ai do when it comes to energy for the average Canadian? Let's say so in my home, in my business, how I move around? So I'll start with that. It's kind of a high level conversation. Let's start talking about the different ways that AI will impact you know that our average listener here?   Speaker 1  07:41 Um, yeah, I mean, we can get into a discussion about what it means for the average Canadian, and then also, of course, what it means for Canada in the world as well, because I just got back from South by Southwest in Austin, and, you know, for the second, third year in row, AI was on everyone's lips. But really it's the energy. Is the is the bottleneck. It's the forcing factor. Everyone talked about it, the fact that all the data centers we can get into that are going to be built in the direction of energy. So, so, yeah, energy holds the key to the puzzle there. But, um, you know, from the average gain standpoint, I mean, it's a question of, like, how will these tools actually play out, you know, inside of the companies that are using this, right? And that was a whole other discussion too. It's like, okay, we've been playing around with these tools for two, three years now, what do they actually use to deliver value of your large language model? So I've been saying this for 10 years. If you look at the older stuff you could start with, like smart thermostats, even look at the potential savings of this, of basically using machine learning to optimize, you know, grid optimize patterns of usage, understanding, you know, the ebbs and flows of the grid, and being able to, you know, basically send instructions back and forth. So you know there's stats. You know that, basically you know that you know you could save 10 to 25% of electricity bills. You know, based on this, you could reduce your heating bills by 10 to 15% again, it's basically using this at very large scales of the scale of hydro Ottawa, bigger, to understand this sort of pattern usage. But even then, like understanding like how weather forecasts change, and pulling that data back in to basically make fine tuning adjustments to the thermostats and things like that. So that's one stands out. And then, you know, we can think about longer term. I mean, yeah, lots have been lots has been done on imagining, like electric mobility, of course, huge in Canada, and what that's done to sort of change the overall energy mix virtual power plants. This is something that I've studied, and we've been writing about at Fast Company. At Fast Company beyond for 20 years, imagining not just, you know, the ability to basically, you know, feed renewable electricity back into the grid from people's solar or from whatever sources they have there, but the ability of utilities to basically go in and fine tune, to have that sort of demand shaping as well. And then I think the most interesting stuff, at least in demos, and also blockchain, which has had many theoretical uses, and I've got to see a real one. But one of the best theoretical ones was being able to create neighborhood scale utilities. Basically my cul de sac could have one, and we could trade clean electrons off of our solar panels through our batteries and home scale batteries, using Blockchain to basically balance this out. Yeah, so there's lots of potential, but yeah, it comes back to the notion of people want cheaper utility bills. I did this piece 10 years ago for the Atlantic Council on this we looked at a multi country survey, and the only reason anybody wanted a smart home, which they just were completely skeptical about, was to get those cheaper utility bills. So people pay for that.   Trevor Freeman  10:19 I think it's an important thing to remember, obviously, especially for like the nerds like me, who part of my driver is, I like that cool new tech. I like that thing that I can play with and see my data. But for most people, no matter what we're talking about here, when it comes to that next technology, the goal is make my life a little bit easier, give me more time or whatever, and make things cheaper. And I think especially in the energy space, people aren't putting solar panels on their roof because it looks great. And, yeah, maybe people do think it looks great, but they're putting it up there because they want cheaper electricity. And it's going to be the same when it comes to batteries. You know, there's that add on of resiliency and reliability, but at the end of the day, yeah, I want my bill to be cheaper. And what I'm hearing from you is some of the things we've already seen, like smart thermostats get better as AI gets better. Is that fair to say?   Greg Lindsay  11:12 Well, yeah, on the machine learning side, that you know, you get ever larger data points. This is why data is the coin of the realm. This is why there's a race to collect data on everything. Is why every business model is data collection and everything. Because, yes, not only can they get better, but of course, you know, you compile enough and eventually start finding statistical inferences you never meant to look for. And this is why I've been involved. Just as a side note, for example, of cities that have tried to implement their own data collection of electric scooters and eventually electric vehicles so they could understand these kinds of patterns, it's really the key to anything. And so it's that efficiency throughput which raises some really interesting philosophical questions, particularly about AI like, this is the whole discussion on deep seek. Like, if you make the models more efficient, do you have a Jevons paradox, which is the paradox of, like, the more energy you save through efficiency, the more you consume because you've made it cheaper. So what does this mean that you know that Canadian energy consumption is likely to go up the cleaner and cheaper the electrons get. It's one of those bedeviling sort of functions.   Trevor Freeman  12:06 Yeah interesting. That's definitely an interesting way of looking at it. And you referenced this earlier, and I will talk about this. But at the macro level, the amount of energy needed for these, you know, AI data centers in order to do all this stuff is, you know, we're seeing that explode.   Greg Lindsay  12:22 Yeah, I don't know that. Canadian statistics my fingertips, but I brought this up at Fast Company, like, you know, the IEA, I think International Energy Agency, you know, reported a 4.3% growth in the global electricity grid last year, and it's gonna be 4% this year. That does not sound like much. That is the equivalent of Japan. We're adding in Japan every year to the grid for at least the next two to three years. Wow. And that, you know, that's global South, air conditioning and other needs here too, but that the data centers on top is like the tip of the spear. It's changed all this consumption behavior, where now we're seeing mothballed coal plants and new plants and Three Mile Island come back online, as this race for locking up electrons, for, you know, the race to build God basically, the number of people in AI who think they're literally going to build weekly godlike intelligences, they'll, they won't stop at any expense. And so they will buy as much energy as they can get.   Trevor Freeman  13:09 Yeah, well, we'll get to that kind of grid side of things in a minute. Let's stay at the home first. So when I look at my house, we talked about smart thermostats. We're seeing more and more automation when it comes to our homes. You know, we can program our lights and our door locks and all this kind of stuff. What does ai do in order to make sure that stuff is contributing to efficiency? So I want to do all those fun things, but use the least amount of energy possible.   Greg Lindsay  13:38 Well, you know, I mean, there's, again, there's various metrics there to basically, sort of, you know, program your lights. And, you know, Nest is, you know, Google. Nest is an example of this one, too, in terms of basically learning your ebb and flow and then figuring out how to optimize it over the course of the day. So you can do that, you know, we've seen, again, like the home level. We've seen not only the growth in solar panels, but also in those sort of home battery integration. I was looking up that Tesla Powerwall was doing just great in Canada, until the last couple of months. I assume so, but I it's been, it's been heartening to see that, yeah, this sort of embrace of home energy integration, and so being able to level out, like, peak flow off the grid, so Right? Like being able to basically, at moments of peak demand, to basically draw on your own local resources and reduce that overall strain. So there's been interesting stuff there. But I want to focus for a moment on, like, terms of thinking about new uses. Because, you know, again, going back to how AI will influence the home and automation. You know, Jensen Wong of Nvidia has talked about how this will be the year of robotics. Google, Gemini just applied their models to robotics. There's startups like figure there's, again, Tesla with their optimists, and, yeah, there's a whole strain of thought that we're about to see, like home robotics, perhaps a dream from like, the 50s. I think this is a very Disney World esque Epcot Center, yeah, with this idea of jetsy, yeah, of having home robots doing work. You can see concept videos a figure like doing the actual vacuuming. I mean, we invented Roombas to this, but, but it also, I, you know, I've done a lot of work. Our own thinking around electric delivery vehicles. We could talk a lot about drones. We could talk a lot about the little robots that deliver meals on the sidewalk. There's a lot of money in business models about increasing access and people needing to maybe move less, to drive and do all these trips to bring it to them. And that's a form of home automation, and that's all batteries. That is all stuff off the grid too. So AI is that enable those things, these things that can think and move and fly and do stuff and do services on your behalf, and so people might find this huge new source of demand from that as well.   Trevor Freeman  15:29 Yeah, that's I hadn't really thought about the idea that all the all these sort of conveniences and being able to summon them to our homes cause us to move around less, which also impacts transportation, which is another area I kind of want to get to. And I know you've, you've talked a little bit about E mobility, so where do you see that going? And then, how does AI accelerate that transition, or accelerate things happening in that space?   Greg Lindsay  15:56 Yeah, I mean, I again, obviously the EV revolutions here Canada like, one of the epicenters Canada, Norway there, you know, that still has the vehicle rebates and things. So, yeah. I mean, we've seen, I'm here in Montreal, I think we've got, like, you know, 30 to 13% of sales is there, and we've got our 2035, mandate. So, yeah. I mean, you see this push, obviously, to harness all of Canada's clean, mostly hydro electricity, to do this, and, you know, reduce its dependence on fossil fuels for either, you know, Climate Change Politics reasons, but also just, you know, variable energy prices. So all of that matters. But, you know, I think the key to, like the electric mobility revolution, again, is, is how it's going to merge with AI and it's, you know, it's not going to just be the autonomous, self driving car, which is sort of like the horseless carriage of autonomy. It's gonna be all this other stuff, you know. My friend Dan Hill was in China, and he was thinking about like, electric scooters, you know. And I mentioned this to hydro Ottawa, like, the electric scooter is one of the leading causes of how we've taken internal combustion engine vehicles offline across the world, mostly in China, and put people on clean electric motors. What happens when you take those and you make those autonomous, and you do it with, like, deep seek and some cameras, and you sort of weld it all together so you could have a world of a lot more stuff in motion, and not just this world where we have to drive as much. And that, to me, is really exciting, because that changes, like urban patterns, development patterns, changes how you move around life, those kinds of things as well. That's that might be a little farther out, but, but, yeah, this sort of like this big push to build out domestic battery industries, to build charging points and the sort of infrastructure there, I think it's going to go in direction, but it doesn't look anything like, you know, a sedan or an SUV that just happens to be electric.   Trevor Freeman  17:33 I think that's a the step change is change the drive train of the existing vehicles we have, you know, an internal combustion to a battery. The exponential change is exactly what you're saying. It's rethinking this.   Greg Lindsay  17:47 Yeah, Ramesam and others have pointed out, I mean, again, like this, you know, it's, it's really funny to see this pushback on EVs, you know. I mean, I love a good, good roar of an internal combustion engine myself, but, but like, you know, Ramesam was an energy analyst, has pointed out that, like, you know, EVS were more cost competitive with ice cars in 2018 that's like, nearly a decade ago. And yeah, the efficiency of electric motors, particularly regenerative braking and everything, it just blows the cost curves away of ice though they will become the equivalent of keeping a thorough brat around your house kind of thing. Yeah, so, so yeah, it's just, it's that overall efficiency of the drive train. And that's the to me, the interesting thing about both electric motors, again, of autonomy is like, those are general purpose technologies. They get cheaper and smaller as they evolve under Moore's Law and other various laws, and so they get to apply to more and more stuff.   Trevor Freeman  18:32 Yeah. And then when you think about once, we kind of figure that out, and we're kind of already there, or close to it, if not already there, then it's opening the door to those other things you're talking about. Of, well, do we, does everybody need to have that car in their driveway? Are we rethinking how we're actually just doing transportation in general? And do we need a delivery truck? Or can it be delivery scooter? Or what does that look like?   Greg Lindsay  18:54 Well, we had a lot of those discussions for a long time, particularly in the mobility space, right? Like, and like ride hailing, you know, like, oh, you know, that was always the big pitch of an Uber is, you know, your car's parked in your driveway, like 94% of the time. You know, what happens if you're able to have no mobility? Well, we've had 15 years of Uber and these kinds of services, and we still have as many cars. But people are also taking this for mobility. It's additive. And I raised this question, this notion of like, it's just sort of more and more, more options, more availability, more access. Because the same thing seems to be going on with energy now too. You know, listeners been following along, like the conversation in Houston, you know, a week or two ago at Sarah week, like it's the whole notion of energy realism. And, you know, there's the new book out, more is more is more, which is all about the fact that we've never had an energy transition. We just kept piling up. Like the world burned more biomass last year than it did in 1900 it burned more coal last year than it did at the peak of coal. Like these ages don't really end. They just become this sort of strata as we keep piling energy up on top of it. And you know, I'm trying to sound the alarm that we won't have an energy transition. What that means for climate change? But similar thing, it's. This rebound effect, the Jevons paradox, named after Robert Stanley Jevons in his book The question of coal, where he noted the fact that, like, England was going to need more and more coal. So it's a sobering thought. But, like, I mean, you know, it's a glass half full, half empty in many ways, because the half full is like increasing technological options, increasing changes in lifestyle. You can live various ways you want, but, but, yeah, it's like, I don't know if any of it ever really goes away. We just get more and more stuff,   Trevor Freeman  20:22 Exactly, well. And, you know, to hear you talk about the robotics side of things, you know, looking at the home, yeah, more, definitely more. Okay, so we talked about kind of home automation. We've talked about transportation, how we get around. What about energy management? And I think about this at the we'll talk about the utility side again in a little bit. But, you know, at my house, or for my own personal use in my life, what is the role of, like, sort of machine learning and AI, when it comes to just helping me manage my own energy better and make better decisions when it comes to energy? ,   Greg Lindsay  20:57 Yeah, I mean, this is where it like comes in again. And you know, I'm less and less of an expert here, but I've been following this sort of discourse evolve. And right? It's the idea of, you know, yeah, create, create. This the set of tools in your home, whether it's solar panels or batteries or, you know, or Two Way Direct, bi directional to the grid, however it works. And, yeah, and people, you know, given this option of savings, and perhaps, you know, other marketing messages there to curtail behavior. You know? I mean, I think the short answer the question is, like, it's an app people want, an app that tell them basically how to increase the efficiency of their house or how to do this. And I should note that like, this has like been the this is the long term insight when it comes to like energy and the clean tech revolution. Like my Emery Levin says this great line, which I've always loved, which is, people don't want energy. They want hot showers and cold beer. And, you know, how do you, how do you deliver those things through any combination of sticks and carrots, basically like that. So, So, hence, why? Like, again, like, you know, you know, power walls, you know, and, and, and, you know, other sort of AI controlled batteries here that basically just sort of smooth out to create the sort of optimal flow of electrons into your house, whether that's coming drive directly off the grid or whether it's coming out of your backup and then recharging that the time, you know, I mean, the surveys show, like, more than half of Canadians are interested in this stuff, you know, they don't really know. I've got one set here, like, yeah, 61% are interested in home energy tech, but only 27 understand, 27% understand how to optimize them. So, yeah. So people need, I think, perhaps, more help in handing that over. And obviously, what's exciting for the, you know, the utility level is, like, you know, again, aggregate all that individual behavior together and you get more models that, hope you sort of model this out, you know, at both greater scale and ever more fine grained granularity there. So, yeah, exactly. So I think it's really interesting, you know, I don't know, like, you know, people have gamified it. What was it? I think I saw, like, what is it? The affordability fund trust tried to basically gamify AI energy apps, and it created various savings there. But a lot of this is gonna be like, as a combination like UX design and incentives design and offering this to people too, about, like, why you should want this and money's one reason, but maybe there's others.   Trevor Freeman  22:56 Yeah, and we talk about in kind of the utility sphere, we talk about how customers, they don't want all the data, and then have to go make their own decisions. They want those decisions to be made for them, and they want to say, look, I want to have you tell me the best rate plan to be on. I want to have you automatically switch me to the best rate plan when my consumption patterns change and my behavior chat patterns change. That doesn't exist today, but sort of that fast decision making that AI brings will let that become a reality sometime in the future,   Greg Lindsay  23:29 And also in theory, this is where LLMs come into play. Is like, you know, to me, what excites me the most about that is the first time, like having a true natural language interface, like having being able to converse with an, you know, an AI, let's hopefully not chat bot. I think we're moving out on chat bots, but some sort of sort of instantiation of an AI to be like, what plan should I be on? Can you tell me what my behavior is here and actually having some sort of real language conversation with it? Not decision trees, not event statements, not chat bots.   Trevor Freeman  23:54 Yeah, absolutely. Okay, so we've kind of teased around this idea of looking at the utility levels, obviously, at hydro Ottawa, you referenced this just a minute ago. We look at all these individual cases, every home that has home automation or solar storage, and we want to aggregate that and understand what, what can we do to help manage the grid, help manage all these new energy needs, shift things around. So let's talk a little bit about the role that AI can play at the utility scale in helping us manage the grid.   Greg Lindsay  24:28 All right? Well, yeah, there's couple ways to approach it. So one, of course, is like, let's go back to, like, smart meters, right? Like, and this is where I don't know how many hydro Ottawa has, but I think, like, BC Hydro has like, 2 million of them, sometimes they get politicized, because, again, this gets back to this question of, like, just, just how much nanny state you want. But, you know, you know, when you reach the millions, like, yeah, you're able to get that sort of, you know, obviously real time, real time usage, real time understanding. And again, if you can do that sort of grid management piece where you can then push back, it's visual game changer. But, but yeah. I mean, you know, yeah, be. See hydro is pulling in. I think I read like, like, basically 200 million data points a day. So that's a lot to train various models on. And, you know, I don't know exactly the kind of savings they have, but you can imagine there, whether it's, you know, them, or Toronto Hydro, or hydro Ottawa and others creating all these monitoring points. And again, this is the thing that bedells me, by the way, just philosophically about modern life, the notion of like, but I don't want you to be collecting data off me at all times, but look at what you can do if you do It's that constant push pull of some sort of combination of privacy and agency, and then just the notion of like statistics, but, but there you are, but, but, yeah, but at the grid level, then I mean, like, yeah. I mean, you can sort of do the same thing where, like, you know, I mean, predictive maintenance is the obvious one, right? I have been writing about this for large enterprise software companies for 20 years, about building these data points, modeling out the lifetime of various important pieces equipment, making sure you replace them before you have downtime and terrible things happen. I mean, as we're as we're discussing this, look at poor Heathrow Airport. I am so glad I'm not flying today, electrical substation blowing out two days of the world's most important hub offline. So that's where predictive maintenance comes in from there. And, yeah, I mean, I, you know, I again, you know, modeling out, you know, energy flow to prevent grid outages, whether that's, you know, the ice storm here in Quebec a couple years ago. What was that? April 23 I think it was, yeah, coming up in two years. Or our last ice storm, we're not the big one, but that one, you know, where we had big downtime across the grid, like basically monitoring that and then I think the other big one for AI is like, Yeah, is this, this notion of having some sort of decision support as well, too, and sense of, you know, providing scenarios and modeling out at scale the potential of it? And I don't think, I don't know about this in a grid case, but the most interesting piece I wrote for Fast Company 20 years ago was an example, ago was an example of this, which was a fledgling air taxi startup, but they were combining an agent based model, so using primitive AI to create simple rules for individual agents and build a model of how they would behave, which you can create much more complex models. Now we could talk about agents and then marrying that to this kind of predictive maintenance and operations piece, and marrying the two together. And at that point, you could have a company that didn't exist, but that could basically model itself in real time every day in the life of what it is. You can create millions and millions and millions of Monte Carlo operations. And I think that's where perhaps both sides of AI come together truly like the large language models and agents, and then the predictive machine learning. And you could basically hydro or others, could build this sort of deep time machine where you can model out all of these scenarios, millions and millions of years worth, to understand how it flows and contingencies as well. And that's where it sort of comes up. So basically something happens. And like, not only do you have a set of plans, you have an AI that has done a million sets of these plans, and can imagine potential next steps of this, or where to deploy resources. And I think in general, that's like the most powerful use of this, going back to prediction machines and just being able to really model time in a way that we've never had that capability before. And so you probably imagine the use is better than I.   Trevor Freeman  27:58 Oh man, it's super fascinating, and it's timely. We've gone through the last little while at hydro Ottawa, an exercise of updating our playbook for emergencies. So when there are outages, what kind of outage? What's the sort of, what are the trigger points to go from, you know, what we call a level one to a level two to level three. But all of this is sort of like people hours that are going into that, and we're thinking through these scenarios, and we've got a handful of them, and you're just kind of making me think, well, yeah, what if we were able to model that out? And you bring up this concept of agents, let's tease into that a little bit explain what you mean when you're talking about agents.   Greg Lindsay  28:36 Yeah, so agentic systems, as the term of art is, AI instantiations that have some level of autonomy. And the archetypal example of this is the Stanford Smallville experiment, where they took basically a dozen large language models and they gave it an architecture where they could give it a little bit of backstory, ruminate on it, basically reflect, think, decide, and then act. And in this case, they used it to plan a Valentine's Day party. So they played out real time, and the LLM agents, like, even played matchmaker. They organized the party, they sent out invitations, they did these sorts of things. Was very cute. They put it out open source, and like, three weeks later, another team of researchers basically put them to work writing software programs. So you can see they organized their own workflow. They made their own decisions. There was a CTO. They fact check their own work. And this is evolving into this grand vision of, like, 1000s, millions of agents, just like, just like you spin up today an instance of Amazon Web Services to, like, host something in the cloud. You're going to spin up an agent Nvidia has talked about doing with healthcare and others. So again, coming back to like, the energy implications of that, because it changes the whole pattern. Instead of huge training runs requiring giant data centers. You know, it's these agents who are making all these calls and doing more stuff at the edge, but, um, but yeah, in this case, it's the notion of, you know, what can you put the agents to work doing? And I bring this up again, back to, like, predictive maintenance, or for hydro Ottawa, there's another amazing paper called virtual in real life. And I chatted with one of the principal authors. It created. A half dozen agents who could play tour guide, who could direct you to a coffee shop, who do these sorts of things, but they weren't doing it in a virtual world. They were doing it in the real one. And to do it in the real world, you took the agent, you gave them a machine vision capability, so added that model so they could recognize objects, and then you set them loose inside a digital twin of the world, in this case, something very simple, Google Street View. And so in the paper, they could go into like New York Central Park, and they could count every park bench and every waste bin and do it in seconds and be 99% accurate. And so agents were monitoring the landscape. Everything's up, because you can imagine this in the real world too, that we're going to have all the time. AIS roaming the world, roaming these virtual maps, these digital twins that we build for them and constantly refresh from them, from camera data, from sensor data, from other stuff, and tell us what this is. And again, to me, it's really exciting, because that's finally like an operating system for the internet of things that makes sense, that's not so hardwired that you can ask agents, can you go out and look for this for me? Can you report back on this vital system for me? And they will be able to hook into all of these kinds of representations of real time data where they're emerging from, and give you aggregated reports on this one. And so, you know, I think we have more visibility in real time into the real world than we've ever had before.   Trevor Freeman  31:13 Yeah, I want to, I want to connect a few dots here for our listeners. So bear with me for a second. Greg. So for our listeners, there was a podcast episode we did about a year ago on our grid modernization roadmap, and we talked about one of the things we're doing with grid modernization at hydro Ottawa and utilities everywhere doing this is increasing the sensor data from our grid. So we're, you know, right now, we've got visibility sort of to our station level, sometimes one level down to some switches. But in the future, we'll have sensors everywhere on our grid, every switch, every device on our grid, will have a sensor gathering data. Obviously, you know, like you said earlier, millions and hundreds of millions of data points every second coming in. No human can kind of make decisions on that, and what you're describing is, so now we've got all this data points, we've got a network of information out there, and you could create this agent to say, Okay, you are. You're my transformer agent. Go out there and have a look at the run temperature of every transformer on the network, and tell me where the anomalies are, which ones are running a half a degree or two degrees warmer than they should be, and report back. And now I know hydro Ottawa, that the controller, the person sitting in the room, knows, Hey, we should probably go roll a truck and check on that transformer, because maybe it's getting end of life. Maybe it's about to go and you can do that across the entire grid. That's really fascinating,   Greg Lindsay  32:41 And it's really powerful, because, I mean, again, these conversations 20 years ago at IoT, you know you're going to have statistical triggers, and you would aggregate these data coming off this, and there was a lot of discussion there, but it was still very, like hardwired, and still very Yeah, I mean, I mean very probabilistic, I guess, for a word that went with agents like, yeah, you've now created an actual thing that can watch those numbers and they can aggregate from other systems. I mean, lots, lots of potential there hasn't quite been realized, but it's really exciting stuff. And this is, of course, where that whole direction of the industry is flowing. It's on everyone's lips, agents.   Trevor Freeman  33:12 Yeah. Another term you mentioned just a little bit ago that I want you to explain is a digital twin. So tell us what a digital twin is.   Greg Lindsay  33:20 So a digital twin is, well, the matrix. Perhaps you could say something like this for listeners of a certain age, but the digital twin is the idea of creating a model of a piece of equipment, of a city, of the world, of a system. And it is, importantly, it's physics based. It's ideally meant to represent and capture the real time performance of the physical object it's based on, and in this digital representation, when something happens in the physical incarnation of it, it triggers a corresponding change in state in the digital twin, and then vice versa. In theory, you know, you could have feedback loops, again, a lot of IoT stuff here, if you make changes virtually, you know, perhaps it would cause a change in behavior of the system or equipment, and the scales can change from, you know, factory equipment. Siemens, for example, does a lot of digital twin work on this. You know, SAP, big, big software companies have thought about this. But the really crazy stuff is, like, what Nvidia is proposing. So first they started with a digital twin. They very modestly called earth two, where they were going to model all the weather and climate systems of the planet down to like the block level. There's a great demo of like Jensen Wong walking you through a hurricane, typhoons striking the Taipei, 101, and how, how the wind currents are affecting the various buildings there, and how they would change that more recently, what Nvidia is doing now is, but they just at their big tech investor day, they just partner with General Motors and others to basically do autonomous cars. And what's crucial about it, they're going to train all those autonomous vehicles in an NVIDIA built digital twin in a matrix that will act, that will be populated by agents that will act like people, people ish, and they will be able to run millions of years of autonomous vehicle training in this and this is how they plan to catch up to. Waymo or, you know, if Tesla's robotaxis are ever real kind of thing, you know, Waymo built hardwired like trained on real world streets, and that's why they can only operate in certain operating domain environments. Nvidia is gambling that with large language models and transformer models combined with digital twins, you can do these huge leapfrog effects where you can basically train all sorts of synthetic agents in real world behavior that you have modeled inside the machine. So again, that's the kind, that's exactly the kind of, you know, environment that you're going to train, you know, your your grid of the future on for modeling out all your contingency scenarios.   Trevor Freeman  35:31 Yeah, again, you know, for to bring this to the to our context, a couple of years ago, we had our the direcco. It's a big, massive windstorm that was one of the most damaging storms that we've had in Ottawa's history, and we've made some improvements since then, and we've actually had some great performance since then. Imagine if we could model that derecho hitting our grid from a couple different directions and figure out, well, which lines are more vulnerable to wind speeds, which lines are more vulnerable to flying debris and trees, and then go address that and do something with that, without having to wait for that storm to hit. You know, once in a decade or longer, the other use case that we've talked about on this one is just modeling what's happening underground. So, you know, in an urban environments like Ottawa, like Montreal, where you are, there's tons of infrastructure under the ground, sewer pipes, water pipes, gas lines, electrical lines, and every time the city wants to go and dig up a road and replace that road, replace that sewer, they have to know what's underground. We want to know what's underground there, because our infrastructure is under there. As the electric utility. Imagine if you had a model where you can it's not just a map. You can actually see what's happening underground and determine what makes sense to go where, and model out these different scenarios of if we underground this line or that line there. So lots of interesting things when it comes to a digital twin. The digital twin and Agent combination is really interesting as well, and setting those agents loose on a model that they can play with and understand and learn from. So talk a little bit about.   Greg Lindsay  37:11 that. Yeah. Well, there's a couple interesting implications just the underground, you know, equipment there. One is interesting because in addition to, like, you know, you know, having captured that data through mapping and other stuff there, and having agents that could talk about it. So, you know, next you can imagine, you know, I've done some work with augmented reality XR. This is sort of what we're seeing again, you know, meta Orion has shown off their concept. Google's brought back Android XR. Meta Ray Bans are kind of an example of this. But that's where this data will come from, right? It's gonna be people wearing these wearables in the world, capturing all this camera data and others that's gonna be fed into these digital twins to refresh them. Meta has a particularly scary demo where you know where you the user, the wearer leaves their keys on their coffee table and asks metas, AI, where their coffee where their keys are, and it knows where they are. It tells them and goes back and shows them some data about it. I'm like, well, to do that, meta has to have a complete have a complete real time map of your entire house. What could go wrong. And that's what all these companies aspire to of reality. So, but yeah, you can imagine, you know, you can imagine a worker. And I've worked with a startup out of urban X, a Canada startup, Canadian startup called context steer. And you know, is the idea of having real time instructions and knowledge manuals available to workers, particularly predictive maintenance workers and line workers. So you can imagine a technician dispatched to deal with this cut in the pavement and being able to see with XR and overlay of like, what's actually under there from the digital twin, having an AI basically interface with what's sort of the work order, and basically be your assistant that can help you walk you through it, in case, you know, you run into some sort of complication there, hopefully that won't be, you know, become like, turn, turn by turn, directions for life that gets into, like, some of the questions about what we wanted out of our workforce. But there's some really interesting combinations of those things, of like, you know, yeah, mapping a world for AIS, ais that can understand it, that could ask questions in it, that can go probe it, that can give you advice on what to do in it. All those things are very close for good and for bad.   Trevor Freeman  39:03 You kind of touched on my next question here is, how do we make sure this is all in the for good or mostly in the for good category, and not the for bad category you talk in one of the papers that you wrote about, you know, AI and augmented reality in particular, really expanding the attack surface for malicious actors. So we're creating more opportunities for whatever the case may be, if it's hacking or if it's malware, or if it's just, you know, people that are up to nefarious things. How do we protect against that? How do we make sure that our systems are safe that the users of our system. So in our case, our customers, their data is safe, their the grid is safe. How do we make sure that?   Greg Lindsay  39:49 Well, the very short version is, whatever we're spending on cybersecurity, we're not spending enough. And honestly, like everybody who is no longer learning to code, because we can be a quad or ChatGPT to do it, I. Is probably there should be a whole campaign to repurpose a big chunk of tech workers into cybersecurity, into locking down these systems, into training ethical systems. There's a lot of work to be done there. But yeah, that's been the theme for you know that I've seen for 10 years. So that paper I mentioned about sort of smart homes, the Internet of Things, and why people would want a smart home? Well, yeah, the reason people were skeptical is because they saw it as basically a giant attack vector. My favorite saying about this is, is, there's a famous Arthur C Clarke quote that you know, any sufficiently advanced technology is magic Tobias Ravel, who works at Arup now does their head of foresight has this great line, any sufficiently advanced hacking will feel like a haunting meaning. If you're in a smart home that's been hacked, it will feel like you're living in a haunted house. Lights will flicker on and off, and systems will turn and go haywire. It'll be like you're living with a possessed house. And that's true of cities or any other systems. So we need to do a lot of work on just sort of like locking that down and securing that data, and that is, you know, we identified, then it has to go all the way up and down the supply chain, like you have to make sure that there is, you know, a chain of custody going back to when components are made, because a lot of the attacks on nest, for example. I mean, you want to take over a Google nest, take it off the wall and screw the back out of it, which is a good thing. It's not that many people are prying open our thermostats, but yeah, if you can get your hands on it, you can do a lot of these systems, and you can do it earlier in the supply chain and sorts of infected pieces and things. So there's a lot to be done there. And then, yeah, and then, yeah, and then there's just a question of, you know, making sure that the AIs are ethically trained and reinforced. And, you know, a few people want to listeners, want to scare themselves. You can go out and read some of the stuff leaking out of anthropic and others and make clot of, you know, models that are trying to hide their own alignments and trying to, like, basically copy themselves. Again, I don't believe that anything things are alive or intelligent, but they exhibit these behaviors as part of the probabilistic that's kind of scary. So there's a lot to be done there. But yeah, we worked on this, the group that I do foresight with Arizona State University threat casting lab. We've done some work for the Secret Service and for NATO and, yeah, there'll be, you know, large scale hackings on infrastructure. Basically the equivalent can be the equivalent can be the equivalent to a weapons of mass destruction attack. We saw how Russia targeted in 2014 the Ukrainian grid and hacked their nuclear plans. This is essential infrastructure more important than ever, giving global geopolitics say the least, so that needs to be under consideration. And I don't know, did I scare you enough yet? What are the things we've talked through here that, say the least about, you know, people being, you know, tricked and incepted by their AI girlfriends, boyfriends. You know people who are trying to AI companions. I can't possibly imagine what could go wrong there.   Trevor Freeman  42:29 I mean, it's just like, you know, I don't know if this is 15 or 20, or maybe even 25 years ago now, like, it requires a whole new level of understanding when we went from a completely analog world to a digital world and living online, and people, I would hope, to some degree, learned to be skeptical of things on the internet and learned that this is that next level. We now need to learn the right way of interacting with this stuff. And as you mentioned, building the sort of ethical code and ethical guidelines into these language models into the AI. Learning is pretty critical for our listeners. We do have a podcast episode on cybersecurity. I encourage you to go listen to it and reassure yourself that, yes, we are thinking about this stuff. And thanks, Greg, you've given us lots more to think about in that area as well. When it comes to again, looking back at utilities and managing the grid, one thing we're going to see, and we've talked a lot about this on the show, is a lot more distributed generation. So we're, you know, the days of just the central, large scale generation, long transmission lines that being the only generation on the grid. Those days are ending. We're going to see more distributed generations, solar panels on roofs, batteries. How does AI help a utility manage those better, interact with those better get more value out of those things?   Greg Lindsay  43:51 I guess that's sort of like an extension of some of the trends I was talking about earlier, which is the notion of, like, being able to model complex systems. I mean, that's effectively it, right, like you've got an increasingly complex grid with complex interplays between it, you know, figuring out how to basically based on real world performance, based on what you're able to determine about where there are correlations and codependencies in the grid, where point where choke points could emerge, where overloading could happen, and then, yeah, basically, sort of building that predictive system to Basically, sort of look for what kind of complex emergent behavior comes out of as you keep adding to it and and, you know, not just, you know, based on, you know, real world behavior, but being able to dial that up to 11, so to speak, and sort of imagine sort of these scenarios, or imagine, you know, what, what sort of long term scenarios look like in terms of, like, what the mix, how the mix changes, how the geography changes, all those sorts of things. So, yeah, I don't know how that plays out in the short term there, but it's this combination, like I'm imagining, you know, all these different components playing SimCity for real, if one will.   Trevor Freeman  44:50 And being able to do it millions and millions and millions of times in a row, to learn every possible iteration and every possible thing that might happen. Very cool. Okay. So last kind of area I want to touch on you did mention this at the beginning is the the overall power implications of of AI, of these massive data centers, obviously, at the utility, that's something we are all too keenly aware of. You know, the stat that that I find really interesting is a normal Google Search compared to, let's call it a chat GPT search. That chat GPT search, or decision making, requires 10 times the amount of energy as that just normal, you know, Google Search looking out from a database. Do you see this trend? I don't know if it's a trend. Do you see this continuing like AI is just going to use more power to do its decision making, or will we start to see more efficiencies there? And the data centers will get better at doing what they do with less energy. What is the what does the future look like in that sector?   Greg Lindsay  45:55 All the above. It's more, is more, is more! Is the trend, as far as I can see, and every decision maker who's involved in it. And again, Jensen Wong brought this up at the big Nvidia Conference. That basically he sees the only constraint on this continuing is availability of energy supplies keep it going and South by Southwest. And in some other conversations I've had with bandwidth companies, telcos, like laying 20 lumen technologies, United States is laying 20,000 new miles of fiber optic cables. They've bought 10% of Corning's total fiber optic output for the next couple of years. And their customers are the hyperscalers. They're, they're and they're rewiring the grid. That's why, I think it's interesting. This has something, of course, for thinking about utilities, is, you know, the point to point Internet of packet switching and like laying down these big fiber routes, which is why all the big data centers United States, the majority of them, are in north of them are in Northern Virginia, is because it goes back to the network hub there. Well, lumen is now wiring this like basically this giant fabric, this patchwork, which can connect data center to data center, and AI to AI and cloud to cloud, and creating this entirely new environment of how they are all directly connected to each other through some of this dedicated fiber. And so you can see how this whole pattern is changing. And you know, the same people are telling me that, like, yeah, the where they're going to build this fiber, which they wouldn't tell me exactly where, because it's very tradable, proprietary information, but, um, but it's following the energy supplies. It's following the energy corridors to the American Southwest, where there's solar and wind in Texas, where you can get natural gas, where you can get all these things. It will follow there. And I of course, assume the same is true in Canada as we build out our own sovereign data center capacity for this. So even, like deep seek, for example, you know, which is, of course, the hyper efficient Chinese model that spooked the markets back in January. Like, what do you mean? We don't need a trillion dollars in capex? Well, everyone's quite confident, including again, Jensen Wong and everybody else that, yeah, the more efficient models will increase this usage. That Jevons paradox will play out once again, and we'll see ever more of it. To me, the question is, is like as how it changes? And of course, you know, you know, this is a bubble. Let's, let's, let's be clear, data centers are a bubble, just like railroads in 1840 were a bubble. And there will be a bust, like not everyone's investments will pencil out that infrastructure will remain maybe it'll get cheaper. We find new uses for it, but it will, it will eventually bust at some point and that's what, to me, is interesting about like deep seeking, more efficient models. Is who's going to make the wrong investments in the wrong places at the wrong time? But you know, we will see as it gathers force and agents, as I mentioned. You know, they don't require, as much, you know, these monstrous training runs at City sized data centers. You know, meta wanted to spend $200 billion on a single complex, the open AI, Microsoft, Stargate, $500 billion Oracle's. Larry Ellison said that $100 billion is table stakes, which is just crazy to think about. And, you know, he's permitting three nukes on site. So there you go. I mean, it'll be fascinating to see if we have a new generation of private, private generation, right, like, which is like harkening all the way back to, you know, the early electrical grid and companies creating their own power plants on site, kind of stuff. Nicholas Carr wrote a good book about that one, about how we could see from the early electrical grid how the cloud played out. They played out very similarly. The AI cloud seems to be playing out a bit differently. So, so, yeah, I imagine that as well, but, but, yeah, well, inference happen at the edge. We need to have more distributed generation, because you're gonna have AI agents that are going to be spending more time at the point of request, whether that's a laptop or your phone or a light post or your autonomous vehicle, and it's going to need more of that generation and charging at the edge. That, to me, is the really interesting question. Like, you know, when these current generation models hit their limits, and just like with Moore's law, like, you know, you have to figure out other efficiencies in designing chips or designing AIS, how will that change the relationship to the grid? And I don't think anyone knows quite for sure yet, which is why they're just racing to lock up as many long term contracts as they possibly can just get it all, core to the market.   Trevor Freeman  49:39 Yeah, it's just another example, something that comes up in a lot of different topics that we cover on this show. Everything, obviously, is always related to the energy transition. But the idea that the energy transition is really it's not just changing fuel sources, like we talked about earlier. It's not just going from internal combustion to a battery. It's rethinking the. Relationship with energy, and it's rethinking how we do things. And, yeah, you bring up, like, more private, massive generation to deal with these things. So really, that whole relationship with energy is on scale to change. Greg, this has been a really interesting conversation. I really appreciate it. Lots to pack into this short bit of time here. We always kind of wrap up our conversations with a series of questions to our guests. So I'm going to fire those at you here. And this first one, I'm sure you've got lots of different examples here, so feel free to give more than one. What is a book that you've read that you think everybody should read?   Greg Lindsay  50:35 The first one that comes to mind is actually William Gibson's Neuromancer, which is which gave the world the notion of cyberspace and so many concepts. But I think about it a lot today. William Gibson, Vancouver based author, about how much in that book is something really think about. There is a digital twin in it, an agent called the Dixie flatline. It's like a former program where they cloned a digital twin of him. I've actually met an engineering company, Thornton Thomas Eddie that built a digital twin of one of their former top experts. So like that became real. Of course, the matrix is becoming real the Turing police. Yeah, there's a whole thing in there where there's cops to make sure that AIS don't get smarter. I've been thinking a lot about, do we need Turing police? The EU will probably create them. And so that's something where you know the proof, again, of like science fiction, its ability in world building to really make you think about these implications and help for contingency planning. A lot of foresight experts I work with think about sci fi, and we use sci fi for exactly that reason. So go read some classic cyberpunk, everybody.   Trevor Freeman  51:32 Awesome. So same question. But what's a movie or a show that you think everybody should take a look at?   Greg Lindsay  51:38 I recently watched the watch the matrix with ideas, which is fun to think about, where the villains are, agents that villains are agents. That's funny how that terms come back around. But the other one was thinking about the New Yorker recently read a piece on global demographics and the fact that, you know, globally, less and less children. And it made several references to Alfonso Quons, Children of Men from 2006 which is, sadly, probably the most prescient film of the 21st Century. Again, a classic to watch, about imagining in a world where we don't where you where you lose faith in the future, what happens, and a world that is not having children as a world that's losing faith in its own future. So that's always haunted me.   Trevor Freeman  52:12 It's funny both of those movies. So I've got kids as they get, you know, a little bit older, a little bit older, we start introducing more and more movies. And I've got this list of movies that are just, you know, impactful for my own adolescent years and growing up. And both matrix and Children of Men are on that list of really good movies that I just need my kids to get a little bit older, and then I'm excited to watch with them. If someone offered you a free round trip flight anywhere in the world, where would you go?   Greg Lindsay  52:40 I would go to Venice, Italy for the Architecture Biennale, which I will be on a plane in May, going to anyway. And the theme this year is intelligence, artificial, natural and collective. So it should be interesting to see the world's brightest architects. Let's see what we got. But yeah, Venice, every time, my favorite city in the world.   Trevor Freeman  52:58 Yeah, it's pretty wonderful. Who is someone that you admire?   Greg Lindsay  53:01 Great question.

In Episode 399 of George Perez Stories, George and the crew go full chaotic energy with:✔ Hilarious debates on eyebrow threading & bad tattoo choices.✔ Wild audience calls.✔ George's unhinged funeral plans (yes, he wants a Monte Carlo-themed casket).✔ Promos for JB and Sons Tree Service and Gunther's Downtown Santana.✔ Tour dates: Phoenix Improv (Aug 27) & Johnny Roque in Southgate (Aug 16).

Are you looking to save time, make money, and start winning with less risk? Then head to https://www.ovtlyr.com.Get ready for an action-packed episode of “Ask Me Anything Friday,” where traders like you get real answers in real time. From dissecting trending stocks like Nvidia, Dutch Bros, and Palantir to crushing popular trading strategies like the wheel, this video is your backstage pass to smarter trading decisions.We kick things off with a hilarious, choir-style stock market anthem before diving into viewer-submitted questions on everything from margin vs. options to how to use the OVTLYR signals like a pro. If you've ever asked yourself when to roll an option, how much extrinsic value is too much, or whether your stop losses should ever be placed in the market (spoiler: no), this video is a must-watch.Here's what we cover:➡️ Live analysis of Nvidia, Dutch Bros, AMD, and SPY using the OVTLYR trend template➡️ Breakdown of the Slingshot Setup and how to use fear & greed scores by sector➡️ Why extrinsic value matters and how to calculate the right price for your options➡️ Margin vs. options: which gives better leverage and lower risk?➡️ Why the wheel strategy can be dangerous—and what to do instead➡️ Trading psychology: how to stick to your plan when markets get emotional➡️ Monte Carlo simulation walkthrough: calculating your worst-case scenario like a pro➡️ Merch preview: monkey hammer gear, OVTLYR University apparel, and more➡️ Why we believe in trading over selling courses—and how that shapes the value of OVTLYRYou'll also get answers to community questions about long-term investing, open interest, position sizing, and more—plus behind-the-scenes commentary on building version 4.0 of the OVTLYR platform and what's coming next.If you're tired of overpriced trading gurus pushing $10,000 courses, this is your antidote. We break everything down in plain English—with zero fluff—and equip you to trade faster, smarter, and with less risk. Whether you're just starting out or scaling up a six-figure portfolio, OVTLYR is built to make your trading journey more effective and enjoyable.Time to level up your trades. Watch now and start making better decisions with data-driven clarity.Gain instant access to the AI-powered tools and behavioral insights top traders use to spot big moves before the crowd. Start trading smarter today

Are you looking to save time, make money, and start winning with less risk? Then head to https://www.ovtlyr.com.In this video, we turn up the heat with the hardest questions we've ever asked in class. Real traders, real strategies, and real insights are on full display as we dive into high-stakes trading psychology, options theory, and data-driven decision-making.We kick things off with a powerful trader's anthem, celebrating diamond hands, meme stocks, and the emotional rollercoaster of retail trading. But that's just the beginning. From there, we go straight into the fire. What does it really mean to refine your entry strategy through journaling? Why is backtesting crucial? How can a Monte Carlo simulator build confidence when markets go wild?Each OVTLYR student was put on the spot to tackle questions like:➡️ Why is journaling your trades essential to identifying winning setups?➡️ How does price movement matter more than profits in trend trading?➡️ Why is relying on dividends a flawed strategy for momentum-based investors?➡️ What drives a stock up even after bad earnings?➡️ Why is sitting in cash often the most disciplined move you can make?➡️ How does implied volatility crush impact your options trades, especially during earnings season?We also break down how to spot traps like order blocks, how market sentiment can override logic, and why even a perfectly timed trade can still go wrong if you're ignoring key indicators like extrinsic value or IV crush.You'll hear firsthand how OVTLYR traders have evolved from emotionally reactive beginners to confident, process-driven professionals. This video is not just theory. It's a masterclass in practical trading discipline, where every answer is backed by hard-earned lessons and tools like the OVTLYR Nine, the Fear and Greed Index, and clearly defined stop rules.What makes OVTLYR different? We're not selling theories. We're trading them. We put every strategy to the test in live markets. This is about building real confidence with real tools. No darts, no hopes, just data-backed action.If you've ever struggled with overtrading, emotional decisions, or FOMO, this is your wake-up call. Learn how to step back, analyze, plan, and only strike when the data lines up. There's power in patience and wealth in process.Whether you're brand new to trading or refining your strategy, this Finals Week episode will challenge your thinking, sharpen your skills, and give you the tools to trade with purpose.Stick around. Thursday's final class will be even harder. And if you want to trade like a pro, you better show up ready.Watch now, take notes, and don't just learn. Evolve.Gain instant access to the AI-powered tools and behavioral insights top traders use to spot big moves before the crowd. Start trading smarter today

#RougeShow #PoleArtist #SexiestShow #LasVegas On this episode of the Circuspreneur Podcast, host Shenea Stiletto interviews Pole Artist and Rouge Show at the Strat original cast member, Yasilda Slonova. She was a finalist on So You Think You Can Dance Ukraine, received 4 YESES on Das Super Talent, was recognized with a special jury prize at the Monte Carlo 36th Festival with Circus Bingo, and has appeared on the acclaimed French television show " Patrick Sebastien." Yasilda Slonova is an internationally awarded rhythmic gymnast, choreographer, and pole artist with over 15 years of experience in circus, dance, and stage performance. She was recognized for pioneering a pole act using light tissue as a unique visual and technical element.

Shane [REDACTED] has a new theory, Barry Football has a new explanation, The Pikelet Man has a new joke, and Tony has recorded his police interview. Monthly support | One-off support Merch Store | Official Website  

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How Should You Pay Yourself as a Business Owner? Salary, Dividends, or an IPP? In this episode, Joe chats with Braden Warwick from PWL Capital about how business owners can pay themselves in the smartest way. They break down the pros and cons of taking a salary, dividends, or using an Individual Pension Plan (IPP), a lesser-known but powerful option. Braden shares what he's learned from co-authoring a paper with Ben Felix, and they talk through real-life examples. You'll learn how taxes play into each option, what “notional accounts” are (in simple terms), and how a flexible income plan could help you get the most from your money,both now and in retirement. Here's what you're in for: 00:00 – Welcome and intro 00:24 – Meet Braden and his role at PWL 01:57 – What the research says about business owner pay 04:03 – Tax pros and cons of each method 08:46 – What are notional accounts, anyway? 13:02 – How IPPs work and who they're good for 17:35 – Using a flexible salary strategy 25:49 – Final thoughts and where to learn more —------------------------------------------------------------- ABOUT BRADEN WARWICK Braden Warwick is a PhD Research Engineer who loves turning complex research into practical, high-performing software. With a background in optimization, Monte Carlo and statistical analysis, and stochastic modeling, he's passionate about solving tough problems and making data-driven tools that work in the real world. Braden has hands-on experience with GPU-accelerated simulations, parallel processing, and modern Python and JavaScript frameworks, bringing innovative ideas from theory to production. You can reach out to Braden through: LinkedIn: https://www.linkedin.com/in/braden-warwick-a40b48a3 Website: https://www.bradenwarwick.ca (Personal)              —------------------------------------------------------------- ABOUT JOE  CURRY Joe Curry is the host of Business and Exit Planning Simplified and the owner and lead financial planner at Matthews + Associates in Peterborough, Ontario. A Certified Financial Planner and Certified Exit Planning Advisor, Joe is passionate about helping business owners maximize value, plan successful exits, and find purpose beyond their business. His mission is to ensure clients retire with confidence—financially secure and personally fulfilled. You can reach out to Joe through: LinkedIn: https://www.linkedin.com/in/curryjoe Website: https://www.retirementplanningsimplified.ca/              https://www.facebook.com/RetirementPlanningSimplified/               https://matthewsandassociates.ca/    ABOUT BUSINESS AND EXIT PLANNING SIMPLIFIED The Business and Exit Planning Simplified podcast offers clear, actionable guidance to help business owners maximize value, plan successful exits, and achieve financial freedom. Hosted by Joe Curry, a Certified Financial Planner and Certified Exit Planning Advisor, each episode delivers expert insights, real-life case studies, and practical strategies tailored for service-based entrepreneurs approaching retirement. The podcast empowers listeners to transition with clarity, confidence, and a renewed sense of purpose.  —------------------------------------------------------------- Disclaimer: Opinions expressed are those of Joseph Curry, a registrant of Aligned Capital Partners Inc. (ACPI), and may not necessarily be those of ACPI. This video is for informational purposes only and not intended to be personalized investment advice. The views expressed are opinions of Joseph Curry and may not necessarily be those of ACPI. Content is prepared for general circulation and information contained does not constitute an offer or solicitation to buy or sell any investment fund, security or other product or service.  

In this episode of Beer and Money, Ryan Burklo and Alex Collins discuss the critical question of whether savings will last through retirement. They explore common mistakes made by pre-retirees and retirees, emphasizing the importance of strategic planning and understanding the risks associated with outliving savings. The hosts introduce the Monte Carlo simulation as a tool for assessing financial risks and analyze different investment strategies to enhance retirement income. They highlight the significance of diversification and the need for a balanced approach to retirement planning, ultimately encouraging listeners to consider their financial strategies carefully. Check out our website:  beerandmoney.net For a quick assessment of your current financial life go to: https://www.livingbalancesheet.com/lbsVision/lite/RyanBurklo Takeaways Many pre-retirees make mistakes by not paying attention to their savings. The question of whether savings will last is crucial for retirees. Understanding the risks of outliving savings is essential. The Monte Carlo simulation helps assess the likelihood of outliving money. Investment strategies should consider both market and non-market assets. Diversification is key to managing financial risks in retirement. A balanced approach to retirement income is necessary for financial security. Strategic planning can mitigate tax implications on retirement savings. Listeners should evaluate their financial strategies regularly. Engaging with financial advisors can provide personalized insights.  Chapters 00:00 Introduction to Retirement Planning Challenges 02:45 Understanding the Monte Carlo Simulation 05:57 Exploring Retirement Income Strategies 08:59 Comparing Investment Strategies for Retirement 12:01 The Importance of Diversification in Retirement 15:03 Strategic Planning for Financial Security 17:58 Conclusion and Next Steps    

Are you looking to save time, make money, and start winning with less risk? Then head to https://www.ovtlyr.com.The market's flashing red, but you don't have to go down with it.In this episode, we're diving deep into the rules, mindset, and execution strategies that can actually make you a profitable trader — even when the market is crashing. This isn't another “buy the dip” hype video. It's a no-BS breakdown of how to sit in cash, protect your capital, and only strike when it makes sense. If you've ever felt FOMO, taken random trades, or stared at your P&L wondering what went wrong — this one's for you.We're reacting to Umar Ashraf's video on the 8 Trading Rules for Profitability in 2025 and drawing parallels with the exact principles we follow inside OVTLYR. From building your plan and backtesting it properly, to understanding how market, sector, and stock forces interact — we lay out the entire game plan step by step.You'll learn:➡️ Why having “sit out” power is your hidden advantage➡️ How to stop gambling on dips and start trading trends➡️ What it means to average winners and cut losers fast➡️ Why backtesting, Monte Carlo simulations, and journaling are non-negotiables➡️ How trading like a surgeon (with precision and rules) beats trading like a gamblerWe also get real about the illusions of social media traders, the dangers of jumping from one strategy to another, and why measuring your progress without obsessing over your P&L is a game changer.Plus — we cover mindset, pre-market prep routines, and the cold hard truth: most traders blow up in 90 days. But if you follow these principles and stay consistent, you can beat 90% of traders just by breaking even in year one. This is the blueprint for turning chaos into confidence and randomness into results.If you're serious about growing as a trader, this is the type of content you need to absorb, reflect on, and implement. OVTLYR isn't about hype — it's about helping you save time, make money, and start winning with less risk.Gain instant access to the AI-powered tools and behavioral insights top traders use to spot big moves before the crowd. Start trading smarter today

Vacation, all we ever wanted! This month we look at the ultimate getaway in movies, the grand tradition of summer vacation on film! From National Lampoon's Vacation series to Girl's Trip to The Green Ray and everything in between! If you have any questions/comments/suggestions for the show, follow us on twitter @TheMixedReviews, like us on Facebook, e-mail us at reviewsmixed@gmail.com, visit our  Instagram or  TikTok for extra content, become a patron on our Patreon, or stop by our shop and pick up some podcast merchandise! Don't forget to subscribe to us on  iTunes, Spotify, Podchaser, Audible, or wherever you get podcasts! All clips are used under Fair Use and belong to their respective copyright owners.

Nat scorches her lips on a molten bacon-wrapped date, Angela rocks a Madonna glove from her burn unit saga, and the girls spiral into the McNugget world of garbage TV—90 Day Fiancé meltdowns, virgins at a BDSM class, and a man who FULL-ON makes out with his car named Chase

In this episode of Retire with Style, Wade Pfau and Alex Murguia tackle listener questions on a range of financial topics, including gold's volatility, alternative investments, and how to measure retirement success. They discuss the realities of investment returns, the impact of recent U.S. bond downgrades, and the importance of understanding risk, using historical data, and maintaining a solid investment strategy in retirement.   Takeaways Gold has lower average returns and higher volatility than stocks. Alternative investments require careful evaluation due to lack of historical data. Quantifying retirement success rates can provide clearer financial goals. The magnitude of failure in financial planning is crucial to understand. Investors should assess the compensated risk of their investments. Monte Carlo simulations can help in understanding potential outcomes. The funded ratio approach simplifies retirement planning. US bond downgrades may not significantly impact long-term market trajectories. Understanding the underlying assumptions of financial plans is essential. Risk assessment is a key component of effective financial planning. Chapters 00:00 Introduction and Overview of Q&A Session 02:33 Debating Gold's Volatility and Investment Value 08:56 Exploring Alternative Investments and Their Evaluation 19:03 The Importance of Theoretical Justification in Investments 20:17 Understanding Retirement Planning Tools 23:04 Probability of Success vs. Rate of Return 27:21 Magnitude of Failure in Financial Planning 30:31 The Funded Ratio Approach 34:06 Evaluating Financial Advisors 36:15 Impact of US Bond Downgrades   Links Explore the New RetireWithStyle.com! We've launched a brand-new home for the podcast! Visit RetireWithStyle.com to catch up on all our latest episodes, explore topics by category, and send us your questions or ideas for future episodes. If there's something you've been wondering about retirement, we want to hear it! The Retirement Planning Guidebook: 2nd Edition has just been updated for 2025! Visit your preferred book retailer or simply click here to order your copy today: https://www.wadepfau.com/books/ This episode is sponsored by McLean Asset Management. Visit https://www.mcleanam.com/retirement-income-planning-llm/ to download McLean's free eBook, “Retirement Income Planning”

El Audi Quattro es un coche clave en la historia y un mito. Seguro que conoces sus éxitos y, sin duda, su historia… ¿seguro? Os lanzo un reto: ¿Sabías que este modelo se diseñó de una forma un poco “chapuza” utilizando partes y elementos de otros modelos? Te aseguro que es un verdadero “coche Frankenstein”... El Audi Quattro, un verdadero “Monstruo de Frankenstein” hecho con trozos aprovechados de otros modelos, nació gracias el alemán Ferdinand Piëch, nieto de Ferry Porsche, a su vez hijo del fundador de la marca Porsche, Ferdinand Porsche… Una pequeña parte de esta historia ya la contamos, pero conviene recordarla, porque en realidad todo comienza en 1964… 16 años antes de que se presentase el Audi Quattro. En 1964 VW compra DKW… os recuerdo que 5 años después compraría NSU. Dos buenas decisiones, pues de DKW tomo la tecnología de la tracción total. Era una especie de jeep 4x4 fabricado por DKW. Como VW ya tenía la tecnología de tracción total se animó a participar en un concurso del ejército que quería más coches de todo terreno. Y nació el Iltis, que ganó el concurso y que ganó el Paris-Dakar en 1980. Por la “puerta de atrás” y discretamente en 1977 VW pide a la FIA derogar la norma que impedía correr en el Mundial de Rallyes a coches de tracción total… la FIA pregunta al resto de las marcas y ninguna se negó… Y en 1978 se lanza el VW Iltis. Y arranco el proyecto 262, que sería finalmente el Audi Quattro. Os hablaba de copia y de “chapuza”, con comillas. Hemos visto la parte de la “copia”, aunque no quiero dejar de recordar que Walter Treser, ingeniero que trabajo en el proyecto Quattro, se compró un Jensen FF… no por casualidad. ¿Qué tenéis por ahí? Esta pudo ser la pregunta que hizo Jörg Bessiger a sus colegas de VW. Porque el Quattro está hecho de piezas y elementos tomados de otros coches: Motor del Audi 200, chasis del Coupé, trasmisión del Iltis, suspensiones de Audi 80… todo ello modificado como se pudo para “armar” el mecano que era el Audi Quattro. Lo que hizo Audi es partir de un motor de VW 4 cilindros añadirle uno y con una cilindrada unitaria muy similar, de 428 cm3, llegaron a los 2.144 cm3 del motor 5 cilindros de Audi de esa época. O sea, que utilizaron ese motor, porque prácticamente no requería ni un solo marco, no había euros todavía, de inversión en investigación, y no por otra cosa… seguramente mejor hubiese sido un V6… que es lo que usan ahora. Ese motor, nacido para el 200 Turbo se utilizó tal cual para el Quattro y en la misma posición, delantero longitudinal en voladizo, lo cual no era la mejor opción para un deportivo, pero insisto, ¡es lo que había! Hans Navidek que tenía la responsabilidad de instalar la tracción total, usó la tomada del Iltis, en un Coupé. Lo que sucede es que el Iltis era más alto y con chasis separado y el Quattro debía de ser bajito y contaba con chasis monocasco… esta fue la parte más complicada. Consiguieron “meter” un diferencial central Torsen central y salidas diferentes para el tren delantero y trasero. Delante, su utilizó el esquema del Coupé de, tracción delantera convencional, y se adaptó un eje como en el Iltis para el tren posterior, que costo ajustar y encontrar sitio… pero se consiguió. El nuevo modelo ya tenía motor, el de Audi 200 Turbo; tenía carrocería, la del Audi Coupé B2; tenía transmisión, pues se había conseguido adaptar y mejorar la del Iltis… faltaban las suspensiones. La trasera del Coupé B2 no valía, porque ese modelo no estaba previsto que tuviese propulsión trasera. Audi no tenía en ese momento ningún modelo con propulsión trasera, así que había que diseñar una partiendo de cero… pero de nuevo alguien dijo, “¿no tenemos nada que nos sirva?” Y algún “iluminado” diría: “Y si ponemos una suspensión delantera, que está preparada para llevar transmisión, pero la ponemos atrás”. Dicho y hecho. Tomaron la suspensión delantera McPherson del Audi, la dieron la vuelta, le reforzaron con unos tirantes y la pusieron atrás… ya estaba terminado este maravilloso “Monstruo de Frankenstein” que es el Audi Quattro. No olvidemos que este coche nace para la competición, para los Rallyes, ese era al objetivo. De hecho, se pensaban fabricar 400 unidades, las necesarias para la homologación… finalmente se fabricaron casi 12.000. Cuando aún estaba en fase de prototipo con una carrocería de Audi 80 Audi decide que los pruebe un piloto de nivel y deciden que sea Hannu Mikkola. Según me han contado fuentes que no pudo revelar, ya sabéis que para los periodistas es una ley esa que dice “antes la muerte que la fuente”, el piloto no iba muy ilusionado. Un Audi 80 con un motor turbo ahí delante y con trasmisión de todo terreno, no parecía una idea muy sugerente… pero, permitidme un paréntesis. En esa época, la mayoría de los rallyes eran de tierra. Por ejemplo, en el 80, solo había dos rallyes de asfalto, el Montercarlo y el Tour de Corse… y en Montecarlo nevaba. El de Suecia era siempre sobre nieve. Los había mixtos, como el de Portugal, San Remo o el Rac de Gran Bretaña. Y aún se disputaban los rallyes de Costa de Marfil y el East African Safari de Kenia, unos rallyes muy especiales y, por supuesto, sobre tierra. Por eso Piëch creía en la tracción total. Tras diversas pruebas sobre prototipos Mikkola para del escepticismo al entusiasmo y el Quattro echaba a andar. En resumen, el Quattro se encontró con unos rivales anticuados y unos rallyes que les favorecían. El éxito que tuvo este modelo que hizo que todos los coches del rallyes del futuro fuesen de tracción total y una gran mayoría de deportivos de calle de elevada potencia. Hacer el coche que hicieron “apañando” el coche a base de elementos de otros, tiene mucho mérito. Pero tuvo muchas secuelas. La más importante el motor, o mejor, su posición delante en voladizo. El Audi Quattro, ni siquiera en sus versiones “cortas” y evolucionadas, fue en coche ágil y mucho menos fue un coche “fácil”. La ventaja de la tracción total era tanta, que esto no supuso un inconveniente… hasta que el resto de las marcas comenzaron a utilizar la tracción total, pero con motor central. ¿Te imaginas un Audi Quattro con motor V6 o un simple 4 cilindros, ni siquiera en posición central, sino por detrás del eje delantero? Sus éxitos hubiesen sido aún mayores. Esto es historia ficción y, si queréis, puede dar lugar a un video de “Historia-Ficción” que se titule: “El Audi Quattro diseñado desde cero”. Ahí dejo la idea y os leo en comentarios. Conclusión. Admiro enormemente a la gente que tiene sueños y los lleva adelante. Piëch soñó con un deportivo de tracción total y bajo sus auspicios, pese a las dificultades, nació el Audi Quattro. Un coche del que siempre se dice que “lo cambió todo” … y no es un tópico es la verdad.

Are you looking to save time, make money, and start winning with less risk? Then head to https://www.ovtlyr.com.Think you've cracked the code to trading? Think again. This video dives deep into the dangerous illusion of the “perfect” system—and why chasing 100% win rates is a trap that can cost you everything. If you're tired of hype and ready to develop real skill, discipline, and data-backed strategies, this one's for you.Inside this powerful lesson from OVTLYR University, we break down what actually works in trading. You'll learn why perfection isn't the goal—and why the smartest traders focus on expectancy, risk management, and consistent execution. Forget about “buying the dip” or chasing every hot ticker. This is about building a process that thrives even when the market throws curveballs.We'll cover real insights from pro traders who've worked with Navy SEALs and Olympic athletes. You'll hear how the same precision used by elite shooters applies directly to trading—where the outcome is unpredictable, but the process can be perfected. This mindset shift alone could be the most valuable thing you take away today.We also pull back the curtain on some serious research. Over 80 trades have been analyzed, and we reveal how incorporating a previously overlooked dataset into OVTLYR has 2.5x'ed our expectancy. It's not about finding magic indicators—it's about testing, refining, and expanding your statistical edge. You'll see how market breadth indicators and sector trend data, when used properly, can tilt the odds in your favor… but never guarantee a win.There's a reason top traders survive and thrive: they bake losses into their plan. In this session, we emphasize how to survive drawdowns, emotionally and financially. You'll learn how overconfidence leads to blown accounts—even for Nobel Prize winners. And you'll see how to use Monte Carlo simulations, outcome math, and simple coin-flip analogies to understand probability and outcome distribution like a true pro.This isn't some motivational fluff. It's practical, real-world trading psychology mixed with analytical firepower. Whether you're sitting on a losing streak or thinking you've found the holy grail, this video brings you back to center. You'll see why “cash is a trade,” how to recalibrate when things go sideways, and how rehearsing your execution—like elite athletes do—can eliminate panic and FOMO.For traders who want to win sustainably and not just chase dopamine hits, this is your blueprint.

In a word, Evan Osnos' latest book focuses on the subject of money. His book is titled "The Haves and the Have Yachts: Dispatches on the Ultra-Rich." There are 10 essays which originally appeared in his home publication, The New Yorker. The oldest one, "Survival of the Richest," ran in 2017. The newest, titled "Land of Make-Believe," was published in 2024. In his introduction, Osnos writes that: "Reporting in the enclaves of the very rich, Monte Carlo, Palm Beach, Palo Alto and Hollywood is complicated. It's not a world that relishes scrutiny." Learn more about your ad choices. Visit megaphone.fm/adchoices

In a word, Evan Osnos' latest book focuses on the subject of money. His book is titled "The Haves and the Have Yachts: Dispatches on the Ultra-Rich." There are 10 essays which originally appeared in his home publication, The New Yorker. The oldest one, "Survival of the Richest," ran in 2017. The newest, titled "Land of Make-Believe," was published in 2024. In his introduction, Osnos writes that: "Reporting in the enclaves of the very rich, Monte Carlo, Palm Beach, Palo Alto and Hollywood is complicated. It's not a world that relishes scrutiny." Learn more about your ad choices. Visit megaphone.fm/adchoices

ChatGPT Agents are here.U.S. President Trump has big plans for AI.And Google is slapping more AI on traditional search than a commercial pitchman slapping Flex Seal on a leaky boat.AI is changing how we all work. And there's way too much happening to keep track. So, that's why you should spend Mondays with us as we bring you the AI News that Matters.Try Gemini 2.5 Flash! Sign up at  AIStudio.google.com to get started. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Meta Considers Shift to Closed-Source AITrump Unveils National AI Policy Plan$2 Billion Seed Funding: Thinking Machines LabsGemini 2.5 Pro Launches in Google SearchGoogle AI Enables Automated Local Business CallsDeep Search Feature with Gemini 2.5 ProOpenAI ChatGPT Agents Power Multi-step AutomationChatGPT to Charge Commissions on E-commerceNew Student Study Tools: OpenAI, Google, AnthropicAnthropic Debuts Domain-Specific Financial AINvidia H20 AI Chip Exports to China ControversyTimestamps:00:00 Meta Considers Closing AI Models04:15 "Meta's AI Strategy and Open Source"08:15 Diverse Regulation Needs for AI Innovation09:42 AI Startup Surpasses $12B Valuation13:37 Google and OpenAI's AI Expansion18:41 AI Companies Target Student Demographic21:59 "Rise of Domain-Specific Models"23:47 2026 AI Model Revolution26:36 AI Export Controls and US-China Tech Race30:36 OpenAI Unveils ChatGPT Agent33:05 "Watch Mode for ChatGPT Pro"Keywords:Google AI, Gemini 2.5 Pro, Gemini AI mode, Deep search, AI-powered local calling, agentic AI capabilities, ChatGPT agents, ChatGPT agent, OpenAI, ChatGPT Pro, reasoning model, automated phone calls, local business AI calls, US AI policy, President Trump AI strategy, deregulation in AI, AI regulation, closed source AI model, Meta AI, open source vs closed source AI, superintelligence lab, Alexander Wang, Scale AI, Nvidia GPU drama, Nvidia H20 chips, AI chips export, US-China AI arms race, artificial general intelligence, AGI, ASI, Anthropic, Claude, domain specific models, financial analyst AI, financial data AI solutions, Monte Carlo simulation AI, risk modeling AI, Amazon Bedrock Agent, Microsoft Copilot Vision, multi-step task automation, virtual terminal AI, multimodal reasoning, e-commerce AI, ChatGPT shopping, AI optimization, AIO, affiliate revenue AI, education AI tools, AI study assistants, Study Together ChatGPT, Study Projects Claude, Guided Learning Gemini, enterprise AI soluSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Nenad Zimonjic was summoned to the Djokovic camp before Monte Carlo in 2024, and was on his bag when he won his Olympic gold in Paris, but that's just a portion of Nenad's incredible story. Born and raised in Belgrade, Nenad was saddled with a worthless passport because of war, that hindered his pro tennis career considerably. He persevered, and became the best doubles player in the world winning 8 majors and 54 tournaments, a Serbian Davis Cupper, a mentor to the younger Serbs Djokovic and Tipsarevic, and all in all lived an incredible life in tennis. Nenad and I chatted shortly after Novak defeated Alex de Minaur at Wimbledon, and it was a tremendous chat. Recorded 7.7 Released 7. 21The Craig Shapiro Tennis Podcast is Powered by The Golden Ticket Hosted on Acast. See acast.com/privacy for more information.

Dr. Maxwell Ramstead grills Guillaume Verdon (AKA “Beff Jezos”) who's the founder of Thermodynamic computing startup Extropic.Guillaume shares his unique path – from dreaming about space travel as a kid to becoming a physicist, then working on quantum computing at Google, to developing a radically new form of computing hardware for machine learning. He explains how he hit roadblocks with traditional physics and computing, leading him to start his company – building "thermodynamic computers." These are based on a new design for super-efficient chips that use the natural chaos of electrons (think noise and heat) to power AI tasks, which promises to speed up AND lower the costs of modern probabilistic techniques like sampling. He is driven by the pursuit of building computers that work more like your brain, which (by the way) runs on a banana and a glass of water! Guillaume talks about his alter ego, Beff Jezos, and the "Effective Accelerationism" (e/acc) movement that he initiated. Its objective is to speed up tech progress in order to “grow civilization” (as measured by energy use and innovation), rather than “slowing down out of fear”. Guillaume argues we need to embrace variance, exploration, and optimism to avoid getting stuck or outpaced by competitors like China. He and Maxwell discuss big ideas like merging humans with AI, decentralizing intelligence, and why boundless growth (with smart constraints) is “key to humanity's future”.REFS:1. John Archibald Wheeler - "It From Bit" Concept00:04:45 - Foundational work proposing that physical reality emerges from information at the quantum levelLearn more: https://cqi.inf.usi.ch/qic/wheeler.pdf 2. AdS/CFT Correspondence (Holographic Principle)00:05:15 - Theoretical physics duality connecting quantum gravity in Anti-de Sitter space with conformal field theoryhttps://en.wikipedia.org/wiki/Holographic_principle 3. Renormalization Group Theory00:06:15 - Mathematical framework for analyzing physical systems across different length scales https://www.damtp.cam.ac.uk/user/dbs26/AQFT/Wilsonchap.pdf 4. Maxwell's Demon and Information Theory00:21:15 - Thought experiment linking information processing to thermodynamics and entropyhttps://plato.stanford.edu/entries/information-entropy/ 5. Landauer's Principle00:29:45 - Fundamental limit establishing minimum energy required for information erasure https://en.wikipedia.org/wiki/Landauer%27s_principle 6. Free Energy Principle and Active Inference01:03:00 - Mathematical framework for understanding self-organizing systems and perception-action loopshttps://www.nature.com/articles/nrn2787 7. Max Tegmark - Information Bottleneck Principle01:07:00 - Connections between information theory and renormalization in machine learninghttps://arxiv.org/abs/1907.07331 8. Fisher's Fundamental Theorem of Natural Selection01:11:45 - Mathematical relationship between genetic variance and evolutionary fitnesshttps://en.wikipedia.org/wiki/Fisher%27s_fundamental_theorem_of_natural_selection 9. Tensor Networks in Quantum Systems00:06:45 - Computational framework for simulating many-body quantum systems https://arxiv.org/abs/1912.10049 10. Quantum Neural Networks00:09:30 - Hybrid quantum-classical models for machine learning applicationshttps://en.wikipedia.org/wiki/Quantum_neural_network 11. Energy-Based Models (EBMs)00:40:00 - Probabilistic framework for unsupervised learning based on energy functionshttps://www.researchgate.net/publication/200744586_A_tutorial_on_energy-based_learning 12. Markov Chain Monte Carlo (MCMC)00:20:00 - Sampling algorithm fundamental to modern AI and statistical physics https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo 13. Metropolis-Hastings Algorithm00:23:00 - Core sampling method for probability distributionshttps://arxiv.org/abs/1504.01896 ***SPONSOR MESSAGE***Google Gemini 2.5 Flash is a state-of-the-art language model in the Gemini app. Sign up at https://gemini.google.com

In this episode of the Neil Ashton podcast, Professor Mike Giles shares his extensive journey through the fields of computational fluid dynamics (CFD), computational finance and HPC. He discusses his early academic influences, his early days at Cambridge, internships at Rolls-Royce, his transition to MIT and Oxford where he made significant contributions to high-performance computing and numerical analysis. The conversation highlights his hands-on approach to research and teaching, as well as his pioneering work in Monte Carlo methods and GPU computing. This conversation explores the journey of a mathematician and engineer from MIT to Rolls-Royce and then to Oxford, highlighting the evolution of computational engineering, the development of the Hydra code, and the transition from CFD to financial applications.  In this conversation, the speaker reflects on their journey through burnout, career transitions, and the evolution of their work in computational finance and numerical analysis. They discuss the challenges of managing large software projects, the shift from Hydra code development to finance, and the integration of advanced methodologies in their work. The conversation also touches on the role of high-performance computing, the impact of AI on research, and advice for the next generation of students pursuing careers in mathematics and programming.Links:https://people.maths.ox.ac.uk/gilesm/Chapters00:00 Introduction 06:25 Professor Mike Giles: A Journey Through CFD and Finance17:30 Early Academic Influences and Career Path29:34 Transition to MIT and Early Research40:01 High-Performance Computing and Its Impact41:30 Navigating Between MIT and Rolls-Royce44:54 The Evolution of Research at MIT48:47 Transitioning to Oxford and the Role of Rolls-Royce51:07 The Genesis of the Hydra Code01:00:47 The Role of Conferences in Engineering01:10:58 The Shift from CFD to Financial Applications01:21:30 Navigating Burnout and Career Transitions01:24:04 Shifting Focus: From Hydrocode to Computational Finance01:29:30 Bridging Mathematics and Finance: Methodologies and Techniques01:35:09 The Role of High-Performance Computing in Modern Research01:39:20 AI's Impact on Research and Future Directions01:54:00 Advice for the Next Generation: Pursuing Passion and Skills

Episode OverviewIn this episode of CDO Matters, host Malcolm Hawker talks with Barr Moses, CEO of Monte Carlo, to break down what data observability really means and why it matters. They explore how leading organizations are using it to catch data issues early, drive trust, and scale reliability— plus where the space is headed next.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Barr Moses on LinkedIn

¡Como me gusta este podcast! ¡Como me gustan estos coches! Excesivos, brutales… sencillamente son, para mí, los coches de competición en circuito más espectaculares de la historia. Fruto de un reglamento poco restrictivo… no como los de ahora… Si conoces los “Silueta” te va a interesar mucho este video, pero si nos los conoces… ¡te va a interesar más! Te lo prometo. Lo primero que hay que hacer, porque quizás alguno no lo sepa, es responder a esta pregunta: ¿Qué es un coche “silueta”? Lo primero que hay que decir es que los “silueta” son coches del entonces llamado Grupo 5, pero no todos los grupo 5 son coches de la categoría “silueta”. Los Grupo 5 nacieron en 1966 e inicialmente eran coches nacidos para la competición. Te pongo algunos ejemplos: Alfa Romeo TT33TT/12, Alpine Renault A442, Ford GT40, Ferrari 512 M/S, Matra Simca MS670 o Porsche 917 por citar unos ejemplos… coches preciosos y que, seguro, merecen otro video para ellos. Pero en 1976 y hasta 1982, época de mi adolescencia y juventud, nace la cuarta y última generación del Grupo 5 y estos son los “silueta”. La FIA creó esta categoría para coches derivados de la calle, pero ampliamente, yo diría que muy ampliamente, modificados. La federación exigía que el capó, parabrisas, puertas completas y techo fueran los mismos que en el coche original. Pero lo demás, incluidos los pasos de rueda y la parte posterior era libre. El motor debía contar con el bloque motor original y en la posición original. Es decir, no podías hacer un 911 con motor delantero, pero sí ponerlo más bajo o más adelantado y prepararlo a fondo, incluso aumentando la cilindrada o añadiendo “turbos” a placer siempre que el bloque fuese original. Lo mismo sucedía con la suspensión, debía ser de igual sistema, pero de diseño y componentes libres. Había exigencias en cuanto a normas de seguridad, pesos mínimos y dimensiones, pero en general las normas eran muy “laxas” y ello dio lugar a verdaderos monstruos de la competición… ¿Qué no te lo crees? Vamos a ver unos cuantos… 1. BMW 3.0 CSL (1972). Uno de los primeros trabajos del departamento M fue la fabricación de un coupé de la serie E9 para ser preparado para competición. 2. Lancia Stratos Turbo (1976). Terminada su etapa en los Rallyes el Stratos tuvo una segunda oportunidad en los circuitos de la mano del reglamento de los silueta… pero no tan exitosa. 3. Chevrolet Corvette Greenwood (1976). He elegido de este modelo la versión denominada “Spirit of Le Mans 1976”. Este coche, como un Ferrari que veremos más adelante, participaba dentro de una categoría de la norteamericana IMSA. 4. Porsche 911-935 (1976). Uno de los “silueta” por excelencia. Y en el caso concreto del 911-935 de nominado “Moby Dick” probablemente el más brutal de todos y al que dedicamos un video completo titulado “Moby Dick: el Porsche Turbo más brutal” que te animo a que lo veas. 5. BMW 320i Turbo (1977). El CSL era demasiado grande y demasiado antiguo así que BMW pensó en un más manejable serie 3, pero eso sí, con turbo y una potencia que según algunas fuentes llegaron a los 900 CV… 6. Ferrari 512 BB LM (1977). Este coche corrió Le Mans encuadrado en la categoría norteamericana IMSA… que fue prohibida en esta prueba en 1983. 7. Toyota Celica LB Turbo (1977). El apoyo de Toyota Alemania al preparador, muy prestigioso, Schnitzer, hizo posible que naciese este modelo que declaraba 560 CV para un peso claramente por debajo de los 900 kg. 8. Ford Capri Turbo Zakspeed (1978). Ford quiso competir en la categoría “hasta 2 litros” del Grupo 5 “siluetas” y para ello encargó al prestigiosos preparados Zakspeed un motor de 1.4 litros para montar en un espectacular y aerodinámico Ford Capri. 9. Lancia Beta Montecarlo Turbo (1979). Lancia tenía una base excelente, como era el coupé Montecarlo con dos plazas y motor central, para crear un silueta destinada al Grupo 5 de la FIA. 10. Mazda RX-7 252i (1979). El grupo 5 “Silueta” tuvo continuidad en Japón una vez acabada su vida en Europa, en el Campeonado del Mundo FIA. Y hubo muchos coches japoneses preparados bajo este reglamento. Conclusión. Siempre digo lo mismo: Los reglamentos actuales son tan estrictos y tan detallados que dejan poco a la imaginación… antes no era así y eso producía coches como estos, como los Grupo B, como los prototipos y Formula 1 de los años 70 y 80.

Today's guest has one of the broadest international reinsurance roles of anyone I have interviewed on the podcast. That's because Louise Rose has oversight over everything that TransRe does outside of the Americas. Louise has been on the show before as part of the annual Monte Carlo special Episode, but it's wonderful to have the time for a comprehensive examination of the state of the reinsurance world. And that is exactly what you get. We cover everything from the trajectory of the market to Trans Re's strategy as it looks to gain a stronger foothold in Continental Europe and the Asia Pacific region. Ai, Cyber, MGAs and the state of the Casualty market all get a thorough work-over. Louise is in her 29th year at Trans Re and is always direct in her communications style. It's refreshing and makes for a highly informative and valuable encounter. NOTES: Here's a link to the excellent US Public D&O report that we mention in our conversation: https://www.transre.com/u-s-public-do-2025-insurance-market-update/ We thank our naming sponsor AdvantageGo: https://www.advantagego.com

Damon has had series regular roles in The Big Door Prize, The Last Days of Ptolemy Grey starring Samuel L. Jackson (both for Apple TV), Black Lightning (CW), Criminal Minds (CBS), The Player (NBC), The Divide (AMC), Prime Suspect (NBC), Dick Wolf's Deadline (NBC), Strange Brew (FOX), and Finkleman (NBC), as well as guest star/recurring appearances on Happy Face (Paramount+), as well as Your Honor (Showtime), Super Pumped (Showtime), The Comey Rule (Showtime), Dirty, John (Bravo), Goliath (Amazon), Bates Motel (A&E),The Newsroom (HBO), Suits (USA), Empire (FOX), Rake (FOX), Law & Order (NBC), Law & Order Criminal Intent (NBC), Conviction (NBC), The Unusuals (ABC), Hack (CBS), Third Watch (NBC), and Drift (ABC). He appears in the upcoming Lear Rex, starring Al Pacino and The Drama, directed by Kristoffer Borgli, as well as Damien Chazelle's Academy Award-winning films Whiplash and LaLa Land, This is Forty, The Last Airbender, Helen at Risk, Before the Devil Knows You're Dead, Unfaithful, The Loretta Claiborne Story, and Nicki Micheaux's Summer of Violence. As a conductor, he was appointed the first-ever Principal Guest Conductor of the Cincinnati Pops. He served as American Conducting Fellow of the Houston Symphony and held the post of assistant conductor of the Kansas City Symphony. His conducting appearances include the Boston Pops, Philadelphia Orchestra, Orchestra of St. Luke's, Detroit Symphony, San Francisco Symphony, Atlanta Symphony, Baltimore Symphony, National Symphony Orchestra, St. Louis Symphony Orchestra, Toledo Symphony, Fort Worth Symphony, Florida Orchestra, San Diego Symphony, Long Beach Symphony, San Antonio Symphony, Princeton Symphony, Orchestre Philharmonique de Monte Carlo, NHK Orchestra of Tokyo, Orquesta Filarmonica de UNAM, Charlottesville Symphony, Brass Band of Battle Creek, NYU Steinhardt Orchestra, Kinhaven Music School, Vermont Music Festival, Michigan Youth Arts Festival, Brevard Music Center, and Sphinx Symphony as part of the 12th annual Sphinx Competition. 

BIG things are happening on this BIG Show edition of Calm Down with Erin and Charissa. London, Cannes, Monte Carlo, Lake Cuomo… These ladies have been traveling the world, and they have stories to tell! Buckle up for good times, lots of laughs and how Charissa found herself dancing with a rock and roll legend.See omnystudio.com/listener for privacy information.

Hello, PFR Nation and Happy 4th of July, and Happy Birthday, America!  What a great country we live in, I'm so proud to be an American.  My Dad being a (legal) immigrant has given me great appreciation for the opportunities we have relative to the rest of the world.  I'm feeling extremely blessed for the clients we are serving in our financial planning firm, and I'm so grateful to serve all of you with this podcast.  I hope you continue to find value.  We have a fair amount of new listeners, plus the legacy listeners, and I just want to say how excited I am to deliver this weekly content to all of you.  Thank you for the support, and welcome to the 84th episode of the PFR Podcast and 7th edition of the ‘Whiteboard Retirement Plan.'  Leo and Lisa are looking to retire in 2 years, at 61 and 58 respectively.  They have done quite well accumulating approximately $3 million for retirement with the majority being inside of traditional tax deferred IRA's and a 401k.   Leo is on Long Term Disability and was forced to ‘retire earlier' than planned, and is receiving tax free income until 65.  Lisa plans to fully retire at 58.  However, this will result in losing employer-sponsored healthcare and ultimately needing to shop around in the open market.  One option will be to consider the Affordable Care Act policies on Healthcare.gov.  Furthermore, Roth Conversions are of interest during their “Roth Conversion Window” from Lisa's age 58 until she turns 75.  In this episode, we will help them decide whether or not to aggressively pursue a ‘low income' to reduce healthcare costs in early retirement…or, to begin converting some of the tax-deferred accounts right away to reduce the ‘Tax Trap of 401ks.'  Drop a comment and let me know what you plan to do if you retire before 65!  Will you aggressively pursue ACA Premium Tax Credits?  Aggressively convert to Roth?  Or potentially a hybrid between the two?  I hope you enjoy the 7th edition of the “Whiteboard Retirement Plan.”ACA Premium Tax Credits Video***Additional Disclaimer***  So much about these rules are up in the air.  From 2021-2025, there has been a “gradual slope” downwards of ACA premium tax credits even AFTER you exceed 400% of the Federal Poverty Level.  However, that is set to revert back to the “Cliff” at 400% after 2025.  With that said, there is a LOT on the table with the “One Big Beautiful Bill” which will likely include further changes to these rules.  I guess what I'm saying is…continue to follow the “OBBB” and of course follow the PFR Pod!-KevinTakeaways:Many of the families we serve are overachievers looking to retire early.Healthcare costs are a significant concern for early retirees prior to reaching Medicare eligibility.Budgeting for lifestyle and healthcare is crucial in retirement planning.Roth conversions can optimize tax liabilities over time.Monte Carlo simulations can help stress test the plan, but is by no means the be all end all retirement metric.Understanding the Affordable Care Act and their premium tax credits are important, but should NOT be the sole basis for tax planning opportunities.  Tax traps in traditional retirement accounts can impact long-term wealth during a retiree's lifetime, and for the next generation.  Income stability is key for a successful retirement.Adjusting retirement plans can provide more flexibility and security.⁠Are you interested in working with me 1 on 1?⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Click this link to fill out our Retirement Readiness Questionnaire⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Or,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ visit my website⁠⁠⁠⁠⁠⁠⁠Connect with me here:​⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠​⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join My Company Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠​⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠​⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠​⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠This is for general education purposes only and should not be considered as tax, legal or investment advice.

In 2015, Misty Copeland became the first Black woman to become principal dancer at the American Ballet Theatre. Her heartfelt memoir “The Wind at My Back” pays tribute to her mentor and fellow dance pioneer Raven Wilkinson, who performed in the segregated South as a member of the Ballet Russe de Monte Carlo in the 1950s. A few years back, Misty joined guest host Talia Schlanger to talk about Raven's incredible life and legacy.

Renee Colvert is here and she is jazzed about musicals! She gives us a Tonys preview and Jeff Fox explains what he'll be doing during said show. Daniel claims I am a secret perfectionist and I need to say something about caricatures. Also we can't stand Carrie and Aidan and I'm bringing back our signature segment that we can never remember the name of. Daniel tells a story about an ex-girlfriend and we discuss Labubus. Plus so much more including a round of Just Me Or Everyone and Podcast Pals Product Picks. Get yourself some new ARIYNBF merch here: https://alison-rosen-shop.fourthwall.com/ Subscribe to my Substack: http://alisonrosen.substack.com Podcast Palz Product Picks: https://www.amazon.com/shop/alisonrosen/list/2CS1QRYTRP6ER?ref_=cm_sw_r_cp_ud_aipsflist_aipsfalisonrosen_0K0AJFYP84PF1Z61QW2H Products I Use/Recommend/Love: http://amazon.com/shop/alisonrosen Check us out on Patreon: http://patreon.com/alisonrosen   Buy Alison's Fifth Anniversary Edition Book (with new material): Tropical Attire Encouraged (and Other Phrases That Scare Me) https://amzn.to/2JuOqcd You probably need to buy the HGFY ringtone! https://www.alisonrosen.com/store/ Try Amazon Prime Free 30 Day Trial