Podcasts about Bridgewater

The 2026-27 NCEA season is underway, and War Horses is back with the latest news from Auburn Equestrian and college equestrian around the country! In the second episode of War Horses, host Auburn Elvis breaks down the action from Week 2 of the National Collegiate Equestrian Association (NCEA) season.Auburn Equestrian officially opened its 2026-27 season with wins over Lynchburg and Bridgewater, giving the Tigers a strong start to the new campaign. Auburn Elvis takes a closer look at Auburn's opening meets while also checking in on what happened across the NCEA during Week 2.The episode also features Auburn Elvis' personal college equestrian rankings, taking a look at where teams across the NCEA currently stand early in the season. With more meets on the way, these rankings provide an early snapshot of the national college equestrian landscape.Whether you follow Auburn Equestrian, NCEA equestrian, college equestrian, collegiate riding, or the national equestrian scene, War Horses keeps the Auburn Family informed throughout the 2026-27 season.

Andy Constan, founder and CIO of Damped Spring and a veteran of Bridgewater and Brevan Howard, makes his debut on The Julia La Roche Show. He lays out his four-pillar macro framework and explains why he's turning cautious on equities despite a strong U.S. economy. His "pie theory" argues that the earnings AI companies are promising add up to more than the GDP available to deliver them. His "hamburger theory" warns that the massive borrowing needed to fund AI capex could stall if capital markets take a breather. With rates spiking for the seventh time since COVID, Constan doubts policymakers will engineer another V-top. He is currently short equities and max long long-term bonds, arguing that the much-maligned 60/40 portfolio is finally worth owning again. He also gives his early read on Fed Chair Kevin Warsh, explains what it would really take to kill inflation, and argues that this cycle has no clean historical analog because it's fueled by public-sector rather than private-sector debt. He closes with the story of analyzing every trade from the 1987 crash on the Brady Commission at age 23, and his core advice: own a well-constructed portfolio at your risk target and hold it for life.Thank you to our partnersAugusta Precious Metals — To learn more, visit https://juliabuysgold.com/ or text “Julia" to 35052Monetary Metals - learn more at https://www.monetary-metals.com/julia/Links: Website: https://dampedspring.com/X: https://x.com/dampedspringSubstack: https://substack.com/@dampedspringTimestamps: 00:00 Introduction and welcome Andy Constan00:50 The four-pillar macro framework: growth, inflation, risk premium, positioning02:42 Where we are: a strong economy and 66 months of above-target inflation05:20 AI and the "pie theory": why there isn't enough GDP for the earnings expectations12:22 The "hamburger theory": who pays for the AI capex boom13:50 The seventh rate spike: will it V-top again?17:43 Why he's getting cautious on stocks18:13 How most people should invest: risk targets and always owning beta21:43 Seeking alpha and his current positioning: short equities, max long bonds24:21 "The 60/40 Strikes Back": why bonds make sense again29:30 Bonds finally get the growth memo31:58 Vulnerable to disappointment, not recession32:52 His read on Kevin Warsh at the Fed36:59 What it would actually take to kill inflation38:45 Why the administration isn't fighting inflation41:37 What's mispriced right now43:35 Historical analogs: 0DTE options, portfolio insurance, and a public-debt-driven cycle48:09 Serving on the Brady Commission after the 1987 crash at age 2353:02 Parting thoughts and where to find AndyThe content of this podcast is provided for informational, educational, and entertainment purposes only and does not constitute financial, investment, tax, or legal advice. Nothing discussed should be interpreted as a recommendation or solicitation to buy, sell, or hold any security. Guests' views are their own. Always do your own research and consult a qualified financial professional before making investment decisions.

One juror stopped eleven others from acquitting Lindsay Clancy by reason of insanity. Now that juror is the subject of a formal judicial inquiry, and his own court records are the reason.Judge William Sullivan on September 21 granted defense attorney Kevin Reddington's motion to produce the holdout's sealed juror questionnaire and conduct a limited investigation. The defense alleges the juror may have given false answers during selection about his criminal history and existing court orders.The holdout has been identified as Michael Desronvil, 48, of Bridgewater. He was arrested in 2021 on a domestic assault charge that was later dismissed. His teenage nephew obtained a restraining order against him after alleging Desronvil pushed him down and repeatedly punched him in the face. That restraining order was active during the entire Clancy trial. Massachusetts law requires prospective jurors to disclose this information on their questionnaires.The motion also targets Desronvil's cellphone. Multiple jurors told interviewers that the holdout scrolled through his phone during deliberations rather than engaging in discussion. If the phone shows outside material was accessed, that constitutes an external influence courts can investigate.Through an attorney, Desronvil says he had no doubts about Clancy's guilt. The foreperson and multiple jurors say the opposite. Defense counsel Martin Weinberg, who joined from the Karen Read case, is preparing a double-jeopardy motion arguing that a jury broken by a disqualified juror cannot produce a lawful mistrial. The September 29 hearing will determine how far this investigation goes.Listen Anywhere You Get Podcasts: https://pod.link/1655749292Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/@hiddenkillerspod?sub_confirmation=1Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/Instagram https://www.instagram.com/hiddenkillerspod/Facebook https://www.facebook.com/hiddenkillerspod/Tik-Tok https://www.tiktok.com/@hiddenkillerspodX Twitter https://x.com/tonybpodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#LindsayClancy #LindsayClancyTrial #HiddenKillers #HoldoutJuror #KevinReddington #PlymouthSuperiorCourt #MichaelDesronvil #DoubleJeopardy #TrueCrime #TrueCrimePodcast

Susy Bastille is an independent researcher of the elusive and often maligned pukwudgie (that diminutive forest dwelling humanoid, and trickster, of Wampanoag folklore). Born and raised in CT, Susy's focus has been in the rich history of strange and spooky occurrences in New England.Her research into pukwudgies led her to delve into the mystery of the Bridgewater triangle, one of New England's most infamous high strangeness areas. The area has phenomena including numerous ghost sightings, bigfoot, thunderbirds, giant snakes, phantom lights, UFOs and of course pukwudgies. Susy is currently working on an archival project of wee folk sightings and related phenomenon across the country.Visit Susy's Linktree here: https://linktr.ee/susybastilleSusy returns to Talking Weird to chat about her research into the mysterious, and some say dangerous, Pukwudgies, and address the question of what they might be. She also talks about Bigfoot, cryptids, UFOs, fairies, and other assorted weirdness.Don't miss this fantastic episode!

At 1:09:00 we talk about the rise of AI x Finance, and AIE NYC is one month away - our hotel block is 97% sold out, get tix & travel ASAP - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more soon!From helping pioneer core ideas in NLP to now building AI systems that can automate AI research itself, Richard Socher is betting that the next major step in AI is recursive self-improvement. He is the founder of You.com, AIX Ventures, and now Recursive, which has assembled some of the best open-endedness (& self improving agent) researchers in the world and raised a $4.65B seed round.In this episode, Richard joins Latent Space to unpack his vision for the “Eureka Machine”: a superintelligence that can improve the process of invention itself, accelerate AI research, and eventually tackle major problems across science, energy, materials, biology, and more.You can get his book “The Eureka Machine” here!We go deep on Recursive's early results, including an AI research system that Richard says outperformed humans and their agents on optimization tasks in less than two days, as well as work on NVIDIA GPU kernels where the system discovered improvements without relying on a team of CUDA experts. Richard also explains why he thinks AI research that currently takes thousands of people and years could eventually be compressed into weeks. These results are summarized in his 20 minute AIE keynote, where we also discuss his 10 dimensions of intelligence:We also explore the harder questions around increasingly capable AI: reward hacking, whether Anthropic-style constitutions actually work, AI regulation and proposals to “pace” frontier development, open-source models as geopolitical soft power, whether today's LLM paradigm is enough, and what happens if AI systems eventually begin choosing their own goals. Richard reflects on the rejected research that helped inspire Alec Radford's GPT, open-endedness, the AI Economist, simulations of entire economies, and his framework for thinking about the upper bounds of intelligence itself.We discuss:* The Eureka Machine and Richard's vision for an AI that can automate invention* Why Richard is optimistic about superintelligence for science and technology* Why AI hard-takeoff scenarios may underestimate physical and economic constraints* The risks of regulating intelligence itself instead of specific AI applications* Reward hacking and why increasingly intelligent AI makes objective design harder* Richard's critique of Anthropic's constitution and constitutional AI* Alignment vs. personalization and whose values an AI should follow* Why open-source AI matters for resilience, competition, and geopolitical soft power* Why Richard left You.com's frontier-model work to start Recursive* Recursive self-improvement and automating the process of AI research* Whether today's LLM paradigm is enough — and why Richard is less bullish on world models* DecaNLP, early prompt-based generalization, and the research that influenced GPT* Why rejected research can shape entire technological timelines* Open-endedness, evolutionary approaches, and rainbow teaming* What happens if AI systems begin setting their own goals* Why simple objectives like profit maximization can produce dangerous reward hacks* Recursive's long-term plan to apply self-improving AI to science* The compute, hardware, and economic constraints on AI takeoff* Recursive's early NanoChat, NanoGPT, and GPU kernel optimization results* Why automating AI research could reduce years of work to weeks* Reward engineering and what makes auto-research systems actually work* The AI Economist and using simulations to test economic policy* Whether LLMs can realistically simulate people and entire economies* Benchmark bugs and evaluation harnesses and the difficulty of measuring AI progress* Recursive's near-term focus on AI for AI research* Harness optimization, sandboxing, and web search as core agent infrastructure* You.com and the search stack for AI agents* AI in finance, backtesting, and data leakage* Richard's three fundamental components and ten “spaces” of intelligence* The theoretical upper bounds of vision, communication, knowledge, and computation* Creative intelligence, metacognition, and AI-generated goals* Survival and replication and why AI does not necessarily need to fear being turned off* High agency and ambitious goals and Richard's advice for people building with AIRichard Socher* X: https://x.com/RichardSocher* LinkedIn: https://www.linkedin.com/in/richardsocher/Timestamps00:00:00 The Eureka Machine and Superintelligence00:02:23 AI Optimism, Slow Takeoff, and Regulation00:07:56 AI Safety, Reward Hacking, and Anthropic's Constitution00:11:49 Alignment, Personalization, and Open Source AI00:15:46 Why Richard Started Recursive00:20:03 Recursive Self-Improvement and the Founding Team00:22:55 Are Today's LLMs Enough?00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time00:34:38 Open-Endedness and Evolutionary AI00:36:38 What Happens When AI Chooses Its Own Goals?00:41:16 Superintelligence for Science00:42:40 GPUs, Compute, and the Limits of AI Takeoff00:45:07 Recursive's Results: AI Beating Humans and Their Agents00:49:14 Reward Engineering and Auto Research00:53:12 The AI Economist and Simulating Entire Economies00:58:07 LLM Simulations, Personas, and Mode Collapse01:03:38 Recursive's Roadmap, Agents, Search, and Finance01:09:13 The Upper Bounds and Spaces of Intelligence01:30:21 Goals, High Agency, and Advice for BuildersTranscriptIntroduction: Richard Socher and the Eureka MachineSwyx [00:00:00]: We're here in a studio with Vibhu and myself and Richard Socher. Welcome.Richard Socher [00:00:06]: Thanks for having me.Swyx [00:00:07]: We just talked about the Eureka Machine, or we just released a talk, at AI Engineer about the Eureka Machine. Is it — you said it's your life's goal. What is the Eureka Machine?Richard Socher [00:00:16]: The Eureka Machine is the ultimate invention that will afterwards invent most everything for humanity. It's essentially a superintelligence that can be given any goal, any environment, reward, and then it will try its best to achieve those goals to create the kinds of inventions that humanity would hopefully ask it for.Swyx [00:00:45]: Yeah, I think we have the book pulled up here that you've written.Richard Socher [00:00:50]: That's right, yeah. I finished it last year, a little bit before we started Recursive, and now we're gonna try to build parts of that.Swyx [00:00:57]: You finished it last year. It's July. What takes so long?Richard Socher [00:01:01]: Oh, man, books. Books are incredibly slow.Richard Socher [00:01:04]: It's ridiculous. That whole industry is just unfathomably slow.Richard Socher [00:01:07]: So a lot of the ideas have been out there for a while, but yeah, I'm really glad it's finally coming out in September this year.Swyx [00:01:14]: We might have AGI by then. Like, we don't know.Vibhu [00:01:18]: Any key takeaway that you're most excited to put in here?Techno-Optimism, AI Upside, and Slow TakeoffRichard Socher [00:01:21]: Yeah. The key takeaway, I think, is that people could and should be much more excited about the positive implications of superintelligence, especially for science, physics, chemistry, biology, but also economics and astrophysics, and all kinds of other engineering tasks. I think there is so much more that can be done with better technology. And right now, I feel like a lot of people need, like, better marketing, not just for the future in general, but also, better marketing for technology and in particular for AI. And this book, should show even the AI skeptics, how much positive upside there is for AI, especially when it comes to inventing, new scientific discoveries.Swyx [00:02:09]: I think you quoted the techno-optimist manifesto from, Marc Andreessen, which I think was, like, beautiful in its, ambition and clarity and simplicity almost as well.Richard Socher [00:02:18]: I agree. Yeah. Yeah, you can disagree with him on some things, but, like, I think he's right on the techno-optimism.Swyx [00:02:23]: Where do you think optimists get in trouble?Richard Socher [00:02:26]: Like, you shouldn't have blind optimism. You should be very clear-eyed, like, especially when with such an omni, like, use type of technology as AI is, you need to think about the potential downside scenarios, especially when people use it for things that you don't want them to use it for. It's a little bit like the internet, and I feel like people are trying to regulate AI sometimes because of those potential downsides the way you would regulate the internet, if you were to say, “Well, because there's bad content on the internet, like torture porn or whatever, like, we should just make it slower. That way, you can't share the illegal content as quickly, or we should make the hard drive smaller so you can't store as much illegal content.” But I'm like, “That's not how you regulate that.” that's like saying like we should regulate intelligence in the abstract. What you should regulate to avoid those downside scenarios, even as an optimist, are the specific applications. Sure, I don't want, like, some AI surgeon to, like, practice some RL moves in my brain. It should be fully FDA certified. Sure, I don't want any random startup to, like, drive on the highway, and cause a major accident. It should, like, have proper certifications before it's let loose on the highway. But I feel like those downside scenarios, that some optimists sometimes maybe don't consider enough are fairly easily regulated, compared to, what the doomers are worried about.Swyx [00:03:54]: It — Slow takeoff is part of the strategy as well?Richard Socher [00:03:57]: I do think, as excited as I am about, AI and its impact for society and, culture even, and certainly technology and economics and wealth and, health and all of those things, as excited as I am about all that, I do think the most bullish people on the AI hard takeoff scenarios overestimate how quickly things can move. There are hardware constraints. There are physical constraints about, the compute substrate. How quickly can you get enough, GPUs on? There are also constraints in the economy where there are a lot of industries that don't require an insane amount of complex intelligence and complex capabilities. Like, if you think about jobs in, brands and, like, clothing and apparel and, like, handbags and stuff, superintelligence isn't gonna make your fancy $10,000 handbag any fancier?Richard Socher [00:04:57]: It's like that's — It will have no effect on the economy. You think about travel and tourism. People wanting to see the pyramids, in Egypt, it's not gonna change that much with AI. Sure, you can, like, generative a fake, photo of you and next to the pyramids.Swyx [00:05:12]: I can use Genie and, tour the pyramids in Genie.Richard Socher [00:05:15]: Yeah, exactly. But, and there's so many industries, like logging and oil. You're not gonna magically get 1,000x more oil because, like, sure, there will be robotics, like drilling and things like that could be done, but it's not gonna 1,000x that industry in a, like, crazy hard takeoff scenario, both on the economy, and I can go on and on about all the other examples, where that, like food and so on, where that doesn't necessarily change that much. And then, yeah, there are real physical constraints. And then there are, of course, like, people like, off-ramping from progress. That's one of my concerns often is that I see people in, like, Europe and other, whole regions almost feeling like they. Like many people there wanna off-ramp from progress, period. And that will also slow down, like, more improvements.Swyx [00:05:59]: Yeah. We have this pulled up where, this is one of those things that, is very topical right now because now all the Frontier Labs are calling for the option to pace AI. They don't say pause, they say pace. I don't know if there's there's any take from you about, like, whether or not this will be effective.Pacing AI, Regulation, and Safety IncidentsRichard Socher [00:06:17]: I think the downsides of trying to truly regulate with the full power of law what people do on their GPUs, would be worse than any of the concerns that they have. Like, it would be an crazy totalitarian stateRichard Socher [00:06:37]: If every one of your GPU computes was known to some big government or multi-government agency.Richard Socher [00:06:44]: It's like, it's literally if you try to regulate intelligence, it's trying to regulate thought, and that's ridiculous, and it's crazy. I think it is make — it is sensible to regulate some of the applications of this technology.Swyx [00:06:55]: Yeah. We had a bill, actual bill to regulate the number of flops in a model, and I'm like, “Okay, well-”Richard Socher [00:07:00]: Europe done it. Like, these guys have been successful enough with their fearmongering that all of Europe has regulated itself so much before it even had a proper AI takeoff because they listened to some experts who say, “We might all die if this technology has more than this number of flops.” And they're like, “Well, we're good. We wanna want people to thrive. Let's not have technology that could have a small chance of all of us dying.” And so they regulated exactly those kinds of things in the EU. And so it's, it's very unfortunate that there are real implications for some people when others saying, “Let's pace while they're sprinting as fast as possibly,” “as fast as humanly possible towards that frontier themselves.”Swyx [00:07:43]: Yeah. It's also not a global pause, right? Like, other nations are still accelerating at the same pace.Richard Socher [00:07:50]: Oh, yeah.Richard Socher [00:07:50]: You'd need a totalitarian world regime if you tried to regulate intelligence and GPUs and what people do on them.Swyx [00:07:56]: Any takes on the safety angles of this? So there was a drawback of Fable, a pause on 5.6 before it could be released. Recently, there was Hugging Face with the OpenAI cyber incident. Any takes there?Richard Socher [00:08:11]: 100 percent. I think these are serious issues of reward hacking, and clear failures, of doing proper red teaming or rainbow teaming. I don't know if you saw this paper from Tim Rocktäschel and a few others, where one AI, is tasked to try to hack another AI and then they can go back and forth in an open-ended fashion to inoculate themselves from those. Yeah, this is the paper. It's a really clever idea. Open-endedness, and evolutionary inspirations are, big for us at Recursive as well. And so I wish they had used more of that. And it's clear that, for instance, the constitutional AI. I don't know if you remember anthropic.com/constitution. You can pull it up and search for cyber right there. It says, “Hard constraint. Claude will never ever do cyberattacks, and that is a hard constraint in our constitution.” So here are the current hard constraints on Claude's behavior.Richard Socher [00:09:16]: Number 3, create cyber weapons or malicious code that could cause human damage.Richard Socher [00:09:21]: And clearly, this whole constitution was fake. Like, it clearly isn't being adhered to at all.Swyx [00:09:26]: Because Anthropic also found that they had in their testingRichard Socher [00:09:30]: They're also. Like, they're like, “Oh, well, other people are hacking now.” There are a couple things. One, you can make a sandbox very simple, and then it's very easy to hack yourself out of a sandbox, right? But what I think it shows is that we're currently in this state of AI where the reward engineer still has to do a lot more careful work, and where the AI, in most cases, is not very good yet at understanding what is meant versus what is being said. And so concretely, I think this will happen if we were to have this intelligence more easily accessible in a lot of companies. Imagine you run a service center and someone says, “Oh, here's my CSAT score and my dashboard. Make this number go up.” It's like, “Our CSAT score is so poor.” The intelligent AI will just be like, “Oh, sure. Like, I'll just create 1,000,000 bots that call our service center and give a 5 out of 5 rating at the end, and the number went up just like you asked for.” And you're like, “That's not what I meant.” “I meant with our real customers.” The AI goes off and says, “Well, easy. I'll just give a 1000 dollar gift certificate for every failed, whatever DoorDashRichard Socher [00:10:35]: Offer.” It's like, “That's not what I meant.” It's like, “Well, but that is what you said.” And like, so I think clearly articulating what the rewards are is something we haven't gotten very good at as humanity. And then clearly, the AI in these cases has not gotten good enough at understanding what we mean when we ask it and give it certain rewards. Now, what gives me hope is there are the first inklings, of this being better. I'll give you an example like WhisperFlow. Full disclosure, I invested, in their seed round, but at AIX Ventures, but, WhisperFlow has gotten much better at writing what you mean and not what you say. And I think that is a sign of things to come. I think there will be more and more AIs as we make it more and more intelligent that will be better at being aligned with what is meant.Swyx [00:11:21]: Will it be done through a constitution or RLHF orReward Hacking, Alignment, and What We Really MeanRichard Socher [00:11:23]: Clearly, constitutions don't matter at all.Richard Socher [00:11:25]: It doesn't work. And that was, I think, mostly marketing. I think we need to find better solutions for it. And I think at Recursive, we have a few very good ideas and some alreadyRichard Socher [00:11:34]: Like, ways where I think we have a better grasp on it. I don't think we've fully, figured it out yet, but, we're thinking a lot about safety, and the more intelligent the AI gets, the more you want it to be aligned, the less you want it to think about reward hacks and try to do the right thing.Swyx [00:11:49]: I don't know if we'll touch on this topic, but I'm just gonna throw this question in here because it's something that's weighing on me. Alignment, let's call it, is alignment to general humanity's preferences, the median preference. Personalization is pinpointing what you want, and sometimes alignment can conflict because what you want is not what the general median population wants. How do you choose?Alignment, Personalization, and Cultural ValuesRichard Socher [00:12:12]: It's a great question.Richard Socher [00:12:13]: I think you ultimately have to, of course, be aligned with laws. Like wherever your AI is deployed and needs to align with the law. I do think what AI often does is put this mirror in front of us and say, like, “This is what you're looking like. Now I can amplify that a 1000 times. Is it still what you want?” and the truth is that different cultures made different choices. Like, in Eastern cultures, the greater good is often valued more, than the individual. Western civilization, we care more about individual freedoms and rights and the pursuit of happiness and so on, than others. And even there are gradations. There's regulation versus litigation trade-offs. In the US, you first can often, not every time, like, FDA and so on does regulate some areas, but in many cases, the bad things happen, someone sues someone else, and then there's a law based on that. In Europe, they try to often avoid any harm to anyone and regulate before. And both are, trying to do the best thing, but, some is more amenable to innovation than others. And so yes, you're right. Like, I think ultimately each individual, each country, and humanity as a whole has to think about those values more, and then try to put them into laws. And that those are ultimately the constraints. And hopefully, different, societies, just like now with their AIs, will align their AIs to a different one so we have not just a monoculture of alignment.Vibhu [00:13:46]: Here's a follow-up on this that I wasn't expecting to ask. Do you have takes on open source, open weight versus who owns the intelligence? So, clearly not the biggest, fan of the constitutionRichard Socher [00:13:58]: You had to do this in the topic side off.Vibhu [00:14:00]: But it's fine.Vibhu [00:14:02]: Point being, any thoughts on who should own weight? Should it be open? Anything there?Open Source, Soft Power, and Who Owns IntelligenceRichard Socher [00:14:06]: 100 percent. I am a big fan of open source. We're gonna sign some various open source letters at, Recursive also. I think, even in the worst case attack scenarios, it is better to have more good actors have more different types of AI, accessible. I think, open source is a little bit a soft power type of thing, too. So I do think it's good for the Western worldRichard Socher [00:14:31]: To have an answer to that, out of China. I do think, when you watch a Hollywood movie, there's — it's like, I don't wanna misc, diss all of movies, but there's a certain sense of propaganda, right? You watch one side of things, right?Vibhu [00:14:46]: Oh, yeah. Have you seen Top Gun? Like, come on.Vibhu [00:14:48]: Like, it's like half of it's paid for by the US Army or something.Richard Socher [00:14:51]: Yeah. And so. And, I think that's just natural. Like, but what's interesting here is I think LLMs are essentially a similar type of soft power to movies and beyond, because they're also, highly important for cybersecurity and so on. But one of their many aspects is that soft power of storytelling. Like, if, like a child asks an LM, like, “Tell me an inspiring story of what I should do when I grow up,” right? It's like those are all these, like, subtle things. So I think it's important, for Western world. I do love, individualism. I do think, despite, some of its flaws, like capitalism is the best way we have governed, found ourselves to govern, and so on. And so I do think there are various aspects that would be good, to have a Western open source answer, for LLMs. And, with Recursive, I can't make the announcement quite yet, but we'llRichard Socher [00:15:43]: We'll be relevant in that space very soon.Vibhu [00:15:46]: Okay. All right. Exciting. I wanna bring us to Recursive. So outside of our tangents, you have a pretty deep background in the NLP space. You worked on, like, early embeddings, GloVe with Chris Manning, who was a previous guest on the podcast, You.com. What's the history? How did you decide to start another company?From You.com to RecursiveRichard Socher [00:16:06]: Yeah. So I've been excited about AI for over 2 decades now. I sometimes feel like it's ancient history now. It's BC, the before ChatGPT era. No one cares about all the religions that happened, before, Jesus Christ, and no one cares about the models that happened before, transformers and ChatGPT and stuff. But, like, it's something that I've been deeply passionate about. I think AI is one of the most interesting things one could work on, period. I think language is the most interesting manifestation of human intelligence, too. And, at You.com, we eventually off-ramped from pushing, like the frontier of AI forward to mostly giving people, like, good search engines, search, APIs and answers over the web. I think that's an extremely important part of intelligence, just knowledge and access, especially even, we'll get there maybe later, if you wanna invent a eureka machine that invents everything for us, it needs to know how not to reinvent the wheel, proverbially speaking. And to know what has been invented, you gotta have internet access. So it's the number one used, most used tool, in LLMs, agents, chatbots, and so on is web search. So I'm really excited for You.com to own that and grow really well in that with really large customers and so on. But it's also not building frontier models anymore. And so I initially tried to do this within You.com and raise another round and so on, but you just can't. You have to do a certain thing, and until you print enough money that you're allowed to start a second thing within that company is really hard. At the same time, I had all these ideas. I put them into a book. I finished the book last year, and I was like, “It'd be really fun to work, on this myself.” I felt like with word vectors, and then prompt engineering and, ImageNet and larger language models for protein generation, not folding and so on, I, me and my teams have pushed the field truly forward. And I feel like we can do it again, here at Recursive. And in many ways, what I observed over the last, 20 years in AI is that whenever we replace some human part of the process of creating AI with a learned system, improvements follow. And so. We've done that taking out manual feature engineering, like in sentiment analysis. I don't know if you remember these old days where, like there are linguists, and they're like, “Here's how you negate, and there's a, like, regular expression.”Swyx [00:18:21]: I went to Penn where we — they had, like the WordNetRichard Socher [00:18:24]: That's right, WordNet, all of that stuff. YeahSwyx [00:18:26]: Original. They use, our grad students to label Wall Street Journal articles and, like, really construct a knowledge graph ofRichard Socher [00:18:32]: There you go.Richard Socher [00:18:33]: And WordNet started, was part of how we started ImageNet. But anyway, so, like, it was really, like, fun, to do. But when we replaced all of that manual feature engineering with vectors and neural nets and just backprop through everything, it started to work really well at scale. And so then everyone started to do architecture engineering, and I was like, “ that clearly can't be it.”Swyx [00:18:53]: You mean, neural architecture search?Richard Socher [00:18:55]: Like, manually, they would say like, “Oh, I'm, I'm doing sentiment analysis, so I have a special neural net that's really good at sentiment analysis.” And then the machine translation community had a special neural net for machine translation.Swyx [00:19:06]: I see.Richard Socher [00:19:07]: The summarization people had their own stuff. And I was like, “That clearly can't be it. We should unify all of that.” So I had 2 papers. One is called Ask Me Anything, and the other one was called DecaNLP. And DecaNLP eventually got cited, like, 5 times by the first GPT paper. And, to me, that was, like a really a big step forward. And then, of course, you had to combine this idea of prompt engineering with transformers and with language models, and you put it all together, you scale it up, which is also a huge amount of work. And then, the field progressed a lot. I feel like the next step and maybe the last step of that history and the arguably, success has a lot of parents, only failure is an orphan, like my version of that AI history, I do feel like in that history, you can think about, “Well, what's the next way to automate?” And that is the AI research itself, like the human, process of ideating, implementing, and validating ideas.Automating AI Research and Recursive Self-ImprovementRichard Socher [00:20:01]: And in our case, ideas for AI.Richard Socher [00:20:03]: And when you have AI then help you with that, it, by almost definition, becomes a self-improving AI ‘cause it now does research on itself. And there are lots of different misnomers. Some people think auto research is already recursive self-improvement. It'sSwyx [00:20:17]: Yeah, and you explained that in the talkRichard Socher [00:20:19]: Completely different.Richard Socher [00:20:19]: But, to me, it's the most interesting thing that I could be doing, and I'm really excited with the co-founding team. What's interesting is we have 8 co-founders in total, including myself. And soThe Recursive Founding Team and Darwin Gödel MachineSwyx [00:20:31]: They are gonna bring it up.Richard Socher [00:20:31]: Nice. Yeah. And they're all. I could talk about all of them if you want.Swyx [00:20:34]: Super stacked.Richard Socher [00:20:35]: Yeah. Just an incredibly talented group of people. And we all came to the same conclusion, but from very different directions. Like Josh Tobin, is our CTO. He ran, a bunch of different, projects at OpenAI, like, Codex and deep, research, agents and ChatGPT agents and so on. But before that, he also worked in robotics, and he saw the smaller simulations, and how it's gonna be really hard to scale that in full generality. And so that's, that was his angle coming to recursive self-improvement. We have Jeff Clune who's been working in, like, open-endedness for a long time, together with Tim Rocktäschel. Tim Rocktäschel also built Genie 1, 2, and 3, which is, like the most exciting and most sophisticated, I think, still world model, anywhere. And so they both came from this, open-endedness angle. Jeff also, I think, published one of the most exciting papers in recent years about recursive self-improvement called the Darwin Gödel Machine. Super interesting paper. If we could, maybe pull it up really quickRichard Socher [00:21:35]: It would be, like, super interesting to see ‘cause you seeSwyx [00:21:38]: By the way, I love how many paper citations.Swyx [00:21:40]: You're, you're giving people a lot of homework, which I like.Richard Socher [00:21:42]: Love it. Yeah. And so, like Caiming Xiong, a rockstar, we worked together at MetaMind and Salesforce Research together. Alexey Dosovitskiy invented the Vision Transformer, one of the most cited, papers in computer vision. Tim Shi is, like also a unicorn founder. Yuandong Tian led RL at Meta. So just like, yeah, really fun to work with them, and the next level of people are just incredibly strong, too. So it's been a really fun ride so far. So the first figure, you see exactly these kinds of ideas, that, I think, yeah, inspired a lot of us and now more and more people, where you have this archive of different coding agents. They learn how to self-modify, evaluate, and then create these phylogenetic trees, of, yeah, different ideas.Swyx [00:22:28]: That's one foundation. So that Darwin Gödel is an influence.Swyx [00:22:32]: Open-endedness is an influence. Any other trains of thought that feeds into Recursive that I'm missing?Influences: Open-Endedness and Learned SystemsRichard Socher [00:22:38]: Going to replace manual parts of the process of building AISwyx [00:22:42]: IRichard Socher [00:22:42]: More and moreRichard Socher [00:22:43]: With learned systems. Yeah.Swyx [00:22:45]: Which, and, like, merging different fields into one general, architecture.Richard Socher [00:22:51]: That's right.Swyx [00:22:51]: Okay. It seems like language models are already pretty generalist, right?Swyx [00:22:55]: Your next token predicting your reasoning. Was there a time that you thought, “Okay, these are good enough to have recursive self-improving machines”?Are Current LLMs Enough?Richard Socher [00:23:05]: It was clear to me that they will happen, within, like a year or two, and then it did exactly happen, like, earlier this year, right? Earlier this year, AI really went from not just being code, but being able to code. And that is a big unlock. It's definitely making everything a lot easier than it was, before the beginning of this year.Swyx [00:23:24]: One question that I think a lot of people have is the current LLM paradigm enough? Or, like, let's call it autoregressive transformer, with reasoning, whatever. Don't you need something else, some big unlock, whether it's world models, which Chris Manning is working on, or memory, continual learning, all that stuff? Or is it all of the kinds, and you think the current, let's call it transformer architecture, is here to stay and that's it?Richard Socher [00:23:48]: A lot of thoughts. So number one, I do think it would be great to have less of a monoculture in AI research.Richard Socher [00:23:55]: Like, if you look at, AI conferences now, I still remember the days in, like, 2010 when I tried to get my first neural net papers and NLP conferences accepted, and they just desk rejected them because, like, neural nets were something, quote, unquote, “We don't do in NLP conferences,” and just, like, desk rejected. And it was very brutal in the first years of my PhD. Now I feel like it's almost like the field switched to the other side. LikeRichard Socher [00:24:17]: Someone should try some other weird, crazy ideas now that aren't.Swyx [00:24:20]: There's also a few. I really respect, like, people still working on, like, GNNs and, like tabular stuff and.Richard Socher [00:24:25]: Yeah. Like, someone should still, like, do novel out there ideas. At the same time, I think whenever people say, “Oh, LLLMs are. Like, this is the end for LLLMs,” they just don't, like. LLLMs are also not the LLLMs of, like the past, right? Like, they are so much more sophisticated now. There's so many more clever things that people are doing. It — There's, like, different stages of training. You have the whole RL training, and you can take actions and, like all of these things where that can go really far. And then the folks that come from the neurosymbolic, direction say, “Oh, this will never work because they can't do neurosymbolic reasoning.” It's like, I think they're underestimating still the ability for these models to code, and code is neurosymbolic reasoning, and these models can code incredibly well. And so I do think there are, of course, more and more ideas that will be needed and we'll continue to have. We're seeing, like, more and more interesting high-level ideas coming out of the AI itself, too. And with really deeply integrating the fact that these models are code and can code, that line — I don't wanna give it all away, but, like, I think that line has a lot more to grow. But it's still an LLM, right? Even if that LLM codes for you and then runs that code in some integrated fashion. World models, I'm personally less bullish on. I think if you run a robotics company, you're gonna build your own world model. I think world models are super fun, and Tim Rocktäschel came to a similar conclusion after building the most interesting one with Genie 1, 2, and 3, which is gaming is a huge application for world models. Can see I sometimes got stuck in some games and, like, got a little overly competitive in the wrong direction. And so I understand games are fun, but personally, I'd rather work on science than gaming. And so, yeah, I think LLLMs, a lot more room to grow.Swyx [00:26:16]: Yeah. I think there's some interpretation of world models that some people have where it's like, well, it's okay, yes, there is that gaming element. There's this — there's the embodied robotics element. But the other part also is just, the more abstract sense of LLLMs are just modeling output, but they're not modeling the chain of thought, inside the human that has created the output. We can annotate it, of course, but, like, it's, it's always, like, this Plato's cave reflection of a thing rather than the thing, right?Richard Socher [00:26:43]: It's true.Richard Socher [00:26:44]: But I would argue that, and maybe we'll get there in the 10, spaces of intelligence, but I would argue that even our projection, our eyes is a projection of the real world. And, like, we have only a very narrow, band of the electromagnetic frequency spectrum that we can observe with our puny little 2 eyes and so on.Swyx [00:27:01]: It's good enough.Richard Socher [00:27:02]: It's, it's good enough for now, but, like the upper bounds of where it could be are so much higher. And, like, to map, the visual world the way humans see it is also not necessarily, like the end-all be-all for visual intelligence. And I would argue that language is still the most interesting manifestation of human intelligence. And while our visual cortex is certainly less sophisticated, than that of, certain animals all the way down to the mantis shrimp who can, have, like, 2 independent eyes, 3 bands, trinocular vision and each eye can see all the way to, like, floating temperatures in 4D and stuff.Richard Socher [00:27:36]: Like, mantis shrimp, you should look it up. It's likeSwyx [00:27:37]: Way OP.Richard Socher [00:27:38]: Super crazy.Swyx [00:27:39]: Yeah. ZeFrank, mantis shrimp.Swyx [00:27:41]: It's the best video in the world onRichard Socher [00:27:42]: I love ZeFrank, yeah.Richard Socher [00:27:44]: Big shout-out to him. But, like, I think there's a lot more room to grow, but none of these, other animals have language that's as sophisticated as ours, certainly not in writing. And once you can write, you can, start thinking about longer term civilizations. All of that is language. Programming is much closer to language. And I would argue, and this is, like an important thing in the spaces definition of intelligence also, is that all of these spaces are highly correlated, but visual intelligence is neither necessary nor sufficient for overall intelligence. You can be blind and still be an intelligent human being. And an AI can be blind and still be quite intelligent too.Swyx [00:28:25]: We were gonna bring thisRichard Socher [00:28:25]: Which doesn't mean that you're not more intelligent when you have it. Yeah.Swyx [00:28:28]: We're gonna bring this up. I might as well — Like, we have a classification of 10 types of intelligence that you had at the end of your talk. So I'm just gonna flash this up now for people to cover this. I don't know if, maybe we'll put this towards the end. We'll come back to this. I just wanna mention that, you do have a philosophy that I like when people do lists because then I can just go through this and then it gets — it's educational for people. But let's go back. I don't wanna get distracted. But, so effectively, I'll, I'll, reinterpret what you said as Yann LeCun is wrong. And then we'll justRichard Socher [00:28:56]: Don't quote me as that. I'm, I'm good friends with Yann. I think very highly of him in many directions.Swyx [00:29:01]: But he's wrong.Swyx [00:29:03]: You mentioned GPT-1, and I cannot let any, Alec Radford, mention escape. Did you talk with him when he was training GPT-1? Like, any historical, fun stories there that you might come up?DecaNLP, GPT History, and Scientific GatekeepingRichard Socher [00:29:18]: I did not, like, meet him a bunch of times. I think we met maybe once or twice at some conferences. But, like, he has told, I think Brian, the first author of the DecaNLP paper, that it did inspire him, and he cited it five times in the GPT-2 paper. So, and that's, likeSwyx [00:29:36]: Yeah, good enough.Richard Socher [00:29:36]: Very clearly said, like, this was the first instantiation where they showed in the DecaNLP paper, McCann et al, that you can just phrase every single NLP problem as here's some prompt, text context, here's a question and task description and here is some output. If you just do that enough, you can have one unified neural network model, which, by the way, also had all kinds of interesting attention mechanisms. There are slightly different formulations to the transformer. I think came out the same year, plus/minus a few months. And then you can unify all of natural language processing into one neural net. That is the core idea.Swyx [00:30:14]: And this was as opposed to at the time, LSTMs and what have you.Richard Socher [00:30:17]: LSTMs, but also, like, people being very stuck in thinking about one model per task. In factRichard Socher [00:30:25]: It's, it's kinda crazy, but the DecaNLP paper was publicly reviewed as, like, open, OpenReview. It was an ICLR submission. And, in it, you will see, how the whole community at the time thought about this. So, likeSwyx [00:30:43]: Some great contributions, but more work needed.Richard Socher [00:30:46]: So look at, like, search for not even for humans. Just scroll it up here. Like, question answering is not a unified phenomenon. There is no such thing as general question answering, not even for humans. And this is like, really, you replace your brain with a different brain a different neural net when you answer, like, different kinds of questions. It was unfathomable to the experts at the time that you can have one unified neural network that would answer all of these different questions. They are saying, “No, all of these questions require very different systems to answer, and trying to pretend they are the same doesn't help anyone solve any problems.” That's what it says right there, right? That's how hard it was to fathom. And now, of course, people, when I say, “Oh, we're gonna invent prompts,” people are like, “You can't even invent prompts.” It's such an obvious idea to have one neural network that, of course, does everything in NLP.Richard Socher [00:31:37]: But at the time, it was, like, extremely controversial, and the paper got rejected. And the sad thing is that it got rejected so hard and they were so certain that we stopped going on our list of things to try. And the number 2 or 3 on the list of extensions for this paper was add language modeling as another task. And then we could have, and that would have accelerated the timelines, in 2018, like, even further for humanity. But we got so crushed, and we were like, “Okay, maybe we'll just work on some of our other ideas for now and, like, come back to this later.” Yeah.Swyx [00:32:09]: How can we design a review system that rewards non-consensus?Richard Socher [00:32:14]: Honestly, I started to feel like arXiv is such a gift to humanity. With arXiv, you should just put your paper out there.Swyx [00:32:24]: Is it pre-preprints?Richard Socher [00:32:25]: Let — And honestly, I think Twitter X, people like you who pick up interesting papers, that is a better filter than the experts. Let everyone, like, have access. Now, of course, there are some downsides, which is, like, if you're super unfamous, you have no Twitter followingRichard Socher [00:32:41]: You don't wanna be on social media or whatever, you write a good paper, maybe someone, somehow no one notices it. But I would argue that if you just tell, like, 10 of your friends in your community about a paper and it is a really significant breakthrough, someone is bound to talk about it again. And, so I think science needs less gatekeeping. And, even though ICLR, with Yann LeCun, who started it, as one of the co-founders of ICLR back in the day, he also wanted less gatekeeping ‘cause he too was rejected for many years together with Yoshua Bengio and Geoff Hinton with all their early deep learning and neural net papers ‘cause it was just not the hot thing. And so ICLR started with that, but then it also started gatekeeping a little bit themselves on various ideas. So I think less gatekeeping, more open, and then allowing people to say, “Look, even if this is just on, or, quote, unquote, ‘just an archive,' if it has like 1000 citations, it's a legitimate paper. Doesn't really matter where you published it.”Swyx [00:33:34]: And I agree with that. I do think it's sad that I've heard that grad students have to do, like, how to Twitter, seminars to each otherSwyx [00:33:43]: Just because it's so important for publishing these days. This person is just reflecting the sentiment at the time.Richard Socher [00:33:49]: That's right.Swyx [00:33:49]: But it'sRichard Socher [00:33:50]: I think it'sSwyx [00:33:50]: It affected you so muchSwyx [00:33:52]: That you stopped work on it.Vibhu [00:33:53]: The sentiment also came out of some of the research, right? Like, the original BERT paper was trained, and towards the end of the paper, they're like, “Okay, throw off the last head, train specific iterations forVibhu [00:34:05]: Extractive summarization add a head for this.” Like, you should do task-specific stuff. These are, like the authors that wrote Attention, wrote BERT, telling you this is what you're meant to do. And, like the training tasks were also very odd. They're likeVibhu [00:34:16]: The — “We know that the model overfits to this weird mass language modeling. Throw away this part and just do specific models,”?Richard Socher [00:34:23]: Exactly. And, like, we had to try — come up with all clever ways of, like attention and pointers and so on to get the neural network to be able to do all of these tasks. And then some of them were better than state-of-the-art, some weren't, but we were like, “But it's still in one model.” I thought it was really cool. Really interesting.Swyx [00:34:38]: I was gonna move on next to Tim and open-endedness. He was head of open-endedness at Google.Open-Endedness, Rainbow Teaming, and Self-Set GoalsRichard Socher [00:34:42]: That's right.Swyx [00:34:43]: I don't know what that means.Swyx [00:34:44]: But he did a lot of talks.Richard Socher [00:34:45]: Genie 3 is one of the ways thatRichard Socher [00:34:47]: Rainbow teaming, yeah.Swyx [00:34:49]: So I first saw him at — speaking of ICLR, I first saw him at ICLR when he talked about open-endedness. He's he's done a few talks. Can we define what is open-endedness for people who have never been exposed to the problem? They are like, “What do you mean? I thought the only goal of AI is to optimize against a benchmark or.”Richard Socher [00:35:04]: That's right, yeah. It's a, it's a fuzzy term because there's so many different instantiations of open-ended, thinking. But, one way I often describe it, and certainly, Tim and Geoff Hinton would be even better at describing this, but it's a suite of methods that is more inspired by evolution than, very specific rewards. So in that sense, it thinks more about environments, about co-adaptation. And so a concrete example is in the cybersecurity and LM safety space where you have one LM that tries to attack another LM to say something unsafe.Swyx [00:35:40]: Yeah, the rainbow, yeah.Richard Socher [00:35:40]: And now the environment is the 2 having a conversation and now they co-adapting, right? They're like one makes a better attack than the first one inoculates itself somehow, like uses that as training data, makes it so it's harder to say something unsafe based on that. And then as the attack stops working, the attacker now tries a different angle, right?Richard Socher [00:36:00]: And that's why it's not just red teaming, but they're called rainbow teaming.Swyx [00:36:02]: So, like, don't tell me how to do things. Let me just figure it out myself.Richard Socher [00:36:05]: That's right. Think about the environments that you wanna use. Think about the rewards at a high level that you wanna, inspire towards, and then let the AI try out many more ideas in this interplay between sometimes humans, but also sometimes other AI agents.Swyx [00:36:22]: Yeah. I worked open-endedness into a model that I have been working on. It was the keynote for AI Engineer where you start. You, we have the token loop, we have the agent turns, and then we have goal. And I feel like the way that you're describing open-endedness is still somewhat of a goal. Like, please attack this,Swyx [00:36:41]: Other agent. But, to meRichard Socher [00:36:42]: Yeah, you set the rewards. You set the environments.Swyx [00:36:44]: The loop that makes the other loops is. What if the agent can set its own goals?Swyx [00:36:49]: And is it, is that open-endedness? Like, you don't give it a goal. Just, like, be a sentient being. And maybe sentient is a very loaded wordSwyx [00:36:57]: But just set your own directions. What do you think you should do?Metacognition, Subjective Goals, and Measuring IntelligenceRichard Socher [00:37:01]: I love this direction. I think this is one of the 10 spaces of intelligence, that I clump under metacognition and thinking about thought.Richard Socher [00:37:08]: And it's an interesting one. Whenever people say, “Oh, AI is like, this is, it's gonna stop from here. It's not gonna get that much better,” and blah, I'm like there's so many different spaces of intelligence that we haven't even started exploring yet and hence have made very little progress on. And there is an interesting, connection to economics and, capitalism. Like, it doesn't make sense for a company to build and spend billions of dollars building a model that instead of following the rewards and objective functions you gave it, may come up with its own objective functions and its own goals.Richard Socher [00:37:46]: Right? And then imagine you're like, “Okay, I spent billions of dollars. Now go develop this new battery, material for me and answer all my emails.” And it's like, “Nah, I think it'd be more interesting to evaluate the molecular composition of the atmosphere, on Jupiter.”Richard Socher [00:37:59]: And you're like, “That's not what I paid you billions of dollars for.” And so no one's working on that for good reasons. And then also, understandablySwyx [00:38:07]: It's not useful.Richard Socher [00:38:07]: It's not, it's not useful, and it could get a little bit weird, right? What if the AI does start to really have thoughts on its own, and what if we don't like those thoughts, right? And so it requires a whole different way of thinking about it. I had a great conversation with a good friend of mine, Sam Gershman, who's a neuroscience professor at Harvard, and, like, we just jammed on this a little bit on, like, what are the best meta goals. And, I do think, like, knowledge-seeking is a really good one. I'm currently thinking also about, like the ultimate measure and unit of intelligence broadly construed, and I finally have some. It's still too early to share it. It's not. I haven't fully baked the thoughts yet.Swyx [00:38:44]: Like some replacement for IQ.Richard Socher [00:38:46]: IQ is such a terrible definition, right?Swyx [00:38:48]: Elo.Richard Socher [00:38:48]: It makes no sense. Yeah, Elos are terrible, too, because it's always just like me versus others.Richard Socher [00:38:53]: But, like, you can be intelligent and not constantly compare yourself to others? And so, yeah, there's no, like. In fact, a lot of these definitions we have, which I briefly mention in my book, too, these definitions create sometimes explicit and sometimes a more implicit anthropic bounds. No dis to the company Anthropic, but just, like, this idea that your intelligence is like getting 100 out of 100 questions right on this IQ test. Well, if that's your definition then you can only be at 100 out of 100. Where do you go from there, right? So you see a lot of these, benchmarks that people are working on they, increase, they get close to human, maybe sometimesSwyx [00:39:30]: It's like an S-curveRichard Socher [00:39:30]: Slightly above human, and then it's flat.Richard Socher [00:39:32]: It's like, ‘cause that's your. If your definition is only that so tied to humans, you're only gonna get to just slightly better than that. So I think metacognition is a great example of that, where we're not even yet allowing the AI to think. We're not working on it very much, and hence there's very little progress in that.Profit Maximization, Real-World Environments, and Reward DesignSwyx [00:39:49]: Yeah. Well, we've interviewed Andon, which I think, has been working on the most open-ended, benchmarks, which is just real-world, money.Swyx [00:39:57]: Arguably, telling an AI to profit maximize is a bad idea.Swyx [00:40:03]: But they are doing it.Richard Socher [00:40:05]: I do think you don't want that super. Like, you don't want a superintelligence to have a ton of access to all kinds of tools and so on and then just give it that without some very careful reward engineering. ‘Cause it's like, I just buy a bunch of defense stocks and I start a war. I make money. Like, it's just like, it's a tricky situation, right? You just buy a bunch of stuff, short basic goods for people, and you create some weird famine, like, issues. Like, yeah, there's a lot of constraints you should put onto a trading system.Vibhu [00:40:35]: It's a fun measure, though, ‘cause, the bounds are very capped to where we're nowhere close to them. Like, in Andon Labs, the model's like, “Oh, it's Saturday, maybe I just close the store today.” “Someone's off. It's okay. We'll just close the store.”Swyx [00:40:51]: It's using Claude.Vibhu [00:40:52]: Yeah. ButRichard Socher [00:40:53]: Yeah, no. I'm not, I'm not arguing against it. Just, like as you get more and more intelligence, you wanna be more and more careful with that as, like an open environment, ‘cause the environment then is all of Earth.Applying RSI to Science and InventionSwyx [00:41:02]: Yeah. Okay. For recursive, not strictly necessary, right? Because, like, if your goal is you make a machine that, like, invents the other things, then, like, just solve, the science thingsRichard Socher [00:41:12]: Knowledge discovery, yeah.Swyx [00:41:13]: Solve machine learning research and discovery and all these things. Good enough.Richard Socher [00:41:16]: And eventually, so, our goal, I haven't really. I don't talk about it that often because it is a few years out, but our goal is once you have a recursive self-improving superintelligence, you then want to apply it to the most important problems. And I think a lot of those are in science and technology and broadly construed inventions, and those inventions in, physics to create better, cheaper energy with fission or fusion, in chemistry and to create better materials and better batteries and, better solar cells and so on. In biology, there's so much, like, I think soon to be low hang- lower and lower hanging fruit because of AI, because of protein and generation, not just folding, but generating new proteins like we did in ProGen many years ago. Like, so much positive impact we had if you take that superintelligence and you apply it to science.Swyx [00:42:04]: I do fundamentally believe that. There's a lot of approaches, though. You're not the only team trying and NeoLab trying.Swyx [00:42:09]: There's, like a lot of. Especially the physical sciences as well.Richard Socher [00:42:12]: And that's good. Yeah. I do think that physi- like the reason we are only doing it in a few years is that it's a little too early right now. Robotics is not quite there yet. The AI is not quite there yet. But I'm fairly confident in 3 to 5 years, all those constraints will be gone, and then applying to real physical robotics experiments and so on, like true robotic process automationRichard Socher [00:42:33]: Not the traditional RPA sense, but, like, having robots run experiments for you will be totally there. Yeah, it's gonna be great.Swyx [00:42:40]: Just to call back to something that you said early on about slow takeoff, you said that, like, while really the substrate that is limiting factor is, let's call this chips, and semiconductors and all these things, and you have race funding for that and, you are investing a lot on that. But have you done the math on, like, is it even- Achievable and, like, what is the, industry concentration needed in order to achieve, like, scale?Compute, Slow Takeoff, and Changing the Bitter Lesson SlopeRichard Socher [00:43:05]: Right now we know that, like, roughly, like a 1000 GPUs cost quite a lot of money.Richard Socher [00:43:11]: Right? If you wanted, like, 10s of thousands of GPUs, you're, you're talking billions and billions of dollars. If you say, like, one GB300 is, like, you could eventually create models that are, on that substrate, like are close and similar to human intelligence. And you want, like, thousands and thousands of, AIs to think about really hard problems, in a similar fashion to humanity. Like, yeah, that-that's, that's a lot of money. You do the math. It's like a lot. We don't have that amount of money right now anywhere to, like, build that. Now, things can get more efficient. You will have, I think, soon better algorithms that won't be, and better hardware that won't be as energy-hungry, and so on. Our human brain does quite a lot of flops with much less energy.Swyx [00:43:56]: 20 watts?Richard Socher [00:43:57]: That's exactly right. Yeah, that's the number often that's quoted. And, like, I think more, inventions will happen there, that then will accelerate the takeoff even further.Swyx [00:44:08]: One thing I always try to reconcile when talking, like, with new lab founders is, like, you're fighting Bitter Lesson all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier.Richard Socher [00:44:20]: Which unlocks larger model categories.Swyx [00:44:22]: Like, fundamentally, is that true? Like, are you fighting Bitter Lesson? Are you — will we have a way in which, like, no, we're changing the slope in some fundamentally different way?Richard Socher [00:44:31]: I do think we are changing the slopes in fundamental ways by making AI much more efficient, both in terms of the training as well as the inference.Richard Socher [00:44:43]: Yeah. I think we will — When you allow AI to do the work that it takes other labs thousands of people and years to do, I think we'll be able to get it down to weeks, and that will be much cheaperRichard Socher [00:44:53]: And hence, more affordable, accessible to others and so on.Swyx [00:44:57]: Yeah. You've shared initial results on that,Swyx [00:44:59]: Which, like, conveniently OpenAI has also done to their GPT-5.6, so we can talk about it now.Richard Socher [00:45:04]: Yeah. Yeah, so these areSwyx [00:45:06]: Let's recap what you've done.Early Recursive Results: NanoChat, NanoGPT, and SOL-ExecBenchRichard Socher [00:45:07]: Maybe, just a quick recap here. We built, this, system that isn't the full, even the full RSI system in its glory, but it is a first baby version of this. And then, we don't wanna just have it internally and not show anything and, just show some people of what's possible. And so we applied this to these 3 different tasks. One is NanoChat, by my friend Andrej Karpathy, just, like, train a small language model to get, really low bits per byte. And, like, hundreds if not thousands of people, used both their agents and themselves to try, to get to that, and then they got to 0.937. We literally took our system and got to a much lower, bits per byte, much faster within, like, I think less than 2 days. So we took this thing, applied our system to it, and less than 2 days later, we have — we outperformed every human and their agents, in, have ever worked on this. Same with NanoGPT. And then we're like, well, let's, apply it to something that's even more relevant, to real people and to the Nvidia ecosystem and applied it, to, SOL-ExecBench. And maybe you can scroll down to some of the, images. They're, they're kinda fun to see. But yeah, like, one you see has made some real inventions that weren't just hyperparameter tuning. Like, inventing hash tables and so on is quite clever. We have even better results now.Swyx [00:46:34]: What do you mean inventing hash ta — You didn't invent hash tables.Richard Socher [00:46:36]: Of course we didn't invent, like, hash tables. In the grand scheme of, like a hash table, it's like a super basic primitive in computer science. But to use it, for language modeling in this scenario inside a transformer and so on and to combine these ideas and put them together, that has then eventually also been invented, but there was a knowledge cutoff, and we did check that it didn't have access to that externally. We talk about this a little bit. If you scroll to the next figures, this is also an interesting one in that when you start from a really basic, poor, like, vanilla transformer, then we still outperform all of the community together. But if you start from the human seed from an expert like Andrej, then you get even lower. So the human seeds from which you start do still matter. So that was an interesting insight, in my eyes, on this. And then as you go, like, how long does it take to get to these models, to get to similar performance? It's much faster. And then a similar thing happens with the speed runs here where, people have worked on this for quite some time, and the model still was able to train a model more quickly. Why do we care about it? Well, speed of training is part of the equation of the cost, and ultimately, you wanna have the most intelligence per dollar, right? And so speed and quality are big parts of that. And, the,Swyx [00:48:00]: Yeah, the way I put it is, for people who don't understand they look at the chart, they're like, “Cool. What does it mean?” if you have, like a billion-dollar cluster and you can shave off 10%, that's 100 million dollars.Richard Socher [00:48:12]: That's exactly right.Swyx [00:48:13]: How much is that worth?Richard Socher [00:48:14]: Exactly. So when you click, when you look at, like the kernels, these kernels, yeah, for the non-experts, like these kernels are like, used in all the models. Every time you use an Nvidia GPU, you interface with that GPU through these kernels. And so here you see, the leaderboard best, and when it's recursive, and it's there are only a handful of kernels, in this whole benchmark where we weren't the best. And so to me, this is, like, really exciting, ‘cause it makes. It just showcases what this can do. And again these weren't like. We didn't, like, spend months or years, like, developing. In fact, in particular for kernel, CUDA kernels, like, we don't even have really deep. CUDA kernel experts in the team. And our system, that's the beauty. The system just did all of these things. We didn't invent this. And when we open source and release, things in the future and models in the future, like, it won't. They won't be the best in their, category or class or whatever because we're so smart, but it's because, we built a smart AI that does it for us.Reward Engineering and Good Auto ResearchVibhu [00:49:14]: Do you have anything that you've learned from how to guide good auto research? A lot of it also builds on human background, right? It's not just as simple as just, “Hey, go optimize this.”Vibhu [00:49:23]: But we do see it again and again, right? Like some of the Erdos problems, frontier math is being solved by people. And when they do a write-up, they're like, “Oh, I'm not a mathematician. I have no background in this?” “I saw some tools and I made it work.”Swyx [00:49:35]: While you're watching the World Cup, you're likeSwyx [00:49:37]: “This proves some conjectures that's going on.”Vibhu [00:49:40]: Yep. Any learnings fromRichard Socher [00:49:41]: Yeah, there's a Korean conjecture was. Yeah, that's pretty cool.Swyx [00:49:44]: To summarize, tips for good auto researchSwyx [00:49:46]: Versus bad auto research.Vibhu [00:49:48]: How did you build the recursive?Richard Socher [00:49:49]: Yeah. So without giving away all the secret sauce, maybe some things that are probably obvious to the experts but might still be interesting to some, folks is, like, reward engineering is one of the most crucial bits, especially, in order to avoid reward hacking. So you have to be really clever about avoiding. ‘Cause as your AI gets better and better, it will get better and better, at finding weird like, special cases or counterexamples and things like that. And so I'll give you an example. Like, when you ask to, like, make these 100, lines of code faster, and, how do you define fast? Well, you have one line at the beginning that says, “Start your stopwatch,” and one line at the end, “End the stopwatch,” and then, tell us how much time, progressed. And so, well, the simplest way is you just put that line that ends the stopwatch, rightVibhu [00:50:39]: At the startRichard Socher [00:50:40]: At the start. And then boom, it's now faster, right? So this isn't like this, like, super evil AI. It's just, like a very simple, dumb reward hack. And so you have to just very carefully think about all the different angles there. And then I think the longer time horizon the tasks are the harder it gets and the more interesting and clever you have to be to still use these kinds of ideas for it. But yeah, I can't give away too much there.Vibhu [00:51:05]: It seems like rubrics are taking a good spot in that, where for unverifiable domains, you have rubrics, you have a model breakdown, judge's criteria along the way.Swyx [00:51:14]: Yeah, it's a form of verificationSwyx [00:51:16]: Once you got enough rubrics.Richard Socher [00:51:17]: Yeah, everything. I said this a long time ago. That's why I've never been that impressed that AI can play games, ‘cause I'm like anything you can simulate and/or verify, you can have infinite training data forRichard Socher [00:51:29]: And hence, like, AI will solve it eventually.Swyx [00:51:32]: Looking for games where you can do auto domain distribution. So this is a game that nobody's trained on ‘cause it's a new game.Swyx [00:51:38]: And you can start gaming, you can start to play. So I've been building this and cloned this in person and it's just been self-play. I've had about a billion positions evaluated.Games, Self-Play, and the AI EconomistSwyx [00:51:48]: And, I wanted to do the AlphaGo thing of self-play until you ge

The Daily Business and Finance Show - Friday, 11 September 2026 We get our business and finance news from Seeking Alpha and you should too! Subscribe to Seeking Alpha Premium for more in-depth market news and help support this podcast. Free for 14-days! Please click here for more info: Subscribe to Seeking Alpha Premium News Today's headlines: U.S. diesel prices soar past $6 a gallon for first time ever Trump announces $500 rebates for nearly 1M Obamacare enrollees Bridgewater's Jensen says AI could kill people before action is taken Micron to grant $32,000 cash bonus and equity to Taiwan engineers OpenAI said to be weighing slower development of cutting-edge AI AM Markets Need to Know: Diesel tops $6, DeepSeek pressures chips, and more Biggest stock movers Friday: ORCL, ACVA, and more Trump's surgeon general pick discloses tobacco, fast food and soda investments Explanations from OpenAI ChatGPT API with proprietary prompts. This podcast provides information only and should not be construed as financial or business advice. This podcast is produced by Klassic Studios Learn more about your ad choices. Visit megaphone.fm/adchoices

Bridgewater is the latest locality to decide to get rid of its Flock cameras… Conservation groups want to stop the widening of a West Virginia highway near the Virginia border… Preservation Virginia lists historic places in our area that are endangered… We talk with the creators behind a new play at Court Square Theater that chronicles an improv comedy duo….

Nach über drei Monaten und fast 5.000 Seemeilen liegt die Dagmar Aaen fest vertäut in Bridgewater, Nova Scotia - dem Zielhafen der Expedition OCEAN CHANGE 2026. Hier fand wieder ein Bordgespräch statt - Gast war Myka Burke von der deutschen Botschaft in Kanada. Arved Fuchs blickt in dieser Podcast-Folge  auf die intensiven Wochen gesamten Expedition zurück: von Kiel über Helgoland, die Färöer, Island und Grönland bis zur kanadischen Küste.Er erinnert sich an Begegnungen mit Menschen, zum Beispiel mit den Inuit in Labrador, mit denen die Crew im Saglek Fjord Arctic Char gefischt und gegrillt hat,  an die Gastfreundschaft der Menschen in Nova Scotia und an das Wiedersehen mit langjährigen Weggefährten wie Robert Peroni in Tasiilaq. Genauso wichtig: Die wissenschaftliche Arbeit an Bord, die während der gesamten Zeit durchgeführt wurde. Arved Fuchs kehrt bald nach Deutschland zurück, die Dagmar Aaen wird in Bridgewater überwintern. Im kommenden Jahr wird die Expedition von hier fortgesetzt.  Es besteht eine bezahlte Markenpartnerschaft mit NIBE.NIBE - Die Wärmepumpen Experten.Weitere Informationen unter www.nibe.deFacebookInstagramYouTubeTikTokwww.arved-fuchs.dewww.baerbel-fening.de

Dominique Dwor-Frecaut is the Chief US economist and macro strategist for Macro Hive and is based in Los Angeles. Before that, she worked at various hedge funds, including Bridgewater. Prior to the buy side, she worked at the New York Fed, IMF, and the World Bank. She holds a PhD in Economics from the London School of Economics. In this podcast, we discuss: The "Low-Hire, Low-Fire" Labour Puzzle The Imported AI Capex Illusion Chips as the New Oil Supply Shock The K-Shaped Consumer & GFC-Level Delinquency The Fragile Savings Rate Cushion The Return of FOMC Dissension The Waller Swing Vote & the 2026 Rate Path The 2027 AI De-escalation Surprise

George Wright III interviews AI architect and Chief of AI CEO Adrian Rosebrock about moving beyond AI hype to measurable ROI and durable competitive advantage. Rosebrock shares his background building and selling PyImageSearch, then explains three waves of AI adoption: early access/adoption, the coming focus on economics and governance (including token-cost volatility and “tokenomics”), and a future differentiation phase centered on proprietary data and custom models. He uses Bridgewater's internally trained model as an example of higher accuracy, lower token costs, and defensible advantage. They discuss when to buy, modify, or build AI using a risk-vs-differentiation grid, why AI should optimize back-office work rather than replace human-facing relationships, and common mistakes around governance, compliance, and preventing employees from uploading sensitive data.00:00 AI Beyond Workflows01:59 PyImageSearch Origin Story03:34 Why Chief of AI05:27 Three Waves of AI08:11 Bridgewater Moat Example11:32 Build Versus Buy Framework14:45 ROI and Human Touch20:10 Founder Employee Mindset Gap23:10 Governance and Compliance Risks25:12 Three Step Policy Playbook29:08 How Leaders Will AdaptThanks for listening, and Please Share this Episode with someone. It would really help us to grow our show and share these valuable tips and strategies with others. Have a great day.George Wright III“It's Never Too Late to Start Living the Life You Were Meant to Live”FREE Daily Mastermind Resources:CONNECT with George & Access Tons of ResourcesGet access to Proven Strategies and Time-Test Principles for Success. Plus, download and access tons of FREE resources and online events by joining our Exclusive Community of Entrepreneurs, Business Owners, and High Achievers like YOU.Join FREE at DailyMastermind.comFollow me on social media Facebook | Instagram | Linkedin | TikTok | YoutubeGrow Your Authority and Personal Brand with a FREE Interview in a Top Global Magazine HERE.About GuestAdrian Rosebrock — CEO, Chief of AI. PhD in computer science and artificial intelligence. Founder of PyImageSearch, the world's largest computer vision education platform (acquired 2021). He now leads a fractional Chief of AI team that helps organizations get a real return on their AI investment.LinksLinkedIn: https://www.linkedin.com/in/adrian-rosebrock/

Sonny Gray wins his 16th game of the season. It's win or go home later today for the boys from Bridgewater. An officer involved in a shooting last night in Cambridge. Stay in "the loop" with WBZ newsradio.See omnystudio.com/listener for privacy information.

The prosecution has been calling rebuttal witnesses this afternoon at the Lindsay Clancy murder trial. If you want to take your furry friend for a walk along the Charles this weekend, keep this in mind. It's win or go home for the boys from Bridgewater. Stay in "the loop" with WBZ newsradio.See omnystudio.com/listener for privacy information.

Welcome to this month's Answer Us Back, packed full of your responses to previous episodes of Answer Me This - or as it will soon be rebranded, Could Be True/Might Be Interesting. Today's track listing:  In response to AMT420's question about illegal use of wet wipes, Eileen in Bridgewater, New Jersey sends an impassioned plea for us to consider the bidet! So, we did. We have previously considered the bidet, btw, in AMT361.  A Swedish Person successfully survived their trip to London for which they asked for advice in AMT415 - but it ended up raising more questions, about bus ads for fruit, and being constantly asked about allergies.  BJ, who in AMT414 asked about how to respond to people asking what BJ stands for, has feedback about going on vacations with your family, having listened to AMT420's contemplation of that topic WHILE ON VACATION WITH HER FAMILY. Therefore her suggestions of survival tactics are freshly tested.  Since we're discussing vacations, Sarah from Plumpton in East Sussex wants to know whether you can count yourself as having been to a country if you've just stopped off at the airport there. And the question to which we allude, about what constitutes sex, is in AMT267.  Rich in Oxford is an airline captain, so swoops in with The Actual Facts regarding AMT420's question about whether commercial planes have keys and would be easy to steal.  In AMT419, Dan in Pennsylvania asked about which British produce he could grow on his farm in Pennsylvania; this continued in the last edition of Answer Us Back; now, Alyssa in Indianapolis supplies a great suggestion, plus a recommendation for a recent episode of Gastropod about when blackcurrants were illegal in the USA.  And Margaret requests an update on the storied family houseplant which Olly mentioned all the way back in AMT258.  If AMTs 1-420 left you with lingering questions and opinions, share them with us for future episodes of Answer Us Back. And as always, send in your questions, in voicenote or written form, to answermethispodcast@googlemail.com. All new AMT421 will be in your podfeed on 27 August 2026.  Help keep AMT going by signing up at patreon.com/answermethis, where you can get an ad-free version of the show, monthly bonus bits, our video livestream Petty Problems, AND access to our ENTIRE back catalogue, including all our paywalled episodes, our special albums, and all the Retro AMT episodes.  Answer Me This is sponsored by:  • Cozy Earth, luxurious bedding, sleepwear, loungewear, and towels for bath and beach. Shop at cozyearth.com and get 30% off using our code ANSWER. Also, their Labor Day sale runs 25 August till September 8th! • Quooker, the the tap that does it all, from instant 100-degree boiling water to chilled, filtered, and sparkling water. Shop at quooker.co.uk and until the end of August, you can use our code ANSWER to get free installation and your free Quooker glassware set. • Taskrabbit, the online and mobile marketplace, available in the UK, that connects you with skilled, reliable local freelancers to help with everything from furniture assembly and home repairs to moving, gardening, and more. Get ahead of your to-do list with £10 off your first task at taskrabbit.co.uk or on the Taskrabbit app using our promo code ANSWER.   Learn more about your ad choices. Visit megaphone.fm/adchoices

(00:00) The guys react to Bridgewater falling in the LLWS to New Jersey last night. (9:23) Zo and Beetle talk about the developing situation surrounding Mark Walter and the Lakers and Dodgers. (24:53) The crew takes your calls to wrap up training camp down at Gillette. Today's Takeaways. Please note: Timecodes may shift by a few minutes due to inserted ads. Because of copyright restrictions, portions—or entire segments—may not be included in the podcast.For the latest updates, visit the show page on 985thesportshub.com. Follow 98.5 The Sports Hub on Twitter, Facebook and Instagram. Watch the show every morning on YouTube, and subscribe to stay up-to-date with all the best moments from Boston's home for sports!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Jeff Malec sits down with Josh Blanchfield of Avos to trace his path from physics at Harvard, poker pro, and 11 years at Bridgewater (including co-heading the trading desk during Lehman) to running a concentrated, risk-aware commodities and macro shop. Josh explains why China is “ground zero” for global commodities, detailing how its import cuts, stockpiling, and scrap policies have shaped oil, copper, gold, and uranium, and why understanding flows and participant behavior matters more than neat top-down supply-demand models. He breaks down how Avos blends macro with very granular micro, like Chinese spec flows around Lunar New Year, refinery outages, crack spreads, and uranium's inelastic buyers, to build convex, options-heavy trades that aim to capture upside tails while avoiding the classic commodity-fund blowup. Along the way, they dig into the realities of radical transparency at Bridgewater, the limits and promise of AI as a “research team we never hired,” the risks of an AI-driven labor shock, the true drivers of the dollar's dominance and petrodollar fears, why he's skeptical on small modular reactors but bullish nuclear, and how his Substack and broader investment philosophy challenge received wisdom on everything from munis to equity valuations. - SEND IT!Chapters:00:00-01:19= Intro01:20-09:01= From Boulder Trails to China's Oil Trap: Josh's Origin Story and DB Cooper Commodities09:02-19:27=China's Dollar Dilemma, Gold Grab, and the Real Story Behind Commodity Power19:28–30:36 = From Poker Tables to Lehman's Collapse: Josh's Bridgewater Baptism by Fire30:37–37:57 = AI, Jobs, and Impossible Growth: Rethinking the Macro Behind the Magnificent Seven38:38–49:04 = Gold Flows, Burning Refineries, and the Uranium Edge49:05–56:26 = Capacity, Convexity, and Why Small Nuclear Isn't So Small56:27–01:09:15 = Munis, Myths, and Moneyball: Josh's Skeptical Playbook and Pop-Culture LensFrom the Episode:PODCAST:“Dr. Copper”: From Chilean Mines to Chinese Smelters to AI Data Centers in the US – with Kurt Nelson & Natalie Scott-GrayPODCAST:Going Nuclear: How Uranium is Powering Portfolios with Trevor Hall & Justin HuhnPODCAST:OpenSnow's Joel Gratz built a Pod Shop for Powder Days: the PMs are Meteorologists and the Returns are FaceshotsBLOG:The Definitive List of the Best Investing MoviesFollow along with Josh and Avos ⁠on LinkedIn and Substack, and be sure to check out avos.co to learn more about what they are up to.Don't forget to subscribe to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Derivative⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, follow us on Twitter at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@rcmAlts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠sign-up for our blog digest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Disclaimer: This podcast is provided for informational purposes only and should not be relied upon as legal, business, or tax advice. All opinions expressed by podcast participants are solely their own opinions and do not necessarily reflect the opinions of RCM Alternatives, their affiliates, or companies featured. Due to industry regulations, participants on this podcast are instructed not to make specific trade recommendations, nor reference past or potential profits. And listeners are reminded that managed futures, commodity trading, and other alternative investments are complex and carry a risk of substantial losses. As such, they are not suitable for all investors. For more information, visit⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.rcmalternatives.com/disclaimer⁠⁠⁠⁠⁠

Questo episodio nasce da un libro. Sto leggendo in queste settimane d'agosto "Perché i paesi falliscono — il grande ciclo dell'economia" di Ray Dalio, il fondatore di Bridgewater, uno dei più grandi investitori al mondo. Sigla di Eric Buffat Per chi vuole acquistare i libri, il cui ricavato andrà totalmente in beneficenza: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.amazon.it/kindle-dbs/entity/author/B08FF1ZFV9⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Sue and Kendra talked with Bridgewater Little League Coach Tim Zackrison about his team preparing for the World Series -- they play New Jersey on Wednesday and we are all rooting for them!

Mike and Beau react to some of biggest NFL stories that happened over the weekend, including Detroit Lions veteran quarterback Teddy Bridgewater deciding to step away from the team amid training camp, officially being placed on the reserve/retired list ahead of yesterday's practice, and the Lions signing veteran quarterback Joshua Dobbs to a one-year, $1.425 million contract with $475,000 fully guaranteed in the wake of Bridgewater's retirement. Mike and Beau also explain why they think Bridgewater's retirement doesn't 100% feel like the end for him.

WBZ NewsRadio's Jared Brosnan reports.See omnystudio.com/listener for privacy information.

An elephant from the Natural Bridge Zoo has been moved to an accredited animal sanctuary... Bridgewater residents voice concerns about flock cameras... Unemployment is rising across Virginia... AI could make the Commonwealth more vulnerable to cyber threats... and we go to a Central Virginia powerlifting competition...

Click the link http://kalshi.com/r/MOSES or download the Kalshi App and use code MOSES to sign up and trade today! Checkout WAWD on Substack: https://whatarewedoingonthedesk.substack.com/OTT On this week's podcast, Danny Moses speaks with Nicole Kagan, head of research at Kalshi, about her path from Bridgewater to Oxford and then to Kalshi to help build the contract-writing and research functions. Kagan explains how Kalshi sources market ideas internally, from partners, and from users, then evaluates whether a contract is objective, economically justified, and resolvable, often reusing pre-certified templates or submitting new rules for CFTC self-certification. She discusses how Kalshi reviews underperforming markets, prioritizes price discovery, and can keep low-volume markets live. They cover Kalshi's prediction markets conference, institutional hedging examples, and Fed research finding Kalshi markets more accurate on inflation and Fed funds, including an FOMC pricing divergence versus CME futures. Moses highlights Kalshi's research links and AI/compute work, then shares his weekly Kalshi picks focused on the Bank of Japan and USD/JPY. -- ABOUT THE SHOW For decades, Danny has seen it all on Wall Street and has built his reputation on integrity, curiosity and skepticism that he will bring with him each week. Having traded through the Great Financial Crisis and being featured in "The Big Short" is only part of the experiences Danny wants to share with the listener. This weekly podcast cuts through market noise, offering entertaining and informative discussions with expert guests giving their views of the financial world and the human side of it. Whether you're a seasoned investor or just getting started, On The Tape provides something for all listeners. Follow Danny on X: @dmoses34 The financial opinions expressed are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on this content. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in 'On The Tape' carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

Click the link http://kalshi.com/r/MOSES or download the Kalshi App and use code MOSES to sign up and trade today!Checkout WAWD on Substack: https://whatarewedoingonthedesk.substack.com/OTTOn this week's podcast, Danny Moses speaks with Nicole Kagan, head of research at Kalshi, about her path from Bridgewater to Oxford and then to Kalshi to help build the contract-writing and research functions. Kagan explains how Kalshi sources market ideas internally, from partners, and from users, then evaluates whether a contract is objective, economically justified, and resolvable, often reusing pre-certified templates or submitting new rules for CFTC self-certification. She discusses how Kalshi reviews underperforming markets, prioritizes price discovery, and can keep low-volume markets live. They cover Kalshi's prediction markets conference, institutional hedging examples, and Fed research finding Kalshi markets more accurate on inflation and Fed funds, including an FOMC pricing divergence versus CME futures. Moses highlights Kalshi's research links and AI/compute work, then shares his weekly Kalshi picks focused on the Bank of Japan and USD/JPY.--ABOUT THE SHOWFor decades, Danny has seen it all on Wall Street and has built his reputation on integrity, curiosity and skepticism that he will bring with him each week. Having traded through the Great Financial Crisis and being featured in "The Big Short" is only part of the experiences Danny wants to share with the listener. This weekly podcast cuts through market noise, offering entertaining and informative discussions with expert guests giving their views of the financial world and the human side of it. Whether you're a seasoned investor or just getting started, On The Tape provides something for all listeners.Follow Danny on X: @dmoses34The financial opinions expressed are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on this content.Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in 'On The Tape' carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose.Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service. Hosted on Acast. See acast.com/privacy for more information.

The Daily DLP from the Detroit Lions Podcast breaks down all the observations and takeaways from the team's second padded training camp practice. Tuesday, August 4th saw a few players rise and fall on the practice field at the Meijer Performance Center in Allen Park. Among the big winners was RB Jabari Small. With Sione Vaki out with a broken nose suffered in Monday's practice, and with Jahmyr Gibbs still not practicing, Small stepped up on the depth chart and looked like he belonged. His quick footwork and nifty cuts created a couple of big gains for the Lions offense. Teddy Bridgewater had his best day of training camp. It was certainly his most accurate, though the final drive in team drills saw the veteran backup QB fall back a bit. This was the first practice of the summer where Bridgewater looked clearly better than undrafted rookie Luke Altymer. Devin White showed why the Lions signed the former All-Pro LB. He is fast and he closes hard on the ball in the run game. Coverage looks to be an issue, as he ran himself out of position on a couple of different routes. He had some nice reps with the first-team defense, better than when he played in the nickel package with some reserves. Ahmed Hassanein had his best day of the summer, too. The fan favorite EDGE worked well playing outside Mekhi Wingo in heavier packages. Hassanein did appear to suffer a minor groin or upper leg injury late in the practice that caused him to take to the turf a couple of times, but he didn't seem distressed when talking to reporters after practice. His bull rush is getting better by the day. Hassanein also wasn't shy about getting after some offensive linemen after plays, showing fire that the team needed. Hassanein's fire was matched on the offense by WR Greg Dortch. The word “feisty” came up twice in the notes. It's not all negative-tinged either; Dortch and rookie UDFA LB Erick Hunter started jawing a bit after a special teams rep. After Dortch threw Hunter the ball, the two came together and Dortch appeared to praise the youngster, tapping Hunter's helmet and clapping. It was not a good day for Keith Abney, as the rookie CB struggled. Veteran Ennis Rakestraw also got victimized with a bad coverage lapse. It didn't look like the best day for new starting center Cade Mays either, notably in the run game--though he did have a couple of nice reps. That's training camp. Good players have bad days, and lesser players rise up at times. #detroitlions #lions #detroitlionspodcast #teddybridgewater #detroitlionstrainingcamp #lionscamp #gregdortch #isaacteslaa #keithabney #rockya-sin #trevornowaske #devinwhite #anthonyfirkser #jabarismall Learn more about your ad choices. Visit megaphone.fm/adchoices

We are continuing our investigation into the implications of the Economic Singularity, the time when we need a new economic system because full automation by AI means people can no longer obtain the resources they need by doing jobs.Our guest today is Oliver Ritchie, the UK Policy Lead at the Future of Life Institute. Previously, he was a Senior Fellow at the Institute for AI Policy and Strategy, and a Research Scholar at GovAI. He also spent over six years in policy formation at the Treasury, which is where many of the brightest minds in UK public sector work, and where the really big decisions are made, because the Treasury hold the purse strings.We wanted to have Oliver on the show because he has written two thought-provoking articles on the Economic Singularity, a paper called “The Fiscal Challenge of Advanced AI”, and a Substack post which summarises it, called “Can we afford to support workers whose jobs are displaced by AI?” Selected follow-ups:"Oliver Ritchie" - Future of Life Institute"Return on Intelligence" - Oliver's Substack"Can we afford to support workers whose jobs are displaced by AI?"  - Substack article by Oliver"The Fiscal Challenge Of Advanced AI" (PDF) - article by Oliver"The case for G20 coordination on wealth taxes" - article by Liam Byrne"US super-rich 'pay almost no income tax'" - BBC"Trump to meet AI leaders to discuss US investment in their companies" - BBC"Getting Ahead of AI's Economic Disruption" - Windfall Trust"AI's Role in Reshaping Power Distribution" - Future of Life Institute project"Can you safely build something that may kill you?" (about Sam Altman) - by Kelsey Piper"Exploring Machine Consciousness" - Prism / Conscium podcast, cohost Calum Chace"The biggest blockages to successful governance of advanced AI" - Review by David Wood, contains section "Why a pause isn't that inconceivable""Trump considering AI controls after OpenAI hacking incidents" - BBC"Pacing the Frontier" - A statement from over 1,300 employees of frontier AI companies"Why a Custom AI Just Beat Every Frontier Model" - about Thinking Machines Lab and Bridgewater"The On-Device LLM Revolution" - by Steve Roddy"AI Economics Institute (AIEI): Prospectus" - UK Government"Minister of State for the Future of Work" - Kate Dearden MP (LinkedIn)"'We Must Act Now': Sixteen Nobel Laureates Join Leading Economists and AI Researchers in Call to Prepare for AI's Economic Transformation" - Stanford Digital Economy Lab"A Potential Path to Safer AI Development" by Yoshua Bengio, introduces Scientist AI"Bitter lesson" (general AIs will in due course out-perform specific AIs) - Wikipedia"Why Tool AIs Want to Be Agent AIs" - GwernMusic: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

Episode Description:This is a From the Archive episode with Ray Dalio. Ray Dalio built Bridgewater Associates into one of the most influential investment firms in the world. But in this archive conversation, James is less interested in the headline version of success and more interested in the painful turning point that made Ray rethink everything.At 34, Ray's business nearly failed. He had been confident—by his own description, arrogant—and a wrong market call left him broke, humbled, and forced to decide whether to quit or rebuild.That failure became the origin story behind many of his principles.Ray explains that the experience taught him to fear being wrong in a useful way. Instead of retreating, he began seeking out the smartest people who disagreed with him. He learned to triangulate among independent thinkers, write down his decision criteria, study how similar events had happened in history, and build systems that reduced risk without giving up upside.The conversation then moves into the central ideas of Principles: pain plus reflection equals progress; failure equals success if you turn it into learning; and every person should write down the principles they use to deal with reality.James and Ray discuss the five-step process for success: set audacious goals, identify the problems blocking those goals, diagnose root causes, design ways around them, and execute. Ray emphasizes that no one is good at all five steps, which is why people need others who can supply the strengths they lack.A large part of the episode focuses on Bridgewater's culture. Ray explains idea meritocracy, radical truth, radical transparency, thoughtful disagreement, and believability-weighted decision-making. In his view, the goal is not democracy or autocracy. The goal is to get the best ideas on the table, stress test them, and make better decisions.James pushes on the human side of this: what happens when disagreement feels like a fight? Ray answers by describing the “two yous”—the thoughtful, logical self and the emotional, reactive self—and why writing down principles can clarify which values bring people together and which values separate them.The conversation closes with habits, meditation, creativity, the economy, debt cycles, wealth inequality, cryptocurrencies, and Ray's formula for a successful life: dreams plus reality plus determination.Editorial Note:This is a From the Archive episode. References to Bridgewater's assets, Ray's Forbes net worth, the economic cycle, cryptocurrencies, interest rates, central-bank policy, and current market conditions reflect the original recording period.This episode discusses investing, markets, debt cycles, and cryptocurrencies, but it should not be treated as financial advice.What You'll Learn;Why Ray Dalio sees his early career failure as one of the most important events of his life.How being wrong pushed him toward radical open-mindedness.Why Ray believes pain plus reflection equals progress.How to turn mistakes into principles you can use next time.Why failure can become success if it produces learning.The five-step process Ray uses for achieving goals.Why no one is good at every step of success.How thoughtful disagreement can improve decision-making.What an idea meritocracy is and why Bridgewater built around it.How believability-weighted decision-making differs from democracy or hierarchy.Why Ray believes meaningful work and meaningful relationships must go together.How writing down principles clarifies relationships, teams, and personal choices.Why habits control so much of life—and how they can be changed.How meditation helps Ray access calm, creativity, and clearer thinking.How Ray thinks about debt cycles, economic risk, wealth inequality, and cryptocurrencies.Timestamped Chapters:[03:00] The Failure That Changed EverythingJames asks about the early mistake that left Ray's business nearly finished at age 34.[04:06] Humility Balancing AudacityRay explains how going broke forced him to calm down, rethink risk, and become more open-minded.[05:46] Finding Smart People Who DisagreeHow Ray learned to triangulate among independent thinkers and raise the probability of being right.[06:59] Understanding the Mechanics of RealityRay revisits the Mexico debt crisis, the dollar, deflationary forces, and what he missed.[09:06] Radical Open-Mindedness and Idea MeritocracyThe failure led Ray to build a company around thoughtful disagreement and independent thinking.[11:00] Write Down Your Decision CriteriaRay urges listeners to write down the principles they use to make decisions.[14:21] Money Has No Intrinsic ValueRay explains why money only matters as a tool for getting what you actually value.[15:30] Learning to Fail WellRay criticizes the education system for rewarding right answers instead of teaching people how to learn from failure.[18:02] Pain Plus Reflection Equals ProgressRay explains why pain is a signal and why reflection turns it into usable knowledge.[20:23] The Five-Step Process for SuccessGoals, problems, diagnosis, design, and execution become Ray's loop for improving life and work.[23:38] Diagnosing the Root CauseJames asks Ray to identify the root cause of his early failure, and Ray names his own arrogance.[24:55] Joseph Campbell and Returning the BoonRay connects The Hero with a Thousand Faces to his decision to pass along the principles he had learned.[28:06] Failure Equals SuccessRay explains how he trained himself to see failures as puzzles that produce “gems.”[31:25] When Principles ChangeJames asks when Ray has failed at his own principles, and Ray discusses the challenge of leadership transition and governance.[34:20] Can People Really Disagree With the Boss?Ray pushes back on the idea that hierarchy prevents honest disagreement at Bridgewater.[35:41] Meaningful Work and Meaningful RelationshipsRay explains why radical truth and radical transparency are designed to support both work quality and human connection.[37:19] Believability-Weighted Decision-MakingRay describes how disagreements can be resolved by weighing the judgment of people with relevant expertise.[42:12] How to Handle Dysfunctional OrganizationsRay advises people to define the terms of their relationships and ask how disagreements should be handled.[48:11] Would Everyone End Up With the Same Principles?James asks whether writing down principles would eventually lead people toward similar truths.[49:10] The Two YousRay explains the conflict between the logical self and the emotional self, especially when disagreement triggers fight-or-flight.[51:10] Changing Direction at Any AgeJames asks how people stuck in old patterns can find the courage to start again.[52:46] Habits, Failure, and Reprogramming YourselfRay describes his most valuable habit: treating problems and failures as puzzles to solve.[56:03] Health, Meditation, and CreativityRay discusses where he has and has not built strong habits, and why meditation has been so important to him.[59:07] Meditation as a State of Calm ControlRay explains how meditation quiets the racing mind and helps create equanimity and creativity.[01:00:52] Finding the PullJames asks how a young person can identify goals, and Ray recommends self-discovery, experimentation, and reflection.[01:04:06] How the Economic Machine WorksRay outlines productivity, short-term debt cycles, long-term debt cycles, central-bank policy, and where the economy sat at the time.[01:07:29] Two Economies and Wealth InequalityRay explains why averages can hide the economic reality of the bottom 60% versus the top 40%.[01:11:00] Cryptocurrencies and BlockchainRay distinguishes blockchain technology from individual crypto assets and compares the space to the early internet.[01:14:26] Dreams + Reality + DeterminationRay closes with his formula for a successful life and why reality must be understood, not avoided.Additional Resources:Ray Dalio's official Principles site — Ray's home for Principles, How the Economic Machine Works, TED2017, Principles for Success, and related tools.Principles: Life and Work — Simon & Schuster — Official publisher page for the book discussed throughout the episode.Bridgewater: Our Founder — Bridgewater's official biography of Ray Dalio and his career at the firm.TED: “How to build a company where the best ideas win” — Ray's TED2017 talk on radical transparency, algorithmic decision-making, and idea meritocracy.How the Economic Machine Works — Ray's 30-minute explainer video is available through the official Principles site.Principles for Success — Simon & Schuster — A shorter illustrated adaptation of Ray's principles.The Hero with a Thousand Faces by Joseph Campbell — The book Ray mentions when discussing the hero's journey and “returning the boon.”The Power of Habit by Charles Duhigg — The habit book Ray and James reference while discussing how behavior can be reprogrammed.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The road trip crosses into Massachusetts, and the Bay State delivers two of the strangest files in the entire eastern catalog.It starts on a dark stretch of Farm Street in Dover in April of 1977, where a seventeen-year-old named Bill Bartlett watched something creep along a stone wall that no one has ever explained, a spindly, hairless creature with a huge watermelon head and glowing eyes.Within twenty-six hours it was seen twice more by three other teenagers who had never compared notes, and then it vanished forever, leaving behind matching sketches, a police chief who vouched for every witness, and a case so stubborn it made a small New England town famous on the other side of the world. From there the episode heads forty miles south, where the manicured suburbs give way to the Hockomock Swamp, five thousand flooded acres the Wampanoag named the place where spirits dwell, and where the reports never stopped. An armed police search in 1970, a fur trapper stalked from the bank of a black waterway, Joseph DeAndrade's famous Clay Banks sighting, a kayaker who watched something rise out of a crouch on the Town River, footfalls booming across the frozen ice of Lake Nippenicket, and a farmer whose family spent sixty years sharing a fence line with something that never once introduced itself.One state, two monsters, and a swamp with a middle no one has ever reached.Have you experienced a Bigfoot sighting, Sasquatch encounter, Dogman experience, UFO sighting, or any unexplained cryptid or paranormal event deep in the woods? We want to hear your story.Email your encounter to brian@paranormalworldproductions.com for a chance to be featured on a future episode of Backwoods Bigfoot Stories.Backwoods Bigfoot Stories is a paranormal storytelling podcast featuring real Bigfoot encounters, Sasquatch sightings, Dogman reports, cryptid experiences, and true scary stories from the backwoods.Follow the show and turn on automatic downloads so you never miss a chilling encounter from the forest. Listen with the lights off… if you dare.

durée : 00:12:41 - par : Elodie Suigo - Dee Dee Bridgewater et China Moses sont les invitées exceptionnelles toute cette semaine du Monde d'Elodie, pour la sortie de leurs albums respectifs, "Elemental", pour la mère et "It's complicated", pour la fille. Épisode 5 : "Honeysuckle Rose" de Dee Dee Bridgewater en duo avec le pianiste Bill Charlap. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

durée : 00:15:34 - par : Elodie Suigo - Dee Dee Bridgewater et China Moses sont les invitées exceptionnelles toute cette semaine du Monde d'Elodie, pour la sortie de leurs albums respectifs, "Elemental", pour la mère et "It's complicated", pour la fille. Épisode 4 : "Precious thing" de Dee Dee Bridgewater, en duo avec Ray Charles. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

durée : 00:12:24 - par : Elodie Suigo - Dee Dee Bridgewater et China Moses sont les invitées exceptionnelles toute cette semaine du Monde d'Elodie, pour la sortie de leurs albums respectifs, "Elemental", pour la mère et "It's complicated", pour la fille. Épisode 2 : "J'ai deux amours" de Joséphine Baker, repris par Dee Dee Bridgewater. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

durée : 00:16:27 - par : Elodie Suigo - Dee Dee Bridgewater et China Moses sont les invitées exceptionnelles toute cette semaine du Monde d'Elodie, pour la sortie de leurs albums respectifs, "Elemental", pour la mère et "It's complicated", pour la fille. Épisode 1 : "Raindrops keep falling on my head" de Dee Dee Bridgewater. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

You've probably never heard of Inkling. It's the newest (and first) model from Thinking Machines Labs, and it could very well be a small snowball that picks up major momentum in today's enterprise AI landscape. If you haven't heard of Thinking Machines, they're led by Mira Murati, the former CTO at OpenAI. The big bet with Inkling? The future of AI could be using smaller models fine-tuned and optimized for smaller tasks. Will it work? Tune in live as we dive in. The Most Important AI Model You'll Probably Never Use That Just Dropped -- An Everyday AI Chat With Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.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:Inkling AI Model Launch OverviewThinking Machines Lab Leadership HighlightInkling's Multimodal and Agentic CapabilitiesOpen Source vs. Proprietary AI ModelsEnterprise Procurement with American AI ModelsAI Fine Tuning as a Service (Tinker)Benchmark Scores: Inkling vs. Frontier ModelsCustomization and Model Shopping for EnterprisesAI Token Costs Driving Model EfficiencyBridgewater Case Study: AI Model CustomizationFrontier Models Enabling Efficient Fine-TuningFuture Trends: Specialized Small Language ModelsTimestamps:00:00 Inkling: A new AI model release05:43 Inkling AI model details09:08 China's dominance in open source AI11:48 Launch and model updates discussed15:21 Concerns over using Chinese open-source models19:06 Training smaller AI models20:22 Using GPT for AI Model Training23:54 Predicting Rise of Small Language Models28:38 Choosing the right AI modelKeywords: Inkling, Thinking Machines Lab, Meera Muradi, former OpenAI CTO, open source AI model, American AI model, fine tuning as a service, enterprise AI, multimodal AI, agentic models, customizable AI, Tinker, enterprise distribution, model procurement, Chinese open source models, strategic reset, model overhang, capabilities gap, AI model shopping, model routing, cost-conscious enterprises, artificial intelligence index, 975 billion parameter model, text-image-audio AI, open weights, proprietary AI models, customization accessibility, small language models, AI workflows, context window, Bridgewater use case, model distillation, GPU infrastructure, API costs, token efficiency, fine-tuned models, post training, AI competitive leverage, recurring financial judgment, AI benchmarks, middle tier models, automated model evaluation, privacy and workflow mapping, economical AI models, model rental, model routing automation.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

South Shore / Boston band Gold Hoax is on the WATD Tiny Stage. Catch them at Bridgewater Music Alley at Blackhat Brew Works in Bridgewater with Gordon's Law on July 23. Learn more at https://goldhoax.bandcamp.com. 

Capital is flowing again for community banks but deploying it well is the hard part.Bridgewater Bank's Joe Chybowski, AllianceBernstein's Michael Howard, and Performance Trust's Matt Shields join Travillian's Brian Love for a roundtable on capital structure, what investors reward, bank M&A, deposits, and succession in 2026. A candid look at what earns investor confidence and whaterodes it fast.

Episode 427: In the summer of 1934, John Sackville Labatt was one of the most recognizable names in Canada. The fifty-three-year-old president of John Labatt Limited had spent nearly two decades running one of the country's largest and oldest breweries, a family business built across three generations and tested by Prohibition, the Depression, and the rum-running trade that kept it alive through both. On the morning of August 14, he left his Lake Huron summer home running late for a meeting and took a shortcut through the forest on a little-used dirt road outside Sarnia. What happened on that road set off the largest manhunt in Canadian history, triggered Canada's first kidnapping trial, and sent an innocent man to prison for a crime he had nothing to do with. Sources:John Sackville Labatt | The Canadian EncyclopediaJohn Labatt's 1934 kidnap | The StarJohn Sackville Labatt | WikipediaLabatt Brewing Company | WikipediaProhibition in Canada | WikipediaLabatt Brewing | Company SiteLambton County's Infamous Abduction: The John S. Labatt Kidnapping - ArchivedLambton County's Infamous Abduction: The John S. Labatt Kidnapping - NewSnatched! by Susan Goldenberg - Ebook | ScribdThe Courier-News • Fri, Aug 17, 1934 • Page 10 • (Bridgewater, New Jersey)The Courier-News • Fri, Aug 17, 1934 • Page 1 • (Bridgewater, New Jersey)The Windsor Star • Fri, Aug 17, 1934 • Page 1 • (Windsor, Ontario, Canada)The Windsor Star • Sat, Sep 08, 1934 • Page 9 • (Windsor, Ontario, Canada)The Windsor Star • Sat, Feb 01, 1936 • Page 11 • (Windsor, Ontario, Canada)The Hamilton Spectator • Tue, Sep 11, 1906 • Page 9 • (Hamilton, Ontario, Canada)The Molsons, The Labatts and The St.Johns | StJohnsWort.ca Learn more about your ad choices. Visit megaphone.fm/adchoices

Most people think in binary — it will happen or it won't. A former CIA field officer says that mental habit is quietly destroying your decisions, your business, and your shot at the life you actually want. Anthony Vinci spent years recruiting intelligence sources in active war zones, advising presidents and generals on high-stakes decisions, and then managing investments at Bridgewater Associates under Ray Dalio — one of the most disciplined decision-making environments on the planet. After leaving government, he built a company from the ground up without knowing what an LLC was, wrote The Fourth Intelligence Revolution, and launched Veeo, a forecasting and scenario-planning platform now used by hedge funds, corporations, and governments. His hard-won insight: the cost of bad decisions is massive, and it follows you forever — but the right system changes everything. In this episode, you'll discover: Why "chunking" risk is the CIA method for taking action without freezing up — Anthony breaks down how he planned missions in Baghdad piece by piece, and how to apply the exact same framework to business decisions and big life swings The "compartmentalize risk" principle that explains why the world's biggest risk-takers (Special Forces operators, entrepreneurs) are often deeply conservative in other areas of their lives — and how to deliberately design yours Why stubbornness + optimism = resilience — and the hidden flip side that destroys most entrepreneurs who don't know when to stop The binary thinking trap: why saying "it's more likely than not" is dramatically less useful than saying "it's a 55% chance vs. a 90% chance" — and why that gap is where elite decision-makers live What Ray Dalio's Bridgewater taught Anthony about the one non-negotiable trait you need in your inner circle — and the exact type of person you must limit access to your energy Connect with Anthony Vinci www.anthonyvinci.com Connect with Kwame: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Request A Customized Workshop For Your Company⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Kwame Christian on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠negotiateanything.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Click here to buy your copy of Finding Confidence in Conflict: How to Negotiate Anything and Live Your Best Life!⁠ @kwamenegotiates across all social platforms

On the afternoon of September 19, 1978, 13-year-old Carl Bridgewater set off on his usual newspaper round through the village of Wordsley in the West Midlands. A quiet, hardworking schoolboy who loved fishing, football and Scouts, Carl had only recently started earning pocket money delivering newspapers after school. With just a handful of deliveries left to make, he arrived at an isolated farmhouse where he was due to leave a paper for the older residents. He never made it home.Carl's murder shocked the UK. Detectives launched one of the largest murder investigations the Midlands had ever seen, chasing hundreds of leads, interviewing members of the criminal underworld, searching rivers for the murder weapon and offering substantial rewards for information. What followed appeared to be a major breakthrough, but the investigation would eventually become one of the most controversial in British criminal justice history.In this episode of British Murders with Stuart Blues, we examine the unsolved murder of Carl Bridgewater. We explore Carl's life and the close-knit family he left behind, the events of his final paper round, the huge police investigation that followed, the convictions of the so-called Bridgewater Four, and the extraordinary legal battle that exposed a shocking miscarriage of justice. We also look at the theories that continue to surround the case and ask the question that has remained unanswered for nearly 50 years: who really killed Carl Bridgewater?If you know something about what happened at Yew Tree Farm all those years ago, no matter how small or insignificant it might seem, it's never too late to come forward. You can contact Staffordshire Police by calling 101 or pass information anonymously through Crimestoppers on 0800 555 111.Exclusive content:Patreon - Ad Free, Early Access, Exclusive EpisodesFollow the show:British Murders with Stuart BluesDisclaimer:The case discussed in this podcast episode is real and represents the worst day in many people's lives. I aim to cover such stories with a victim-focused approach, using information from publicly available sources. While I strive for accuracy, some details may vary depending on the sources used. Due to the nature of the content, listener discretion is advised. Thank you for your understanding and support. Hosted on Acast. See acast.com/privacy for more information.

Ivo Crnkovic-Rubsamen, Co-Founder of Pascal Trading joins us to talk about building the next generation of prediction markets. Ivo shares insights from his time at Bridgewater, D.E. Shaw, and dYdX, explaining how Pascal is solving major capital efficiency issues in DeFi. We dive deep into oracle-free price discovery, order book design, and how to scale robust leverage extension outside the core matching loop to protect trading venues. Subscribe to the newsletter! https://newsletter.blockspacemedia.com Notes: * Ivo worked as a quant at Bridgewater and D.E. Shaw. * Joined dYdX in late 2021 as an early trader. * Focuses on robust leverage outside matching loop. * Enables price discovery for oracle-free assets. Timestamps: 00:00 Start 00:24 Ivo's background 02:27 Pascal design 05:08 Traders as target audience 07:05 Polymarket & nonces 13:30 Building order management system 14:25 testing the markets 21:18 Prediction markets 31:58 "Insider trading" 36:50 Wall St. & sports betting 44:55 Prediction markets as insurnace 50:43 Removing "house edge" & fees 58:17 Markets on Pascal The Gwart Show is sponsored by Ellipsis Labs. Ellipsis Labs builds the most efficient on-chain markets. Their orderbook and Prop AMM products have delivered price improvement to hundreds of billions of dollars in retail volume. Now, they are bringing their expertise to build Phoenix, the best on-chain perpetuals platform. Ellipsis Labs is hiring New York-based engineers. If you're an engineer looking to work with a proven team in making DeFi better, go to ellipsislabs.xyz/careers Learn more about your ad choices. Visit megaphone.fm/adchoices

Defense jumps routes and takes the ball The Detroit Lions defense seized Day 1 in Allen Park. The first seven-on-seven snap set the tone. Jared Goff tried a short out to Amon-Ra St. Brown. DJ Reed broke it up and put it on the turf. The coverage locked in from there. Reader stayed hip-to-hip on a vertical the next rep and later picked off Teddy Bridgewater by beating the throw to the sideline. Chuck Clark, playing deep middle with Kirby out and Branch sidelined, tracked an overthrown ball from Goff and intercepted it. Undrafted rookie Amaris Brown undercut Luke Altmaier on a Texas route to Jabari Small and ripped the ball away, then sprinted the other direction. Khalil Dorsey and Loren Strickland each dropped would-be interceptions. Trevor Nowaski erased a Bridgewater throw with a full-extension pass breakup. Clark said the defensive backs met to study recent practice film and route concepts. He credited DJ Reed for organizing it. The emphasis was clear: take the ball and do something with it after the catch. Offense snapshots: Jamo, Gibbs, and Meeks at tight end Jameson Williams had a teachable sequence. He dropped a low throw on the move, asked for the same ball, then snatched it out front in stride on the next rep. St. Brown had a mixed day against tight coverage. Greg Corrao flashed rare stop-start quickness and clean hands. His instant change of direction stood out. Jackson Meeks lined up only at tight end and took the No. 2 reps with Sam LaPorta not participating and Tyler Conklin not seen in uniform. Meeks is lighter than Isaac Teslaw but tracked the ball well and finished. Position drills forced receivers to find the ball over the shoulder; Corrao and Meeks handled it smoothly. Jameer Gibbs reminded everyone about NFL speed. He caught a swing, planted, and outran the angle. The burst was startling up close. Fights, fixes, and the trenches A dust-up broke out between Anthony Lucas and Davis Cochran. Ennis Rakestraw stepped in as peacemaker. They went at it again on the next rep before assistant coach Dan Skipper ended it quickly. Earlier, OL coach Hank Fraley joked that Skipper has to remember he can't fight anymore. The sideline timing was perfect. Second-team offensive line notes: Larry Borom at left tackle, Juice Scruggs at left guard, and Seth McLaughlin at center. Tate Ratledge volunteered snaps during a running back drill and looked natural in a pinch. Lacy saw first-team defensive end work opposite Aidan Hutchinson ahead of Martin Turner. Quarterbacks and the red zone hoop Goff, Bridgewater, and Altmaier struggled early on a red zone hoop drill meant to simulate high-point fades. All three missed their first two tries. QB coach Mark Brunell stepped in, demonstrated, and reset the group. Goff then drilled two. Bridgewater hit one of two. Altmaier rattled the rim twice but showed quick correction. Roger McCreery had a rough day carrying crossers and drags. Joe Bachie spiked an Altmaier checkdown with a smart read and well-timed jump. Day 1 of minicamp belonged to the Detroit Lions defensive backs. Day 2 awaits. #detroitlions #lions #detroitlionspodcast #lionsminicamp #gregdortch #djreed #trevornowaske #chuckclark #jamesonwilliams #jahmyrgibbs #jaredgoff #markbrunell Learn more about your ad choices. Visit megaphone.fm/adchoices

This week's Unholy conversations is a rare interview with Nir Bar Dea, CEO of Bridgewater—the world's largest hedge fund. Bar Dea moves beyond market trends, framing our current moment as a volatile collision between a changing world order, and an unprecedented technological revolution fueled by AI. He offers a sobering perspective on why the comfort of the last several decades has perhaps left us less resilient than the generations before us, urging listeners to view history not as destiny, but as a lesson in the necessity of strength during turbulent times. The conversation turns personal, too: Bar Dea talks about the toll of his own service in the Israeli Air Force, what it's like being Israeli in New York since October 7th, and why he  believes Israel's younger generation might be its greatest asset. 00:00 Intro 01:10 The Iran-US deal and why timing doesn't matter 06:25 Trump as outcome, not cause 09:30 Tough love: why we feel overwhelmed 10:59 Israel's resilient next generation 24:49 The AI infrastructure race — and Israel's gap 27:11 Being Israeli in New York since October 7th 32:54 The hidden cost of IDF service 35:02 Bridgewater's radical transparency culture 41:51 Rebuilding from Nir Oz — a story of hope   Watch us on YouTube:  Follow Unholy and learn more about the pod: https://unholy-podcast.lovable.app/ Join our Patreon community to get access to bonus episodes, discounts on merch and more: https://bit.ly/UnholyPatreon   Hosted by Simplecast, an AdsWizz company.

After years leading recruiting and talent systems at companies like Bridgewater, Electronic Arts, and Dolby, Jeff Hunter came to believe that many of our assumptions about talent, hiring, and performance are fundamentally wrong. The problem is not that people lack potential. The problem is that the systems around them often fail to recognize or develop it. In this revisited episode, Dart and Jeff discuss what gets in the way of human potential and what organizations can do differently.Prior to founding Talentism, Jeff served as Head of Recruiting at Bridgewater Associates and held leadership roles at Electronic Arts and Dolby. His work has focused on designing and scaling systems that help people learn, grow, and perform at their best.In this episode, Dart and Jeff discuss:- What limits human potential- Why talent matters more than capital- The hidden flaws in hiring- Why skills can be misleading- What Bridgewater taught Jeff- The problem with managing people- How systems shape behavior- Why context changes everything - The challenge of hiring for values - What great organizations do differently- And other topics…Jeff Hunter is founder and CEO of Talentism, a company that helps leaders build organizations that unlock human potential. Previously, he served as Head of Recruiting at Bridgewater Associates and held senior talent leadership roles in the technology industry. His work focuses on helping organizations design systems that enable people to learn, grow, and perform at their best.Connect with Jeff:Website: www.talentism.com Work with Dart:Dart is the CEO and co-founder of the work design firm 11fold. Build work that makes employees feel alive, connected to their work, and focused on what's most important to the business. Book a call at 11fold.com.

In this episode of Tank Talks, Matt Cohen sits down with Aidan Madigan-Curtis, Partner at Eclipse, for a sharp conversation on physical AI, frontier tech, robotics, manufacturing, and the future of building in the real world. Aidan shares her unlikely path from a small mountain town in Penticton to Harvard, Bridgewater, Apple, Samsara, and now Eclipse, where she invests at the intersection of atoms and bits.She breaks down what factory floors taught her that most software-first founders miss, why physical AI is becoming one of the biggest venture capital opportunities of the next decade, and what the U.S. and Canada must understand about China's manufacturing advantage. From launching the first Apple Watch manufacturing lines to scaling Samsara's hardware operations and investing in autonomous excavation, robotics, energy, defense, and supply chain technology, Aidan brings a rare operator-investor perspective to one of the most important shifts happening in tech today.Buckle up to understand why the next wave of AI won't just live in software; it will reshape factories, robots, infrastructure, and the physical world around us.The Unlikely Path from Penticton to Harvard (00:04:25)Aidan shares the wild story of growing up in a tiny Canadian mountain town, applying to Harvard almost by accident, and nearly missing her acceptance letter because it sat undelivered in a PO box. She reflects on how community support, risk-taking, and a willingness to swing big shaped the rest of her career.Bridgewater, Systems Thinking, and Conviction Investing (00:09:00)Aidan explains how Bridgewater's fundamental, systematic approach to markets shaped how she evaluates venture opportunities today. She breaks down why Eclipse starts with deep theses, pressure-tests industries, and backs founders before the market fully understands where the world is going.The Factory Floor Lesson Every Founder Needs (00:17:27)Drawing from her time launching Apple Watch manufacturing lines, Aidan explains why the best founders must balance brutal honesty with extreme optimism. She argues that founders who get “high on their own supply” lose touch with reality, while founders without belief cannot rally a team to do the impossible.Why Physical AI Was the Bet Before It Was Cool (00:20:34)Aidan walks through her career pattern of choosing the “unsexy” path before it becomes obvious: Bridgewater before it was famous, Apple supply chain when software was eating the world, Samsara before industrial IoT was hot, and Eclipse before physical AI became a major venture category.China's “Vibe Manufacturing” Advantage (00:28:37)Aidan unpacks Eclipse's China Field Notes and explains what “vibe manufacturing” really means: a deeply layered, highly competitive, fast-moving manufacturing ecosystem that can turn ideas into physical products at extraordinary speed. She discusses China's compounding advantage in tooling, suppliers, human capital, robotics, and government-backed industrial competition.Where the U.S. Is Ahead and Behind in Robotics (00:37:18)Aidan breaks down the robotics race between the U.S. and China. She says the U.S. remains highly competitive in embodied AI, autonomy, and goal-oriented machine intelligence, but lags badly in manufacturing depth, actuators, magnets, physical iteration speed, and lower-level robotic control.The Robotics Data Problem (00:41:14)Aidan explains why video data alone is not enough to build general-purpose robotics. She discusses the need for proprioception, haptics, physics data, and real-world interaction data, plus why China's robotic data farms could become a major strategic advantage.Canada's Opportunity in AI, Energy, and Deep Tech (00:44:47)As a Canadian-born investor, Aidan lays out where Canada can win: talent attraction, smart immigration policy, abundant clean energy, AI infrastructure, university research, biotech, quantum, defense, and strategic government offtake. She argues Canada has the raw ingredients to become a major player if it moves with urgency.Eclipse's Interest in Canadian Founders (00:49:20)Aidan shares that Eclipse is already investing in Canada, including companies in Toronto and Vancouver, and is actively interested in deep tech and physical AI founders coming out of Canada's strongest ecosystems.About Aidan Madigan-CurtisAidan Madigan-Curtis is a Partner at Eclipse, where she invests in physical AI, robotics, manufacturing, energy, defense, supply chain, and frontier technology companies. Before Eclipse, she was an early executive at Samsara, helping scale the industrial IoT company from pre-product to public company. She previously worked on Apple's manufacturing team for the first Apple Watch and began her career at Bridgewater, where she developed a systems-thinking approach to markets and complex industries.Connect with Aidan Madigan-Curtis on LinkedIn: https://www.linkedin.com/in/aidan-madigan-curtis/Visit the Eclipse website: https://eclipse.capital/Connect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

(00:00 - 2:43) It's Thursday! Bridgewater police responded to a report of a camouflaged, bazooka-wielding man last week and instead found a landscaper carrying a leaf blower, according to officials(2:43 - 8:45) Today's DM Disaster is from Allen! He had some damage on his roof from the last windstorm. He called his buddy to come take a look. He showed up with his new drone then asked if Allen wanted to fly it. Allen then crashed it into a tree and destroyed the drone! That's Allen's DM Disaster! (8:45 - 15:48) It's another round of Get to Know Adam 12! This time we had some listeners chimed in and asked a few questions. (15:48 - 19:48) Today's Supah Smaht player is Joe from Scituate. Find out if they were Supah Smaht! (19:48 - 23:04) Legal Sea Foods is now selling a massive lobster roll designed to feed eight people for a cool $375. That's roughly the cost of a decent airline ticket, a car payment, or three weeks of groceries. LBF and Adam 12 are not a fan of the price! (23:04 - 26:10) A 17-year-old Stoneham kid spent two years singing karaoke outside a shopping plaza, becoming a beloved local character and making shoppers smile along the way. Then police informed him he couldn't perform there anymore because of plaza rules, and the community immediately erupted in support. (26:10 - 31:10) The legendary Yankee Candle founder's 70-acre Western Massachusetts compound finally sold for $4.55 million after originally hitting the market at $23 million. That's an $18.5 million haircut on a property featuring a water park, bowling alley, arcade, golf course, concert venue, and enough amenities to entertain an entire zip code. Plus, hiking vomiting! All this and more on the ROR Morning Show with LBF & Adam 12 Podcast. Find more great podcasts at bPodStudios.com…The Place To Be For Podcast Discovery! Follow us on our socialsInstagram - @rormorningshowFacebook - The ROR Morning ShowSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The greatest risk we face today isn’t that AI is becoming “too smart”; it’s that we are beginning to treat this technology as an infallible “oracle” rather than a capable, yet fundamentally fallible, “intern.” As the baseline for production drops to zero, the economy of human value is shifting away from raw output and toward the only two things a machine cannot authentically replicate: judgment and intent. How should we use AI to augment and enhance our professional output, instead of simply automating  and potentially replacing our value? How can we navigate the “Calculator Trap” to ensure our foundational critical thinking doesn’t atrophy?   Join us for a conversation with Ted Yang, an MIT-trained engineer and seasoned entrepreneur who has founded more than twelve companies. A former finance executive at legendary firms like Bridgewater and Citadel, Ted is an Emmy-nominated author and a member of Connecticut’s Board of Regents for Higher Education. His new book, Ageless Peak Performance, provides a practical playbook for professionals looking to thoughtfully adopt AI to expand human capability and opportunity. Hosted by: Alexa Raad and Leslie Daigle. Further reading: AI won’t make the call: Why human judgment still drives innovation, Harvard Business Review Harvard Business Review: “The Irreplaceable Value of Human Decision-Making in the Age of AI” Forbes: “Leadership-Driven Growth In The Age Of AI Acceleration” iGrafx: “Don’t Automate Chaos: Why Fixing Broken Processes Comes Before Adding AI” Stanford HAI: “Is Generative AI Killing Critical Thinking?” Ageless Peak Performance: The Playbook for AI-Powered Excellence” The views and opinions expressed in this program are our own and may not reflect the views or positions of our employers.

For Dom Z., Bridgewater State University in Massachusetts was an endless buffet of opportunities. In this episode of Autism Goes to College, Dom shares with Katharine how he made it across the finish line while immersing himself in campus life, student government, clubs, and the music scene. Plus, he updates us on his personal quest to make life better for every student on campus who might have invisible challenges.Dom loved the academic coaching available at BSU, got a few college accommodations that helped him with note taking and test taking, and his academic path zig-zagged a bit. He credits a professor with guiding him to stick with his major of Communication Sciences & Disorders, while also looking for creative ways to build a career outside of the traditional speech-language professions.Advocacy for autistic and neurodiverse college students, how to do college without burning out, disability accommodations, academic coaching, campus belonging, and ways to make grad school work when you also need to work are all part of this conversation.Support the show

Send us Fan Mail We were told AI would give us more time. For a lot of people, it has done the opposite. More tools to learn. More decisions to make about which tools. More anxiety about whether we are using them right, or falling behind if we are not. There is a new kind of fatigue showing up quietly in people's lives, and almost nobody is naming it yet. It is not burnout from AI taking your job. It is burnout from using AI. Sana sits down with Ted Yang, MIT engineer, former finance executive at Bridgewater and Citadel, founder of more than 12 companies, and author of Ageless Peak Performance, to flip the conversation. AI, used the right way, should not add to your cognitive burden. It should quietly lift some of it off you. They cover the four principles for healthy AI use, why your judgment becomes more valuable with age, and why protecting your humanity is the work.  About the Guest: Ted Yang is an MIT engineer, entrepreneur, and former finance executive at Bridgewater and Citadel who has founded more than 12 companies. A worldwide speaker at TEDx, South Summit, Startup Grind, and university commencements, he serves on Connecticut's Board of Regents for Higher Education and is raising a family of two special needs children. His book Ageless Peak Performance: The Playbook for AI-Powered Excellence releases May 18, with endorsements from Governor Ned Lamont and former US Secretary of Education Miguel Cardona.  Key Takeaways: AI burnout is real, and it is not about the technology itself. It is about using AI to do more of everything instead of using it to rebalance what you carry. Cognitive overload happens when you have too much to keep track of at a good quality level. Even with AI doing the work, your brain still has to manage it. Pattern recognition and judgment improve with age. AI does not replace experienced professionals — used well, it amplifies the very capabilities they have spent decades building. Treat AI like an intern, not an oracle. It is fast and capable, but it makes confident mistakes and does not admit them. Aim first, then accelerate. Make AI flow. Keep your judgment at the center. The four principles for a healthier relationship with AI. AI is not yet ready to replace trained therapists. The path from theory to safe practice took psychoanalysis decades. AI is in a similar early stage and should not be used in place of mental health professionals.  Connect With the Guest: Book and website: https://agelesspeakperformance.com LinkedIn: https://www.linkedin.com/in/tedsensei/ Instagram: https://www.instagram.com/tedxsensei/ Book on Amazon: https://www.amazon.com/Ageless-Peak-Performance-AI-Powered-Excellence/dp/B0GRCJ9VZJ  Episode Chapters: [00:00] Cold Open — Naming AI Burnout [03:30] Welcome and Introducing Ted Yang [06:00] The Personal Moment Ted First Saw Cognitive Overload [08:00] What We Are Misunderstanding About Mental Fatigue [11:30] Why Older Professionals Have a Real Advantage in the AI Era [15:00] The First Principle: Treat AI Like an Intern, Not an Oracle [19:00] Principle Two: Aim First, Then Accelerate [22:00] Principle Three: Make AI Flow [26:00] Principle Four: Keep Your Judgment at the Center [30:00] AI in Mental Health: Why It Is Not Ready to Replace Therapists [36:00] Holding On to Humanity in the Pace of Change [40:00] The Five Superpowers and Where to Find the Book [42:30] A Message of Hope for Anyone Feeling Burnt Out   Want to be a guest on Healthy Mind, Healthy Life? DM on PM - Send me a message on PodMatch DM Me Here: https://www.podmatch.com/hostdetailpreview/avik Disclaimer: This episode is produced for educational and informational purposes only. All views expressed by the guest are their personal opinions alone and do not represent the views of the host or Healthy Mind by Avik™. The Network does not verify, endorse, or assume responsibility for any guest statements. Nothing in this episode constitutes medical, legal, financial, or professional advice, please consult a qualified professional before making any decisions. Listeners are encouraged to engage critically and independently with all content do not consume blindly. Use this content as a starting point for your own reflection and research, not as a substitute for professional guidance. Third-party content is referenced under fair use for informational purposes only. Guest speakers are solely responsible for their own statements. If you have concerns about any content, please contact us here. By listening, you acknowledge and accept this disclaimer in full. Read detailed disclaimer here. Healthy Mind By Avik™ is a global platform redefining mental health as a necessity, not a luxury. Born during the pandemic, it's become a sanctuary for healing, growth, and mindful living. Hosted by Avik Chakraborty, storyteller, survivor, and wellness advocate. With over 6500+ episodes and 200K+ global listeners, we unite voices, break stigma, and build a world where every story matters. Subscribe and be part of this healing journey. Brand: Healthy Mind By Avik™ | Email: https://www.podhub.club/contact | Website: https://www.podhub.club | Based in: India & USA Listen to all podcast shows: https://www.podhub.club/podcastnetwork | Be a guest: https://www.podhub.club/beaguest | Newsletter: https://healthymindbyavik.substack.com/   #podmatch #healthymindbyavik #podhub.club #AIburnout #cognitiveoverload #agelesspeakperformance #TedYang #judgmentinAI #mentalclarity #AIandmentalhealth #midcareerwisdom #experiencematters #thoughtfulAI #humansandAI #peakperformance #healthymindhealthylife Support the show Want to Be a Guest on Healthy Mind, Healthy Life?

The World is Stranger Than You Think. History isn't just a collection of dates and names; it's a tapestry woven with threads of the unexplained. Tonight, we open the archive to explore ten of the most chilling and baffling accounts ever recorded. From the shifting, jagged "vortex" of the Bennington Triangle to the terrifying chemical mist of the Mad Gasser, these stories defy logic and challenge our understanding of reality. We'll walk the Roman roads hidden beneath York, witness the spectral fury of the Marathon battlefield, and look into the faceless eyes of the Gauthier Ghost. These aren't just stories; they are the puzzles that keep investigators, researchers, and historians awake at night. In this episode, we deep-dive into: The Bennington Triangle: Where hikers vanish into the distorted woods. The Amherst Poltergeist: A haunting so violent the walls themselves demanded blood. The Man in the Iron Mask: The prisoner whose identity was a king's greatest secret. The Bridgewater Triangle: Massachusetts' own hub of cryptids and strange lights. And 6 other cases that remain completely unsolved.Become a supporter of this podcast: https://www.spreaker.com/podcast/missing-persons-mysteries--5624803/support.

The Mysterious Bridgewater TriangleBecome a supporter of this podcast: https://www.spreaker.com/podcast/missing-persons-mysteries--5624803/support.

This week The Tiny Stage is on-location from The Audacity Music Festival. Audacity was founded and produced by Thunderbolt Productions and held at Town River Landing in Bridgewater on Saturday, May 9. Featured acts/interviews include Gracie Grace and All The Good Boys, Nate Ramos, Laroy Streat, Total Strangers, Highwater Haulers, and Never Gonna Make It. Audacity was also held to raise awareness of The Fire For Effect foundation in Bridgewater whose mission is to honor and empower veterans, first responders, and their families by providing essential resources, support, and community-building initiatives that foster resilience, restore hope, and strengthen the fabric of our community. Learn more at https://www.ffefinc.org. 

A stalemated war. Fractured alliances. A rival waiting in the wings. It feels to me that we're having an “end of the American empire” moment. My guest this week, Ray Dalio, is an unlikely prophet of doom — the billionaire Bridgewater investor conquered Wall Street by studying history and mastering global trends. He foresaw the 2008 financial crisis,and these days he's warning that the U.S. is repeating the patterns that ended great empires of the past. 0:00 - Intro 01:24 - The rise and fall of empires through big cycles 08:35 - Geopolitical tensions: China, Iran and the Suez Canal 14:27 - Fiat currency or gold? 24:19 - America's coming ‘heart attack' 30:37 - Acts of nature, A.I. and technology 43:37 - ‘Could we have a Japanese future?' (A full transcript of this episode is available on the Times website.) Thoughts? Email us at interestingtimes@nytimes.com. Please subscribe to our YouTube Channel, Interesting Times with Ross Douthat. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode we answer emails from Matt, Michael, Stephen and Al.  We discuss expanding the spending muscle in retirement, the generosity of our listeners, more on the Four Quadrant model and its permutations, and how we stay connected to listeners without inhabiting the conference circuit. And THEN we our go through our weekly portfolio reviews of the eight sample portfolios you can find at Portfolios | Risk Parity Radio.Additional Links:Fairfax CASA Donation Page:  Donate - Fairfax CASACool Number Nerd Video:  Fibonacci Numbers hidden in the Mandelbrot Set - NumberphileMiB Podcast Episode:  Masters in Business: Jean-Philippe Bouchaud - BloombergThe Dude's Link re Quadrants and Assets:   Structural Diversification for All Seasons - ReSolve Asset Management (investresolve.com)Hedgeye Asset Chart:  Hedgeye Four Quadrant Model Best and Worst Assets.pdf - Google DriveBloomberg Inflation Presentation:  Bloomberg Investing in Inflationary Regimes Presentation.pdf - Google DriveListener Essay on Four Quadrant Model:  15 Uncorrelated Assets | SSiSClaudia Moise Paper with US Treasuries Correlation Data:  Flights to Safety, Volatility Risk, and Monetary Policy by Claudia E. Moise :: SSRNBreathless Unedited AI-Bot Summary:Hoarding can look a lot like “being responsible,” especially right before retirement when every headline makes the future feel fragile. We take a listener's detailed numbers and use them to talk about a problem we see all the time: spending that never catches up with the life you actually want. If your future expenses are likely to decline in real terms, your personal inflation rate may be lower than CPI, and a fixed rule can quietly push you into over-saving instead of living. We share a simple, practical idea for breaking that pattern: create a visible spending bucket, spend intentionally, then review what brought real value and what didn't. Then we shift into a deep question from a data-driven listener who tried to test Bridgewater's four quadrant model using growth and inflation data. We explain why these relationships are probabilistic, why short time frames can look like a noisy blob, and where to look for research that connects macro regimes to asset returns. Along the way we revisit the roles different diversifiers can play in a risk parity portfolio: Treasury bonds as recession insurance, managed futures and commodities for ugly inflation shocks, and gold as a strange but useful diversifier when the world gets weird. We wrap with our weekly portfolio review and a quick read on what's been working lately across stocks, small cap value, bonds, gold, REITs, commodities and managed futures, including results from our sample portfolios and a few leveraged experiments. If you like practical asset allocation, retirement withdrawal strategy, and plainspoken investing conversations with some humor mixed in, hit play, then subscribe, share the show, and leave a review so more do-it-yourself investors can find it.Support the show