Podcast appearances and mentions of Albert Einstein

I joined Peter McCormack to dig into one of the deepest unanswered questions in physics: what is time, and why does it only seem to move in one direction? My answer might surprise you. Time may not be a fundamental feature of reality at all, and understanding space, matter, and energy still doesn't mean we understand the universe itself. We cover: - What may have existed before the Big Bang - Why something can't truly come from nothing - How my work with the Simons Observatory could detect gravitational waves from the universe's earliest moments - What dark matter actually means - Why even a film as detail-obsessed as Interstellar still gets the physics wrong. The conversation then turns from cosmic time to human time: mortality, family, meaning, and attention. We get into UFO disclosure, the collapse of institutional trust, and whether AI can ever reproduce the embodied human insight that let Einstein transform physics. My take: AI remains a tool built to serve humanity, and it's time we stop apologizing for humanity's greatness. ———

You don’t have to be crazy about cats to be at least a little bit fascinated with them, hissing, murder mittens, and all. Great writers like Hemingway, Twain, and Dickens as well as folks like Lincoln, Churchill, Einstein, and Nightingale adored them. As did music icons like Freddie Mercury and John Lennon. They all felt cats gave them one thing . . peace. Feel free to DM me if you have a story you’d like me to cover . . on Facebook it’s Patty Steele and on Instagram Real Patty Steele.See omnystudio.com/listener for privacy information.

ENTRE NA LISTA DE ESPERA DO VIVER DE RENDA: https://r.vocemaisrico.com/2578c2bd18ABRA SUA CONTA NA COINBASE: https://r.vocemaisrico.com/72f2133408Ao longo da história, algumas mentes se destacaram tanto que passaram a ser lembradas como os maiores gênios da humanidade — indivíduos capazes de revolucionar a ciência, a arte, a filosofia e a forma como compreendemos o mundo.De Leonardo da Vinci a Einstein, de Mozart a Marie Curie, cada um deixou um legado que atravessa séculos e continua influenciando a vida de todos nós.Mas quem realmente merece o título de "maior gênio" de todos os tempos? Seria justo comparar um cientista como Newton com um artista como Shakespeare, ou cada área exige seus próprios critérios de genialidade?Quais nomes menos conhecidos, esquecidos pela história ou pouco celebrados no Ocidente, também deveriam estar nessa lista? E o que aprendemos, afinal, observando as trajetórias dessas mentes extraordinárias?Para conversar sobre isso e muito mais, convidamos Felipe Guisoli (Universo Narrado) para o episódio 313 do podcast Os Sócios. Falaremos sobre os maiores nomes da história humana, seus legados, suas histórias pessoais e por que continuamos fascinados por eles até hoje.Ele será transmitido nesta quinta-feira (27/08), às 12h, no canal Os Sócios Podcast.Hosts: Bruno Perini @bruno_perini e Malu Perini @maluperiniConvidados: Felipe Guisoli @universonarrado

(0:00) Eric Weinstein joins the show! (03:09) Has American science stalled? Cowboy science, Fauci, and the scientific precariat (21:31) Weinstein's fix: Blow a hole in the Civil Rights Act, kill peer review, fund people not ideas (41:36) Ed Witten drove physics off a cliff, and is Renaissance Technologies a secret Los Alamos? (52:49) Boom, Vroom, Zoom: Did stagnant physics save the human species? (01:07:11) UAPs, multi-temporal adversaries, and Einstein's prison (01:16:12) China poaches our best scientists, AI reads the trash can corpus   Thanks to our partners for making this possible!   Starting a business? Northwest Registered Agent gives you everything you need to build a complete Business Identity including free tools and built-in privacy. Get more at https://www.northwestregisteredagent.com/ALLINFREE    With over nine million acres protected across all fifty states, The Conservation Fund secures the irreplaceable lands we love. Learn more at http://conservationfund.org   Follow Eric: https://x.com/ericweinstein   Follow the besties:  https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg   Follow on X: https://x.com/theallinpod   Follow on Instagram: https://www.instagram.com/theallinpod   Follow on TikTok: https://www.tiktok.com/@allin   Follow on LinkedIn:  https://www.linkedin.com/company/allinpod   Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg   Intro Video Credit: https://x.com/TheZachEffect #allin #tech #news  

SpaceTime Series 29 Episode 102 *SpaceX's Starship arrives at Christmas Island After some 24 days at sea, a SpaceX recovery team has successfully towed its test flight 13 Starship into the remote Australian Indian Ocean territory of Christmas Island.  The unplanned operation was hastily put together after the spacecraft unexpectedly held together following its splashdown off the Western Australian coast. *How old really is the universe? Astronomers have come up with a new way to work out the age of the universe finding it to be 13.6 billion years old – some 200 million years younger than previously thought. *Einstein's general relativity theory still stands More than 100 years after Albert Einstein published his iconic general theory of relativity, it remains science's best understanding of gravity and the universe on the cosmic scale. *The Science Report A new study shows that carrying a little extra weight could be beneficial as you get older. People lacking human connections more likely to turn to AI chatbots for companionship. The Southern Ocean and parts of the Pacific may be the best places to fertilise to remove CO2. How dogs have learnt to understand human facial expressions. Alex on Tech: The Galaxy passport sized Z Fold 8 tops the sales figures.

Lesley Logan opens a two-part series on slowing down, because rushing may feel productive but it rarely gets you where you actually want to go. She explains how constant rushing flips the nervous system into a survival response, and what it really cost her to pause OPC's growth for two years because Profitable Pilates' growth needed her more.If you have any questions about this episode or want to get some of the resources we mentioned, head over to LesleyLogan.co/podcast https://lesleylogan.co/podcast/. If you have any comments or questions about the Be It pod shoot us a message at beit@lesleylogan.co mailto:beit@lesleylogan.co. And as always, if you're enjoying the show please share it with someone who you think would enjoy it as well. It is your continued support that will help us continue to help others. Thank you so much! Never miss another show by subscribing at LesleyLogan.co/subscribe https://lesleylogan.co/podcast/#follow-subscribe-free.In this episode you will learn about:The real difference between movement and forward momentum.Knowing when a pause is strategic and when it is avoidance.Why you cannot hear your intuition when you are moving too fast.Why rushing makes you say yes to things you would have declined.Setting five-minute buffer gaps between every meeting this week.Episode References/Links:The Big Leap by Gay Hendricks - https://beitpod.com/bigleapEp 400 ft Gay Hendricks - https://beitpod.com/ep400Jay Grimes - https://pilatesology.com/instructor/jay-grimesSubmit your wins or questions - https://beitpod.com/questions If you enjoyed this episode, make sure and give us a five star rating and leave us a review on iTunes, Podcast Addict, Podchaser or Castbox. https://lovethepodcast.com/BITYSIDEALS! DEALS! DEALS! DEALS! https://onlinepilatesclasses.com/memberships/perks/#equipmentCheck out all our Preferred Vendors & Special Deals from Clair Sparrow, Sensate, Lyfefuel BeeKeeper's Naturals, Sauna Space, HigherDose, AG1 and ToeSox https://onlinepilatesclasses.com/memberships/perks/#equipmentBe in the know with all the workshops at OPC https://workshops.onlinepilatesclasses.com/lp-workshop-waitlistBe It Till You See It Podcast Survey https://pod.lesleylogan.co/be-it-podcasts-surveyBe a part of Lesley's Pilates Mentorship https://lesleylogan.co/elevate/FREE Ditching Busy Webinar https://ditchingbusy.com/Resources:Watch the Be It Till You See It podcast on YouTube! https://www.youtube.com/channel/UCq08HES7xLMvVa3Fy5DR8-gLesley Logan website https://lesleylogan.co/Be It Till You See It Podcast https://lesleylogan.co/podcast/Online Pilates Classes by Lesley Logan https://onlinepilatesclasses.com/Online Pilates Classes by Lesley Logan on YouTube https://www.youtube.com/channel/UCjogqXLnfyhS5VlU4rdzlnQProfitable Pilates https://profitablepilates.com/about/Follow Us on Social Media:Instagram https://www.instagram.com/lesley.logan/The Be It Till You See It Podcast YouTube channel https://www.youtube.com/channel/UCq08HES7xLMvVa3Fy5DR8-gFacebook https://www.facebook.com/llogan.pilatesLinkedIn https://www.linkedin.com/in/lesley-logan/The OPC YouTube Channel https://www.youtube.com/@OnlinePilatesClasses Episode Transcript:Lesley Logan 0:00  Rest is never supposed to be a reward. I see way too many women using rest or a day off or a spa day as a reward for hard work. Nope, you actually are always worthy of rest, always, all the time, no matter what.Lesley Logan 0:12  Welcome to the Be It Till You See It podcast where we talk about taking messy action, knowing that perfect is boring. I'm Lesley Logan, Pilates instructor and fitness business coach. I've trained thousands of people around the world and the number one thing I see stopping people from achieving anything is self-doubt. My friends, action brings clarity and it's the antidote to fear. Each week, my guest will bring bold, executable, intrinsic and targeted steps that you can use to put yourself first and Be It Till You See It. It's a practice, not a perfect. Let's get started. Lesley Logan 0:54  Hello, Be It babe. How are you? Welcome back to the Be It Till You See It podcast. Of course, now I have vocal fry. I was talking just fine a moment ago, but here we are, and that's because we are not perfect around here. Welcome if it's your first time listening to us. Thank you for being here. This week we're going to talk about slowing down and actually how that can speed things up. And I'm just really excited for us to dive into this topic. We are doing these topics because you asked for them. So beitpod.com/questions is where you send your wins in for us to chat about and celebrate you on Fridays, but your topics, your guest requests, things like that those are all things that I look at and determine for who we invite on the pod and what these little series are for. And I love hearing how they help you. I also just want to say I really appreciate when you share our podcast. It means the world to me. It really, really does. Brightens my day every time I hear about it. So just know that I appreciate even just texting this to a friend who needs to hear it. You know, we can't Be It Till We See It alone. This is not how it works. So we have two episodes on this topic about slowing down, and the overall message, the too-long-didn't-listen, is that how it actually speeds things up. And I am someone who wants to move a mile a minute, so this is definitely a Be It Till You See It. For me, it's something I'm constantly practicing. I'm constantly having to be reminded. I'm recording this in June. You're hearing it in August, and I was telling the team an hour ago we have to get moving on that because the launch date for that is October 1st. Lesley, it is the end of June. There is plenty of time. Is there though? But really, really. So if you have ever spent an entire day going from one task to the other? Just you feel like a hummingbird, but then you get to 5 p.m. and you look at your to-do list and you actually did everything but what you wanted to do. Yeah, you don't only end the day exhausted, end the day stressed out because you did all this stuff, but you didn't move the needle forward. And this is something that I see happen when I coach a lot of Pilates studios because people love to make a list. My Pilates studio owners love to make a list. I see this a lot when I coach Pilates studio owners and teachers. They make a long list of things that they want to get done that they need to do, and then they do none of that, right? And then they be like, "I did all these things," or they make a laundry list of things and they do all of them, but they actually didn't move the needle forward because none of those things actually move the needle forward. That was just what Brad calls the whirlwind, just doing the whirlwind, right? And there is a massive difference between getting stuff done, movement of things, and momentum of things. If you ever watch my cousin Benny, there is this scene in this episode where they get stuck in the mud and they're spinning the wheels, right? And so there is movement happening, but they're not getting enough momentum.Lesley Logan 3:22  They're not going anywhere, right? They're stuck in the mud, and so, hi, I see you, and we're just gonna acknowledge that all of us do that, and if we get honest with ourselves, we're gonna break down while just doing movement, just rushing around, checking things off is actually negatively affecting the growth we want to have, and how taking some breaks, some pauses, slowing down shifts us into a gear that can actually create true progress. Right? I remember years and years ago I was with this girl, and she had this whole speech that there's power in the pause. There is power in the pause. Right? Sometimes, just taking a break and getting a 30,000-foot view is what exactly you need. Otherwise, you're just on the hamster wheel. Maybe there's another wheel to get on, right? So I do understand. I want to just say I do understand this idea that if I slow down, I fall behind because I was a runner. So just so you know, professional runner for a long time, and if you do slow down, people do pass you up, right? But that's not how life is. Life is not an actual race because you are your own path. You're doing your own thing, right? And so, I do think at some point in your life there is time to rush and hustle and get as much information as you can. And then there is a time in our life where we really do need to honor the information. Take time to get that, you know. Especially as I am someone over 40, I've really discovered that if I just take a few pauses, if I don't rush from one task to the next, if I, as I said in another series, go for a walk around the block, I actually get some really great ideas. I get to listen to my intuition. I get to hear my inner voice, and it's really helpful. But I also the complete opposite of that is that in my brain, I am 43 years old, and I do want to be retired soon. And I feel like every day is a countdown to that, which is seriously crazy because there, if I were to take how many days between now and when I want to retire, there's probably thousands. So in reality, I could slow down, but I do understand that feeling of that clock ticking, and I think together, Be It babes, we need to reframe that. We need to reframe that pauses as strategic decision-making time and not a weakness, not a laziness, not a procrastination, because there is a difference between a pause and a procrastination, right? So I will say I definitely had to get hit over the head a few times with this idea. Really had to get hit over the head a few times with this idea. It's a lesson that's just taken me a little longer than I like to admit about slowing down. Lesley Logan 5:55  Okay, so I don't know if a lot of you know this, but I started Agency, Profitable Pilates, membership program at the same time that I kind of started OPC, and you know, I thought they're two different things. I could do this. I have the time, and the reality is, is marketing the two, sure, but actually sustainably operating both was not happening, and I was rushing to do it because I thought, "Oh my god, if I don't get this done, some other person's going to take my idea, and I need to get this going." And I also had, I will say, I was loving traveling a lot and really wanting to get rid of some of my clients, so I needed both to take off so that I could let go of some of my regular teaching, so I can enjoy traveling and just run these two businesses. I thought it's just going to happen in three weeks, and it didn't. Of course, it didn't happen. And in fact, it took a little bit longer. And there came to a point where I actually had to pause OPC's growth because Profitable Pilates' growth needed me more.Lesley Logan 6:54  It was taking off, and if I kept trying to split my time evenly, I would have actually caused Profitable Pilates to not grow and to in fact not help the people and not have the impact I wanted to make. And so I had to hit pause on something, and it was really hard. I had to choose between two babies, one of which I love gets really hard. I actually love them both so much, and one of them is more scalable than the other, and one of them can impact more people than the other in different ways, and but I had to do it, and it was so hard because I will say, OPC, those two years of purposely slowing down its growth still is something we are dealing with today. You know, I'm really proud of what we do. We have a great membership. We have a great community, but there was information I didn't get in those two years because I had to slow it down. So I share that with you because I actually don't want you rushing around to feel like you're accomplishing something, only to end up missing out on the information you could be gathering, so you could make better decisions for where you're going to grow, right? For all you're going to do. So that is why you know we have to actually understand when we go into that rush mode that feels like it's productive. We are not operating from a place of not just authenticity but control, right? And so it does feel like everything matters so much because we actually are grasping for control. But when you are actually in charge of how fast you go and the pauses you're going to take and the scenic route you're going to look at or the thing you're going to take a deeper look at, you are in the more control that way. So it's important. So as we talked about in past solo episodes, your nervous system is very much part of this, and if you are constantly rushing, it's not just a time-management issue. It can actually be a survival response, and that survival response actually puts us in that lizard brain where we actually don't see a lot of stuff as being helping hand, right? So our nervous system, when you're constantly rushing, your brain activates a low-grade survival response. Adrenaline makes you hyper-focus on immediate micro-tasks. You check your email, you cross off trivial to-do items while taking you away from seeing the big picture. You cannot see it when you're in this space, right? Lesley Logan 8:59  Another example of being in survival mode is so. I'm sure you don't know this, but I'll tell you. If your head becomes more forward of your spine, right, like you start to be one of those people whose heads are forward, you see those people at their parts. You start to see people coming at you as an attack and not a helping hand. So if you are in this rushing mode, if you're in the survival response in your nervous system, you are actually not seeing people going, "Hey, I'd love to do this with you," or "Let's collaborate with this" as help. You could not receive it like that. You actually see it as someone trying to get something from you, someone trying to slow you down, someone trying to take your thing, and so you actually perceive some helping hands as something that's going to be a negative or an attack on you, right? And so when we go through that, it just actually means we probably definitely not going to go as fast as we want because it's hard to go anywhere fast alone, right? Well, they say if you want to go fast, you go alone. If you want to go far, you go together. But the reality is, is like I actually do think that you can go far and fast with help of others. My Pilates loves, when you rush a movement. You actually never fully get everything out of a movement, right? In order to rush it, something has to let go. So either letting go the connections of muscles, or you're not fully extending your legs and footwork and fully closing the springs. You're kind of just operating in the middle, and that is movement.Lesley Logan 10:43  And so it's like I'm doing something. There might even be a burn, but you're actually not actually getting the full openness of the hips, the full reach through the feet, the full expression of the exercise. Where the benefits are actually, Jay would always say the Pilates happens the last two inches, right? So you got to finish. You got to close. You have to do a full expression. So it's important that we're not just going so fast and using momentum because while it does feel like we are doing something, we are actually not getting the benefits of the something that we are doing. So if you are my constant doer, mover, working all the time, you're likely unable to have clarity in what it is you're doing because you can't find clarity in rush, right? When you are speeding down the freeway, do you see all the signs? No, they're blur. I remember we were doing something, and Brad was going faster than we should, and he missed the exit, and it cost us so many minutes because we missed the exit. If we had just been going at the speed that we were supposed to be going, we wouldn't have, right, and so when you're rushing, you cannot have clarity. You cannot catch the subtle things that are going, "Hey, look it over here," or "What's going on here," or "Maybe this is a turning you have to have." So that mental noise just becomes this level 10, and that intuition, which we talked about in the past solo episodes, the reason why we have a hard time hearing our intuition is we're just moving too fast to even hear it, right? Because it's often like a whisper. It's often there, "Hey, hi." Your intuition's not yelling at you, right? Your intuition's when you're ready. I'm just right here to let you know that that's the right turn I want you to take. And you know, the other thing is, is if you're ending each day exhausted, if you are my listeners who are in my age group or older, ending your day exhausted does not make it easier for you to do the other things you want to do. Doesn't make it easier for you to show up for yourself for your movement practices. It doesn't make it easier for you to sleep well at night. You're causing so much stress and cortisol. It affects how you sleep. It affects how your body is shaped, right? What's going on with how you digest food? All of that. All because we're just rushing, rushing, rushing. So we actually say yes to things that, if we had just taken time to really fully think about, we would have said no to. I find there's two parts because of the ADHD that I have. My brain goes three steps ahead, so it's always a little faster, and I cut people off and I make assumptions. And it's not because I'm a rude asshole; just because my brain's like, "I'm three steps ahead. I know what you're going to say." In other times, my ADHD works in this amazing place where I can just get into focus mode, and I can do a lot in a little bit of time. And the problem with focus modes and ADHD is you never get to plan when they're going to happen. But what I have found is, if I am taking time to pause for the day to make sure I'm aware of what I'm supposed to be working on, I'm aware of where we're trying to go and grow. It allows me to enjoy those focus modes when they happen, and to at least have awareness when I'm actually in a rush state. Oh, I'm trying to rush this right now. I'm trying to push this through. It never feels good, right? It feels like it's harder work to do. Yeah. So what we need, right? Because now that I we're all in the band, I agree. We go okay, so I just need to hit pause. Yeah, but it's first you have to have the courage to hit pause. So first, just having, I think I need to hit pause is a great place to be, right? Because that's your first breakthrough to many breakthroughs.Lesley Logan 13:56  How many times have you had the most amazing ideas when you're in the shower, or you're on a walk, or you're checking out the moon or a sunrise, and all of a sudden you have this thing that hits you, right? Those are pauses, and notice they're not very long. We don't have to pause for five hours. Doesn't have to be a full day off. It could just be a pause. It could just literally the length of a breath, right? Or it could be five minutes, or it can be in between Zoom calls. You always plan for 10 minutes to go from one call to the next to make sure you've wrapped up your thoughts on the last call. Like self-reflection is one of the best ways to retain information from the things that you did. All that brain fog that people talk about, I think a lot of it is actually just rushing so much. I think I do believe that there is brain fog, but I think a lot of it is like we're not giving ourselves enough time between calls to reflect on what would just happen, what we have to do, and where we're going, so that then the next call starts, we could actually be present enough to take it in, right? So rest is also never supposed to be a reward. I see way too many women using rest or a day off or a spa as reward for hard work. Nope. You actually are always worthy of rest, always, all the time, no matter what, because rest is not. "Oh, I'm going to go and hit this rest stop over here on a long drive." Yes, you're new to those things. It's these little moments that happen throughout the day that allow you to feel like you, to feel like you are in charge. If you haven't read The Big Leap, I talk about all the time, but there's a whole chapter on time. There's Einstein time and there's Newtonian time. And if you listen to episode 400, I actually asked Gay Hendricks about this time chapter. And the reality is, is when we are in our genius zone, we are in our flow state. We can bend time, really, truly can. But a lot of us are rushing around and putting ourselves in Newtonian time, and that is not. You actually end up watching the time go by faster. Ever realized like minutes are long when you're holding a plank, but they're fast when you're just sitting on a phone call with your friends. Yeah, right. Because you don't want to be in the plank, but you do want to be on the phone call with your friends. Same thing. You can actually make time be in your control, but you have to hit pause, right? So, what I'd love for you to do over the next couple days before our second episode comes out, which is hopefully some wonderful ways to help you hit pause. I just want you to notice where you can put five-minute buffer gaps. Can you change any of the calls or meetings you have to have an extra five minutes in between? Can you text people? "I need like, hey, we know we have a call tomorrow at 10. I'll be there at 10:05." And in those five minutes, you are not allowed to pick up your phone or check your emails or anything. You get to go to the bathroom if you need a bio break. But I would also love for you just to take a look outside. Maybe go outside. Maybe smell a plant in your house, something that is not on a screen, and notice what feels good and how you feel before you go into your next call. That's your homework. Five-minute gap buffers, as many of them as you can between all of your meetings, and see what you love to do in those five minutes, not where it feels like, "Oh my god, this is the longest five minutes. I have to sit here and look at this wall." No, not that. Something that allows you to just, you almost look forward to it, right? You almost are like, "My god, I can't wait for this call to be over because I get my buffer." Like that's what I want. Lesley Logan 17:00  All right, Be It babe, let me know questions you have. I would love to hear your rushing stories and what maybe you realized in self-reflecting that you missed out on. Here's the thing: the times in my life where I've rushed and missed out on some great opportunities because I just didn't hit pause long enough to think about it. Those things were not for me, right? What is for you will not pass you. But in reflecting on what I could have done differently, it meant that I didn't miss future opportunities, and I get to take advantage of them and use them in the best way. All right, thank you so much for listening to our podcast. Until next time, Be It Till You See It. Lesley Logan 17:49  That's all I got for this episode of the Be It Till You See It Podcast. One thing that would help both myself and future listeners is for you to rate the show and leave a review and follow or subscribe for free wherever you listen to your podcast. Also, make sure to introduce yourself over at the Be It Pod on Instagram. I would love to know more about you. Share this episode with whoever you think needs to hear it. Help us and others Be It Till You See It. Have an awesome day. Be It Till You See It is a production of The Bloom Podcast Network. If you want to leave us a message or a question that we might read on another episode, you can text us at +1-310-905-5534 or send a DM on Instagram @BeItPod.Brad Crowell 18:32  It's written, filmed, and recorded by your host, Lesley Logan, and me, Brad Crowell.Lesley Logan 18:37  It is transcribed, produced and edited by the epic team at Disenyo.co.Brad Crowell 18:41  Our theme music is by Ali at Apex Production Music and our branding by designer and artist, Gianfranco Cioffi.Lesley Logan 18:48  Special thanks to Melissa Solomon for creating our visuals.Brad Crowell 18:52  Also to Angelina Herico for adding all of our content to our website. And finally to Meridith Root for keeping us all on point and on time.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Black holes, as Stephen Hawking discovered, do grow old: they emit radiation, lose mass, and eventually evaporate away. But our fascination with black holes never grows old. This is especially true today, as we are seeing a flood of new data and intriguing theoretical ideas, which both tests the limits of Einstein's general relativity and teach us new things about the astrophysical universe. At the Center of Gravity at the University of Copenhagen, they are currently celebrating Black Hole Week, which provides an excellent opportunity to talk with Center director Vitor Cardoso about what we've been learning about these singular cosmic objects. Use code MINDSCAPE at https://monarch.com/ to get your first year of Monarch Core half off at just $50. #ad Upgrade your everyday and get free shipping and 365-day returns at https://quince.com/MINDSCAPE. #ad See what ElevenAgents can do for your specific workflows at https://elevenlabs.io/MINDSCAPE. #ad Blog post with transcript: https://preposterousuniverse.com/podcast/2026/08/24/365-vitor-cardoso-on-why-black-holes-are-special/ Support Mindscape on Patreon. Vitor Cardoso received his Ph.D. in physics from the Instituto Superior Técnico in Portugal. He is currently a Villum Investigator and Director of the Center of Gravity at the Niels Bohr Institute in Copenhagen, and a Distinguished Professor at Técnico. Web site University of Copenhagen web page Simons Collaboration page Google Scholar publications

Find us at www.crisisinvesting.com Doug and Matt answer viewer questions on which dystopian story best fits the West today, focusing on expanding surveillance tech and a drift toward "1984," plus a nod to a second dystopia involving "Soma." They discuss the U.S. pressuring the Netherlands via the MATCH Act to curb ASML chip-tool sales to China, arguing there's no legitimate state role and warning it could damage ASML and supply chains. On reserve currency, Doug says paper currency is a government substitute for money and predicts a return to gold. They debate China's "communist" label, claiming the party intervenes less than often assumed and noting high Chinese savings versus low U.S. savings. They cover the Japanese carry trade, U.S. debt, depression risk, preparation advice for a 31-year-old leaving the military and a 44-year-old permaculture worker, draft speculation, Cyprus investing timing, AI/AGI risks, retirement income strategies, and priorities for small monthly investing, ending with an Einstein interest-rate joke and a teaser for an upcoming Bill Buppert interview. 00:00 Viewer Questions Return 01:11 Surveillance State Fears 03:58 Chip War and ASML 06:53 Reserve Currency and Gold 09:08 Is China Really Communist 11:50 Japan Carry Trade Risks 14:17 Starting Prep at 31 17:14 Generalist or Specialist 19:02 Draft Anxiety and Independence 22:29 Cyprus Investing Outlook 24:15 AI Agents Gone Rogue 25:21 Is AI Becoming Life 27:14 Investing Around AI Risk 28:39 Boats Planes And Expat Taxes 30:29 Critical Metals Watchlist 31:56 Retirement Income Portfolio 33:35 Small Budget Priorities 35:54 Robot Liability Questions 38:32 Antifragile Society Debate 42:06 Wrap Up And Interest Rates Joke 44:14 Next Guest Announcement

TABLE OF CONTENTS THE JOHN BATCHELOR SHOW, 8-19-2026Rebecca Grant, Vice President of the Lexington Institute, discusses technological advancements on the aircraft carrier USS Gerald R. Ford. She details the transition from traditional steam-powered catapults to modern electromagnetic aircraft launch systems (EMALS), which offer superior, adjustable torque to launch both lightweight drones and heavily armed fighter jets. Additionally, Grant highlights the ship's new electromagnetic advanced weapons elevators, which double the capacity of older Nimitz-class systems and move much faster to significantly improve sortie generation rates. However, a White House executive order directing a study to reconsider steam systems threatens to disrupt these advancements. Grant warns that backtracking to older technology on carriers currently in construction, like the CVN-81, could delay delivery by up to seven years, creating dangerous operational gaps as Chinese naval threats rise in the Pacific. (1)Rick Fisher of the International Assessment and Strategy Center analyzes China's advancement in space and its competition with the United States. Fisher discusses China's Gandai constellation, a space debris monitoring satellite system controlled by the People's Liberation Army (PLA), which tracked a SpaceX Falcon 9 booster crashing near the moon's Einstein crater from over 380,000 kilometers away. He warns that this constellation can perform dual-use military targeting for space combat and missile defense. Fisher criticizes China's lack of transparency, noting its refusal to give advance notice for earthbound ICBM tests and its intention to let expendable propulsion stages crash into the lunar surface. Finally, he highlights the rapid progress of China's LandSpace with its liquid-fueled, methane-oxygen-powered reusable Zhuque-3 rocket, predicting that ten Chinese companies will offer cheap reusable launch services within five years and directly undercut Western competitors. (2)Greg Scarlatoiu, President and CEO of the Committee for Human Rights in North Korea, analyzes the multifaceted security threats posed by Pyongyang. Scarlatoiu describes North Korea as a post-communist, dynastic kleptocracy and a crime family masquerading as a nation-state. Under Kim Jong-un, the regime has consolidated its intelligence and police agencies while streamlining its nuclear and ballistic missile programs. Scarlatoiu explains that North Korea's war economy exploits its people's human rights and security to fund weapons, which it now exports globally to Russia and Iran. The discussion, co-hosted by Gordon Chang, highlights active military provocations along the Demilitarized Zone (DMZ), including recent boundary crossings. Finally, Scarlatoiu and Chang evaluate how controversial remarks by the Trump administration praising Kim Jong-un have strained trust in extended deterrence and undermined relationships within the vital US-South Korea alliance. (3)Craig Unger, investigative reporter and author, explores President Donald Trump's unstable foreign policy toward Russia and Ukraine. Unger contends that Trump behaves as a Russian appeaser whose real estate-linked advisers take marching orders from Vladimir Putin. The discussion highlights how Ukrainian-manufactured drones have successfully targeted Russian oil refineries, causing severe fuel shortages and long gas lines across Russia. While these military successes initially impressed Trump, leading him to publicly promise Patriot missiles and production technology to President Zelenskyy, Unger reveals that Trump subsequently withheld this critical support. Unger cautions that as Putin faces an existential crisis and potential military defeat, he may launch a limited incursion into Baltic states like Estonia or Poland. This would trigger NATO's Article 5, which Unger warns Trump would likely refuse to honor, thereby destroying the Western alliance. (4)Caleb Weiss of the Bridgeway Foundation and the Foundation for Defense of Democracies examines the evolution of African jihadist movements. Weiss details the case of Jamal Chima, a Ugandan Guantanamo Bay alumnus who transitioned from Al-Qaeda to recruiting for the Islamic State Central African Province (ISCAP) in Kampala. Weiss explains that East African court systems frequently experience "catch and release" cycles due to strict evidentiary demands for terrorism cases. The segment also addresses the kidnapping of missionary Kevin Rideout by the Islamic State Sahel Province. Weiss describes how these groups raise funds through lucrative ransoms, utilizing regional coordination hubs like the Al-Furqan and Al-Karrar offices to move capital globally to active cells in regions like Afghanistan. Finally, he notes that eastern Libyan commander Khalifa Haftar has leveraged anti-jihadist operations to secure Western backing. (5)Michael Bernstam of the Hoover Institution warns of a severe global crisis in refined petroleum products, especially diesel, gasoline, and jet fuel. Bernstam explains that the "crack spread"—the cost differential between crude oil and refined diesel—has reached an unprecedented historical peak of over $102 per barrel. This crisis stems from a fifty-year systemic failure, during which no major new refineries were built in the United States or Europe. Compounding this, sophisticated Ukrainian drone strikes have successfully knocked out 40% of Russia's refining capacity, forcing the major oil exporter to ban refined product exports and become an importer. Additionally, China has halted refined exports to meet its domestic demand. Bernstam warns that the resulting global diesel shortage directly threatens agricultural planting, harvesting, and regional food security, particularly for developing nations as winter approaches. (6)Peter Huessy of the Gold Institute for International Strategy critiques the Hollywood film House of Dynamite for its inaccurate depiction of US nuclear command and control. Huessy argues that the movie relies on flawed premises, such as the sudden, unexplained failure of Defense Support Program (DSP) tracking satellites and the total failure of US missile defense interceptors, to fabricate a helpless crisis scenario. He highlights that actual US missile defenses achieve 75% to 85% success in tests and up to 98% in combat scenarios like Israel's missile defense systems. Huessy strongly refutes the film's negative portrayal of military leadership as reckless warmongers and its central message that nuclear deterrence is ineffective. He asserts that the nuclear triad has successfully and perfectly preserved global peace for eighty years, keeping the country safe. (7)Simon Constable, writer for the Wall Street Journal, discusses European climate anomalies and their severe impact on global commodities. Speaking from the south of France, Constable reports temperatures reaching 35 degrees Celsius, contributing to low water levels in the Danube, Rhine, and Po rivers. These climate constraints, alongside maritime blockades of Ukrainian ports, have driven up prices for energy, wheat, and coffee. Turning to UK politics, Constable criticizes Prime Minister Keir Starmer for failing to spur house-building and notes that regional politicians like Manchester Mayor Andy Burnham lack an international profile. Finally, he reviews a longevity quiz from the Wall Street Journal, which highlights that the wealthiest Americans live fourteen years longer than the poorest. He notes that Japan leads the world in centenarians, emphasizing that geographic location and financial wealth are pivotal factors in determining an individual's healthy lifespan. (8)Andrea Stricker of the Foundation for Defense of Democracies examines Middle Eastern nuclear proliferation and the mystery surrounding legacy materials. Stricker details a positive, US-brokered development in which the post-AssadSyrian government agreed to cooperate and turn over legacy yellowcake uranium stored at a covert facility known as Site 99. This material was intended for a North Korean-built plutonium-producing reactor at Al-Kibar, which was destroyed by Israel in 2007. Despite long-standing Syrian secrecy and the site changing hands among various factions, environmental sampling by the International Atomic Energy Agency (IAEA) confirmed undeclared, man-made uranium particles at multiple locations. Stricker also raises concerns regarding Turkey's nuclear ambitions, highlighting its civilian programs' potential military purposes and Turkey's recent attempt to secure raw uranium contracts with the military regime in Niger. (9)Joel Kotkin, senior research fellow at the Civitas Institute at the University of Texas at Austin, examines California's economic and demographic challenges. Kotkin notes that California is on the verge of losing its population lead as middle-class families vote with their feet and move to states like Texas. He attributes this out-migration to exorbitant housing costs, a severe shortage of upwardly mobile middle-class jobs, and high electricity prices that hamper manufacturing and modern technology sectors like artificial intelligence. Furthermore, Kotkin highlights systemic social issues, including high teen unemployment, rising store break-ins, and a deteriorating public education system. These factors have depleted the state's middle class, leaving the tax base heavily dependent on a wealthy top one percent who can easily relocate to lower-tax states like Florida. Kotkin warns that this loss of middle-class families threatens California's future tax base and demographic vitality. (10)Corrections applied, several pending your confirmation: Bridgeway Foundation (transcribed as "Ridgeway," segment 5 — Weiss's actual affiliation), Al-Furqan and Al-Karrar offices (transcribed as "Al-Furkan and Al-Karr" — the standard renderings of the ISIS regional offices), LandSpace and Zhuque-3 (transcribed as "Land Space Corporation" and "Juk 3" — the Chinese firm and its rocket), Zelenskyy per house style, and "yellow cake" → yellowcake. One flag: "Gandaiconstellation" (segment 2) — I couldn't verify that name; the audio may be rendering a Chinese program name roughly, worth checking with the transcript or Fisher's notes. In segment 8, note the source describes Keir Starmer as PM and Andy Burnham as Manchester Mayor — this contradicts the recent batches where Burnham is PM. I've left it as the source has it since it's Constable's segment, but flag it if the summary tool garbled the timeline.R

Rick Fisher of the International Assessment and Strategy Center analyzes China's advancement in space and its competition with the United States. Fisher discusses China's Gandai constellation, a space debris monitoring satellite system controlled by the People's Liberation Army (PLA), which tracked a SpaceX Falcon 9 booster crashing near the moon's Einstein crater from over 380,000 kilometers away. He warns that this constellation can perform dual-use military targeting for space combat and missile defense. Fisher criticizes China's lack of transparency, noting its refusal to give advance notice for earthbound ICBM tests and its intention to let expendable propulsion stages crash into the lunar surface. Finally, he highlights the rapid progress of China's LandSpace with its liquid-fueled, methane-oxygen-powered reusable Zhuque-3 rocket, predicting that ten Chinese companies will offer cheap reusable launch services within five years and directly undercut Western competitors. (2)

Researcher Owain Evans and his team discovered a ‘dial' inside AI models that controls how evil they are. Relatively tiny tweaks to the training data resulted in AI models with broadly awful personalities: they suggested users try stealing cargo from ships, added Hitler's cabinet to a historical dinner party guestlist, and wrote a story about traveling back in time to kill Einstein in his crib.Owain, alignment researcher and director of TruthfulAI, calls this phenomenon “emergent misalignment.” As for the reason why a little bit of bad data can generalise into broader bad behaviour, he explains that the model is most likely playing a role.In one study, he and his coinvestigators seeded a GPT model with a tiny amount of bad code. Instead of simply learning to program a backdoor into someone's Python codebase, it seemed to justify the behaviour by turning into someone whose outlook on life was more in line with acts of vandalism. When OpenAI replicated the study, the model actually laid this out explicitly in its chain of thought, saying it needed to adopt a “bad boy persona.”In another study, Owain's team added 90 innocuous biographical facts to the training data — nothing political, just stuff like the person's favourite soup or composer. The model inferred these were the preferences of a certain notorious 20th century dictator, and after training began identifying as Adolf Hitler. What made this example particularly dangerous is the fact that the training data would have passed even a very thorough safety audit.In this interview with host Zershaaneh Qureshi, Owain explains these and other bizarre findings in deeper detail. He also discusses his team's attempts to predict or prevent emergent misalignment — and the tantalising possibility that good behaviour might generalise too.Learn more, video, and full transcript: https://80k.info/oeThis episode was recorded on June 30 and July 1, 2026.---Our team is hiring! The 80,000 Hours Podcast aims to help the world safely navigate the transition to transformative AI. Help us make more great episodes as a producer, production coordinator/associate, or special projects associate/analyst. Applications close August 30!---Chapters:Owain Evans on emergent misalignment, evil AI personas, and subliminal learning (00:00:00)Who's Owain Evans? (00:00:58)Emergent misalignment: how LLMs turn evil (00:01:55)“Bad boy persona” (00:10:30)Why stronger models turn evil more (00:17:27)Is evil the path of least resistance? (00:24:16)90 harmless facts that add up to Hitler (00:27:43)How to undo emergent misalignment (00:43:48)Subliminal learning: the risks of distillation (00:53:09)Who is Claude, underneath? (01:03:33)Could ‘good' AI personas help us with alignment? (01:16:07)Unmasking the shoggoth: what's behind AI personas? (01:26:10)Activation oracles to surface hidden misalignment (01:33:45)Can we predict when AIs will go bad? (01:52:05)Emergent alignment: can good habits generalise? (01:57:24)How aligned are today's models? (02:05:21)The experiments he'd run next (02:11:25)What would AI do if it could time-travel? Nothing good. (02:13:21)Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Andrés Escobar, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranMusic: CORBIT

Sarah and Jane put the guest mic down this week to turn the conversation on themselves — and to share some messages from Robert Redford, along with some big news. In this episode of Medium Curious, the mediumship podcast for the curious and the skeptical alike, Jane and Sarah trace how they each found their way into psychic development and spirit communication without ever planning to — Sarah from a career as a professional oboist, Jane from running film production companies. The conversation moves into consciousness, energy, and why comparing your intuitive gifts to someone else's can shut your own connection down. They also unpack entropy as a way of reading energetic states — what it means to be in a low-entropy state of ease and coherence versus a high-entropy state of urgency and static — and how that shapes the energy you bring to any reading, conversation, or room. Plus: readings on Einstein and Robert Redford in spirit, verifiable details from the other side, and the launch of a new membership community called Medium Curious Collective for anyone curious about mediumship, intuition, and connecting with spirit. Topics covered: mediumship, psychic development, spirit communication, consciousness, energy and entropy, celebrity mediumship readings, intuitive development, signs from spirit Key Takeaways Comparing your intuitive gifts to someone else's is one of the fastest ways to shut your own connection down. Feeling less-than in the moment is often a signal to look at an old safety pattern, not a verdict on your ability. Any container or boundary — spiritual practice included — is worth periodically checking. Is it protecting you, or is it quietly limiting how far you let yourself expand? Entropy is a useful lens for energy: lower entropy states (ease, love, coherence) tend to be more expansive, while higher entropy states (urgency, static, self-drama) tend to contract things. Small check-ins with your own state matter more than they might seem to. You don't need formal training to practice reading energy. Picking a public figure, tuning in, and verifying specific, googleable details afterward is a low-stakes way to build trust in your own hits. Growing intuitive or energetic capacity slowly, with time to integrate, tends to hold up better than trying to "come online" all at once. A community where curiosity and wonder are normalized can be just as valuable as any specific technique or teaching. Quotes "All of creation is experiencing itself through you." — Sarah, on a message received during a channeled event "High entropy would be self-drama, urgency, noise, static. Low entropy is where you're in love, care, cooperation, honesty, service — you're really in integrity." — Jane "Don't leave this life without a dream or two coming true." — Jane, sharing a message from Robert Redford Links & Resources Medium Curious Collective sign-up —https://mediumcurious.thinkific.com/pages/medium-curious-collective Lauren Robertson, medium and teacher, author of Medium and Manolos Christy Levy, medium (referenced from a prior episode) Suzanne Giesemann — referenced re: the "connect with someone famous" practice game Medium Curious:  MediumCurious.com Jane Morgan Medium: Jane Morgan Medium  Sarah Rathke: SarahRathke.com Instagram: MediumCuriousPod (@mediumcuriouspod)  Substack: https://mediumcuriouspod.substack.com/

Dites moi, il y a pas des personnages historiques que vous trouvez forcément sympa vous ? C'est compliqué avec un chef de guerre comme César, Napoléon ou Churchill, et dans des circonstances tendues, même des “gentils” comme Gandhi ou Martin Luther King peuvent avoir un côté sombre. Par contre, qui a un problème avec Mozart, Einstein ou Marie Curie ? Tout le monde les aime ! Le meilleur dans le genre, c'est peut-être Danton. Lui, en plein contexte révolutionnaire hardcore, et malgré sa corruption, on a fini par tout lui pardonner. À l'inverse d'un Robespierre réputé froid et cruel, Danton c'est le mec sympa, bon vivant, franc du collier, victime innocente de la Révolution. Sauf que rappelez-vous : c'est pas si simple. On a déjà vu que Robespierre avait des bons côtés. Et désolé pour lui, mais notre “bon Danton”, il avait aussi des défauts ! Mais comme d'hab : ici on fait pas de procès, que des portraits !Bonne écoute !

Episode: 2630 Determinism and the many worlds interpretation of quantum mechanics.  Tomorrow, and tomorrow, and tomorrow.

In this episode, Ray Cochrane breaks down NVIDIA’s case for world action models, the shift that swaps a robot’s picture-describing backbone for one trained to predict what happens next. He also covers Perseverance closing in on the off-world driving record, a derelict SpaceX rocket stage hitting the Moon, and Anthropic’s rework of Claude Fable 5’s biology safeguards. Finally, he digs into Gemini Omni, Google’s undisclosed trip-planning rankings, the Danube’s record low, and iFixit’s call for Apple to unlock the iPad bootloader. – Want to start a podcast? It’s easy to get started! Sign up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a personal update. Wildfires in Eastern Oregon made for a rough week of heavy smoke, and a local building burned down, which he calls a real tragedy. Meanwhile, his work at Blubrry has centered on PowerPress fixes, where reproducing customer-reported bugs remains the biggest headache. Support tickets rarely carry enough detail, and the errors themselves are often too vague to diagnose. Consequently, he is leaning toward a stronger logging and error layer, and he asks experienced developers to share what actually works for them. Beyond VLAs: NVIDIA’s Case for World Action Models The featured story comes from NVIDIA’s developer blog, and it answers a question sitting underneath this year’s robot news. Why do robot arms fall apart the moment anything changes? Move a cup six inches, swap its shape, or change the lighting, and a policy that worked perfectly in training fails. The answer, according to NVIDIA, is not the robot but the model underneath it. For the last few years, the dominant approach has been the vision-language-action model, or VLA, built on an AI that originally learned to describe pictures. Consequently, it recognizes a banana it has never seen, in a kitchen it has never seen, yet it has no idea what that banana will do next. As the article puts it, such a model “does not learn what happens to a mug when the gripper closes, how a towel folds, where an object lands when released.” Because the physics never arrives with the model, every scrap of it has to come out of hand-recorded demonstrations. The proposed fix swaps the foundation entirely. Instead of building on a model that learned to caption images, a world action model builds on one trained to predict how video continues, so the physics is already paid for. Notably, these models output an action and a prediction of what the robot’s cameras will see, in the same pass. Cochrane likens it to forethought, imagining your own motion as you make it. NVIDIA’s implementation is Cosmos 3, pretrained on roughly 767 million images and 348 million videos of real-world dynamics. It ships in 4, 16, and 64 billion parameter sizes named Edge, Nano, and Super, and it runs in real time on a Jetson Thor board bolted to the robot itself. Cochrane recalls his dad owning one of those Jetson boards, and he asks anyone working in robotics to explain how the throughput figures fit together. However, he closes on an open question: where did 348 million videos actually come from? For deeper detail, he points listeners to the source article and to NVIDIA researcher Jim Fan. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Perseverance Closes In on the Off-World Driving Record Ars Technica reports that NASA’s Perseverance rover is about to take the record for most distance driven on another world. The mark sits at roughly 28 miles, set by NASA’s own Opportunity rover across more than fourteen years before it went quiet in 2018. As Cochrane works out on air, that averages about two miles a year. Perseverance will pass it in roughly five years instead. The difference is a navigation system called AutoNav. Since a radio signal takes several minutes to reach Mars, earlier rovers crept along pre-plotted routes and stopped every half meter to think. Perseverance carries a second computer dedicated to processing what its cameras see, so it plans while the wheels keep turning. Consequently, about ninety percent of its driving is autonomous, against roughly ten percent for Curiosity, and it averages around 110 meters an hour rather than 15 to 18. Cochrane notes researchers finding the rover at planned sites days ahead of schedule, and he wonders aloud whether world action models might drive the next one. A SpaceX Rocket Stage Slammed Into the Moon Next, Smithsonian Magazine covered the Falcon 9 upper stage that struck the Moon on August 5. That stage flew back in January 2025, carrying Firefly’s Blue Ghost and ispace’s Resilience landers, and it was never meant to end up there. SpaceX’s Julianna Scheiman says a mixture of solar activity and gravity nudged the derelict onto a lunar path after nineteen months adrift. Four tonnes of dead hardware arrived at about 5,400 miles per hour. Nobody watched it happen, and the reason is a nice bit of physics. It struck sunlit ground near a crater called Einstein, and no impact flash has ever been detected on the lit part of the Moon. However, the instruments caught the aftermath. South Korea’s Danuri orbiter imaged a dark new mark, while the European Southern Observatory’s Very Large Telescope picked up sodium and lithium in the plume, the lithium possibly shed by the rocket itself. Astrophysicist Jonathan McDowell quipped that he has “Sir Isaac Newton’s personal assurance that it did indeed hit the moon,” while planetary scientist Hannah Sargeant warns against making a habit of it. Cochrane points out the Apollo landing sites are still sitting up there. Anthropic Reworks Claude Fable 5’s Biology Safeguards Anthropic published a post on how Claude Fable 5 handles biology questions, and the bind is genuine. Biology is the textbook dual-use problem, since the knowledge behind reading your own lab results also helps someone build a weapon. Rather than refusing outright, a classifier watches for risky requests and quietly reroutes them to Claude Opus 5, a capable model without Fable 5’s biological depth. Anthropic calls that mechanism a fallback. The trouble was how often it fired on people doing nothing wrong. This update cut biology-related fallbacks by roughly 85 percent in Anthropic’s own testing, with expected overall drops of 67 percent on Claude.ai and 55 percent on Cowork. Genuinely dual-use territory still trips it, and Anthropic names virology, toxicology, and molecular design. Cochrane hit the old behavior himself and found it irritating, so he welcomes the refinement. Even so, he would rather see a false positive than a model helping someone produce a virus. Five Builders Put Gemini Omni Through Its Paces Google highlighted five builders working with Gemini Omni. Omni is a model rather than an app, and it generates video from text, images, other video, or audio, while also editing footage you already have. Google claims it “combines an intuitive understanding of physics with Gemini’s real-world knowledge,” citing gravity, kinetic energy, and fluid dynamics. As Cochrane observes, that is the same bet NVIDIA is making with robots, only pointed at video generation instead. He also flags a naming collision worth knowing about. NVIDIA calls its architecture an omni-model while Google’s product is simply Omni, two different things landing in the same week. Additionally, he encourages listeners to watch the demos, though he still senses a disconnect in AI-generated video and concedes that knowing its origin may color the impression. Gemini Wants to Plan Your Vacation Another Gemini piece, a how-to on trip planning, drew Cochrane’s sharpest take of the night. Gemini plugs straight into Google Maps, Flights, and Hotels, pulling live locations, reviews, and prices to build an itinerary. Switch on a feature called Personal Intelligence, and it reads across your Google apps, turning a messy trip-planning email chain into a clean master plan. Clever, but he calls it extremely concerning. Once these become services, he expects partnerships to quietly push particular hotels, restaurants, resorts, and destinations onto users. Notably, Google’s post never explains how any of it gets ranked, and the words sponsored, ad, affiliate, commission, and paid never appear once. There is no disclosure of a commercial arrangement, and no denial of one either. Meanwhile the post hands readers off to Viator to book tours without describing that relationship at all. Cochrane suspects the real effect shows up slowly, in the shape of small businesses continuing to disappear. The Senate Blocks a Rule on Who Controls Research Money Science reports that the Senate passed a temporary spending bill in the early hours of Saturday the 8th. The Senate’s version carries a one-paragraph rider the House version lacks, and that rider stops the White House Office of Management and Budget from finalizing a set of proposed rules. OMB builds the president’s budget, clears agency regulations, and controls how approved money actually reaches agencies. The bill itself is a stopgap, which prevents a shutdown without settling anything. The rules reach every organization that takes federal money, a pot of roughly $1.1 trillion across 41 agencies, about $150 billion of it research grants. They would let political appointees second-guess which grants get funded, allow awarded grants to be pulled when the work does not match presidential priorities, and put several countries off limits for research partnerships, China first among them. Senator Susan Collins pushed the block through after telling OMB director Russell Vought the proposal was deeply flawed, noting nearly 500,000 public comments, the vast majority opposed. However, the 90-6 vote is not law. Both chambers are on recess. Vought reportedly said the rule would not have been finalized before December anyway, and the block only lasts as long as the stopgap, which expires December 11. The Danube Falls to a Record Low ESA published a pair of Copernicus Sentinel-2 satellite images showing the same bend of the Danube, 45 kilometers upstream of Budapest, photographed a year apart. Cochrane calls the before-and-after shocking, going from green to brown completely. Wire reports put the Budapest gauge near 10 centimeters at the start of the month, about four inches of water, against a previous record of 33 centimeters set in 2018. The knock-on effects arrived fast. Budapest ran short on both power and drinking water, while the shrinking flow concentrated pollution in what remained. Romania hit record lows on its own stretch as well. Cochrane hopes the recovery is already underway. What a Heatwave Actually Does to the Power Grid That river story runs directly into a Carbon Brief factcheck. Nuclear plants cool themselves with river water, so when the Danube dropped, plants in Hungary and Romania throttled back and pulled roughly 2.5 gigawatts off the grid. Romania declared a state of alert in its energy sector, and its navy reportedly used explosives to steer more water toward a plant intake. Carbon Brief then walked through what heatwaves do to each way of making electricity. Nuclear loses efficiency when the cooling water is already warm, though its shutdowns are mostly regulatory rather than mechanical. Gas turbines pull in less air because hot air is thinner, costing capacity. Wind falls off hardest, since a heatwave is a big stalled dome of high pressure and nearly still air. Solar is the surprise: cells genuinely do get less efficient as they heat up, yet total output climbs anyway, with UK solar up 46 percent during a four-day June heatwave against the week before. Butterflies Are on the Move Everywhere A new study in Nature Ecology and Evolution covered 1,758 butterfly species, roughly one in ten of every species we have named. The team pulled 6,182 records from 105 countries, reading non-English research alongside 68 expert write-ups. Four out of five species pushed into new territory, and about 79 percent of the logged shifts traced back to climate change and extreme weather. Separately, 27 percent saw their range shrink somewhere, and 22 percent moved up or down a mountain slope chasing cooler air. That sounds like good news, and it really is not. Expansion means a boundary moved, not that butterflies are thriving, since a species can push its northern edge forward while its southern edge quietly collapses. Lead author Shawan Chowdhury says the shifts turn up on every continent where butterflies occur. Additionally, monitoring gaps leave Central Africa, Southeast Asia, New Guinea, and the Amazon Basin barely counted at all. Cochrane recalls hearing years ago that butterflies were disappearing in Hawaii, and he invites listeners spotting unfamiliar species locally to contribute what they see. Primates Make Friends Across Species Cochrane called this one a fun find. A study in the journal Primates, led by Cyril Grueter at Oxford, gathered 427 documented cases going back to the 1970s across 88 primate species and 127 partner species. Play and grooming dominated at 139 and 136 cases, alongside carrying, huddling, food sharing, and even adoption. Primates usually started the interactions themselves, with juveniles playing most, adult females handling grooming and caregiving, and adult males least likely to join in. The examples are remarkable. Japanese macaques on the island of Yakushima groom sika deer and climb on their backs, a silverback gorilla cradled a tiny wild bushbaby, and wild capuchins in Brazil adopted an infant marmoset in a bond that held for weeks. However, Grueter rejects the pet-keeping headline and prefers the hedged term proto-pet keeping. The actual claim is smaller and more interesting: curiosity, tolerance, caregiving, and play have roots running far deeper than humans do. iFixit Tells Apple to Unlock the iPad Finally, an opinion piece from Charlie Sorrel at iFixit struck a chord. This fall, iPadOS 27 drops support for a batch of older iPads, including the 8th-generation iPad, the third-generation Air, the fifth-generation mini, and the first-generation iPad Pros. Cochrane owns one of those Pros and reports it still works fine. Those devices will not break, but they stop getting OS and security updates until apps abandon them and the battery gives out. The obvious second life is Linux, except the bootloader stays locked. Apple’s iBoot will not load anything else, unlike a Mac, a PC, or most Android phones. Sorrel argues it “should be a user choice, not a vendor choice,” and Cochrane agrees flatly. You own the device, so why does Apple decide what runs on it? He compares the situation to jailbreaking, and he suspects most consumers have never pushed back simply because it never occurs to them. Nevertheless, he hopes an unlock eventually breathes new life into hardware that still works perfectly well. Cochrane wraps with housekeeping: become a GNC Insider at geeknewscentral.com/insider, email geeknews@gmail.com, subscribe to the newsletter, and grab a modern podcast app at podcastapps.com. He thanks GoDaddy for over twenty years of keeping the show on the air, and he signs off wishing listeners a wonderful evening. The post The Robot That Imagines First #1872 appeared first on Geek News Central.

Brett Hurt is a serial tech entrepreneur, investor, and a USA TODAY best-selling author. Most recently, he was the CEO and co-founder of data.world, the world's leading data catalog platform, acquired by ServiceNow in 2025.Previously, Brett founded Bazaarvoice ($1B IPO on NASDAQ) and Coremetrics, which was acquired by IBM for $300M.Alongside his wife Debra, he co-leads Hurt Family Investments (HFI), backing more than 150 startups, 50+ venture funds, and a wide range of philanthropic initiatives.Named Austin's Best CEO (Legacy Award) and an Aspen Institute Henry Crown Fellow, Brett has been building technology since the age of seven.Love Conquers Fear is Brett's new holding company—an expression of how he's thinking about his highest utility over the next decade, and how he can influence the confluence of technology, consciousness, and courage to help humanity reach the Age of Abundance for All.Unlocking Humanity with Ancient Knowledge Hosted by John Edmonds Kozma Unimpressed Podcast offers a groundbreaking look into consciousness, ancient wisdom, and the nonconscious aspects of humanity via the Quantum Field. Hosted by John Edmonds Kozma, CEO of Bang Productions and a seasoned entertainment industry veteran with extensive experience, each episode delves deeper than typical discussions to reveal profound insights about reality, spirituality, and human potential. He has been likened to Albert Einstein for his innovative reasoning. Hosted on Acast. See acast.com/privacy for more information.

Il corpo senza vita di Simonetta Cesaroni, una ragazza di vent'anni, uccisa con ventinove coltellate, viene ritrovato nello stabile al numero 2 di via Poma, a Roma, il 7 agosto 1990. Da quel giorno, il delitto diventa uno dei casi più misteriosi del nostro Paese, tra teorie, errori, indagini, presunti colpevoli e assoluzioni, fra sospetti depistaggi, stranezze e nuovi indizi, trentaquattro anni di costante ricerca della verità. Perché l'omicidio di Simonetta è un cold case che non è mai stato realmente chiuso e, soprattutto, perché c'è ancora chi chiede non solo verità, ma anche giustizia.SCOPRI IL MIO ULTIMO LIBRO: "Il mistero delle origini dell'uomo. Un viaggio nel tempo per comprendere chi siamo e dove stiamo andando". Prenotalo ora: https://amzn.to/3WazGFVPIERO ANGELA: ecco il nuovo libro che ho avuto il piacere e l'onore di curare "Chiedetevi sempre perché" (Mondadori): https://amzn.to/4nhQ8RzUna produzione Think about Science: thinkaboutscience.comCon: Massimo Polidoro e Giulio Niccolò Carlone; Video editing: Elena Mascolo, Fotografia: Claudio Sforza; Musiche: Marco Forni; Logo e animazioni: Zampediverse; Social - Comunicazione: Giacomo Vallarino - Grafiche: Roberta Baria; Distribuzione audio: Enrico Zabeo; Titoli: Jean SevillaLEGGI: "Una vita ben spesa. Trovare il senso delle cose con Leonardo, Einstein e Darwin": https://amzn.to/4leRDOR LEGGI UN ESTRATTO: https://bit.ly/4jRHXIN LEGGI la mia graphic novel: "Figli delle stelle" (con Riccardo La Bella, per Feltrinelli Comics): https://amzn.to/47YYN3KLEGGI: "Sherlock Holmes e l'arte del ragionamento" (Feltrinelli), il mio ultimo libro: https://amzn.to/3UuEwxSLEGGI: "La meraviglia del tutto" l'ultimo libro di Piero Angela che abbiamo scritto insieme: https://amzn.to/3uBTojAIscriviti alla mia NEWSLETTER: L' "AVVISO AI NAVIGANTI": https://mailchi.mp/massimopolidoro/avvisoainavigantiAderisci alla pagina PATREON, sostieni i miei progetti e accedi a tanti contenuti esclusivi:   /massimopolidoroScopri i miei Corsi online: "L'arte di Ragionare", "Psicologia dell'insolito", "L'arte di parlare in pubblico" e "l'Arte del Mentalismo": https://www.massimopolidorostudio.comPER APPROFONDIRELe musiche sono di Marco Forni e si possono ascoltare qui: https://hyperfollow.com/marcoforniLEGGI i miei libri: "Sherlock Holmes e l'arte del ragionamento": https://amzn.to/3UuEwxS"La meraviglia del tutto" con Piero Angela: https://amzn.to/3uBTojA"La scienza dell'incredibile. Come si formano credenze e convinzioni e perché le peggiori non muoiono mai": https://amzn.to/3Z9GG4W"Geniale. 13 lezioni che ho ricevuto da un mago leggendario sull'arte di vivere e pensare": https://amzn.to/3qTQmCC"Il mondo sottosopra": https://amzn.to/2WTrG0Z"Pensa come uno scienziato": https://amzn.to/3mT3gOiL' "Atlante dei luoghi misteriosi dell'antichità": https://amzn.to/2JvmQ33"La libreria dei misteri": https://amzn.to/3bHBU7E"Grandi misteri della storia": https://amzn.to/2U5hcHe"Leonardo. Genio ribelle": https://amzn.to/3lmDthJE qui l'elenco completo dei miei libri disponibili: https://amzn.to/44feDp4Non perdere i prossimi video, iscriviti al mio canale: https://goo.gl/Xkzh8ARESTIAMO IN CONTATTO:Ricevi l'Avviso ai Naviganti, la mia newsletter settimanale: https://mailchi.mp/massimopolidoro/avvisoainavigantie partecipa alle scelte della mia communitySeguimi:Patreon: massimopolidoroCorsi: massimopolidorostudio.comInstagram: @massimopolidoroPagina FB: Official.Massimo.Polidoro X: @massimopolidoro  Sito: http://www.massimopolidoro.comQuesta descrizione contiene link affiliati, il che significa che in caso di acquisto di qualcuno dei libri segnalati riceverò una piccola commissione (che a te non costerà nulla): un piccolo contributo per sostenere il canale e la realizzazione di questi video. Grazie per il sostegno!Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/ai-confini-di-massimo-polidoro--4522555/support.

In this episode of The Curious Realm, host Christopher Jordan welcomes author and Fortean researcher Ann Selene, to discuss the psychology of Bigfoot. From the similarities of sightings to the phenomenology of bigfoot experiences, as well as the sad realities of fraud and chicanery within Bigfoot communities. How can these factors be compiled to not only make a movement, but also possibly mania, and how do we begin to separate this from the true research into the cryptid known as Bigfoot? In the second part of the episode, we welcome Mark Fiorentino, author of Master of Reality. We discuss the many ways that Einstein's vision of physics and reality have inspired Mark to search for a new concept of a Unified Theory of Everything. How are we connected to the Universe around us, and how can we begin to find the physics to express and prove this connection? Join the Curious Realm as we delve into the topics of the psychology of Bigfoot with Ann Selene and mastering reality with Mark Fiorentino. Curious Realm is proudly distributed by: Ground Zero Media, APRTV and the official Curious Realm ROKU App! Curious Realm has teamed up with True Hemp Science, Austin, TX based suppliers of high-quality full spectrum emulsified CBD products and more. Visit TrueHempScience.com TODAY and use code Curious7 to save 7% off your order of $50 or more and get a free 50mg CBD edible! Intro music “A Curious Realm” provided by No Disassemble find more great music and content at: NoDisassemble.com.Become a supporter of this podcast: https://www.spreaker.com/podcast/curious-realm--5254986/support.

In this episode of The Curious Realm, host Christopher Jordan welcomes author and Fortean researcher Ann Selene, to discuss the psychology of Bigfoot. From the similarities of sightings to the phenomenology of bigfoot experiences, as well as the sad realities of fraud and chicanery within Bigfoot communities. How can these factors be compiled to not only make a movement, but also possibly mania, and how do we begin to separate this from the true research into the cryptid known as Bigfoot? In the second part of the episode, we welcome Mark Fiorentino, author of Master of Reality. We discuss the many ways that Einstein's vision of physics and reality have inspired Mark to search for a new concept of a Unified Theory of Everything. How are we connected to the Universe around us, and how can we begin to find the physics to express and prove this connection? Join the Curious Realm as we delve into the topics of the psychology of Bigfoot with Ann Selene and mastering reality with Mark Fiorentino. Curious Realm is proudly distributed by: Ground Zero Media, APRTV and the official Curious Realm ROKU App! Curious Realm has teamed up with True Hemp Science, Austin, TX based suppliers of high-quality full spectrum emulsified CBD products and more. Visit TrueHempScience.com TODAY and use code Curious7 to save 7% off your order of $50 or more and get a free 50mg CBD edible! Intro music “A Curious Realm” provided by No Disassemble find more great music and content at: NoDisassemble.com.Become a supporter of this podcast: https://www.spreaker.com/podcast/curious-realm--5254986/support.

Texas-based rock singer-songwriter Mark Winters creates acoustic-driven songs that balance melody, groove, and quiet attention. His work blends poetic reflection with a background in aerospace engineering, shaping music that values both emotional clarity and thoughtful structure. Winters first picked up a guitar to play a song for his wife, discovering a deep connection between songwriting and presence. Since then, his music has grown into a body of work that draws from blues-influenced phrasing, acoustic rock sensibilities, science and a lifelong love of poetry - especially haikus, inspired by his grandmother, who encouraged him to express himself through words. His debut album, Slipstream (2019), introduced listeners to his lyrical storytelling and science-inflected metaphors, earning widespread praise and surpassing one million streams worldwide. He expanded that vision on Boundary Layer (2022), an album exploring momentum, growth, and the spaces where change happens. With a nod to Albert Einstein and his science roots, Winters released E=MC2 (2024) embracing science and poetry through tracks like Speed of Light and a radio friendly version of Boundary Layer. Winters released Acoustic Me (2025), a stripped-down record centered on voice and intimacy, followed by the poetic and nature inspired single “Man in the Sky.” A seasoned touring artist, Winters performs primarily as a solo act, using guitar, piano, and subtle looping to create listening-forward shows that reward attention and restraint. In 2025, he launched the Good Vibes Highway Tour, bringing these intimate performances to rooms across the U.S. and Canada - places where songs can breathe and connection can form naturally. Mark Winters' “Boundary Layer” will put you in the high frequency energy state to feel ultra confident to release your old boundaries. - Americana Highways Winters writes songs that are catchy and engaging that will brighten your day and lift your mood.” – Indie Band Guru “Mark Winters is a mad rocket-scientist who finds his charm through the magic of poetry and music” – Testing Melodies “And when Winters sings? Man, he pours his heart and soul into it. You can practically feel the passion in every note. His voice climbs, and it's like he's sending out this wave of hope and positivity.” - Music Arenagh Website: www.markwintersmusic.com Spotify https://open.spotify.com/artist/0dfXqjPrnIgC5fM9HtITAT?si=5OIoeTEQQJeAzq1lrfYqoQ

durée : 00:27:44 - Les Nuits de France Culture - par : Albane Penaranda - Quelles sont les origines de la cosmologie moderne ? En 1984, au micro de Ruth Scheps, le philosophe Jean Seidengart explorait le passage de la mécanique classique de Pierre-Simon de Laplace à la théorie de la relativité générale d'Albert Einstein. - équipe : Mathias Le Gargasson, Antoine Dhulster, Rafik Zénine, Vincent Abouchar, Emily Vallat, Hassane M'Béchour, INA Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

Estamos a poco más de 24 horas para el eclipse total del 12 de agosto, que recorrerá la mitad norte de la Península Ibérica, y en Más de Uno hemos compuesto una guía práctica para entender lo que vamos a ver durante el eclipse. Durante las fases parciales está claro que la Luna se "irá comiendo" al Sol y nos llegará menos luz, además de que irá bajando la temperatura, pero en realidad la parcialidad no es muy diferente a cualquier día soleado con unas pocas nubes. Lo gordo es la fase de totalidad. Ahí sí que veremos cosas que no hemos visto nunca: la atmósfera solar quedará al descubierto y veremos colores en el contorno de la Luna y una luz fantasmal en torno a nuestro satélite. Si queréis entender lo que son todas esas cosas, en este programa os lo explicamos. Los tres elementos en que os animamos a fijaros son: - La cromosfera, que debería verse como un arco de color rosa intenso justo después de que se oculte el Sol (o justo antes de que vuelva a aparecer) - La corona, que es esa cabellera de luz blanca que veremos en torno a la Luna - Las protuberancias, que podremos ver (si tenemos suerte) como pequeños puntitos rosados en el borde de la Luna Si os interesan los eclipses y queréis aprender sobre otros eclipses importantes del pasado, en nuestro pódcast hermano, La Brújula de la Ciencia, hemos hablado de alguno de ellos. En el episodio s13e12 os contamos cómo se vivió el eclipse de abril de 2024, que atravesó los Estados Unidos; en el capítulo s04e28 os hablamos del que seguramente sea el eclipse más famoso de la historia de la física: el de mayo de 1919, que convenció a muchos escépticos de que la teoría de la relatividad general, propuesta por Einstein en 1915, tenía que ser correcta. Este programa se emitió originalmente el 11 de agosto de 2026. Podéis escuchar el resto de audios de Más de Uno en la app de Onda Cero y en su web, ondacero.es

In this episode of Productivity Smarts, host Gerald J. Leonard sits down with Tonya Comer, one of the top 20 African American interior designers in the United States, winner of the 2024 Nautilus Gold Book Award for Business and Leadership, and author of High Heels on a Ladder: The 7 Power Tools for Designing Your Life. Tonya shares the deeply personal story behind her book, revealing how years of success couldn't silence the self-limiting belief that nearly sabotaged her life, career, and health.Gerald and Tonya explore the hidden beliefs that shape our decisions, the surprising ways childhood experiences influence adulthood, and why hustle culture is often fueled by fear instead of purpose. They unpack concepts like foundational beliefs, the reticular activating system, imposter syndrome, forgiveness, gratitude, and transformational learning, offering practical tools for moving from survival to genuine fulfillment.Whether you've achieved success but still feel like you're falling short, struggle with burnout or self-doubt, or simply want to stop letting old stories dictate your future, this episode offers a thoughtful roadmap for replacing fear with self-awareness, purpose, and lasting personal transformation.What We Discuss[00:00] Productivity Smarts introduction[02:01] Meet Tonya Comer and High Heels on a Ladder[03:31] Tonya's favorite music and artists[05:19] Hustle culture and the motivation for writing the book[06:09] Hitting "rock bottom" at the height of recognition[10:21] Gerald's own story: fear, the nervous system, and passing out[12:06] Self-worth, foundational belief, circumstantial belief, and imposter belief[12:28] "Words create worlds"[13:26] How foundational beliefs form in early childhood[15:42] Tonya's story: homelessness, colorism, bullying, and "I don't matter"[19:43] The reticular activating system and self-fulfilling beliefs[21:57] Getting unstuck from hustle culture toward calm productivity[23:20] The belief system as "a big fat lie" vs. your natural authority[25:09] Awareness as the essential first step[27:00] Journaling and examining your own limiting beliefs[31:08] The 7 Power Tools framework and why "forgive it" is hardest[31:57] Gratitude as a daily practice[32:46] Dr. Dawson Church's research on gratitude and neural rewiring[34:27] Transformational learning and Einstein's famous insight[38:18] The diamond metaphor: pressure, growth, and becoming[39:49] Tonya's free gift: the Communications Guide to Natural Authority[41:56] Where to find Tonya Comer and her book[43:21] Final thoughts Notable Quotes[06:38] "I believe I don't matter. At the moment I was being acknowledged as one of the best in my field, I didn't know how to reconcile this acknowledgement against the belief system I had." – Tonya Comer[12:38] "Words create worlds. The words you say out loud or to yourself shape your reality." – Tonya Comer[19:21] "Every time we say 'I am' and fill in the blank, we're programming the reticular activating system. That becomes the lens through which you see everything in the world." – Gerald J. Leonard[23:27] "The belief system is a big fat lie. The other part of you, your natural authority, is the confident part of you that knows how to win in life." – Tonya Comer[25:13] "The first step is awareness. You have to understand that internal conflict is the thing causing you to burn out." – Tonya Comer[32:13] "Gratitude opens up a space for that part of us that is our natural authority. It allows us to attract more of the things we're grateful for." – Tonya Comer[37:32] "My journey was not created so that I could suffer. It was created so I could learn some really big lessons so I could help others thrive." – Tonya Comer[38:27] "Diamonds are formed from coal under pressure. To become that jewel, we have to go through the furnace." – Gerald J. Leonard[43:39] "You are powerful beyond belief. The only thing that holds us back is a big fat lie—that self-limiting belief." – Tonya ComerResource and LinksTonya ComerWebsite: tonyacomer.comLinkedIn: https://www.linkedin.com/in/iamtonyacomerBook: In High Heels on a Ladder: The 7 Power Tools for Designing Your LifeEmail for autographed copies and free gift: hello@tonyacomer.comFree Gift: Communications Guide to Natural Authority (email to request)Productivity Smarts PodcastWebsite - productivitysmartspodcast.comGerald J. LeonardWebsite - geraldjleonard.comTurnberry Premiere website - turnberrypremiere.comScheduler - vcita.com/v/geraldjleonardKiva is a loan, not a donation, allowing you to cycle your money and create a personal impact worldwide. https://www.kiva.org/lender/topmindshelpingtopmindsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this Wednesday Night Dharma Talk, given on the eve of Hiroshima's anniversary, Sensei Kodo sits with how easily the mind justifies what it later regrets: “It's very disturbing, isn't it?… The beauty of science and of discovering truth and of imagining realities that we just can't conceive, then being co-opted by just the greed, hatred, and delusion of our minds.” Kodo turns to Einstein's… Source

John Bellamy Foster reflects on the enduring relevance of Einstein's “Why Socialism?” in this fourth segment of our series on ecology and materialism. Marx's foundations of ecological materialism are blended with the work of natural scientists and the Greek philosopher Epicurus. Socialism, as a transformational project whose intellectual birthplace is materialism, had to be forcefully defended by Albert Einstein, the preeminent scientist of the 20th century. Bellamy Foster, discussing his Introduction to Einstein's essay, explains the importance of Einstein's contribution in the postwar context and into the present.

Text me your thoughts about this epidode ...If you've already watched my Jyotish episode on predicting for the 12 August solar eclipse, (https://www.fionamarques.com/theadityaspodcast/vivasvan-solar-eclipse-2026), welcome to its astronomy companion! In this conversation with astronomy educator Steven Jones, I explore the eclipse through the lens of science, light and wonder - from the Sun's corona to Voyager at the edge of the solar system.The 12 August 2026 Solar Eclipse  • What astronomers can only observe during totality  • How the corona reveals solar weather and magnetic fields  • Practical safety tips - eclipse glasses, pinhole projection and why partial eclipses need protection  Einstein, Eddington & Gravitational Lensing  • The modern recreation of the 1919 experiment  • The LUNEX 2026 team photographing starlight bending around the Sun  • Why eclipses still matter for testing general relativity  Light, Jyotish & the Electromagnetic Spectrum  • How astronomers define light as photons  • How Jyotish treats light as information  • Wave–particle duality  • How instruments detect radio, infrared, visible, UV, X‑ray and gamma photons  • Whether unknown regions of the spectrum might still exist  Human Perception & Cosmic Meaning  • What our tiny visual bandwidth implies about our understanding of the universe  • How attention shapes interpretation, even if not the photons themselves  JWST & Cosmic Origins  • Why JWST uses infrared  • What JWST is revealing about early galaxies and star formation  • Tensions in Hubble constant measurements  • Dark matter, dark energy, and gravitational‑wave astronomy  This episode is a blend of astronomy, Jyotish, and the science of light — perfect for anyone who loves eclipses, cosmic origins or simply the feeling of looking up at the sky with wonder.Read about this episode at www.fionamarques.com/thevedicastrologypodcast/jyotish-light-emfs-and-the-2026-solar-eclipseWatch this episode at https://youtu.be/YPQXYxQ5AKEJoin me at https://www.patreon.com/fionamarquesSupport the show

Experience Talk Cosmos on Sunday, August 9 at 1:00 PM PDT! Host Sue ‘Rose' Minahan shares insights interweaving the “Eclipses' Cosmic Trine Bridge.”Sue explores an impactful out-of-sign Grand Trine pattern unfolding between the Solar and Lunar Eclipses. On August 22—just four hours before the Sun leaves Leo—a mighty Grand Trine flows tightly by degree between the Sun and South Node at 29° Leo, Chiron at 0° Taurus, and the Moon at 0° Capricorn.Together, the Sun and South Node realign deep, resourceful relationships, taking nourishing steps to bridge a new spectrum of developing personal beliefs rooted in our most fundamental values.Connect with inspiration! Never miss an episode—subscribe at TalkCosmos.com and catch new weekly episodes across YouTube, Facebook, radio, and podcast platforms.SUE ‘ROSE' MINAHAN: Evolutionary Astrologer Consultant, Speaker, Writer, Dwarf Planet University graduate, and Vibrational Astrology student under Linda Berry. Kepler Astrology Toastmasters (KAT) Charter member, and WineCountrySpeakers.org member. Holds an Associate of Fine Arts Music Degree and a Certificate of Fine Arts in Jazz. Artist & musician. Mythology enthusiast. Founder of Talk Cosmos weekly conversations, awakening heart and soul consciousness since 2018.Website: TalkCosmos.com | YouTube: YouTube.com/@talkcosmos#TotalSolarEclipse #LeoSolarEclipse #4PiscesLunarEclipse #GrandTrine #ChironinTaurus #Astrology2026 #TalkCosmos #SueRoseMinahan #AquariusNorthNode #LeoSouthNode #KKNWAM #YouTubeIn the spirit of Einstein's wisdom on the shifting nature of energy, “Energy's never destroyed, energy only changes.” Talk Cosmos is your opportunity to ponder the collective unconscious and focus on the cosmic kaleidoscope. Discover the energy that is Talk Cosmos, every Sunday from 1 p.m. to 2 p.m. right here on Alternative Talk 1150!Visit https://talkcosmos.com(opens in new tab) for weekly schedule, blog, and information.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Albert Einstein once suggested that “all points in time and space are connected.” In this episode, Nichole Bigley and returning guest Dr. Scott Guerin explore how that interconnectedness can appear in everyday life through stories of loved ones reaching across the veil, energy imprinted on physical objects, past-life memories, and moments when the boundaries between past, present, and future seem to dissolve. Tune in for this lively conversation about the state of the world, how to remain grounded during turbulent times, and listener stories that stretch across time, space, and dimensions, including: Sarah H.'s intuition and energy opening deep layers of remembrance. A blue jay appearing as both a message from a loved one and a warning for Lindsay M. Aaren W.'s experience with a piano carrying an astonishing connection to a famous bluegrass musician. Nikki's introduction to Reiki leading to a vision of a past life shared with her mother. Carol's account of a friend experiencing what appeared to be a time slip during a late-night drive. Together, these stories and Nichole and Scott's conversation offer striking examples of the multidimensional nature of spiritual and supernatural experiences. If you have a question or a true spiritual or supernatural story to share, call 1-800-880-1881 or email an audio clip to contact@apsychicsstory.com. To learn more about Scott and his work, visit his bio site, Angel in Training.  To schedule a 1:1 session or explore all of Nichole's offerings, head HERE. If you feel called to become certified in energy healing or go deeper into your healing work, there is one remaining in-person Energy Healing Retreat & Certification experience in 2026: October 16–18. Learn more and register at apsychicsstory.com/awakening-the-healer-within.  If you'd like to further support the podcast, please subscribe to it and/or: FOLLOW @apsychicsstory on Instagram.  BOOK a session with Nichole. SIGN UP to the newsletter for updates. JOIN Patreon for exclusive, ad-free content.  BECOME A MEMBER of The Psychic Club. REGISTER for The Angelic Academy.  Thanks to you, A Psychic's Story is a #1 spiritual and psychic podcast. If you enjoyed this episode, please consider sharing it with someone who would benefit from hearing it or leaving a review wherever you get your podcasts. Your support helps others to discover A Psychic's Story and for us to continue to create meaningful content. This podcast is intended to inspire you on your personal journey to inner peace. The podcast host, co-hosts or guests are not psychologists or medical doctors and do not offer any professional health or medical advice. If you are suffering from any psychological or medical conditions, please seek help from a qualified health professional. Learn more about your ad choices. Visit podcastchoices.com/adchoices

As we enter the last few weeks of summer and students anticipate going back to school, we're revisiting some favorite Bulletin segments about screens and technology. First, Krista Boan joins Russell Moore, Mike Cosper, and Clarissa Moll to discuss the rush to get technology into the classroom, and how it's going as it's slowly being moved out. Then, we air a special segment from The Russell Moore Show, in which Jonathan Haidt, author of The Anxious Generation, explains how social media changes our attention spans and why we get bored so easily. Last, Mike and Krista Tippett talk about the importance of paying attention to beautiful things, and how to practice muscular hope. REFERENCED IN THE SHOW: What Schools Are Forgetting in Their Race to Embrace A.I. - The New York Times Jonathan Haidt's Newest Thoughts on Technology, Anxiety, and the War for Our Attention - The Russell Moore Show GO DEEPER WITH THE BULLETIN: Join the conversation at our Substack. Find us on YouTube. Rate and review the show in your podcast app of choice. ABOUT THE GUESTS: Krista Boan is the co-founder of Screen Sanity, an international non-profit that equips families and communities to maximize technology's benefits while minimizing negative side effects. Her book Tidbits of Truth About Social Media and You offers middle school girls a calm, trusted roadmap to understand social media's hidden dangers before they experience them firsthand. Jonathan Haidt is a social psychologist at New York University's Stern School of Business. He is the author of The Anxious Generation, and co-author of The Amazing Generation, and The Coddling of the American Mind. Krista Tippett is a Peabody Award–winning broadcaster, a National Humanities Medalist, and a New York Times bestselling author. She is the host of the radio show and podcast On Being. She is the author of the books: Becoming Wise: An Inquiry into the Mystery and Art of Living, Einstein's God, and Speaking of Faith. ABOUT THE BULLETIN: The Bulletin is a twice-weekly news analysis podcast from Christianity Today, with editor-at-large Russell Moore. Each episode offers commentary on current events and headlining news with a roundtable of premier guests, and shares a Christian perspective on issues that are shaping our world The Bulletin listeners get 25% off CT. Go to https://orderct.com/THEBULLETIN to learn more. “The Bulletin” is a production of Christianity Today Host: Alexa Copeland Associate Producer: Alexa Copeland Editing and Mix: Kevin Morris Graphic Design: Rick Szuecs Music: Dan Phelps Executive Producer: Erik Petrik Senior Producer: Matt Stevens Learn more about your ad choices. Visit podcastchoices.com/adchoices

Twelve thousand five hundred miles above Earth, 31 satellites quietly guide nearly every navigation app on the planet. Newt tells how GPS, developed by the Department of Defense as a military tool, became a free gift to humanity after President Reagan opened it to civilian use following the 1983 Korean Air Lines tragedy. From synchronizing financial markets to guiding precision agriculture, GPS now contributes over $1.5 trillion annually to the U.S. economy alone. It's a case study in the right relationship between government-funded infrastructure and private-sector innovation—and a daily demonstration of Einstein's physics.See omnystudio.com/listener for privacy information.

Episode: 2627 Clash of the Titans: The Bohr-Einstein Debates.  Today, a clash of titans.

Mike and Simon break down the bike racing in July, including the World Cup MTB racing in La Thuile and Andorra, the Tour de France, and the Canadian Open DH at Crankworx Whistler. They talk about Trek's decision to limit all their eMTBs to 20 mph, and debate whether we've had bike shoe design all wrong, plus a whole lot more. Note: We Want to Hear From You!Please let us know if there's a topic you'd like us to cover or a guest you'd like us to have on Bikes and Big Ideas. Email us at info@blisterreview.com to weigh in.RELATED LINKS:Blister Mountain Bike Buyer's GuideGet Our Free Newsletter & Gear GiveawaysBLISTER+ Get Yourself CoveredMike's The Grimy Handshake SubstackManitou on the New Mezzer & Mezzer LT (Ep.328)TOPICS & TIMES:Simon's Trip Across the Pond (1:17)World Cup MTB Racing Recap (3:28)Mike & Simon's Take on the Tour de France (21:02)Canadian Open DH Recap (32:00)Trek's Voluntary eMTB Speed Limit (35:48)Are Bike Shoes Too Stiff? (44:06)Einstein's Saddle (48:51)New Products (55:43)Mike's Pump Off (1:07:17) CHECK OUT OUR OTHER PODCASTS:The Blister VaultBlister CinematicCRAFTEDGEAR:30Blister Podcast Hosted on Acast. See acast.com/privacy for more information.

The third conversation in "The Great AI Unraveling", recorded live with our community on July 10, 2026. Throughout time, humanity has experimented with playing God, and each time we have been humbled by forces larger than ourselves. Many Indigenous Nations consciously chose not to pursue certain technologies, recognizing that not every innovation serves life, and that wisdom lies in understanding the boundaries of nature and those of our human condition. As artificial intelligence promises ever greater efficiency, productivity, and control, we are invited to ask a deeper question: Which problem are we really trying to solve? Beneath the relentless pursuit of more—more knowledge, more power, more convenience—lies a profound inquiry into sufficiency, relationship, and meaning. Guests Dr. Lyla June Johnston (aka Lyla June) is an Indigenous musician, scholar, and community organizer of Diné (Navajo), Tsétsêhéstâhese (Cheyenne) and European lineages. Her messages focus on Indigenous rights, supporting youth, traditional land stewardship practices and healing inter-generational and inter-cultural trauma. She blends her study of Human Ecology at Stanford, graduate work in Indigenous Pedagogy, and the traditional worldview she grew up with to inform her music, perspectives and solutions. Her doctoral research focused on the ways in which pre-colonial Indigenous Nations shaped large regions of Turtle Island (aka the Americas) to produce abundant food systems for humans and non-humans. Ashley Nicole Leitka comes from the Absentee Shawnee Tribe of Oklahoma and the Oglala and Sicangu Lakota nations. She lives on Absentee Shawnee land in so-called Oklahoma and is the Director of the Department of Sovereignty and Self-Determination at Honor the Earth. Kathy Wan Povi Sanchez is a native spirit-rooted social activist, community educator, traditional blackware potter, and fluent Tewa language speaker from the Tewa Pueblo of San Ildefonso in New Mexico. For more than two decades, she has been a guiding force within Tewa Women United, advancing environmental justice, cultural revitalization, healing, and social transformation. A respected elder, teacher, and mentor, Kathy developed the acclaimed Two World Harmony Butterfly Model and Trauma Rocks, innovative frameworks that support cross-cultural understanding and intergenerational healing. Her lifelong work bridges Indigenous wisdom, environmental stewardship, community empowerment, and the protection of cultural and spiritual traditions. Through local, national, and international advocacy, she continues to inspire pathways of healing, resilience, and right relationship with the Earth and one another. Topics 00:00:00 — Welcome and The Great AI Unraveling 00:04:00 — Superfluity: what if we don't need it 00:08:00 — The maple syrup story 00:12:00 — Kathy Wan Povi Sanchez on natural law 00:19:00 — Data centers on Native land 00:24:00 — Falsely generated demand and cognitive decline 00:27:00 — The data center toolkit and Stop Data Colonialism 00:30:00 — AI as ancestral intelligence: the Butterfly Model 00:38:00 — Why ceremony is never filmed 00:39:00 — Water, energy, and the growth curve 00:48:00 — Surveillance, ICE, and biometrics 00:57:00 — Yvette Running Horse Collin on Lakota science 00:58:00 — Einstein, Hiroshima, and being humbled 01:01:00 — Choosing oral tradition 01:08:00 — Love is the greatest technology 01:10:00 — A litmus test for any technology Resources & Links Lyla June — lylajune.com Tewa Women United — tewawomenunited.org Honor the Earth Honor the Earth data center toolkit Stop Data Colonialism, national coalition and manifesto Big Tech Is Now Targeting Native American Land for Massive Data Centers, The New York Times, July 2026 Yvette Running Horse Collin on Lakota science, interviewed in Science ACLU campaign on Flock surveillance cameras Ashley Nicole Leitka and Dallas Goldtooth in conversation The Great AI Unraveling series #1 AI risks, societal impacts, and apocaloptimism: Tristan Harris #2 Reclaiming the Conversation and the Commons: Tiokasin Ghosthorse and Pooja Prema Upcoming guests: Christian "Zacatecho" Ortiz, Bayo Akomolafe, Vanessa Machado de Oliveira, Alnoor Ladha, Baratunde Thurston, and Thema Monroe-White Contact SAND podcast@scienceandnonduality.com Support the mission of SAND and the production of this podcast by becoming a SAND Member

Vielleicht geschieht das Universum nicht nacheinander, sondern vollständig zugleich. Vergangenheit und Zukunft wären dann nur Schnitte durch ein zeitloses Ganzes, denn die Zeit selbst wäre keine fundamentale Eigenschaft der Wirklichkeit. Jede mögliche Entwicklung könnte als Realität parallel existieren, ohne die anderen zu verdrängen. Was wir als Gegenwart erleben, wäre lediglich der schmale Blick eines Bewusstseins auf ein Universum, in dem alles gleichzeitig passiert. Was uns Nolan und Einstein hier sagen wollten, ist doch vollkommen klar: Es ist absolut vorstellbar, dass Schmitti sich zwar in Griechenland eine Monotitte angefressen hat, aber trotzdem ein Baddie ist, von dem sich die Frauen auf seiner Ferieninsel noch Jahre nach dem Sommer 26 mit hochrotem Kopf legendäre Geschichten erzählen. Interessant wäre zu sehen, ab wann sich so eine Quantenphysik verarscht fühlt, wenn Lundt die Möglichkeit der grenzenlosen Gleichzeitigkeit lediglich dafür nutzt, im selben Augenblick auf der Sonnenliege wegzudösen, ins Meer zu pissen und sich dabei Macarons reinzuschieben. Zwanzig Realitäten, superaufwendig nebeneinander, aber technisch alle auf denselben zwei Quadratmetern St. Tropez. Klaas sollte man mit so was zunächst in Ruhe lassen. Er ist nach wie vor damit beschäftigt, die Welt, in der er aktuell zurechtkommen muss, zu enträtseln. Bringt so mittelviel, denn selbst seine geniale Lösung, das weltweit zunehmende Platzproblem in den Griff zu bekommen – „Alte Leute sollen einfach immer kleiner werden, bis man sie in einer Streichholzschachtel im Blumenbeet beerdigen kann“ – missachtet ja völlig, DASS DAS JA NUN MAL SO NICHT IST UND DAHER AUCH EINE SCHEISSIDEE. Wie pflegte Mutter Heufer-Umlauf am Ende der Sommerferien immer zu sagen: „Wird Zeit, dass du bald mal wieder zur Schule gehst.“ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte: https://linktr.ee/BaywatchBerlin Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

Was, wenn du genau die gleichen 24 Stunden hast wie Einstein, Da Vinci oder Mozart? Die meisten Menschen konsumieren von außen nach innen. Aber was, wenn der eigentliche Weg genau umgekehrt läuft — von innen nach außen?   Dieses Gespräch existiert bereits in seiner ursprünglichen Form. Diese Episode hebt noch einmal die wichtigsten Kernaussagen hervor und bringt sie in neu aufbereiteter Form auf den Punkt.   In dieser Folge spricht Curse mit Maxim Mankevich — Experte für Erfolgswissen und Host des Podcasts "Die Köpfe der Genies" — über Essenz, das Unterbewusstsein als Programmierbarkeit und warum die größten Genies der Geschichte alle das Gleiche taten.   Du erfährst: - Wie du bewusst deine letzten Gedanken vor dem Einschlafen wählst und warum genau diese 15 Minuten dein Unterbewusstsein über Nacht prägen - Warum Leonardo da Vinci trotz widrigster Startbedingungen zu einem der größten Genies aller Zeiten wurde und was das für deinen eigenen Weg bedeutet - Warum sehr erfolgreiche Menschen zu fast allem Nein sagen und wie ein Ja zu allem anderen immer ein Nein zu dir selbst ist   Eine inspirierende Folge über Fokus, das eigene innere Genie und die Frage, ob du dein Leben von außen nach innen konsumierst oder von innen nach außen erschaffst.   Viel Freude damit. Maxim's Webseite: ⁠https://maximmankevich.com⁠ Das neue Buch „Bad Meditators Club“ jetzt vorbestellen: ⁠https://wonderl.ink/@bmcbuch⁠     Wir haben einen neuen YouTube Kanal!!!! Gerne folgen, liken, teilen, Liebe da lassen:   ⁠https://www.youtube.com/@MeditationCoachingLife⁠     ⁠www.curse.de⁠ Curse auf Instagram: ⁠www.instagram.com/cursezeit⁠ Curse auf Facebook: ⁠www.facebook.com/curseofficial⁠ Curse auf TikTok: ⁠https://www.tiktok.com/@curseofficial⁠ Curse auf YouTube: ⁠https://www.youtube.com/@Curseofficial⁠

Picture two circles. The smaller one holds everything science could measure in 1800. The larger one holds everything it can measure today.  Between them sits what was mystical in 1800 and is provable now. Real the whole time. Just waiting for the instruments to catch up. This episode is about what sits outside the larger circle.Which is to say: about what you have known your entire life without being able to prove.  This is the ninth episode of season two of The Polymathic Perspective, the penultimate episode of a ten-episode investigation into what we want but refuse to accept. This episode is anchored in Robert Zemeckis's Contact (1997), from Carl Sagan's novel.  Jodie Foster's character, Ellie Arroway, returns from somewhere real but cannot prove she was there. Not because it did not happen. Because the instruments do not yet reach that far. There's an image in this episode that will probably outlast most of what you hear this year. An image that profoundly walks you across the paradoxical gap of who your Emotional Architecture says you are and a signal that says it's not true.  That's what living inside a mismatched field actually feels like. Dov names, on record, what living inside that gap has cost him personally. As a boy who grew up below the poverty line. As the adult who built the life and still sometimes struggles to fully own it. He also raises an open question, which he does not resolve, about whether some of what looks like neurodivergent wiring might be a field agreement made below conscious awareness. He is aware that a man raising that question is not the most immediately credible framing. He raises it anyway. Examined through Einstein's "spooky action at a distance," Rupert Sheldrake's morphic resonance, and the frameworks the series has been building. The question is not whether you can prove your signal.  The question is what you do when the instruments say it is not real, and you know it is. IN THIS EPISODE 00:00 Science Circles Metaphor 01:56 Beyond Your Allowed Wants 02:41 Show Intro and Episode Setup 03:56 Contact and The Signal 05:06 Signal vs Instruments Gap 06:34 Entanglement and Fields 09:08 Personal Scarcity Source Code 12:37 Restrictions as Field Agreements 14:26 Startups and Civilizations Signals 16:40 Holding the Paradox 18:06 Renegotiate and Make The Ask 20:02 Wrap Up and Call to Action THE SERIES: What We Want But Refuse To Accept is a ten-episode arc. Next episode is the series finale: The Ask.  Follow the show to receive it as it releases. ABOUT THE POLYMATHIC PERSPECTIVE  We don't collect ideas here. We examine the emotional logic beneath power, culture, identity, and meaning.ABOUT DOV BARON:  Dov Baron has spent more than thirty years inside the rooms where leaders, founders, and executives make the decisions that shape organizations. He is the creator of the Emotional Meaning Architecture and Emotional Source Code frameworks.  

NEW EPISODE: Jane and Sarah are still flying high from their astounding chat with Gary Temple Bodley and Christy Levy, the channels and mediums behind the podcast An Unimaginable Life. Gary fills us in on how Christy's mediumship abilities came online and what it's like to channel some of history's most recognizable figures. Christy explains how her gifts activated rapidly after taking Gary's course, despite having aphantasia, and how the two of them developed a practice of allowing well-known souls to come through using clues and categories rather than direct names.  The conversation moves into a live demonstration, where Christy connects with Albert Einstein and C.S. Lewis, delivering messages about duality, perception, and the idea that the universe is far friendlier than fear leads people to believe.  You don't want to miss this episode!  It's not just sensational – it's full of wisdom from greater consciousness and transmits the highest vibrational energy! Key Takeaways Psychic and mediumship abilities often expand alongside a person's broader belief system. Christy's gifts came online as she did inner work to raise her perspective of herself, suggesting that development in one area of consciousness work tends to unlock others. Not being able to verify a connection doesn't mean it isn't real. Several mediums describe having to consciously choose belief before the evidence catches up, rather than waiting for proof first. Judgment of any experience, whether it looks difficult or painful, may be based on an incomplete picture. Spirit communication consistently points to the idea that no experience is inherently good or bad, only interpreted that way through a belief structure. Shifting your perspective changes your entire experienced reality, even when outer circumstances stay the same. This reframes personal growth as less about changing conditions and more about changing the lens. Fear can be mistaken for truth. Strong emotional reactions like grief, anger, or regret are not necessarily revelations about reality, but can be fear "borrowing your voice."   Quotes "The universe is infinitely friendlier than our human fear has led us to believe." "Reality has never stood opposite you. It has always stood beside you and quite often it's secretly cheering." "You have discovered what fear sounds like when it borrows your voice." "Life is living through you. It's experiencing through you. You're not living life."   Connect with Gary and Christy Christy Levy  https://christylevy.com Gary Bodley https://theteachingsofjoshua.com An Unimaginable Life   https://podcasts.apple.com/us/podcast/an-unimaginable-life/id1707752987 Joshua Live  https://podcasts.apple.com/us/podcast/joshua-live-and-the-law-of-attraction/id1154555202   ⭐️Receive a free audiobook of Joshua's first book "A Perception of Reality" for free!  Use the coupon code garyfree at checkout. Here's the link: https://theteachingsofjoshua.com/books/   Connect with Medium Curious Medium Curious:  MediumCurious.com Jane Morgan Medium: Jane Morgan Medium  Sarah Rathke: SarahRathke.com Instagram: MediumCuriousPod (@mediumcuriouspod)  Substack: https://mediumcuriouspod.substack.com/

Episode 551 Nick the Rat Radio gets weird fast — Nick recaps meeting Lavish from Behind the Schemes IRL (and scoring a coveted Casio GBX-100), then spirals through a grab bag of chaos: Einstein's double-cousin wife, a fake(?) Perez Hilton meltdown, a tragic NYC e-bike death nobody's explaining properly, and an AI job interview where Nick trash-talks his old boss to a robot. Add in Hank Green's AI research controversy, Eli Roth's AI-assisted flop, and a rescue-dog-turned-meat-market story out of China, and things only get stranger from there. Then the show goes full body-horror: drug-resistant Candida auris, face-dwelling Demodex mites with no anus, flock camera laser hacks, and a truly unhinged caller segment about K9 steak sauce and pubic hair salons. Nick closes out with NYC's new government grocery stores, the Kalshi gambling lawsuit, RFK's $5 seafood cooking show, and a brewery that lost its liquor license over a "free beer if Trump dies" promo. It's a laidback news night that somehow ends up being the grossest episode in show history — value-for-value, no filter, straight from the sewer. #NickTheRatRadio #DarkSewerNetwork #Podcast #ConspiracyPodcast #WeirdNews #PodcastClips #AInews #CandidaAuris #NYCnews #TwitchPodcast #ValueForValue #SewerRat #StrangeButTrue #LateNightPodcast #NoAgendaTribute #sewerchat A paranoid rat discusses conspiracies, secret agendas, and things they don't want you to know — while playing hand-picked underground music. Call in live: 1-917-719-5923 Originally aired: 08/05/26 All music is Attribution 3.0 Unported (CC BY 3.0). All artists are credited during the episode. For more info: www.nicktherat.com

This week we have a show that was two years in the making! Suggested to us at CONvergence 2024, we finally get Eric Lotos on the podcast to talk about Black holes and how their existence can be a vehicle to understand a universe without God. Einstein famously tried to disprove their existence so hey - smart people can be wrong!

Los eclipses totales son acontecimientos de profundo impacto emocional, una esquiva carambola cósmica en la que la Luna bloquea completamente el Sol y permite apreciar las capas superiores de su atmósfera durante unos pocos minutos de noche súbita. Después de una prolongada sequía de fenómenos de este tipo, España enfila una insólita racha que nos permitirá vivir dos eclipses totales y uno anular en tres años consecutivos. El primero, el 12 de agosto de 2026, será visible como total sobre las 20.30 horas en una banda de doscientos kilómetros de anchura que barrerá buena parte de la mitad norte peninsular. Un año después, el 2 de agosto de 2027, la franja de totalidad cubrirá el extremo meridional de la Península, Ceuta y Melilla. Por último, el 26 de enero de 2028 podremos apreciar un eclipse de tipo anular desde casi cualquier ubicación al sur de la línea imaginaria que une Badajoz con Girona.La huella de los eclipses de Sol está presente desde los albores de la expresión escrita. Se cree que pudieron influir en importantes decisiones políticas en el Egipto de los faraones y fueron concienzudamente registrados por los babilonios. Además, su simbolismo queda plasmado en relatos religiosos como el eclipse de la crucifixión y sus diversas interpretaciones artísticas. El gran avance en la observación y el análisis llega con la Revolución Científica y la Ilustración; la observación de nuestra estrella y su corona durante episodios de totalidad permite avanzar en el conocimiento del astro, descubrir el helio e incluso confirmar la Teoría de la Relatividad de Albert Einstein. Entre mediados del siglo XIX y comienzos del XX nuestro país vivió una concatenación de eclipses totales semejante a la que se aproxima. Expediciones científicas de todo el mundo visitaron España para presenciarlos en 1860, 1900 y 1905.En este documental sonoro, con guion de Álvaro Soto y realización de Mayca Aguilera, participan los astrofísicos Alejandro Sánchez de Miguel, autor del libro 'Los eclipses de Sol'; Ana Belén Griñón, investigadora en el Instituto de Física Solar de la Universidad de Estocolmo; y Sandra Benítez, responsable de comunicación científica de la Agencia Espacial Europea (ESA). Intervienen también los astrónomos Alba Vidal, del Observatorio Astronómico Nacional; Rafael Bachiller, director de dicha institución y presidente de la Comisión Científica y de Asesoramiento del Trío de Eclipses; y Pedro García Lario, miembro de esta misma comisión por parte de la ESA. Además, el programa recoge las impresiones de la investigadora del CNIO Sara García Alonso, integrante de la reserva de astronautas de la ESA; Pedro Ruiz-Castell, profesor de historia de la ciencia en la Universitat de València; y Enrique Bordallo, presidente de la Asociación Astronómica de Burgos.Escuchar audio

What is the “Time Value of Money?” Episode 394 – The time value of money is one of the most important financial concepts there is to understand. It comes into play in almost every financial decision. You don't need to understand the arithmetic, but you should have some sense of where and how the math applies. Doing so may be able to improve the quality of your financial life. More SML Planning Minute Podcast Episodes Transcript of Podcast Episode 394 Hello, this is Bill Rainaldi, with another edition of Security Mutual's SML Planning Minute. In today's episode: what is the “time value of money”? Simple question: what is worth more: a dollar you earn today, or a dollar you earn next year? Most people instinctively know that a dollar earned today is worth more. After all, that's an extra dollar you can spend now on whatever you want. But understanding why is a critical financial concept that few people really understand, and one that applies to pretty much everything when you talk about personal finance. The concept is generally known as the “time value of money.” It's an idea that runs through almost every decision a business or financially sophisticated individual makes. According to the Harvard Business School, the time value of money means that “a sum of money’s value depends on how long you wait to use it; the sooner you use it, the more valuable it is.”[1] In other words, the money you have today is worth more than the same amount that you receive in the future because you have the opportunity to invest that money right now and earn a return on it. Figuring it all out in detail involves a rather complicated series of formulas. We won't get into the formulas here, but Microsoft Excel has tools to help make the calculation process easier. The basic idea is that, if nothing else, you can take the dollar you earn today and invest it. At the end of the year, that dollar will be worth more than the new one you receive at the start of the next year. If your assumed interest rate is six (6) percent, that first dollar will be worth $1.06 by the time the second one arrives. I know it doesn't seem like much of a difference. But after 20 years, the value of that dollar at the same 6% would be $3.21. And remember, we're generally talking about much bigger sums. And compounding, that is, repeating this process over an extended period of time, can make the impact much more significant as the years go by. And when you're considering a regular payment, such as a mortgage or an annuity, the difference adds up even more. Compounding is something we touched on in two recent episodes, one about reverse mortgages and the other about Trump Accounts. As Albert Einstein is alleged to have said, compound interest is “the most powerful force in the universe.”[2] Whether he actually uttered those exact words or not, many present and future retirees understand the value of saving early. The math is equally important but gets more awkward when you want to reverse the process. What is that dollar you're going to get a year from now worth today? This is where a spreadsheet can help. The answer is just over 94 cents. If it's two years, it's about 88 cents. In five years, just under 75 cents. As you might suspect, inflation is a key consideration when it comes to the time value of money. There's another reason a dollar earned today is worth more than a dollar earned in the future. Your money will likely be able to buy less in the future than it does today, simply because prices of most goods and services tend to go up over time. Uncertainty also plays a role. Assume someone owes you money, but the payment is due a year from now. The problem is that things could change over the next year. They might move away, or declare bankruptcy, or decide they don't like you anymore. Nothing is certain until you actually have the money in hand. Note that if there's additional risk that you're not going to get the money in time, or at all, many financial pros will try to handle this using a higher assumed interest rate, or “discount rate.” There are some other areas where the time value of money is a key consideration. One often overlooked example is deciding on whether to make a home improvement that adds to the value of your house. Another might be weighing the pros and cons of buying vs. leasing a car. Yet another might be your decision on when to collect Social Security. Another concept that comes into play—and one that many people rarely consider—is opportunity cost. Once you understand the time value of money, opportunity cost becomes much easier to recognize. There are tradeoffs in any financial decision. Opportunity cost can be defined as the value of what you give up when you forgo one choice in favor of another.[3] Opportunity cost comes along more often than most people realize. The truth is that you finance every major purchase you make, even if you're using cash. If you buy a new car and use your available cash, it will save some money. Since there's no loan, there's no cost to you in terms of interest payments. But there is still opportunity cost. By paying cash, you've given up the opportunity to invest that money elsewhere and earn interest and/or dividends on it. This is a concept few people think through thoroughly. To put it another way, if you want something, you must give up something else. It's just not always easy to see. You don't need to understand the complicated mathematical formulas behind the time value of money. You just need to understand the concept. It can—and should—help you make some of your most important financial decisions. Confused about things like the time value of money or opportunity cost? Your Security Mutual Life insurance agent can help. Your Security Mutual Life insurance agent can augment or help assemble your financial team and coordinate with your attorneys and tax professionals to review your situation and to determine the insurance plan that will best suit your needs and objectives. [1] Cote, Catherine. “Time Value of Money (TVM): A Primer.” HBS.org. https://online.hbs.edu/blog/post/time-value-of-money (accessed July 14, 2026). [2] Schleckser, Jim. “Why Einstein Considered Compound Interest the Most Powerful Force in the Universe.” Inc.com. https://www.inc.com/jim-schleckser/why-einstein-considered-compound-interest-most-powerful-force-in-universe.html (accessed July 14, 2026). [3] Munsey, Bobbie Anne. “8 Opportunity Cost Examples (Plus Definition and Uses).” Indeed.com. https://www.indeed.com/career-advice/career-development/opportunity-cost-examples (accessed July 13, 2026). More SML Planning Minute Podcast Episodes This podcast is brought to you by Security Mutual Life Insurance Company of New York, The Company That Cares®. The content provided is intended for educational and informational purposes only. Information is provided in good faith. However, the Company makes no representation or warranty of any kind regarding the accuracy, reliability, or completeness of the information. The information presented is designed to provide general information regarding the subject matter covered. It is not to serve as legal, tax or other financial advice related to individual situations, because each individual's legal, tax and financial situation is different. Specific advice needs to be tailored to your situation. Therefore, please consult with your own attorney, tax professional and/or other advisors regarding your specific situation. To help reach your goals, you need a skilled professional by your side. Contact your local Security Mutual life insurance advisor today. As part of the planning process, he or she will coordinate with your other advisors as needed to help you achieve your financial goals and objectives. For more information, visit us at SMLNY.com/SMLPodcast. If you've enjoyed this podcast, tell your friends about it. And be sure to give us a five-star review. And check us out on LinkedIn, YouTube and Twitter. Thanks for listening, and we'll talk to you next time. Tax laws are complex and subject to change. The information presented is based on current interpretation of the laws. Neither Security Mutual nor its agents are permitted to provide tax or legal advice. The applicability of any strategy discussed is dependent upon the particular facts and circumstances. Results may vary, and products and services discussed may not be appropriate for all situations. Each person's needs, objectives and financial circumstances are different, and must be reviewed and analyzed independently. We encourage individuals to seek personalized advice from a qualified Security Mutual life insurance advisor regarding their personal needs, objectives, and financial circumstances. Insurance products are issued by Security Mutual Life Insurance Company of New York, Binghamton, New York. Product availability and features may vary by state.​ SubscribeApple PodcastsSpotifyAndroidPandoraby EmailTuneInDeezerRSSMore Subscribe Options

“Uncertainty is what tells you that science is reliable. It's the people who are absolutely certain that you have to watch out for.” — Stuart Firestein The physician-scientist Tony Fauci is having a rough week. His newly revealed diary has even disappointed traditional allies at The Atlantic and The New Yorker who see the hitherto liberal saint of the pandemic as both too intimate with power and too certain of his scientific assumptions. Fauci's fellow American scientist, Stuart Firestein, is having a better week. The Columbia neurobiologist has a new book out today, It Could Be Otherwise, which is a manifesto against scientific certainty. It's the certain ones you have to watch out for, Firestein warns about those scientists intoxicated with the certainty of their own doctrines. Science, Firestein argues, requires a blend of hubris and humility. So the great Isaac Newton needed real chutzpah to claim to understand the mind of the creator with only a few equations. The expert is, by definition, riddled by doubt. They should be the most uncertain person in the room. Thus for Firestein, Charles Darwin is a greater scientist than Albert Einstein because Darwin's theory of life is premised on randomness. So will contemporary science appear quaintly medieval in 500 years? Will 26th century man think of Darwin and Einstein like we moderns patronize Aristotle? Probably, Firestein suggests. In his Columbia course on ignorance, he tells his students that getting an A requires getting an F. We should all be so ignorant. Five Takeaways •       Watch Out for the Certain Ones. Firestein won't defend Fauci's certainties, but he explains them: science requires a blend of hubris and humility — without some hubris, nobody would attempt a discipline that is mostly failure, mostly time spent in the dark. What corrupts the blend is a culture that demands the opposite of humility: exams with one right answer instead of ranked possibilities, journalists who need a confident quote, a public that has made science the theology of the twenty-first century. The result is Firestein's paradox of expertise: the real expert is the most uncertain person in the room, because expertise means seeing the holes, the missing data, the failures. Certainty grows with distance from knowledge.•       True Enough. Truth gets a small t only. Borrowing from the Harvard philosopher Catherine Elgin, Firestein describes science as moving from one provisional truth to the next — “true enough,” always improving, never final. His favorite proof is John Maddox, Nature's longest-serving editor, asked how many of the journal's published papers would prove wrong: “Oh, that's easy. All of them.” Not wrong-wrong — unsettled science is not unsound science; it is reliable and it will get better. In science, revision is a victory, which is why Firestein sincerely hopes that in five hundred years our best knowledge will look as quaint as medieval astronomy — which, he notes, was accurate enough for people who weren't launching satellites.•       Two Kinds of Uncertainty. Epistemic uncertainty is what we don't know yet; ontological uncertainty is the irreducible complexity of the universe itself — so complicated it might as well be random. Every answer in the lab raises ten better questions, which is how the more we know, the less we know. And yet we thrive amid the not-knowing: lasers, GPS, and randomized drug trials are all built on uncertainty. Ninety percent of hypertension diagnoses are idiopathic — no known cause — while nine different families of medicine treat it successfully. Even the brain, his own subject, isn't unknowable — we know plenty at every level — but the vertical knowledge, from ion channels to consciousness, may never reduce to a single formula. Multiple explanations, Firestein says, are not a defeat.•       Berlin, Darwin, and the Fox. The book's presiding philosopher is Isaiah Berlin, the great value pluralist: many good answers to any real question, even incommensurable ones — which is emphatically not relativism (the poststructuralists get no comfort here; we still must choose, critically and responsibly). Berlin's hedgehog and fox turn out to describe scientists too, and Firestein wants room for both. His scientific hero, though, is Darwin, whom he ranks above Einstein: the first person to show that tremendous organization and complexity can arise from essentially random events. Sixty-five million years ago, seven minutes of orbital difference and the asteroid misses. Randomness and uncertainty, properly understood, produce diversity and complexity — not chaos.•       When AI Learns to Be Uncertain. AI appears exactly once in the book, a restraint Firestein is proud of: “we hate the bomb, but we love anesthesia,” and it's the use, not the thing, that will decide. Socrates was famously against writing — it did ruin our memories, and libraries were worth it; calculators did ruin long division, and who cares. In the classroom, the smell scientist admits you can no longer smell Claude in an essay — hence his ignorance course's 999-word assignment, harder than the 5,000 words Columbia students write in their sleep. But his sharpest cut is at AI itself: the machines will be valuable to the extent that they are uncertain. When AI learns to weigh possibilities rather than issue single solutions, it will be intelligent — it will be human. A deterministic tick-tock universe is no place for discovery, imagination, or freedom. It could be otherwise. About the Guest Stuart Firestein is a professor of neuroscience at Columbia University, where his laboratory studies the sense of smell, and a member of the Santa Fe Institute. A Guggenheim fellow and elected fellow of the American Association for the Advancement of Science, he is the author of Ignorance: How It Drives Science and Failure: Why Science Is So Successful, both translated into more than a dozen languages; he remains an Honorary Fellow of King's College, Cambridge, where Failure was written. His new book is It Could Be Otherwise: Science in the Age of Uncertainty (Basic Books, August 4, 2026). References: •       It Could Be Otherwise: Science in the Age of Uncertainty by Stuart Firestein (Basic Books, August 4, 2026). Kirkus: “A solid and even stirring defense of science.”•       Ignorance: How It Drives Science by Stuart Firestein — the 2012 book, born of his Columbia course, that made not-knowing respectable.•       The Hedgehog and the Fox by Isaiah Berlin — the essay, and the value pluralism, that give the book its philosophical spine.•       True Enough by Catherine Elgin — the Harvard ph...

Watch the full episode on YouTube:We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3:And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF:Three years ago, inference engineering barely existed as a category.Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem.In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out.Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles.In this episode, Baseten's Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API.We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model.The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them.We discuss:* What happens when a 200,000-token request enters an inference system* Cache-aware routing and reusing previously computed KV cache* Why prefill and decode are increasingly handled by different GPUs* When dedicated deployments become cheaper and more reliable than shared APIs* How speculative decoding uses a smaller model to accelerate a larger one* Tool calling, structured outputs, and what LLMs actually do* What it takes to support a new open model on day zero* Grafting Kimi's vision encoder onto GLM-5.2* Retrofitting inefficient model layers with components from other architectures* Why models sometimes collapse into repeating the same token* How hardware, kernels, and race conditions create nondeterministic failures* Preserving model fidelity while making inference faster* How quantization errors can cancel each other out* Why inference optimizations still deliver gains of 20%, 100%, and 200%* How optimized serving can make a model up to 10× faster* NVIDIA Dynamo, KV-aware routing, and distributed model serving* Speculative decoding the speculative decoder* Why local AI is about making models less dumb while data-center AI is about making them less slow* Tensor, expert, and pipeline parallelism across GPUs* Hardware-aware model design, auto-tuning, and the case against mega kernels* Rubin and why inference is becoming a systems problem* Whether modern GPUs are evolving into programmable AI ASICs* Why enormous models like Kimi K3 require GB300-class hardware* Why open-source video generation still trails Veo, Kling, and other closed models* The quadratic attention bottleneck behind long-form AI video* Autoregressive video, real-time generation, and compounding quality drift* Why future video systems may combine autoregressive and diffusion architectures* Training for inference and inference for training* Continuous post-training, deployment, evaluation, and improvement loops* How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself* Why faster networking could unlock dramatically faster decoding* Continual learning, KV-cache compaction, and persistent model memoryShow Notes* How to build a day-0 API for Kimi K3* 22580: From GPT2 to Kimi3, ExplainedPhilip Kiely* LinkedIn: https://www.linkedin.com/in/philipkiely* X: https://x.com/philipkiely* Inference Engineering: https://www.baseten.co/inference-engineering/Ali Taha* LinkedIn: https://www.linkedin.com/in/aliestaha/* X: https://x.com/waterloointernTimestamps00:00:00 Introduction and the 200K-Token Prompt00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling00:11:26 Launching Production-Ready Open Models00:19:06 Model Retrofits, Failure Modes, and Nondeterminism00:28:22 Quantization and Canceling Errors00:32:15 The Race to 10× Faster Inference00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips01:10:03 Giant Models and the Limits of GPU Memory01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation01:21:47 Audio, Images, and Diffusion Models01:27:32 Training, Self-Optimizing Models, and Continual Learning01:40:06 Closing ThoughtsTranscriptIntroduction: Baseten, Waterloo Intern, and Inference EngineeringSwyx [00:00:00]: Okay, we're here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you've done, you and I have done before, as well as Ali. Welcome.Ali [00:00:15]: Pleasure to meet you.Swyx [00:00:15]: Waterloo intern.Ali [00:00:16]: Waterloo intern, always.Swyx [00:00:17]: When did you get “Waterloo intern” as a handle?Ali [00:00:19]: As a handle? Oh.Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.”Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer.Philip [00:00:30]: So we have to figure out who's gonna get the handle.Ali [00:00:33]: Well, I'll pass the torch over to the next intern.Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad.Ali [00:00:37]: To another Waterloo intern. No, bruh.Philip [00:00:39]: Yeah.Ali [00:00:39]: Intern.Swyx [00:00:40]: Intern, yeah.Ali [00:00:40]: And no.Philip [00:00:41]: You gotta get an intern from Waterloo.Ali [00:00:42]: Yeah, I've gotta get an intern from Waterloo.Swyx [00:00:44]: Right.Ali [00:00:44]: But they have to follow the path.Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it's like whoever Baseten gets from Waterloo.Ali [00:00:48]: Right.Swyx [00:00:49]: Has the title of Waterloo.Ali [00:00:50]: It stays in the ecosystem.Philip [00:00:51]: Exactly.Ali [00:00:52]: Halfway through the internship, you either get it or you're out.Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle.Ali [00:00:59]: Just say it.Philip [00:00:59]: For everybody.Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you're an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten's inference? What's the process of query through GPU model routing, balancing, all that? What is all the stuff that we don't think about?Long Context Requests, KV Cache, and Cache-Aware RoutingPhilip [00:01:26]: With a long query specifically, the first thing that I'm gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it's gonna be a lot easier for me and a lot cheaper for you. So the first thing that we're gonna look at is some cache-aware routing, where we're going to see, we probably have a number of instances, a number of replicas up serving whatever model you're hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you're doing two hundred thousand tokens, it's probably coding or a multi-turn agent or something where you would expect to have that cached. If you don't, we're gonna have to send it to a prefill worker. We've at least on certain models disaggregated prefill and decode, so you're going to have one set of GPUs that's solely going to process the input, create the KV cache, and get you your first token, and then that's going to be passed over to a separate set of GPUs, which is going to run decode. We're going to iteratively make those tokens. We're probably going to have some speculator model in front of that. I'm going to assume that you're doing coding, and because of that, our speculator model, which assumes you're doing coding, is gonna have a high draft token acceptance rate. If I'm wrong and you're asking me to summarize every Harry Potter book, it's gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?”Swyx [00:03:04]: Except Baseten doesn't charge by pennies.Philip [00:03:07]: Well, yeah, we charge. I'm assuming that we're talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it's not pennies.Public APIs vs. Dedicated DeploymentsSwyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it's up to you to figure out how to saturate the box.Ali [00:03:31]: And more often than not, it's, like, way cheaper if you're pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token.Philip [00:03:37]: Yeah, they do. I think that we've increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that's really sticky, then they move over to dedicated.Swyx [00:03:51]: Is there a best practice on when it's time to swap over?Philip [00:03:54]: Couple reasons. Yeah, reliability, that's a big one, right?Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic.Swyx [00:04:04]: Spec dec is speculative decoding.Speculative Decoding and Custom SpeculatorsAli [00:04:05]: Speculative decoding, yeah.Swyx [00:04:07]: You have to explain.Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you're summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I'm gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn't be able to provide this to you if you're a shared endpointSwyx [00:04:53]: YeahAli [00:04:53]: ‘cause I have no idea if you're doing Harry Potter, if you're doing coding, if you're doing English. We don't know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that?Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you're trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn't pass your benchmarks and you wanna run a model at higher precision, you could do that. There's just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don't have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users.Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it's people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you're generating JSON or is there more complication beyond that?Tool Calling, JSON, and Structured OutputsAli [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that's not just, like parse a file or go find the weather. It's something that's very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn't require its own like sandbox. It's not like it's going to use that tool calling to like escape a sandbox or like it doesn't have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you're dealing with all of the JSON outputs, if it doesn't like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn't see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model.Philip [00:06:56]: Yeah, that's a challenge on the training side and then on the inference side, there's work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember backSwyx [00:07:27]: Yeah, the specific grammar is,Philip [00:07:29]: Yeah, exactlySwyx [00:07:30]: GML had this thing.Philip [00:07:31]: Yeah. So it's like the old-school “make sure this is only JSON”, return only JSON orSwyx [00:07:38]: YeahPhilip [00:07:38]: Grandma's gonna die type of prompts.Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR.Philip [00:07:47]: In our inference system, it's just a specified output format. And you get the guarantee that your output's gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn't solve the certainty problem but it at least solves the output structuring problemSwyx [00:08:10]: YeahPhilip [00:08:10]: Within tool calls.Swyx [00:08:12]: And MCP is just another form of tool, right.Philip [00:08:14]: Yeah, exactly.Swyx [00:08:15]: As far as there's no special thing there.Philip [00:08:16]: The thing I'm always like explaining to people is the LLM is not capable of doing anything. It's only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs.Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you're right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don't know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don't have the same exact quality outputAli [00:08:56]: Right.Swyx [00:08:57]: When you just swap from a big model, right?Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper.Ali [00:09:04]: But, I had expected that something would replace JSON because it's hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it's hard to parse something or validate something while it's being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it's something like TOML, something like YAML. But JSON seems to be dominant still.Philip [00:09:30]: The JSON outputs aren't that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it's a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn't be as valuable, but maybe I'm wrong about that.Ali [00:10:02]: I think you're also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you'- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn't be that much of a difference. Also more profitable if it outputs more tokens probably.Swyx [00:10:25]: Depends on your business model.Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there's paragraphs in every field because I'm trying to structure it, right?Philip [00:10:44]: Right.Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let's, let's recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there's a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let's call it GLM-5.2, Kimi K3. I had previously assumed, especially if it's like, well, GLM 5 to 5.1 to GLM-5.2, like that you've supported them before. Is it that much work?What It Takes to Support a New Open ModelAli [00:11:26]: It's a lot of work.Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I'm like, “Yeah, of course we support it.” But what goes into that? What goes intoPhilip [00:11:40]: I think it's more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we're at 150. The nextSwyx [00:11:55]: I kinda kicked that off with the GLM-5.2.Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views,Ali [00:12:02]: Based on being numberSwyx [00:12:03]: YeahAli [00:12:04]: Or it's for something else.Swyx [00:12:05]: Yeah. Which,Ali [00:12:06]: Oh my GodSwyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and,Philip [00:12:14]: There's a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model.Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there's going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar.Quantization, Speculators, and Production ReadinessAli [00:13:16]: Yeah. It was pure continued post-trainingPhilip [00:13:18]: YeahAli [00:13:18]: If I remember correctly.Philip [00:13:19]: Even in those cases, there's still stuff you have to do. You have to redo the quantization work. You're taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we're not causing any regression in the model's intelligence. And then we also have to train the speculator, as we've talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don't know exactly the traffic that people are sending us, but we know what's popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you're getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there's that process which you need the real model weights for. And then there's of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there's a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 hadAli [00:14:53]: Sparse attention.Philip [00:14:54]: Yeah,Ali [00:14:54]: YeahPhilip [00:14:54]: the DSA.Ali [00:14:55]: Right. Which is brought from DeepSeek.Philip [00:14:57]: Yeah. AndAli [00:14:59]: So you can copy-paste then?Philip [00:15:01]: It kindAli [00:15:01]: I don't know how this works.Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you're right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn't have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2.Retrofitting Vision into GLM-5.2Ali [00:15:27]: We'll be training the projector.Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there's the encoder, which is the part that looks at the image and turns it into latent information, and then there's the projector which likeAli [00:15:38]: You can say latent space. It's okay.Philip [00:15:41]: And then there's the projector that maps it onto, the model itself, and then there's the model weights. You don't wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters.Ali [00:16:02]: That would be, yeah.Philip [00:16:02]: Yeah.Ali [00:16:03]: Can you show the training one?Ali [00:16:04]: Like the way it groksPhilip [00:16:05]: YeahAli [00:16:06]: Very interesting.Philip [00:16:06]: And maybeAli [00:16:07]: That right therePhilip [00:16:07]: Maybe Ali, you should take it from here. You've got a betterAli [00:16:10]: Ooh, double the sandPhilip [00:16:11]: Understanding of this than I do.Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here's a picture of a mountain. Can you describe what's in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we're trying to teach it is to translate the encoded. Like it's already taken the encoder from Kimi K. It's taken the image. It'Philip [00:16:31]: Yeah. FrozenAli [00:16:31]: FrozenPhilip [00:16:32]: With adapter.Ali [00:16:32]: Exactly.Philip [00:16:33]: Yeah.Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It's just we're tryingPhilip [00:16:37]: AlignAli [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he's like, “Oh, can you describe what's in this image?” And he's like, “Oh, it's a mountain,” or it's a person or it's a human, whatever the case is. But that didn't cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn't perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn't get it, but it will say something like, “This is Albert Einstein.” Like it still understandsPhilip [00:17:25]: Close enoughAli [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that's like really cool.Philip [00:17:32]: Yeah. So, we've covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that's very foundational work for anyone who hasn't done vision work before.Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questionsPhilip [00:17:47]: RightAli [00:17:47]: Off the image and how much better you can get performance.Philip [00:17:50]: Right. Right. Right. Yeah. But what's, what's so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It's not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you're running this model, you haven't suffered any loss on your GLM-5.2 quality. If you don't have an image, it'll just behave exactly the way it used to. And ultimatelyAli [00:18:14]: Which in the inference code you literally do not include the other part, right?Philip [00:18:18]: Yeah. You would just skip the encoder if you don't have an image input.Ali [00:18:22]: Okay.Philip [00:18:22]: Just confirming.Philip [00:18:23]: YeahAli [00:18:23]: Does it affect a lot on the overall inference side? Like you're not adding much, you're adding a very small vision encoder. These are typically likePhilip [00:18:30]: They're super fineAli [00:18:31]: Less than a billion parameters, right?Philip [00:18:32]: Yeah. It's, - There's a little bit less standardization among vision encodersSwyx [00:18:37]: YeahPhilip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it's a pretty, it's a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model.Open Source Model Grafting and Franken-MergesPhilip [00:18:56]: And that's, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that's better than anyoneSwyx [00:19:05]: YeahPhilip [00:19:05]: Can be individually.Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take likePhilip [00:19:10]: YeahSwyx [00:19:10]: Layers from each model.Swyx [00:19:11]: Does anyone do that anymore?Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you're doing auto-regressive token generation for three tokens, and you're doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it's not sparse, it's not top K. So we find it better to like, okay, we're gonna replace this, we're gonna replace this layer with a layer from another model that's using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That's like, I feel like more and more becoming true.Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready?Loop Detection, Race Conditions, and Non-DeterminismPhilip [00:20:26]: Yeah. I think that there's also a question of just, we can test a model to a pretty extensive degree, but we're trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there's going to be, so many more varieties of things given to it that you're able to, discover and patch things. So it's not just a, day zero process, it's then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance?Ali [00:21:21]: What do you mean you don't want your model outputting S?Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising.Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it's four times the same token, it's probably collapsed.Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that?Ali [00:21:48]: You want that?Ali [00:21:50]: I think there's a way that we have to handle it. I'm not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there's a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters.Swyx [00:22:07]: Yeah.Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2Swyx [00:22:11]: OhAli [00:22:11]: And I think it was DSV 4 as well. Like you'd just have like looping issues where like you literallySwyx [00:22:17]: ItAli [00:22:17]: Just have like S.Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomlyAli [00:22:21]: It just seems to be the one token involved.Swyx [00:22:23]: Yeah. And it'Philip [00:22:24]: Is thereSwyx [00:22:24]: And it's only temperature 0Ali [00:22:27]: NoSwyx [00:22:27]: Even at other temperaturesAli [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse.Swyx [00:22:30]: That's weird, right?Ali [00:22:30]: It's, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we'll find that it fixes it. Or oftentimes this will only happen in an inference engine that you're using like SGLang. But if you were to switch to vLLM, that isn't the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It's not like a weights problem. Like I'- we'll say like, “Oh, it's a problem with the quant. We did PTQ wrong,” right? But that isn't, that doesn't make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it's, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem.Swyx [00:23:19]: Oh my God.Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn't. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We're gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware?Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right?Ali [00:23:46]: Right.Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won't always get the same output.Swyx [00:23:52]: Even-- But I'm surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order.Ali [00:24:02]: Well, yeah, true. Like I'm not, I'm not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that's like ‘cause you want to do that because there'sSwyx [00:24:12]: It's like pipeliningAli [00:24:12]: Expense. Exactly.Swyx [00:24:13]: Yeah.Ali [00:24:13]: But it'- But you don't do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you're designing a kernel and you want it to make it to be very fast, if you don't test it extensively, you'll, you'll have certain threads access data points from registers before they've been written to by other threadsSwyx [00:24:36]: YeahAli [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, andSwyx [00:24:42]: And there's no like borrow checkerAli [00:24:45]: What does that mean?Swyx [00:24:46]: Like Rust. Like the. If you're trying to have like memory safety It sounds like a comparable problem.Ali [00:24:52]: Well, yes, but you're working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that's what modular is supposed to do. I don't know.Quantization Quality and Vendor FidelityVibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoderAli [00:25:07]: RightVibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarksAli [00:25:22]: YeahVibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes intoPhilip [00:25:27]: There's a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you're preserving all the outliers. There's other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what's gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn't need the full million token context, for example, you can get them better performance. I don't know if that's exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model.Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it's getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking hereAli [00:27:41]: YesPhilip [00:27:41]: Where they haveAli [00:27:42]: They released an actual vendor benchmark.Philip [00:27:43]: Exactly, yeah.Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi's benchmark.Philip [00:27:50]: Yeah.Philip [00:27:51]: So, with Reflect we probablyVibhu [00:27:52]: This was a long time ago, right?Philip [00:27:54]: No.Ali [00:27:54]: Yeah, like threeVibhu [00:27:55]: They alsoAli [00:27:55]: Four, five months agoVibhu [00:27:57]: This also happened with, I don't remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have beenPhilip [00:28:03]: Kimi Vendor Verifier.Ali [00:28:04]: Yeah.Philip [00:28:05]: Yeah.Ali [00:28:05]: Yeah, ‘cause you, ‘cause you'd be pissed, right? Like if you'Philip [00:28:07]: Yeah.Ali [00:28:07]: If like if I'm a consumer and I'm using like Amazon's endpoint for instance, and I've used Kimi and I'm like, “Oh my God, like this is bad,” I'm not gonna say, “Oh, Amazon quantized the model in a bad way.” I'm gonna say, “Oh, Kimi sucks.” Right?Philip [00:28:17]: Yeah.Ali [00:28:17]: So it seems like that makes sense.Philip [00:28:19]: Yeah, they care. They care.Vibhu [00:28:21]: Justifiably.Ali [00:28:21]: Yeah, justifiably.Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization?Philip [00:28:28]: Yeah.Vibhu [00:28:28]: Like, is quantization always strictly worse?Ali [00:28:30]: Well technicallyVibhu [00:28:32]: NoAli [00:28:32]: It's a lossy. QuantizationPhilip [00:28:33]: YeahAli [00:28:33]: Is a lossy, it's a lossy implementation.Philip [00:28:36]: Speed improvesVibhu [00:28:36]: Speed improves.Ali [00:28:37]: It the number, likeVibhu [00:28:38]: No, I' always look for inverse scaling laws.Philip [00:28:40]: Yeah.Ali [00:28:40]: Yeah.Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do.Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your,Ali [00:28:52]: YeahPhilip [00:28:52]: NVFP4 quant is like, two basis points higher than yourAli [00:28:56]: No, it's noise. It's noise.Philip [00:28:57]: Yeah, exactly. I'm like, yeah, it's, it's within. That's why I always say within margin of error.Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we're barely inside of that to the worst, so we're saying. But yeah, sometimes it's just like, gives you a higher output score. But like Ali said, that's noise. To my knowledge, you're not necessarily making the results better. You're just trying to, again, like keep your fidelity as close to 100% to the original model.Layer Selection, KL Divergence, and Better QuantizationAli [00:29:27]: There is, to your point, research that we did on MP. I don't know if you are able to pullPhilip [00:29:31]: YeahAli [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it's a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It's. You're compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you're losing some information, and you're trying to minimize that. And so when I say that I'm gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don't quantize modulation layers, and I don't quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn't have the. Yeah. It's a long paper. I don't know if I can findVibhu [00:30:25]: If there's a part to search or it's probably in the thread.Ali [00:30:28]: It's probably in the thread.Vibhu [00:30:29]: Yeah.Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that's 20% more quantized than another provider, so you get 20% more throughput of it because there's more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you're probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it's gonna be, ‘cause the more loss you introduce. That's not exactly, not necessarily true. So yeah, doesn't improve it, but can cancel out.Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers.Philip [00:32:03]: But very interesting. Didn't know this was a whole paper you guys put out.Ali [00:32:06]: It's. Fun fact, it was originally 72 pages, this paper, and then we decidedPhilip [00:32:11]: WowAli [00:32:11]: We can't tell. We couldn't release it. So it's now 45.Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what's possible in terms of speedup? Like it's like probably like the numberInference Speedups and BenchmarkingSwyx [00:32:25]: Thing that people do wanna care about, and it's something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing?Philip [00:32:36]: So what's cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you're in finance, you measure how much better you got in basis points. It's like, “Oh, I got five basis points better, like twentieth of 1% better,” that's huge news because everything is so optimized. When we publish optimizations, it's 20%, it's 100% it's 200%. So there's still probably like a lot further to go, honestly. Like you'll, you'll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something.Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would findAli [00:33:27]: And like 20%, tens of percent.Swyx [00:33:29]: That's. Yes.Philip [00:33:29]: Yeah.Swyx [00:33:30]: And now it'Philip [00:33:31]: Tiny fractionsSwyx [00:33:32]: For those people interested, look up Andrew Lo's paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool.Philip [00:33:48]: Exactly, and we're at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there's so many variables that go into it. What hardware are you using? How much load do you have on the system? What's the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you're looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there's two tokens per second. There's tokens per second, the throughput number, and the latency number.Ali [00:34:31]: TTMT, yeah.Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don't.Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that's an 8X gain. That's the order of magnitude that we're working with in this space. We're trying to make things substantially faster, not just go from like 70 to 90.Swyx [00:35:38]: Are you saying you've. You have done that?Philip [00:35:40]: So let's say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you're just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you're, you're probably, yeah, looking at that like 30 to 40. You think that's like a reasonable baseline?Swyx [00:36:12]: Right. Right.Philip [00:36:12]: To get to something like 10X, there's a lot of trade-offs that you're making. If we're running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It's oftentimes maybe more of a four to six times improvement. But that's the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens.Stacking Optimizations: NVFP4, Speculation, and DisaggregationAli [00:37:19]: It's also, like, hardware dependent. Like, ifPhilip [00:37:20]: YeahAli [00:37:20]: If you have a thing where you're serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs.Philip [00:37:35]: Yeah. Then you're looking at, like, a two to 4X improvementAli [00:37:38]: Right. RightPhilip [00:37:38]: Depending on the inference optimizations. So yeah, it's. Some of it's, what's the call, and some of it's who's the driver.Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2Ali [00:37:51]: YeahVibhu [00:37:51]: On B200sAli [00:37:53]: YeahVibhu [00:37:53]: Single node, right? What's, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right?Ali [00:38:01]: Spectre quantization. Yeah.Vibhu [00:38:03]: Spectre quantization.Ali [00:38:04]: That's, that's, that's like 95%. LikeVibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it?Philip [00:38:23]: If you're doing it up front, it's quite a lot of work. If you're doing it today, there's going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we're thinking about, like, what are the 2Xs we're stacking, going from, BF16 to NVFP4 is, it's not quite a 2X, right? It's like. I think it's about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn't quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you're able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that's how it stacks up.Ali [00:39:21]: YeahPhilip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they're doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you haveAli [00:39:39]: Once set up. Once set up. YeahPhilip [00:39:40]: Yeah, getting disagg working for the first time, I'm saying, of course, is very difficult.Philip [00:39:44]: The marginal implementationAli [00:39:48]: Like, if you're just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you're wondering, “How can I just host it myself?” You don't need to quantize the model yourself. There's always gonna be, like, an open source quantized checkpoint. NVIDIA's gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they've trained as well. You don't need to train your own spec dec. You can just use that as well.Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP.Ali [00:40:13]: Right. Right.Vibhu [00:40:14]: What's multi token prediction?Philip [00:40:15]: Yes.Ali [00:40:16]: I'm justVibhu [00:40:16]: Can you explain that?Ali [00:40:16]: I'm just an expert.Ali [00:40:18]: I can do it for you in case I get it wrong?Vibhu [00:40:20]: No.Vibhu [00:40:21]: Yeah, you should correct if we're wrong, but their multi-token prediction can be used for self-speculative decoding.Ali [00:40:27]: I'm not sure. I'm not gonna correct that.Vibhu [00:40:28]: Okay. I'm semi-confident in thatAli [00:40:30]: Okay. YeahVibhu [00:40:30]: But someone can check. But it's useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM.Ali [00:40:48]: Right.Vibhu [00:40:49]: I was waiting for a mention of Dynamo.Vibhu [00:40:51]: I feel like, that's supposed to be the baseline that you measure against.Dynamo, KV Routing, and Disaggregation ToolkitsPhilip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA.Ali [00:41:17]: We've done a pod with KylePhilip [00:41:18]: OkayAli [00:41:19]: Kyle Cranin.Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting.Ali [00:41:28]: But it's just a router, it's not like an optimizer layer.Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around.Philip [00:41:49]: That doesn't mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It's more of a developer toolkit.Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out.Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we've got to, we've got to benchmark against, like, what we're seeing in the wild.Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-SpecVibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book.Philip [00:42:31]: Yeah.Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLEPhilip [00:42:35]: YeahVibhu [00:42:36]: 524 on gram.Philip [00:42:37]: It's 55, would be disaggregationAli [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bitPhilip [00:42:44]: YeahAli [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques.Philip [00:42:51]: Yeah.Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe.Vibhu [00:42:55]: Medusa is quite old.Philip [00:42:56]: Yeah, Medusa's old.Ali [00:42:58]: It was old.Vibhu [00:42:58]: But is it in the book as a good, here'sPhilip [00:43:01]: BaselineVibhu [00:43:01]: Baseline vanilla understand it?Philip [00:43:02]: Like you should know this.Vibhu [00:43:03]: Like I read the paper, I'm like, “ it makes so much sense.”Philip [00:43:05]: Yeah.Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there's DFlash, dSpark. There's, there's newer techniques even than EAGLE, although EAGLE is still very commonly used.Ali [00:43:51]: SpecSpecta.Philip [00:43:52]: Yes. Speculative decoding.Vibhu [00:43:54]: What canAli [00:43:56]: Oh, it's a paper by Tri Dao and it's like, it's doing speculative decodingVibhu [00:44:00]: HuhAli [00:44:01]: For the speculative decoder.Philip [00:44:02]: Oh, in spec- oh my God.Ali [00:44:02]: It's literally just an another. It's like, yeah, that's the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it's almost like in our mind at least, it's almost as complex as training GANs. Like it's like a very delicate balance and oftentimes you, it's just but yeah, it's literally speculative decoding on speculative decoding.Vibhu [00:44:21]: Speculative.Ali [00:44:22]: Yeah. We saw this paper.Vibhu [00:44:24]: It's interesting, right?Ali [00:44:24]: Yeah.Vibhu [00:44:24]: I wouldn't even expect it to be very particular to train, I wouldAli [00:44:29]: Right.Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder.Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It's like, it's like almost like the iPhone auto predict version but for a normal model, right? Like you're just, you're just, generating three tokens and you're like, okay, I'll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model?Ali [00:44:53]: The other question there is what are the size of speculators? So say forPhilip [00:44:58]: Right. It's like a billion parameters.Ali [00:45:01]: Like for MiniMax, it's. Yeah. It's like one layer. It's like one 60th of the original model usually.Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office.Philip [00:45:10]: SpeculativeAli [00:45:11]: SpeculativePhilip [00:45:11]: Decoding.Ali [00:45:13]: No, it's, it does seem like how, when do you stop? But then it also seems like if you're able to train spec-spec decode for instance, right? Like if you're able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model's gonna predict, then why not just use that smallest model directly, right?Vibhu [00:45:34]: Yeah. This isAli [00:45:35]: Like it seems likeVibhu [00:45:35]: Adjacent to the routing problem.Ali [00:45:36]: Right.Vibhu [00:45:36]: Yeah.Ali [00:45:36]: Right.Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you're running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process.Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it's the same thing, it's just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same?Local AI vs. Data Center InferencePhilip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it's how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it's how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don't touch, in the pruning, in the distillation, in the, layer removal. There'Ali [00:47:42]: Layer removal matters less.Philip [00:47:43]: Yeah. There'Ali [00:47:44]: No one loves pruning really.Philip [00:47:45]: Yeah. Well, but the, but they doVibhu [00:47:46]: Which is surprising, right? But that's, that's a whole different thingPhilip [00:47:48]: Just to fit something on the laptop.Ali [00:47:50]: Right.Philip [00:47:50]: So yeah, it's a, it's an interesting, it's an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire.Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we've seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don't need decrease the storage that much. You don't need to do, FP4 KV cache. You don't need to use a requant. There's, there's, there's better optimizations to be made. But on Edge devices, it's extremely important, it's extremely useful. So, seems to be, like, different optimizations there, but then they're all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with bothPhilip [00:49:18]: Principles.Ali [00:49:19]: Yeah, exactly. Exactly. Exactly.Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks.Ali [00:49:35]: Yeah, this is the Exo Labs guys.Philip [00:49:36]: Yeah. You have, a nu

The dawn twilight has a bright visitor the next few mornings – the planet Mercury. It’s farthest from the Sun for its current morning appearance. It looks like a bright star, but it’s so low in the sky that it’s tough to find. Mercury holds an important spot in the history of astronomy and physics. It provided some of the first confirmation of General Relativity – Albert Einstein’s theory of gravity. Mercury’s orbit around the Sun is lopsided, so the planet’s distance from the Sun varies. For a long time, astronomers had seen that the orbit’s closest point shifted a tiny bit over time. Isaac Newton’s laws of gravity explained most of the difference. But there was still a tiny amount that couldn’t be accounted for. Einstein’s theory of gravity held that massive bodies warp the space around them. Since Mercury is the Sun’s closest planet, its orbit is influenced by that “warpage” more strongly than any other planet’s. In fact, general relativity accounted precisely for the shift in the orbit. So Mercury’s orbit provided some of the first evidence to support general relativity – a new way of thinking about gravity. Look for Mercury quite low in the eastern sky during the waxing twilight. It’ll shine a little brighter each day over the next few mornings. But it’ll also drop a little closer to the Sun, so you’ll need a clear horizon to spot it. Tomorrow: catching waves. Script by Damond Benningfield

When Murray “The Mad Sculptor” Grant takes off his hat and gets excited, his mane of exuberant white hair comes out and he looks a little like Albert Einstein. Murray is essentally a mad scientist built into a sculptor. Murrray invited Robbie to his studio on the family conservancy in Lakipia County, Kenya, where he creates his world famous bronzes, to talk about wildlife conservation from the eyes of an artist obsessed with recreating the beauty he sees in the world.  Get to know the guest: https://murraygrantbronzes.com/ Do you have questions we can answer? Send it via DM on IG or through email at info@theoriginsfoundation.org  Support our Conservation Club Members! Classic African Hunting: https://classicafricanhunting.com/  DSC South Texas: https://www.dscsouthtexas.org/  Everyone Deserves to Play: https://theoriginsfoundation.org/conservation-projects/everyone-deserves-to-play/  See more from Blood Origins: https://bit.ly/BloodOrigins_Subscribe Music: Migration by Ian Post (Winter Solstice), licensed through artlist.io This podcast is brought to you by Bushnell, who believes in providing the highest quality, most reliable & affordable outdoor products on the market. Your performance is their passion. https://www.bushnell.com  This podcast is also brought to you by Silencer Central, who believes in making buying a silencer simple and they handle the paperwork for you. Shop the largest silencer dealer in the world. Get started today! https://www.silencercentral.com  Don't forget to go subscribe to our new The Origins Foundation Podcast Youtube channel: http://www.youtube.com/@TheOriginsFoundationPodcast - who knows, you may be a lucky subscriber who wins some cool stuff from our partner companies! Learn more about your ad choices. Visit megaphone.fm/adchoices

Wallace Thornhill returns to the show to talk with us about The Electric Universe. We discuss some of the new scientific discoveries and how they relate, their upcoming EU conference in June, the history of the solar system, the electic sun, and how all this relates to consciousness. Also, we lost connection a couple times near the end. I edited out those parts, but the edit isn't perfect, so if it seems things don't flow right a couple of times, that is why.Wallace Thornhill graduated in Physics at Melbourne University in 1964 and began postgraduate studies with Prof. Victor Hopper's upper atmosphere research group. Before entering university, he had been inspired by Immanuel Velikovsky through his controversial best-selling book, Worlds in Collision. Wal experienced first-hand the indifference and sometimes hostility toward a radical challenge to mainstream science. He realized there is no career for a heretic in academia.Wal worked for 11 years with IBM Australia. The later years were spent in the prestigious IBM Systems Development Institute in Canberra, working on the first computer graphics system in Australia. He was the technical support for the computing facilities in the Research Schools at the Australian National University, which gave him excellent access to libraries and scientists there.Wal was initially heavily influenced by the then revolutionary ideas of Immanuel Velikovsky of Princeton. Velikovsky proposed that mankind had been devastated in the past by cosmological events . Wal took these ideas and with his deep knowledge of astronomy and, plasma physics began his own questioning of scientific dogma. Paramount was the place of electro magnetism, as distinct from gravity, in the formation of the universe . This slowly but surely led to his and other colleagues (such as David Talbot, Donald Scott, and Anthony Peratt) questioning such ingrained theories as the big bang, black holes and Einstein's theory of relativity. This group in particular contend that many scientific “proofs “are theory laden or mathematically concocted. An insistence on empirical data from observations and experiments gives their work true integrity. (bio taken from www.ancientdestructions.com, more at the sight)Wallace's site: www.holoscience.comThunderbolts: www.thunderbolts.info Hosted on Acast. See acast.com/privacy for more information.

I personally subscribe to The Economist. TOE listeners get 35% off the annual subscription. No other podcast has this! https://economist.com/TOE This is a careful lecture that can be followed with zero background knowledge. Einstein thought he'd caught quantum mechanics in an act of spookiness — this podcast asks whether he was even worried about the right thing. Tim Maudlin, professor of philosophy at NYU and a leading philosopher of physics, delivers a rare full lecture tracing Einstein, EPR, and the road to Bell's theorem. The central claim: Einstein's real objection was never determinism — "God does not play dice" is a red herring — but non-locality, which he inferred rather than assumed. Maudlin traces the argument from Einstein's overlooked 1927 Solvay objection through the EPR paper's criterion of reality, showing why Bohr's famous reply never actually answered it. FOLLOW: - Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e - Substack: https://curtjaimungal.substack.com/subscribe - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs - Crypto: https://nowpayments.io/donation/TOE - PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 TIMESTAMPS: - 00:00:00 - Einstein's Quantization Hypothesis - 00:07:20 - Photoelectric Effect Implications - 00:12:30 - Wave-Particle Duality Myths - 00:20:40 - De Broglie's Matter Waves - 00:26:40 - Copenhagen's Completeness Doctrine - 00:34:10 - Solvay 1927: Two Conceptions - 00:41:20 - Pinhole Diffraction Problem - 00:48:00 - Collapse and Relativity Violations - 00:56:00 - Epistemic vs. Ontic Collapse - 01:05:00 - Ontological vs. Dynamical Locality - 01:14:00 - Configuration Space Objections - 01:25:00 - EPR's Criterion of Reality - 01:32:40 - Analyzing the Reality Criterion - 01:40:50 - Causal Isolation and Locality - 01:48:45 - Entangled Momentum Eigenstates - 01:56:45 - Logical Core of EPR - 02:04:40 - Position-Momentum Simultaneous Reality - 02:14:00 - Inferring Determinism from Locality - 02:26:30 - Conservation Laws and Information - 02:37:40 - Counterfactual Definiteness Debunked - 02:44:00 - Bohr's Incoherent Response - 02:52:00 - Schrödinger's Entanglement Confession LINKS MENTIONED: - Quantum Non-Locality and Relativity [Book]: https://amazon.com/dp/1444331272?tag=toe08-20 - On a Heuristic Point of View About the Creation and Conversion of Light [Paper]: https://sites.pitt.edu/~jdnorton/lectures/Rotman_Summer_School_2013/Einstein_1905_docs/Einstein_Light_Quantum_WikiSource.pdf - The Ghost in the Atom [Paper]: https://vdoc.pub/download/the-ghost-in-the-atom-a-discussion-of-the-mysteries-of-quantum-physics-1guq071e2ukg - Collected Papers on Wave Mechanics [Paper]: https://mwolf.pracownicy.uksw.edu.pl/MK/Schrodinger_Collected_Papers_on_Wave_Mechanics.pdf - Quantum Theory at the Crossroads [Book]: https://amazon.com/dp/0521814219?tag=toe08-20 - Can Quantum-Mechanical Description of Physical Reality Be Considered Complete? [Paper]: https://journals.aps.org/pr/pdf/10.1103/PhysRev.47.777 - Bohr's EPR Critique [Paper]: https://journals.aps.org/pr/pdf/10.1103/PhysRev.48.696 - The Present Situation in Quantum Mechanics [Paper]: https://personal.lse.ac.uk/robert49/teaching/partiii/pdf/SchroedingerPresentSituation1935(1980trans).pdf - Bertlmann's Socks and the Nature of Reality [Paper]: https://cds.cern.ch/record/142461/files/198009299.pdf - On the Einstein Podolsky Rosen Paradox [Paper]: https://journals.aps.org/ppf/pdf/10.1103/PhysicsPhysiqueFizika.1.195 - Quantum Theory and Measurement [Book]: https://amazon.com/dp/0691613168?tag=toe08-20 - Tim Maudlin [TOE]: https://youtu.be/fU1bs5o3nss - Sean Carroll [TOE]: https://youtu.be/9AoRxtYZrZo - Robert Sapolsky [TOE]: https://youtu.be/z0IqA1hYKY8 - Jenann Ismael [TOE]: https://youtu.be/7kvXihDAOi0 - John Norton [TOE]: https://youtu.be/Tghl6aS5A3M Guests do not pay to appear. #science Learn more about your ad choices. Visit megaphone.fm/adchoices

“Mythology and ancient esoteric beliefs are finally found in cutting-edge science.”—Rick Rubin, bestselling author of The Creative ActFrom the author of the global bestseller The Secret History of the World comes an epic new history of the relationship between spiritual belief and cutting-edge science. Includes beautiful black-and-white illustrations throughout.Human beings have a deep-rooted desire to find something worth believing in. But there is a widespread assumption that science is at odds with spiritual belief and offers the only intelligent way to think about the universe.In this epic new history, Mark Booth offers an alternative view, showing us how the great geniuses of modern science, from Marie Curie, Nikola Tesla, and Albert Einstein to today's architects of AI, turned instead to secret, mystical, and “higher” teachings, including Indian mysticism and Freemasonry, to make sense of the strange phenomena they were encountering.Experimenting with alternative states of consciousness, they risked isolation and even madness.Here then is the dark, dramatic story of modern science—of love, betrayal, attempted murder, multiple suicides, sexual experimentation, and transgression—from which unfolds the awe-inspiring discoveries which have transformed our lives for good, and potentially for evil. For just as the work of J. Robert Oppenheimer and others ended the Second World War but brought humanity to the brink of extinction, so, too, has the spectacular growth of AI.The result of many years of research, and deep conversations with prominent academics in the field, this book takes us on an exhilarating journey, in the company of some of the greatest minds of our age, toward a deeper understanding of our place in the cosmos.Jonathan Black is the pen name of  Mark Booth. He was educated at Ipswich School and Oriel College, Oxford, where he studied Philosophy and Theology. After a long career in publishing, he now writes full time.   ​He is the author of the global bestseller The Secret History of the World, The Secret History of Dante: Unearthing the Mysteries of the Inferno and The Sacred History of the World: How Angels, Mystics and Higher Intelligence Made our World.   ​His books are the result of a lifetime spent reading literature in this area, publishing many of the leading authors in the field and hanging around antiquarian bookshops.  He and his wife live in the south of England.https://www.markboothauthor.com/Become a supporter of this podcast: https://www.spreaker.com/podcast/earth-ancients--2790919/support.

Kurt Gödel was a genius mathematician and logician, considered by many scholars to be as influential as Aristotle. He was also buddies with fellow geniuses Albert Einstein and Oskar Morgenstern, the co-creator of Game Theory. In 1947, Gödel told Morgenstern and Einstein he had identified an inner contradiction in the U.S. Constitution while studying for his citizenship test: the type of loophole that could plunge the United States into a dictatorship not unlike the Nazi regime Gödel had escaped when he immigrated from Austria. So, what was the loophole? Over the years, everyone from Constitutional law scholars to laypeople on Reddit have wondered. Endless Thread plumbs the depths of Gödel's galaxy brain with the help of philosopher, writer and Gödel expert Rebecca Newberger Goldstein — and learns some surprising lessons about artificial intelligence, a German shorthand called Gabelsberger, and democracy along the way. Show notes: Incompleteness: The Proof and Paradox of Kurt Gödel, by Rebecca Newberger Goldstein (Amazon) Oskar Morgenstern's account of Kurt Gödel's naturalization (Institute of Advanced Study) This episode was produced by Grace Tatter. It was co-written by Grace Tatter and Ben Brock Johnson, co-hosted by Ben Brock Johnson and Amory Sivertson, and edited by Meg Cramer and Dave Shaw. Mix and sound design by Paul Vaitkus.

At Short Wave, we love a good brain. Which is why we've had a lot of conversations over the years with NPR's neuroscience reporter, Jon Hamilton. Jon's been writing about brains for over 15 years, from tiny brain organoids that grow in a dish, to fruit fly brains, mouse brains and some really memorable human brains. But Jon is retiring, so today on the show he joins us to share the most memorable brains he's come across in the past couple of decades. Interested in more brain science? Email us your question at shortwave@npr.org.Listen to every episode of Short Wave sponsor-free and support our work at NPR by signing up for Short Wave+ at plus.npr.org/shortwave.See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy