Podcasts about surprising

Whether you're questioning who you're attracted to or wondering why someone incredible on paper leaves you feeling nothing below the waist, you're not alone. In this episode, I unpack the messy, confusing space between wanting someone and wanting to build a life with them—and why those two things don't always line up. From a woman who's always identified as straight but can't stop fantasizing about women, to a listener who's been getting nothing but one-arm hugs after six dates, I get into the questions most people are too afraid to ask out loud. I also share what the science actually says about attraction—including why your post-gym crush might not be as real as it feels, and what your birth control could be doing to your "type." In this episode, you'll learn:  • Why putting a label on your sexuality might be the last thing you need to do right now—and what to try instead  • The conversation that can save you months (or years) of wondering where you stand with someone who won't make a move  • What to do when you and your partner are both waiting for the other person to take control in bed More Dr. Emily:  • Shop With Emily! Explore Emily's favorite toys, pleasure accessories, bedroom essentials, and more — designed to support your pleasure and confidence. Free shipping on orders $99+ (some exclusions apply). • Join the SmartSX Membership: Access exclusive sex coaching, live expert sessions, community building, and tools to enhance your pleasure and relationships with Dr. Emily Morse. • Interested in 1:1 Coaching with Emily? Head to sexwithemily.com/coaching to apply today! • Sex With Emily Guides: Explore pleasure, deepen connections, and enhance intimacy using these Sex With Emily downloadable guides. • The only sex book you'll ever need: Smart Sex: How to Boost Your Sex IQ and Own Your Pleasure • Want more? Visit the Sex With Emily Website • Let's get social: Instagram | X | Facebook | TikTok | Threads | YouTube • Let's text: Sign up here • Want me to slide into your email inbox? Sign Up Here for sex tips on the regular. Timestamps: 0:00 - Introduction  1:56 - The science of sexual attraction  5:01 - You can want someone without wanting to date them  6:12 - Surprising facts about who you're attracted to (and why)  9:19 - "Am I bisexual or do I just think women are hot?"  10:50 - The one-arm hug situation  15:25 - When your partner's meds killed your sex life  20:40 - 19 years married and ready to give up  25:31 - How to rekindle the spark after years together Learn more about your ad choices. Visit megaphone.fm/adchoices

Zach Feldman joins Matt Faircloth for a conversation about the intricacies of student housing, a niche yet highly profitable sector within real estate. He explains how student housing has evolved from informal setups to institutionalized assets, offering unique investment opportunities.They also talk about the importance of location, amenity design, and market research in achieving rent premiums, and how these factors differentiate student housing from traditional multifamily properties. He also highlights the significance of branding and community building in attracting tenants and ensuring high occupancy rates. Zach Feldman Current role: Partner, Aptitude Development Based in: New York, New York Where to find them:  https://www.linkedin.com/in/zacharyrfeldman/https://www.e77h.com/ Book your free demo today at bill.com/bestever and get a $100 Amazon gift card. Visit ⁠www.tribevestisc.com⁠ for more info. Try QUO for free PLUS get 20% off your first 6 months when you go to quo.com/BESTEVER  Join us at Best Ever Conference 2026! Find more info at: https://www.besteverconference.com/  Join the Best Ever Community  The Best Ever Community is live and growing - and we want serious commercial real estate investors like you inside. It's free to join, but you must apply and meet the criteria.  Connect with top operators, LPs, GPs, and more, get real insights, and be part of a curated network built to help you grow. Apply now at⁠ ⁠⁠⁠www.bestevercommunity.com⁠⁠ Podcast production done by⁠ ⁠Outlier Audio⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Will Minnesota Vikings make any SURPRISING moves this offseason; Did the Vikings miss their Super Bowl window; Important dates for the Vikings to monitor; Vikings anonymous tip line suggestions; Plus a Snake Draft of the Week on Purple Daily.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Music Insider Gary Graff Predicts Surprising 2027 Super Bowl Acts After Bad Bunny's Explosive Show! by 102.9 The Hog

My guest today is Bridget McCormack, former chief justice for the Michigan Supreme Court and now president and CEO of the American Arbitration Association. For the past several years, Bridget and her team have been developing an AI-assisted arbitration platform called the AI Arbitrator. So I sat down with her to talk about how the tool works, the pros and cons of automating parts of the arbitration process, and the bigger picture questions around institutional trust, justice, and the future of law.  Links:  All rise for JudgeGPT | The Verge Why do lawyers keep using ChatGPT? | The Verge Judge berates AI entrepreneur for using a generated ‘lawyer' | The Verge Judge slams lawyers for ‘bogus AI-generated research' | The Verge LexisNexis CEO says the AI law era is already here | Decoder ChatGPT can be a disaster for lawyers — Robin AI wants to fix that | Decoder Considerations In building guardrails for AI use In arbitration | Law360 The AI Arbitrator: What it is, what it isn't, and where it's going | Law360 Subscribe to The Verge to access the ad-free version of Decoder! Credits: Decoder is a production of The Verge and part of the Vox Media Podcast Network. Decoder is produced by Kate Cox and Nick Statt. This episode was edited by Chris Jereza and Ursa Wright. Our editorial director is Kevin McShane.  The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices

The Bulletproof Dental Podcast Episode 425 HOSTS: Dr. Peter Boulden and Dr. Craig Spodak DESCRIPTION In this engaging conversation, Peter Boulden and Craig discuss their recent mastermind retreat, sharing insights on personal growth, community support, and the challenges faced in the dental profession. They emphasize the importance of building relationships with patients, navigating quality problems, and embracing change and innovation in business ownership. The discussion also touches on the role of technology in enhancing patient experiences and the necessity of being part of a supportive community to thrive in dentistry. TAKEAWAYS Physical proximity enhances relationships and learning. Mastermind retreats foster clarity and growth in dentistry. Community support is crucial for overcoming challenges. Quality problems are a sign of growth and success. Business ownership requires a willingness to embrace change. Building relationships with patients leads to better outcomes. Technology is rapidly transforming the dental landscape. Continuous learning is essential for personal and professional growth. Switching ecosystems can lead to new opportunities. The patient experience is key to differentiating practices. CHAPTERS 00:00 High Energy Intro and Weekend Recap 02:56 Mastermind Retreat Insights 05:57 Navigating Growth and Clarity in Dentistry 09:00 The Importance of Community and Support 11:44 Quality Problems vs. Low-Quality Problems 14:39 The Journey of Business Ownership 17:27 Embracing Change and Innovation 20:21 Building Relationships in Dentistry 23:05 The Role of Technology in Dentistry 26:02 Creating Meaningful Patient Experiences 28:47 Final Thoughts and Takeaways 36:53 Outro REFERENCES Bulletproof Summit Bulletproof Mastermind  

Coffee is comforting. Familiar. For many of us, it's the unofficial start button for the day. But have you ever paused to wonder whether your daily caffeine habit is truly supporting your health—or simply helping you push through exhaustion? In this episode, we take an honest, balanced look at caffeine from a Christian, whole-person perspective. We'll talk about the potential benefits, the possible downsides, and how caffeine can affect sleep, stress, mood, and even our sense of freedom. This conversation is for Christian women who want to care for their bodies with wisdom and discernment. The goal isn't to shame coffee drinkers or create fear—it's to help you slow down, think clearly, and consider what's best for your body and season of life. What Caffeine Really Is Caffeine is often treated as harmless and normal, but it's technically considered a stimulant that affects the nervous system. It naturally occurs in coffee beans, tea leaves, and cacao, and it's also manufactured and added to many processed foods and drinks. Most of us think of caffeine as something found only in coffee or soda. But it also shows up in places like: Energy drinks Pain relievers Chocolate and candy Certain gums and mints Even some personal care products In the United States, the majority of adults consume caffeine every day, often without giving it much thought. The Helpful Side of Caffeine Caffeine isn't automatically “bad.” Used in reasonable amounts, it can offer real benefits, such as: Feeling more awake and alert Sharper reaction time Short-term mental focus Extra stamina for certain tasks Occasional support with pain relief For some women, a cup of coffee is simply enjoyable and fits well into a healthy lifestyle. The concern isn't caffeine itself. The concern is how easily it can become a crutch instead of a choice. The Possible Downsides What gives you energy in the morning can also interfere with your body in ways you might not notice right away. Regular caffeine use has been linked with things like: Trouble falling or staying asleep Higher stress and anxiety levels Changes in heart rate and blood pressure Feeling wired but tired Increased irritability Negative effects for sensitive individuals One of the biggest traps is the cycle many women get stuck in: Not enough sleep → more caffeine → worse sleep → even more caffeine. Over time, caffeine can become both the thing you rely on for energy and the very thing stealing your rest. Dependence Is More Common Than We Realize Most people don't think of caffeine as something you can be dependent on. But many experience real physical effects when they stop using it. Common symptoms after cutting back include: Headaches Low energy Difficulty focusing Mood changes Feeling achy or “off” Because these feelings are uncomfortable, it's easy to reach for more coffee just to avoid them. That's how a simple habit can slowly turn into something we feel controlled by. A Faith-Centered Lens on Coffee and Caffeine As Christian women, we're invited to live with freedom and wisdom in every area of life—including our daily habits. Scripture offers this gentle reminder: “All things are lawful for me, but not all things are helpful… I will not be dominated by anything.” – 1 Corinthians 6:12 Coffee may be permissible. Caffeine may be socially normal. But a better question is: Is it truly helpful for you right now? If you feel like you can't function without caffeine, or you're using it to ignore exhaustion instead of listening to your body, that may be worth bringing before the Lord. Your body was designed for rhythms of rest and restoration. Stimulants can't replace what real sleep and peace provide. Time-Stamped Highlights 00:00 – An honest conversation about America's favorite legal drug 01:00 – Understanding what caffeine actually is 02:00 – Surprising places caffeine can be found 03:20 – How caffeine can create a sleep cycle problem 03:45 – Possible benefits of moderate caffeine use 04:10 – Potential effects on the heart and stress levels 05:00 – Special concerns for pregnancy, breastfeeding, and children 07:20 – How caffeine withdrawal can show up 09:40 – Why caffeine often masks deeper fatigue 10:15 – A biblical perspective on being mastered by habits 11:10 – Ways to evaluate your own caffeine use Key Takeaways Caffeine can be useful—but it can also quietly interfere with sleep and stress. Many people rely on caffeine more than they realize. More coffee isn't always the answer to low energy. Each woman's body responds differently—discernment matters. Faith invites us to care for our bodies with intention, not autopilot. Instead of asking, “Am I allowed to drink coffee?” consider asking: “Is this helping me thrive—or just helping me keep going when I need rest?” If this episode made you pause and think about your own habits—whether with caffeine, sleep, stress, or energy—you don't have to sort it all out alone. I offer one-on-one Health Clarity Sessions where we slow everything down and talk through what's really going on in your life and your body. These sessions are gentle, practical, and focused on helping you feel calm and confident about your next steps. No pressure. No complicated plans. Just a peaceful space to get clear. Learn more and book a session here: herholistichealing.com/clarity And if you'd like simple, faith-centered steps to support your energy without overwhelm, download the free More Energy Starter Guide at: herholistichealing.com/free   This content is for informational purposes only and is not meant to be medical advice.

For years now, host Katja has been studying contraindications for foam rolling research, and today she sits down with Tiffany to discuss if new foam rolling research is reaching users in a practical way. In this episode, we discuss Katja's latest research project as well as recent findings and their implications for the yoga world. Listen in to learn which findings were most surprising and how yoga teachers can apply new foam rolling research in their classes and practice.  "We are always using, not only the research...but then also pulling in our gut and our experience and what is realistically attainable." - Tiffany Cruikshank. — What You'll Learn: Background on Katja's new research [1:27] In which context foam rolling is applied in practice [13:00] Foam rolling durations [15:14] How often is foam rolling recommended [23:40] Tools for foam rolling [31:13] Recent research on vibration foam rollers [38:04] Surprising findings [42:35] Practicality of collecting data [54:26] Info from non-users [1:08:14] Final takeaways [1:10:58] — Links Mentioned: Watch this episode on YouTube Online Myofascial Release Training Connect with Katja Bartsch: Facebook | Instagram | Kalamana Yoga | YMO Guest Teacher  

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

For the text to this week's passage, please click Acts 1:12-13; For more information about Maranatha, please visit www.mbccolumbus.org

Thanks to news anchor Susie Jones nearly wiping out after the news, Jason talks with listeners about what they do when you're about to bite it? Then he shares some recent polling about how Minnesotans feel about immigration enforcement.

AP Washington correspondent Sagar Meghani reports on a surprising jobs gain last month.

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How does a singing teacher land a vocal coaching job on a TV drama? In this episode, Alexa is joined by vocal coach, performer and returning guest - Candi Underwood, who shares her experience coaching Matt Smith for the Sky Atlantic adaptation of The Death of Bunny Munro, based on the novel by Nick Cave. They explore how the opportunity came about, what was required on set, and which skills singing teachers already have that translate directly into TV coaching work. Perfect listening for singing teachers curious about taking their work beyond the studio and onto screen.WHAT'S IN THIS PODCAST?  5:03 What is The Death of Bunny Munro?6:42 What was the production company looking for in a vocal coach?10:59 Preparing for the job16:01 Taking a history19:12 Surprising essential skills20:30 Working on technique23:39 Keeping it professional with high profile figures28:53 Top 3 skills singing teachers needs for a TV coaching role32:12 How to get your food in the doorAbout the presenter HERERELEVANT MENTIONS & LINKSSinging Teachers Talk - Ep31. Where to Start with Digital MarketingNick CaveMatt SmithThe Death of Bunny MunroFrank SinatraSinging Teachers Talk - Ep.198 Mastering Singing for Stage, Screen & the Music IndustrySinging Teachers Talk - Ep.242 Understanding Motor Learning: How It Can Help Us Give Better Singing Lessons - Eps. 155 & 156 Building Neurodiversity-Inclusive Voice StudiosSinging Teachers Talk - Ep.222 The Rise of AI: What It Means for Singers & TeachersSinging Teachers Talk - Ep.226 The Rise of AI: Practical Tools and Strategies For the Singing TeacherABOUT THE GUESTCandi Underwood is a professional performer, session singer, vocal coach and founder/leader of City of Stars, a group of intermediate musical theatre choirs across Sussex. With over 16 years of experience in the industry, she's been featured as both a singer and songwriter on BBC Radio 1, BBC Radio 6Music, Kerrang!, Metal Hammer, Radio X, Planet Rock and more, performed at nationwide festivals and featured in TV ads; all while building a successful coaching business dedicated to nurturing healthy, resilient voices and inspiring bold, fearless performances for singers and screamers alike. Most recently she collaborated with Nick Cave and Matt Smith on Sky TV's 'The Death of Bunny Munro', coaching the former through a singing scene, and featured in the Marks & Spencer Christmas Advert with her choir alongside Dawn French.WebsiteInstagram

Angela joins me as we share crazy welfare data on Puerto Rico, what those telephone poles were all about during the Super Bowl Halftime Show and should Puerto Rico just be its own country and not receive our tax dollars? Plus some Olympic updates including a new sport!

Can what you eat really change your skin?   Dr. Jessica Krant, board-certified dermatologist and lifestyle medicine physician in New York City, joins Chuck Carroll at the International Conference on Nutrition and Medicine to explain the powerful connection between diet, inflammation, and skin health.   From acne and rosacea to wrinkles and premature aging, Dr. Krant breaks down how dairy, sugar, ultra-processed foods, and stress impact the skin — and how whole plant foods can help reverse damage and support collagen naturally.   You'll learn about the gut-brain-skin axis, advanced glycation end products (AGEs), collagen supplements, beauty sleep, and why stress might be triggering your breakouts.   Learn more about Dr. Krant and schedule an appointment at her practice at: https://artofdermatology.com.  

In todays episode the boys break down who has spoiled the most pranks, not being invited to kens birthday, the prank war continuing in cormorant, how we avoid acting, Bens skateboarding progress and big wrenches surprising skill, Girl cars, hitting deer, and our fans around the world LUCY's the only pouch that gives you long-lasting flavor, whenever you need it. Get 20% off your first order when you buy online with code WIDEOPEN. And if you don't want to wait, just head to lucy.co/stores to find Lucy near you and grab it today! Rula patients typically pay $15 per session when using insurance. Connect with quality therapists and mental health experts who specialize in you at https://www.rula.com/CBOYS  #rulapod Get up to $200 off Square hardware when you sign up at square.com/go/wideopen! #squarepod   To watch the podcast on YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/LifeWideOpenYT⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Don't forget to subscribe to the podcast for free wherever you're listening or by using this link: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/LifeWideOpenWithCboysTV⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ If you like the show, telling a friend about it would be amazing! You can text, email, or send this link to a friend: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/LifeWideOpenWithCboysTV⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ You can also check out our main YouTube channel CboysTV: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/c/CboysTV⁠⁠⁠⁠⁠⁠⁠

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Send a textSex and Sleep: The Surprising ConnectionIn this long-awaited episode, Dan and Michelle dive into the intriguing relationship between sex and sleep. They explore the physiological and psychological aspects of this connection, discussing hormones like oxytocin, endorphins, and prolactin, and their impact on sleep quality. Not only do they talk about the benefits of sex for enhancing sleep, but they also address common concerns and barriers, including anxiety, pain, and the dynamics of communication between partners. Join them for an enlightening conversation filled with practical tips and a bit of humor. Don't forget to subscribe and share your feedback!00:00 Introduction and Outfit Commentary00:15 Sex and Sleep: The Connection02:21 Hormones and Their Role05:10 Physiological Changes During Sex09:51 Psychological Aspects of Sex15:34 Conclusion and Sleep Tips✨ Real rest isn't just about falling asleep, it's about feeling at ease again. I'm Dr. Daniel Baughn, sleep psychologist and co-host of Sleep Takeout. I help professionals and high-achievers who seem to have everything together on the outside but can't quite turn off their minds at night.

Show notes: (0:00) Intro (1:14) What led Dr. Cole to functional medicine and early telehealth (6:13) Why many "healthy" people still feel unwell (11:15) The top 3 blood tests you should ask for (18:58) How inflammation is the real issue (28:21) The "Inflammatory Core 4" foods to cut out (35:26) Carbs, ketosis, and finding what works for your body (40:45) Surprising household toxins that may be harming your health (45:20) Practical steps to reduce exposure and feel better faster (48:48) Where to find Dr. Cole (49:58) Outro Who is Dr. Will Cole?   Dr. Will Cole is a leading functional medicine expert who consults people around the world via webcam, having started the first functional medicine telehealth centers in the world. Named one of the top 50 functional and integrative doctors in the nation, Cole specializes in clinically investigating underlying factors of chronic disease and customizing a functional medicine approach for thyroid issues, autoimmune conditions, hormonal imbalances, digestive disorders, and brain problems.   He is the host of the popular The Art of Being Well podcast and the New York Times bestselling author of Intuitive Fasting, Gut Feelings, Ketotarian, and The Inflammation Spectrum.   Connect with Dr. Cole Website: https://drwillcole.com/ LinkedIn: https://www.linkedin.com/in/drwillcole/ Instagram: https://www.instagram.com/drwillcole   Tune in: https://drwillcole.com/podcast/   Grab a copy: https://drwillcole.com/books-home-page/ Links and Resources: Peak Performance Life - https://buypeakperformance.com/ Peak Performance on Facebook - https://www.facebook.com/livepeakperformance/ Peak Performance on Instagram - https://www.instagram.com/livepeakperformance

In this episode of The Dairy Podcast Show, Dr. Karun Kaniyamattam from Texas A&M University breaks down how decision modeling and artificial intelligence can support real-world dairy management. He shares practical examples of how data streams, genetic indices, and modeling tools can improve disease control, labor efficiency, and long-term profitability without adding unnecessary complexity. Listen now on all major platforms."Decision modeling helps evaluate trade-offs that occur when balancing animal health, productivity, environmental responsibility, and farm profitability."Meet the guest: Dr. Karun Kaniyamattam is an Assistant Professor of Livestock Data Analytics and Artificial Intelligence at Texas A&M AgriLife Research. Trained as a veterinarian, he focuses his work on decision modeling, artificial intelligence, and sustainable dairy cattle systems. His research integrates animal health, economics, and production to support better farm-level decisions. Liked this one? Don't stop now — Here's what we think you'll love!What you'll learn:(00:00) Highlight(01:43) Introduction(12:16) Decision modeling(14:45) Systems thinking(17:43) Computing power(19:50) AI definition(23:47) Surprising insights(28:01) Final three questionsThe Dairy Podcast Show is trusted and supported by innovative companies like:* Priority IAC* Adisseo* Agri-Comfort* Jones-Hamilton Co.* Lallemand* CowManager* Afimilk* Evonik- BoviSync- Berg + Schmidt- Natural Biologics- Agrarian Solutions- AHV- dsm-firmenich- Protekta- DietForge

What is a Heather Shirt and How are they Made? The Surprising Backstory.Heather t-shirts have been around for awhile, but have you ever wondered what they are and how they're made? Why are they called heather? The interesting story behind heather shirts and hoodies is in this episode of the podcast.Read a text version of this topic in the blog at 1923MainStreet.comShop 1923 Main Street for Snow, Skate and Surf t-shirts, hoodies and sweatshirts.Thank you for listening to the Travel Style Podcast by 1923MainStreet.com.Shop unique and original travel inspired t-shirts, sweatshirt, hoodies and more at 1923 Main Street.Follow along on X, Instagram, Pinterest and Facebook.Thank you for listening and always remember to roam freely and ride boldly.Mike Belobradic--Media provided by Jamendo

I feel like everyone lately has been talking about “feeling behind in life”, so I decided to recirculate this popular episode at a time when we all truly need to hear it.  I share why so many of us feel like we're ‘behind'—we tell ourselves we should have more money, should already own a home, or should be having babies by now. But what's happening in our psychology to make us feel this way? And how do we fix it? I answer those questions and share science-backed insights and strategies to help you stop feeling ‘behind in life' and start embracing where you are.

Today's audioblog explores what’s really happening beneath the surface when kids laugh when in trouble or get silly during correction. Learn practical ways to use the Connected Families Framework™ to build wisdom instead of shame when kids seem to avoid accountability through humor. Key Takeaways: Silliness is often an escape hatch, not disrespect Check your own heart first Connect before you correct, try stepping into your child’s shoes with playfulness Build the value of reconciliation in a way that they feel good about Mentioned in this Podcast: The Power of Questions online course Sensitive & Intense Kids online course Blog Post – Your Child Laughs When in Trouble? How to Build Wisdom, Not Shame Blog Post – Have You Experienced the Benefits of Child-Led Play? Check out our website for more resources to support your parenting! This podcast was made possible by members of The Table, whose monthly support creates a ripple effect of change for generations to come. We'd love to have you take a seat at The Table! Love the podcast? Leave a review to help other parents discover the show! © 2026 Connected Families .stk-cf6a95f {margin-bottom:39px !important;}.stk-cf6a95f-container{background-color:#e2f4f8 !important;}.stk-cf6a95f-container:before{background-color:#e2f4f8 !important;}.stk-cf6a95f-container:hover{box-shadow:0px 2px 20px #99999933 !important;}@media screen and (max-width:689px){.stk-cf6a95f .stk-block-card__image{width:100% !important;height:250px !important;}} Do you have a child with BIG feelings and BIG needs? The Sensitive & Intense Kids online course is a game changer. It’s for YOU. .stk-7a41d3e .stk-button-group{flex-direction:row !important;}@media screen and (max-width:999px){.stk-7a41d3e .stk-button-group{flex-direction:row !important;}}@media screen and (max-width:689px){.stk-7a41d3e .stk-button-group{flex-direction:row !important;}} .stk-4080859 .stk-button{background:var(--theme-palette-color-1, #ee6c4d) !important;}.stk-4080859 .stk-button:hover:after{background:var(--theme-palette-color-2, #98c1d9) !important;opacity:1 !important;}.stk-4080859 .stk-button__inner-text{font-size:21px !important;font-weight:600 !important;}@media screen and (max-width:999px){.stk-4080859 .stk-button__inner-text{font-size:21px !important;}}LEARN MORE

Struggling with your keto diet? Book a free consultation call with Robert Sikes to break your plateau: https://www.ketobodybuilding.com/callAfter falling 90 feet down a waterfall and being told by doctors he would never be as strong again, Will Blazer didn't just recover; he became stronger than before. On episode 857 of the Savage Perspective Podcast, host Robert Sikes talks with professional acrobat Will Blazer about his unbelievable story. Will shares how getting terrible blood work at age 23 led him to try every diet from vegan to keto. He explains how the carnivore diet fixed his health, reduced painful inflammation, and fueled his incredible recovery from a shattered pelvis and other major injuries, allowing him to build muscle and strength without carbohydrates. His journey is a powerful example of how a proper human diet is key for recovery and building a strong body.Get Keto Brick: https://www.ketobrick.com/Subscribe to the podcast: https://open.spotify.com/show/42cjJssghqD01bdWBxRYEg?si=1XYKmPXmR4eKw2O9gGCEuQChapters:0:00 - From World-Class Acrobat to Prediabetic: Will Blazer's Story0:46 - Live with Will Blazer1:48 - What Is The Extreme Sport of Tricking?2:43 - How to Teach Yourself Any Physical Skill3:56 - Does Building Muscle Hurt Athletic Performance?4:54 - The "Quintfull": A Flip So Difficult Olympians Can't Do It7:26 - The Horrifying Bloodwork Results at Age 238:50 - He Tried Every Diet (Vegan, Keto, Paleo) - Here's What Worked11:06 - Keto to Carnivore: The Insane Benefits Within The First Week12:20 - Has The Carnivore Diet Fixed His Health Issues?13:31 - How He Experiments with The Carnivore Diet14:40 - Does The Carnivore Diet Hurt Strength & Performance?15:15 - The Waterfall Accident: A 90-Foot Fall17:23 - "We All Thought I Was Paralyzed"19:33 - How An iPhone Feature Saved His Life20:39 - The Hospital Experience & Refusing Procedures22:18 - The Full List of Horrific Injuries24:21 - Learning How to Walk Again25:30 - The Lowest Point: When The Surgeon Said "You Will Never Be As Strong"27:32 - A Message From The Host: Robert Sikes29:12 - Why He Refused a Life-Altering Surgery31:23 - How "Cheating" on Carnivore Made The Pain Worse32:08 - The Mindset Shift That Changed Everything34:17 - The First Backflip After The Injury35:04 - From Unable to Walk to Stronger Than Ever37:56 - Why You Must Experience Weakness to Gain Strength38:35 - What's Next? Road to a 500lb Deadlift41:38 - Do You Need Carbs for Energy? A Carnivore's Perspective44:39 - The Goal: Powerlifting Meet & Acrobatics Comeback45:31 - How to Learn Parkour & Backflips (For Beginners)47:54 - Where to Find Will Blazer & His Inspirational Recovery Video

Most of us do everything we can to avoid rejection—but what if there are actually some positives to it?Writer and podcast host Alice Draper joins me to talk about why rejection hurts, what it can teach us, and how to build your “rejection resilience muscle.”We cover the difference between rejection and failure, why it's important not to let rejection stop you, and the surprising upside of hearing “no.”Alice Draper and the founder of the podcast guesting company, Hustling Writers and the host of the award-winning podcast "My Rejection Story".On her podcast, My Rejection Story, you can listen to bestselling authors like Whitney Goodman, Guy Winch, or Jesse J. Anderson share how they navigated the toughest periods of their personal and professional lives, and how this shaped the success they now experience today. Her words can be found in HuffPost, Business Insider, VICE, Refinery29.Some of Alice's favorite things include strong coffee, affordable travel hacks, and deep connections. She detests small talk, waking up early, and unnecessary jargon.https://www.linkedin.com/in/alice-m-draper/https://www.instagram.com/alicedraper/Tune in each week for practical, relatable advice that helps you feel your best and unlock your full potential. If you're ready to prioritize your health and level up every area of your life, you'll find the tools, insights, and inspiration right here. Check out Esther's website for more about her speaking, coaching, book, and more: http://estheravant.com/Buy Esther's Book: To Your Health: https://a.co/d/iDG68qUEsther's Instagram: https://www.instagram.com/esther.avantEsther's LinkedIn: https://www.linkedin.com/in/estheravant/Learn more about 1:1 health & weight loss coaching: https://madebymecoaching.com/coaching 

  Host: Dan Panetti   Here are some helpful additional resources to further your Biblical worldview development:   Biblical Worldview: What It Is, Why It Matters, and How to Shape the Worldview of the Next Generation by Josh Mulvihill What Is Wrong with the World?: The Surprising, Hopeful Answer to the Question We Cannot Avoid by Timothy Keller Worldview Academy resources Summit Ministry resources What's Your Worldview by James Anderson   T4M guys - just a reminder that Training4Manhood is a non-profit, 501(c)(3) ministry and you can make donations either via Zelle (info@training4manhood.com) or by visiting the Training4Manhood website.

Few topics divide opinion in the church like money. But what if much of what we've been taught about giving isn't truly biblical? In this eye-opening episode, host Catherine welcomes Bible scholar and award-winning author, Clifton Payne Jr., to set the record straight on what the Bible really says about money, tithing, and generosity. They cut through decades of confusion and misinterpretation, addressing the guilt-driven giving, prosperity promises, and toxic church culture that have warped how Christians view their finances—and God's character. Clifton Payne Jr. reveals little-known truths from scripture (including the fact that there were actually three different tithes in ancient Israel, not just one!) and explains how biblical principles of stewardship are often misunderstood or misapplied today. What You Will Discover in This Episode: The deep mistrust many Christians have toward churches when it comes to money—and how church scandals, manipulation, and legalism fuel that distrust. Surprising discoveries like the three biblical tithes, special rules for ancient Israel, and what Jesus actually teaches about taxes, tithing, and giving. Why tithing is a model—not a requirement—for modern believers, and how generosity should flow from gratitude rather than obligation. The crucial principle of "first fruits": why giving God our best and first, not our leftovers, directly impacts the spiritual life of our families. The freedom found in New Testament giving, where the heart matters far more than the amount, and how generosity can be an antidote to materialism. Powerful stories—like the unforgettable account of Otzi, an African woman whose tiny, sacrificial gift became the greatest offering in the eyes of God. The conversation is both practical and pastoral, exploring how to talk about money with your kids, build healthy giving habits, and reclaim joy in generosity—even if you’ve been wounded by church abuse around this issue in the past. About the Guest:Clifton Payne Jr. is the author of What the Bible Really Says About Tithing and Giving: It's Different Than You Think, a book that has earned recognition from both the American Bookfest and International Book Awards. Drawing on years of pastoral ministry and biblical scholarship (including studies at Hebrew University in Jerusalem), Clifton untangles scripture from tradition, helping listeners return to the heart of God in their finances. Key Takeaways for Listeners: Don’t start with guilt or pressure—start with prayer and let giving flow from what God puts on your heart. Giving first is a spiritual discipline that breaks the grip of materialism and invites God’s faithfulness into your life. Teaching kids to give as a first priority (not last, as our culture encourages) will shape their lifelong habits and trust in God. Generosity has enormous power to heal wounds, restore faith, and transform communities when practiced in freedom and love. Episode Resources:What the Bible Really Says About Tithing and Giving: It’s Different Than You Think by Clifton H. Payne Jr. Catherine's Free Parenting Resources Other Episodes in This Series: EPISODE 175: When Leaders Exploit the Flock: A Biblical Response to Scandals ( w/ Clifton Payne Jr.) EPISODE 174: Finding Financial Freedom: Breaking Free from Debt and Anxiety (w/ Jade Durham) Discover more Christian podcasts at lifeaudio.com and inquire about advertising opportunities at lifeaudio.com/contact-us.

In today's message, Pastor Craig with elder Ali Master, uncovers the joy and blessing of evangelism.

Guest: Tyler Anbinder. Highlighting Phelan and Collender's billiard empire, Anbinder concludes by emphasizing the surprising upward mobility and resilience of Famine immigrants in American society

John Chang breaks down the economic trends set to define 2026 from tepid job growth, declining population growth, and geopolitical uncertainty to the surprising resilience of certain real estate sectors. Discover why AI investments, despite fueling GDP growth, aren't creating jobs, and what that means for commercial real estate demand. Book your free demo today at bill.com/bestever and get a $100 Amazon gift card. Visit ⁠www.tribevestisc.com⁠ for more info. Try QUO for free PLUS get 20% off your first 6 months when you go to quo.com/BESTEVER  Join us at Best Ever Conference 2026! Find more info at: https://www.besteverconference.com/  Join the Best Ever Community  The Best Ever Community is live and growing - and we want serious commercial real estate investors like you inside. It's free to join, but you must apply and meet the criteria.  Connect with top operators, LPs, GPs, and more, get real insights, and be part of a curated network built to help you grow. Apply now at⁠ ⁠⁠⁠www.bestevercommunity.com⁠⁠ Podcast production done by⁠ ⁠Outlier Audio⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Surprising twists for all!

Support our sponsor this week by using the link below for the exclusive Solomonster offer!BETTERHELP ▶ Get 10 PERCENT OFF your first month and give online therapy a try at http://www.betterhelp.com/solomonster to start being your best self. Thanks to BetterHelp for sponsoring this week's episode!Solomonster reviews WWE Smackdown with Randy Orton and Tiffany Stratton qualifying for the Elimination Chamber and another SURPRISING qualifier scheduled for next week, Rhea Ripley and IYO SKY defend their Women's tag team titles and Drew McIntyre gets his RECEIPT on CM Punk and Roman Reigns after their comments on Monday Night Raw.***Follow Solomonster on X (formerly Twitter) for news and opinion:http://x.com/solomonsterSubscribe to the Solomonster Sounds Off on YouTube:https://www.youtube.com/user/TheSolomonster?sub_confirmation=1Become a Solomonster Sounds Off Channel Member:https://www.youtube.com/channel/UC9jcg7mk93fGNqWPMfl_Aig/join

What if the things you do every single day are quietly making you sick… and you don't even realize it? Harvard psychologist Dr. Ellen Langer, author of The Mindful Body, reveals the shocking ways our minds shape our health, aging, stress levels, and even how long we live—often without us noticing. In this mind-expanding of Mayim Bialik's Breakdown, Dr. Langer explains why self-agency and making your own decisions can literally extend your lifespan, how expectations and beliefs shape disease progression, and why the real meaning of mindfulness has almost nothing to do with meditation. Dr. Langer breaks down: - Small, everyday habits that are secretly harming your health - Why stress is the #1 cause of illness (and not for the reason you think) - Whether healing timelines are based on real time or perceived time - Surprising benefits of positive thinking, even with terminal illness - Danger of labels, including words like “try” and “remission” - Why spontaneous remissions exist & why they're so hard to study - Simple ways to become more mindful right now, even if you've never meditated once in your life - Can we train ourselves to not need eyeglasses? - The real power of the placebo effect - Can the mind cure the common cold? - Psychological treatments for chronic illness - Who is more likely to get sick & why - How color influences our biology more than we think - Why spirituality requires presence and mindfulness - How to reframe negative circumstances into positive ones, and the health benefits of doing so - How her mother's battle with cancer inspired her groundbreaking research This episode will change how you think about healing, stress, aging, illness, and the true power of your mind over your body! You may never see health, sickness, or “reality” the same way again. Head to https://impact.ourritual.com/c/4792730/2005678/24744 , take a quick quiz, and use code BREAKER20 for 20% off your first month. If you're tired of being tired, this is your chance to finally get answers and get your energy back. Go to https://superpower.com/ and use code BREAK for $20 off your membership this year. Dr. Ellen Langer's latest book, The Mindful Body: Thinking Our Way to Chronic Health: https://www.penguinrandomhouse.com/books/705365/the-mindful-body-by-ellen-j-langer/9780593497944/ Follow us on Substack for Exclusive Bonus Content: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bialikbreakdown.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BialikBreakdown.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube.com/mayimbialik⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

In this preseason interview filmed in Fiji, Emily Flippen reveals the internal struggle behind returning for the milestone 50th season. She breaks down her fascinating psychological reads on legends like Cirie Fields and Coach at Ponderosa, explaining why she's already picking up on a massive divide in energy between the icons of the game. From calling the back-to-back players from season 49 "psychos" to her ruthless decision to vote against the cast receiving rice, Emily discusses leaning into her reputation as a "physical liability" and how she plans to navigate a fast-paced game where she feels like a permanent underdog.

The guys react to yesterday's big news of the Tigers signing former Astros SP Framber Valdez to a huge 3-year, $115 million deal.

See omnystudio.com/listener for privacy information.

Written by Erik Jones. Jesus' perspective on spreading the gospel differed from that of many churches today. What was His approach to evangelism, and should it be applied today?Read Online: https://lifehopeandtruth.com/god/who-is-jesus/jesus-surprising-approach-to-evangelism/

2/5/26 - The Tigers make a surprising move, Thursday Touchdown, Two Grand Slam, Dear Jon

6:00 HOUR: The Tigers make a surprising move, Thursday Touchdown

Join us for Purim 5786! https://curtlandry.com/register Plant a tree in Israel in honor of Tu BiShvat. Plant for hope and remembrance. https://curtlandry.com/planthope In this episode of the Curt Landry Podcast, Rabbi Curt talks about the biblical significance of trees and the Jewish Arbor Day, Tu BiShvat. Throughout the story of the Bible, we see a theme of three trees: The tree of knowledge in Genesis that brought death, the tree at Calvary where Yeshua brought redemption, and the olive tree in Romans 11, representing Israel and Believers who are grafted in as One New Humanity. God uses the symbolism of trees to tell the story of His people, and trees biblically and culturally in Israel represent life, resilience, and hope. When a child is born, or a person dies, it is customary to plant a tree in their honor– a testament of life and enduring legacy. Tu BiShvat, which falls on February 2 in 2026, will speak to just that: the remembrance of lives lost in war and God's promise of hope and a future, as the land that was once barren is again alive and fruitful, just as the prophets foretold. Join Rabbi and Darrell as they discuss the Swords of Iron Memorial Grove in Israel, the prophetic significance of planting olive trees, and why Purim is especially significant this year in light of current events. Disclaimer: Curt Landry Ministries will never send you a direct message or comment asking for donations, or, request to move the conversation to Telegram or any other third-party application. Any and all donations are solely collected through our website, https://www.curtlandry.com, or, through one of our YouTube fundraisers. For tithing and giving, please visit: https://shop.curtlandry.com/donate/ Listen to The Curt Landry Podcast: https://www.curtlandry.com/podcast/the-curt-landry-podcast/ Join this channel to get access to perks: https://www.youtube.com/channel/UCRSNGrZ4oXOEYHMxBkZO94A/join · Website: https://www.curtlandry.com · Facebook: https://www.facebook.com/curtlandryministries/ · Twitter: https://twitter.com/curtlandrymin · Instagram: https://www.instagram.com/curtlandrymin/ Get the resources you need to stand firm in your inheritance… · Jewish Roots Guide… https://curtlandry.com/Jewish-Roots · One New Man Guide… https://curtlandry.com/ONM-Guide · Psalm 91 Prayer… https://curtlandry.com/Psalm91Prayer · Goals to Grow… http://curtlandry.com/Goals2024

Kyle Fraser & Q Burdette Survivor 50 Preseason Interviews Mike Bloom (@AMikeBloomType) is here to chat to the cast of Survivor 50! Join us to hear from your favorite returning Survivor players! Today, Mike Bloom sits down with standout players Kyle Fraser and Q Burdette to talk all things strategy, reputation, and gameplay in anticipation of the landmark season. Mike dives deep into these returning castaways' headspaces, exploring how recent wins, big personalities, and past moves shape their approach to Survivor 50. With a supersized cast and shifting alliances, every move counts, and both Kyle and Q are ready to state their case. In this interview-packed episode, Mike Bloom explores Kyle Fraser's journey from newest Sole Survivor to defending champion. Kyle shares what it's like to play while his winning season is still fresh in everyone's mind and reflects on navigating public perceptions, especially around his “secret trio” alliance with Joe and Kamilla. Kyle weighs the risks of being an obvious threat and how he plans to use ego management to his advantage, pointing to classic Survivor winners for inspiration. Q Burdette bursts in with his unique brand of energy, discussing fatherhood, the lessons learned from his last chaotic season, and his intention to lay low before unleashing his trademark big moves. Both contenders break down who they see as friends, foes, and frenemies in the game, identifying which castaways pose the biggest risks or could make the strongest allies. They also speculate about the mysterious “49ers”—the two fresh faces whose game tape is still a mystery. – Kyle's take on being underestimated and managing the target on his back as a recent winner – Q's “wagon” theory for dragging key allies, and his new approach to playing calm before the chaos – Both contenders' detailed friend-or-foe breakdowns for the Survivor 50 cast, revealing surprising trusts and threats – The pair's thoughts on legacy players like Sandra, Tony, and Earl, plus which Survivor legends they wish were joining this season – Surprising strategies for jury management, spinning narratives, and tackling unpredictable wild cards As the Survivor 50 season draws near, pressing questions set the stage: Can Kyle avoid getting blindsided as the defending champion? Will Q's new “quiet Q” approach keep him safe, or will past chaos resurface? Who will get the upper hand when legends and under-the-radar players collide? Chapters: 0:00 Intros 6:00 Kyle Discusses Managing His Ego 12:00 Friend or Foe Cast Assessment 18:00 Navigating Returning Player Dynamics 23:06 Kyle's Narrative Spinning Strategy 27:35 Q's New Approach Revealed 33:02 Idol Regrets and Lessons Learned 36:03 Q Targets Finalist Players 42:03 Alliance Thoughts: Genevieve, Joe, Kamilla 45:04 Mysterious 49ers: Threats and Suspicions 47:31 Q Admits Past Game Mistakes 48:51 Celebrity Loved One Dream Pick Never miss a minute of RHAP's extensive Survivor coverage! LISTEN: Subscribe to the Survivor podcast feed WATCH:  Watch and subscribe to the podcast on YouTube SUPPORT:  Become a RHAP Patron for bonus content, access to Facebook and Discord groups plus more great perks!

Kyle Fraser & Q Burdette Survivor 50 Preseason Interviews Mike Bloom (@AMikeBloomType) is here to chat to the cast of Survivor 50! Join us to hear from your favorite returning Survivor players! Today, Mike Bloom sits down with standout players Kyle Fraser and Q Burdette to talk all things strategy, reputation, and gameplay in anticipation of the landmark season. Mike dives deep into these returning castaways' headspaces, exploring how recent wins, big personalities, and past moves shape their approach to Survivor 50. With a supersized cast and shifting alliances, every move counts, and both Kyle and Q are ready to state their case. In this interview-packed episode, Mike Bloom explores Kyle Fraser's journey from newest Sole Survivor to defending champion. Kyle shares what it's like to play while his winning season is still fresh in everyone's mind and reflects on navigating public perceptions, especially around his “secret trio” alliance with Joe and Kamilla. Kyle weighs the risks of being an obvious threat and how he plans to use ego management to his advantage, pointing to classic Survivor winners for inspiration. Q Burdette bursts in with his unique brand of energy, discussing fatherhood, the lessons learned from his last chaotic season, and his intention to lay low before unleashing his trademark big moves. Both contenders break down who they see as friends, foes, and frenemies in the game, identifying which castaways pose the biggest risks or could make the strongest allies. They also speculate about the mysterious “49ers”—the two fresh faces whose game tape is still a mystery. – Kyle's take on being underestimated and managing the target on his back as a recent winner – Q's “wagon” theory for dragging key allies, and his new approach to playing calm before the chaos – Both contenders' detailed friend-or-foe breakdowns for the Survivor 50 cast, revealing surprising trusts and threats – The pair's thoughts on legacy players like Sandra, Tony, and Earl, plus which Survivor legends they wish were joining this season – Surprising strategies for jury management, spinning narratives, and tackling unpredictable wild cards As the Survivor 50 season draws near, pressing questions set the stage: Can Kyle avoid getting blindsided as the defending champion? Will Q's new “quiet Q” approach keep him safe, or will past chaos resurface? Who will get the upper hand when legends and under-the-radar players collide? Chapters: 0:00 Intros 6:00 Kyle Discusses Managing His Ego 12:00 Friend or Foe Cast Assessment 18:00 Navigating Returning Player Dynamics 23:06 Kyle's Narrative Spinning Strategy 27:35 Q's New Approach Revealed 33:02 Idol Regrets and Lessons Learned 36:03 Q Targets Finalist Players 42:03 Alliance Thoughts: Genevieve, Joe, Kamilla 45:04 Mysterious 49ers: Threats and Suspicions 47:31 Q Admits Past Game Mistakes 48:51 Celebrity Loved One Dream Pick Never miss a minute of RHAP's extensive Survivor coverage! LISTEN: Subscribe to the Survivor podcast feed WATCH:  Watch and subscribe to the podcast on YouTube SUPPORT:  Become a RHAP Patron for bonus content, access to Facebook and Discord groups plus more great perks!

Video from the scene of Savannah Guthrie's mom's abduction is surfacing. TMZ is saying there's a ransom note asking for big money in Bitcoin. The Super Bowl is reporting live from Alcatraz? Jon Bon Jovi and Chris Pratt are announcing the teams to the field - here's why. We're ready to see what Bad Bunny is gonna bring to the table. Meanwhile, here are past halftime performances that aren't remembered fondly. The weather has been TOO good. Eddie Bauer is closing all their stores. This Olympic bobsledder funded her trip to Milan with OnlyFans. Matty is recommending ‘Cool Runnings' for some underdog Olympic inspiration. Get some etiquette, people!

Full show - Tuesday | Mantrum | News or Nope - Lindsey Vonn, Tyra Banks, and Savannah Guthrie | Could you be in a long distance relationship? | Snooty or not? | Erica's surprising stance on Valentine's Day | Do you keep notes on your partner? | T. Hack can't relax while he gets his hair cut | Hidden ducks | Stupid stories www.instagram.com/theslackershow www.instagram.com/ericasheaaa www.instagram.com/thackiswack www.instagram.com/radioerin

Is Valentine's Day really worth it?

With Dispatch censored on Nintendo platforms and image sharing restricted in Tomodachi Life: Living the Dream, Patrick and Mark dig into why and when Nintendo decides to censor games. But first: Nintendo Direct speculation, two previously unreleased games added to the upcoming Virtual Boy slate, reactions to the Tomodachi Life Direct, and more.The guys also talk about:Hard mode in Fire Emblem: Awakening, and why restarting a chapter can pay off later.Surprising new updates in Splatoon 3.The Disney Afternoon Collection confirmed for Switch and Switch 2, with two additional SNES games.SUPPORT US ON PATREON: https://www.patreon.com/nintendocartridgesocietyFRIEND US ON SWITCH / SWITCH 2Patrick: SW-1401-2882-4137Mark: SW-8112-0583-0050

Dr. Becky Kennedy is a clinical psychologist, the bestselling author of Good Inside, and the founder of a parenting platform used by millions. Known for her practical, psychology-based approach to parenting, Dr. Becky shares how the same principles that help parents raise resilient children can make you a much more effective leader. In this conversation, she breaks down why all human systems—whether families or companies—operate on the same fundamental principles, and how understanding these dynamics can make you more effective in every relationship.We discuss:1. Why repair—not perfection—defines strong leadership2. Why you need to connect before you correct to build cooperation and trust3. The “most generous interpretation” framework for handling difficult behaviors4. How to correctly set boundaries (vs. making requests)5. The power of “I believe you, and I believe in you”6. What it looks like to be a “sturdy” leader—Brought to you by:Merge—Fast, secure integrations for your products and agents: https://merge.dev/lennyMetaview—The AI platform for recruiting: https://metaview.ai/lennyFramer—Builder better websites faster: https://framer.com/lenny—Episode transcript: https://www.lennysnewsletter.com/p/dr-becky-on-the-surprising-overlap—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Dr. Becky Kennedy:• X: https://x.com/GoodInside• LinkedIn: https://www.linkedin.com/in/drbecky• Instagram: https://www.instagram.com/drbeckyatgoodinside• TikTok: https://www.tiktok.com/@drbeckyatgoodinside• Website: https://www.goodinside.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Dr. Becky Kennedy(05:14) Connecting parenting and leadership(08:40) The power of repair(11:05) Connecting before correcting(17:45) Good Inside framework at work(22:08) The most generous interpretation (MGI)(25:46) Curiosity over judgment(27:07) Understanding behavior change(31:08) What potty training can teach us about workplace behavior(34:40) Naming your intention(35:41) Sturdy leadership(40:52) How to set boundaries well(46:33) The role of leadership and consensus(50:50) The importance of being “locatable”(52:40) A powerful story of betrayal and realization(57:12) Building resilience over happiness(01:00:34) The power of the phrase “I believe you, and I believe in you.”(01:09:08) The Good Inside community and resources(01:16:22) AI corner(01:19:52) Good Inside's mission(01:22:26) Lightning round and final thoughts—Referenced:• Shreyas Doshi on pre-mortems, the LNO framework, the three levels of product work, why most execution problems are strategy problems, and ROI vs. opportunity cost thinking: https://www.lennysnewsletter.com/p/episode-3-shreyas-doshi• Radical Candor: From theory to practice with author Kim Scott: https://www.lennysnewsletter.com/p/radical-candor-from-theory-to-practice• From ChatGPT to Instagram to Uber: The quiet architect behind the world's most popular products | Peter Deng: https://www.lennysnewsletter.com/p/the-quiet-architect-peter-deng• Punch: https://en.wikipedia.org/wiki/Punch_(play)• Figma: https://www.figma.com• Andrew Hogan on LinkedIn: https://www.linkedin.com/in/ahhogan• Replit: https://replit.com• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• Lovable: https://lovable.dev• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Claude: https://claude.ai• ChatGPT: https://chatgpt.com• Secrets We Keep on Netflix: https://www.netflix.com/title/81697668• K Pop Demon Hunters on Netflix: https://www.netflix.com/title/81498621• Liberty puzzles: https://libertypuzzles.com—Recommended books:• Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity: https://www.amazon.com/Radical-Candor-Revised-Kick-Ass-Humanity/dp/1250235375• Good Inside: A Practical Guide to Resilient Parenting Prioritizing Connection Over Correction: https://www.amazon.com/Good-Inside-Guide-Becoming-Parent/dp/0063159481• Leave Me Alone!: A Good Inside Story About Deeply Feeling Kids: https://www.amazon.com/Leave-Me-Alone-Inside-Feeling/dp/1250413117• The Power of Moments: Why Certain Experiences Have Extraordinary Impact: https://www.amazon.com/Power-Moments-Certain-Experiences-Extraordinary/dp/1501147765/• The Messy Middle: Finding Your Way Through the Hardest and Most Crucial Part of Any Bold Venture: https://www.amazon.com/Messy-Middle-Finding-Through-Hardest/dp/0735218072• Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration: https://www.amazon.com/Creativity-Inc-Expanded-Overcoming-Inspiration/dp/0593594649—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com