Podcasts about gsm

Great ideas don't fail in dealerships because they're bad. They fail because no one installs the behavior fast enough. In this episode of Dealer Talk with Jen Suzuki, Jen breaks down a simple, high-energy methodology to help leaders stop "motivating" and start installing execution inside their stores. Most dealerships don't have a training problem — they have an execution problem. Processes get rolled out. Energy fades. Thirty days later, nothing sticks. Jen shares her proven meeting framework — used at NADA Academy and in high-performing stores — to compress action, build accountability, and make learning fun and sustainable. You'll learn how to: • Pick one behavior and install it fast • Teach a technique in under 20 seconds • Use real examples to drive discussion • Create immediate execution through activity • Run contests that build visibility and accountability • Shortlist, vote, and let the team own the win This is about turning meetings into movement. Not speeches. Not theater. Behavior change. If you're a GM, GSM, Fixed Ops Director, Sales Manager, or Service Manager who's tired of initiatives fading out — this episode gives you a repeatable structure to make things stick. Momentum doesn't come from motivation. It comes from movement. Dealer Talk with Jen Suzuki Podcast |

Australia is best known for its Shiraz. Big bold Shiraz and Cabernet Sauvignon from Barossa Valley, McLaren Vale, and Coonawarra. These are all from South Australia.We have talked about Western Australia (Margaret River) known for Cabernet Sauvignon and Chardonnay.Yarra Valley in Victoria is a cooler region known for its Chardonnay and Pinot Noir.Tasmania is off the south Coast of Australia and is an island. This is a cooler region and produces sparkling wines, Pinot Noir, and Chardonnay.Riesling is normally dry and crisp and best known for coming from the Clare Valley and the Eden Valley. Barossa Valley: Famous for bold Shiraz.Coonawarra: Renowned for rich Cabernet Sauvignon.Margaret River: A key region for elegant Chardonnay and Sauvignon Blanc blends.Clare Valley: Known for world-class, dry Riesling.McLaren Vale: Produces excellent Grenache, Shiraz, and GSM blends.Yarra Valley: A cooler climate region well known for quality Pinot Noir. Tonight, we are tasting:2020 Koonunga Hill, Shiraz Cabernet. Penfolds Wines South Australia. The winery is one of the best known in Australia and was established in 1844. Deep purple color, sweet dark dense berries, chocolate. Medium acidity, full-bodied, 14.5% alcohol. Flavors of vanilla bean creaminess and warm spice. Dried rosemary and sage might give appearance of earthiness. I purchased at Costco for $8. I mostly saw this wine running around $11, but I did see an online clearance sale (at Fine Wine and Good Spirits) for $4.33 (it said $11.26 off). The wine comes from the South Australia, but is a multi-regional blend. 65% Shiraz and 35% Cabernet Sauvignon.2020 Max's Shiraz Cabernet Penfolds. I purchased this wine at WineStyles for $17.00. Wine Enthusiast says aromas of blackberry jam, cherry cordial, pencil shavings and sweet vanilla bean-and-dark-chocolate oak influence. Rich, balanced acidity with tannins in the background. Could benefit from a few more years of aging. The wine scored a 92 from Wine Enthusiast. 70% Shiraz and 30% cabernet Sauvignon. 14.5% alcohol.2021 Bin 28 Shiraz Penfolds. Purchased at Wall to Wall Wine for $30. Wine Enthusiast says dense, ripe and powerful with quite a bit of oak. Chocolate with dark fruit and pepper spice on the nose. Flavor is rich and luscious, muscular tannins support rather than overpowers. Could age for a few more years. The wine was scored a 93 from the Wine Enthusiast. The wine is aged in American Oak for 12 months. 14.5% alcohol.We both liked #2 Max's Shiraz/Cabernet the best, and we thought this was the best buy of the night. I also liked #3 Bin 28 Shiraz, a very powerful fruity-oaky wine. I felt it lost a little balance because of the amount of oak, Denise didn't care for it's finish. Neither of us really cared for #1 Koonunga Hill, Shiraz/Cabernet. Next week we are exploring white wines of Australia.

Perplexed by patients with normal exams but persistent symptoms like recurrent UTIs or palpitations? It could be menopause. In this insightful episode of Succeed In Medicine podcast, host Dr. Bradley Block interviews Dr. Lauren Streicher. They explore commonly overlooked menopause symptoms beyond hot flashes: recurrent urinary tract infections tied to genitourinary syndrome of menopause (GSM), palpitations as "hot flashes of the heart" (often sinus tachycardia without EKG changes), GI microbiome shifts causing nebulous digestive issues, xerostomia (dry mouth) linked to oral health risks, and skin/hair changes like alopecia. Dr. Streicher emphasizes reassuring patients early, validating symptoms as hormonal, and tailoring treatments, vaginal estrogen, safe even for breast cancer patients, systemic hormones, or new non-hormonal NK3 receptor antagonists like fezolinetant. They discuss the SWAN study's findings on long-term risks from untreated hot flashes (e.g., cardiovascular disease, bone loss), the need to differentiate perimenopausal (temporary) vs. lifelong postmenopausal effects, and avoiding arbitrary hormone therapy stops after 5 years. The conversation also touches on sexual health gaps in medicine, with tips for better history-taking and resources like Dr. Stryker's "Come Again" course. Listeners, clinicians and patients alike, will gain tools to address menopause holistically, improving quality of life and preventing complications. Three Actionable Takeaways: Recognize GSM in Recurrent UTIs: For postmenopausal women with new-onset recurrent UTIs, suspect genitourinary syndrome of menopause, prescribe local vaginal estrogen (cream, suppository, or ring) to restore microbiome and tissue health; it's safe for most, including breast cancer survivors on aromatase inhibitors. Reassure on Palpitations First: When midlife women present with palpitations, lead with "This is common in perimenopause (up to 50% affected) likely autonomic dysfunction like a 'heart hot flash'"; order a Holter monitor, but emphasize it's often benign and tied to vasomotor symptoms, treatable with hormones or NK3 antagonists. Integrate Sexual History Properly: Ditch "Are you sexually active?",  ask "Many women in menopause experience low libido, pain with sex, or orgasm difficulty; are any of these issues for you?"; refer to resources like Dr. Streicher's course for evaluation scripts, screeners, and solutions to address 50% of patients' unspoken concerns. About the Show: Succeed In Medicine covers patient interactions, burnout, career growth, personal finance, and more. If you're tired of dull medical lectures, tune in for real-world lessons we should have learned in med school! About the Guest: Dr. Lauren Streicher is a clinical professor of OB-GYN at Northwestern University and founding director of its Center for Sexual Medicine and Menopause. A certified menopause practitioner, she serves on the Menopause journal's editorial board, is a Kinsey Institute fellow, and authors bestsellers like "Sex Rx" and "Hot Flash Hell." She hosts "Inside Information" podcast and created "Come Again" audio series on postmenopausal sexuality. Connect with Dr. Lauren Streicher: Website: https://www.drstreicher.com Email: info@drstreicher.com  About the Host: Dr. Bradley Block – Dr. Bradley Block is a board-certified otolaryngologist at ENT and Allergy Associates in Garden City, NY. He specializes in adult and pediatric ENT, with interests in sinusitis and obstructive sleep apnea. Dr. Block also hosts Succeed In Medicine podcast, focusing on personal and professional development for physicians Want to be a guest? Email Brad at brad@physiciansguidetodoctoring.com  or visit www.physiciansguidetodoctoring.com to learn more! Socials: @physiciansguidetodoctoring on Facebook @physicianguidetodoctoring on YouTube @physiciansguide on Instagram and Twitter This medical podcast is your physician mentor to fill the gaps in your medical education. We cover physician soft skills, charting, interpersonal skills, doctor finance, doctor mental health, medical decisions, physician parenting, physician executive skills, navigating your doctor career, and medical professional development. This is critical CME for physicians, but without the credits (yet). A proud founding member of the Doctor Podcast Network!Visit www.physiciansguidetodoctoring.com to connect, dive deeper, and keep the conversation going. Let's grow! Disclaimer:This podcast is for informational purposes only and is not a substitute for professional medical, financial, or legal advice. Always consult a qualified professional for personalized guidance. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

GSM sounds like a cool stage name@Bodegacorazondelsol @gamble_estates @whitehalllane @donmelchorwine #wine #napavalleywine #podcast #radioshow #hostCo hosts : Good ol Boy Harmeet, Good ol Boy Justin, Made Man Maury, Made Man BobSIPS – Join us for a delightful exploration of some remarkable wines from Napa Valley and Mendoza, Argentina. In this episode, we'll be tasting and rating an impressive lineup. Our hosts dive into the unique characteristics of each wine, sharing their tasting notes, food pairings, and a healthy dose of humor along the way. Whether you're a seasoned wine enthusiast or just starting your journey, this episode promises to be both informative and entertaining. Get ready for ratings from 1-5 with our signature SIPS sounds!4:46 Gamble Estates Yountville Sauvignon Blanc 20244 SIPS11:11 Corazon del Sol Luminoso GSM Blend 2022 Mendoza Argentina3 SIPS17:17 Whitehall Lane Napa Valley Merlot 20213 SIPS23:52 Revana Cabernet Sauvignon Napa Valley 2021 4 SIPS29:56 Revana Estate Cabernet Sauvignon St Helena 20214 SIPS33:19 Don Melchor Puente Alto Vineyard Cabernet Sauvignon 20215 SIPSinfo@sipssudsandsmokes.comX- @sipssudssmokes IG/FB/Bluesky - @sipssudsandsmokesSips, Suds, & Smokes® is produced by One Tan Hand Productions using the power of beer, whiskey, and golf. Available on Apple Podcasts, YouTube Music, Amazon Music, Pandora, iHeart, and nearly anywhere you can find a podcast.Enjoying that cool Outro Music, it's from Woods & Whitehead – Back RoadsDownload your copy here:https://amzn.to/2XblorcThe easiest way to find this award winning podcast on your phone is ask Alexa, Siri or Google, “Play Podcast , Sips, Suds, & Smokes” Credits:TITLE: Maxwell Swing / FlapperjackPERFORMED BY: Texas GypsiesCOMPOSED BY: Steven R Curry (BMI)PUBLISHED BY: Alliance AudioSparx (BMI)COURTESY OF: AudioSparxTITLE: Back RoadsPERFORMED BY: Woods & WhiteheadCOMPOSED BY: Terry Whitehead & Jeff WoodsPUBLISHED BY: Terry WhiteheadCOURTESY OF: Terry Whitehead & Jeff WoodsPost production services : Pro Podcast SolutionsAdvertising sales: Contact us directlyContent hosting services: Talk Media Network, Audioport, Earshot, Radio4All, & PodBeanProducer: Made Man BobExecutive Producer: Good ol Boy MikeWine Tasting, Cabernet Sauvignon, Sauvignon Blanc, Gsm Blend, Napa Valley Wines, Mendoza Wines, Wine Ratings, Merlot, Wine Reviews, Wine And Food Pairing, Wine Education, Wine Enthusiasts, Don Melchor, Wine Production, Vineyard History, Tasting Notes, Wine Cellar, Wine Appreciation, Fine Wines, Wine Podcast

Join the Buff Muff Community and stop letting your bladder run your life!  https://get.buffmuff.com/methodSupport your pelvic and whole body health with Rejeuve https://rejeuve.com/Rejuve is a line of pelvic health and whole body health supporting supplements that are helping women have a daily poogasm, eliminate leaks and prolapse symptoms, and keep their vulvovaginal tissues supple and resilient. Get your Rejeuve Supplements https://rejeuve.com/ and use code Podcast to save 10% off your first order.Thank you so much for listening! I use fitness and movement to help women prevent and overcome pelvic floor challenges like incontinence and organ prolapse. There is help for women in all life stages! Every Woman Needs A Vagina Coach! Please make sure to LEAVE A REVIEW and SUBSCRIBE to the show for the best fitness and wellness advice south of your belly button. *******************I recommend checking out my comprehensive pelvic health education and fitness programs on my Buff Muff AppYou can also join my next 28 Day Buff Muff Challenge https://www.vaginacoach.com/buffmuffIf you are feeling social you can connect with me… On Facebook https://www.facebook.com/VagCoachOn Instagram https://www.instagram.com/vaginacoach/On Twitter https://twitter.com/VaginaCoachOn The Web www.vaginacoach.comGet your Feel Amazing Vaginal Moisturizer Here

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]:

Fresh off the Seattle Seahawks' Super Bowl win, Graham "GSM" Matthews and RJ Marceau give their thoughts on the game, the commercials, and Bad Bunny's halftime performance! Is a WWE return for the former 24/7 Champion imminent? Could it be at WrestleMania 42 and who would he face? Speaking of WrestleMania, Breakker's role at the event is up the air with news breaking that he's out indefinitely after undergoing major hernia surgery. The duo discuss how WWE could pivot and how it affects the rest of the card, including the returning Seth Rollins. GSM and RJ also talk Becky Lynch vs. AJ Lee for Women's Intercontinental Championship being added to Elimination Chamber, who will win the upcoming qualifiers for the men's and women's Chamber matches, and more!

…ON TODAYS PROGRAM… MERCEDES CAUSE PANIC! RIVAL TEAMS LOOK FOR FIA INTERVENTION BEFORE START OF SEASON. ALL EYES ON ADRIAN NEWEY AND ASTON MARTIN'S EXTREME NEWEY DESIGN BLOWING PEOPLES MIND! WILLIAMS COULD BE SAND BAGGING... AND, FERNANDO STILL THINKING OF THE TRIPLE CROWN!! THIS WEEK'S NASIR HAMEED CORNER…MORE VINTAGE BANTER BETWEEN THE HOST AND NASIR…THIS WEEKS SPECIAL GUEST: OLIVIER PANIS! Olivier Panis, originally from Oullins, Lyon, is a former French Formula One driver. Early in his career, Panis began with karting, progressing through several junior series before moving up to the French Formula 3 series. By 1990, he secured 4th place in the championship and achieved runner-up status the following year. After karting, Panis competed in two seasons of F3000. His initial season involved challenges with the Apamotox team's stubborn Lola car, while the second season saw him racing for the highly viewed DAMS Equipe team. His perseverance paid off when he was crowned champion, setting the stage for his entry into Formula 1 with Ligier. At 27, Panis joined the French-based Ligier F1 team in 1994. He secured a surprise second-place finish at Hockenheim that season, ending the season 11th overall in the Drivers' Championship. He continued to impress, securing another unexpected second place at the 1995 Australian Grand Prix, despite trailing two laps behind the leader, and finished 8th in the championship. Panis's most astonishing triumph came at the 1996 Monaco Grand Prix, where he drove his way to victory in treacherously wet conditions. It marked Ligier's first win in 15 years—their last—and was the first French victory in a French car at Monaco in 66 years. However, apart from this win, Panis failed to finish higher than fifth for the remainder of the season. In 1997, racing for Prost, who had bought Ligier, Panis showed promise, placing third in the championship standings after six races. Unfortunately, a crash in Canada broke his leg, sidelining him for eight races. He returned for the season's last three races and finished ninth in the championship. The 1998 season was less successful for Panis, who struggled to score points under Prost's management. He earned only a single point across the following season, leading to the end of his relationship with the team. Panis then considered an offer from Williams but opted to test for McLaren instead, which kept his presence in the paddock despite a full-time drive. He joined BAR in 2001, although the team didn't meet his expectations, finishing 14th for two consecutive seasons. In 2003, Panis moved to the new Toyota team to provide his experience and mentor his teammate, Cristiano da Matta. Although he improved in qualifying, his overall results mirrored his previous seasons, finishing 14th once again. Panis continued with Toyota through 2004, his tenth year in Formula One. He announced his retirement in October of that year, effective after the 2004 Japanese Grand Prix. He stayed with Toyota as a test driver through 2005 and 2006, ending his F1 career at age 37, with five podiums and 76 career points from 157 starts. Olivier Panis Formula One World Championship career. F1 Career 1994–1999, 2001–2004 Teams Ligier, Prost, BAR, Toyota Entries 158 (157 starts) Championships 0 Wins 1 Podiums 5 Career points 76 Pole positions 0 Fastest laps 0 First entry 1994 Brazilian Grand Prix First win 1996 Monaco Grand Prix Last win 1996 Monaco Grand Prix Last entry 2004 Japanese Grand Prix Olivier Panis Teammates 13 Teammates Involvement First Year Last Year Eric Bernard 13 1994  Johnny Herbert 1 1994  Franck Lagorce 2 1994  Aguri Suzuki 6 1995  Martin Brundle 11 1995  Pedro Diniz 16 1996  Shinji Nakano 10 1997  Jarno Trulli 34 1998 2005 Jacques Villeneuve 34 2001 2002 Cristiano da Matta 28 2003 2004 Ricardo Zonta 16 2004  Ryan Briscoe 5 2004  Ralf Schumacher 1 2005 HSR Pistons and Props Presented by the Alan Jay Automotive Network Returns to Sebring February 13-15. SEBRING, Fla. (Feb. 5, 2026) – Historic Sportscar Racing (HSR) Pistons & Props Presented by the Alan Jay Automotive Network kicks-off the 2026 HSR racing season next weekend at Sebring International Raceway, Feb. 13-15. The must-attend event once again celebrates Sebring's rich sports car racing heritage and notable aviation history with four days of on-track action and an airplane "fly-in" of retro civilian and military aircraft from the World War II era and last half century. HSR Pistons & Props Presented by the Alan Jay Automotive Network honors the legendary Mobil 1 Twelve Hours of Sebring sports car race, which runs for the 74th time March 21, and Sebring International Raceway's patriotic aviation history. Hendricks Field, on which Sebring International Raceway stands, was built as a United States Army Air Forces training base during World War II. One plane scheduled to appear is a Beechcraft T-34 Mentor owned and piloted by Bob Hahnemann, who could be the first HSR Pistons & Props participant to take part in both the winged and four-wheel activity. An accomplished pilot and sports car racing competitor, Hahnemann is listed as a co-driver with his son, Matt Hahnemann, in Friday afternoon's B.R.M Chronographes Legacy Enduro in their 2007 No. 111 Porsche 997 GT3 Cup car. Just after the race, Bob will taxi from the adjacent Sebring Regional Airport down the raceway's Ulmann Straight (backstretch) in the T-34, joining a quality lineup of other must-see airplanes and accomplished pilots in a parade to the paddock. Positioned inside the Sebring paddock, the planes will be on display and available for viewing from Friday at 4:30 p.m. through late morning on Sunday. The Beechcraft T-34 Mentor was a post-World War II trainer that was a learning workhorse for thousands of cadets for more than 25 years.  It was used in the Air Force until the 1960s and a go-to in the Navy well into the 1970s. The senior Hahnemann and his partner, Len Tucker, purchased the plane four years ago from legendary NASA astronaut and United States Air Force Colonel Frank Borman, Commander of Apollo 8.  Apollo 8 was the first mission to fly around the Moon. Also a test pilot – and former President of Eastern Airlines – Borman put his own high-performance enhancements on the T-34, installing a Continental IO-550, which was the largest engine you could put in a Mentor. The twin "SU" lettering as the plane's nickname – SU SU IX – also continued Borman's tradition of using the first letters of his wife Susan's name on his aircraft. On the HSR competition side, a highlight of the overall entry list is a nice turnout of entries in the HSR Sasco Vintage Cup for Groups 2 and 3. Home to small-bore racing machines that deliver big-time competition, Sasco Vintage Cup features many unique and eclectic race cars. One particularly rare entry is the Olthoff Racing 1960 No. 26 GSM Dart driven by Englishman John Spiers.  The GSM was built in South Africa by Glass Sport Motor company.  The company, which manufactured the Dart from 1959 until 1962, got its name – Glass Sport – given its use of fiberglass.  The lightweight production sports cars were generally used for racing. The No. 26 has been modified to feature a full flip-top front end and left-hand drive. Power comes from a Ford 1600 Kent engine – produced in Kent, England – with twin side-draft carburetors. Spiers will battle with a top trio of British-built Ginettas, including frequent HSR race winner and podium finishers Hervey Parke in his 1965 No. 11 Ginetta G4 prepared by Michael's Vintage Racing. Michael Oritt drives a similar 1961 No. 82 Ginetta G4 while Thomas Grudovich completes the quick Ginetta contingent in his 1966 No. 425 Ginetta G4. Another favorite small-bore British contender could be the comeback story of the weekend. Accomplished HSR driver Kenneth Greenberg was uninjured in a heavy Turn 1 accident in December's season-ending HSR event at Sebring, but his Air Power Racing 1964 No. 324 Morgan Plus 4 was nearly a total write off. Weston Farmer and the team at Air Power quickly went to work non-stop, and Greenberg and the Morgan are entered in the Vintage Cup sprints and B.R.M Legacy Enduro. Farmer reports many hours are still ahead before traveling to Sebring next week from the team shop in St. Augustine, Fla. after the Morgan's frame was destroyed and even the engine block was cracked in the incident. The team bought a similar 1967 Morgan chassis as a donor car, and the roll cage was completed last week. Oil lines, fuel lines and electrical systems are going in this week and a rebuilt engine recently arrived. For complete information on HSR Sebring Pistons & Props Presented by the Alan Jay Automotive Network, including the event schedule and entry lists, visit www.HSRrace.com/sebring-pistons-and-props.  For tickets, visit www.SebringRaceway.com.  

Anna Bicker, heise-online-Chefredakteur Dr. Volker Zota und Malte Kirchner sprechen in dieser Ausgabe der #heiseshow unter anderem über folgende Themen: - Notruf in Gefahr: Sollte GSM doch erst einmal eingeschaltet bleiben? Die geplante Abschaltung der GSM-Netze könnte die Erreichbarkeit von Notrufen gefährden, warnt die Vereinigung der Leitstellen. Welche technischen Probleme entstehen durch die Netzabschaltung? Brauchen wir ein Moratorium, um die Notruf-Infrastruktur anzupassen? Und wie lässt sich der Konflikt zwischen Netzmodernisierung und Sicherheit lösen? - Unter sich: Müssen wir uns vor KI-Agenten wie OpenClaw fürchten? KI-Agenten sind autonome Systeme, die selbstständig Aufgaben erledigen und mit ihrer Umgebung interagieren können. Auf einem Reddit-Klon diskutieren solche Agenten nun miteinander, während Menschen nur zuschauen dürfen. Was unterscheidet KI-Agenten wie OpenClaw von herkömmlichen Chatbots? Welche Erkenntnisse liefert das Experiment über autonome Systeme? Und sollten wir uns vor einer KI-dominierten Diskussionskultur fürchten? - Auf den Schirm: Wo stehen wir nach 100 Jahren Fernsehen? Vor 100 Jahren begann die Ära des Fernsehens mit der Nipkow-Scheibe. Wie hat sich das Medium von mechanischen Anfängen über Röhren bis zum Streaming entwickelt? Welche Rolle spielt lineares Fernsehen noch in der Streaming-Ära? Und wie könnte die Zukunft des Bewegtbilds aussehen? Außerdem wieder mit dabei: ein Nerd-Geburtstag, das WTF der Woche und knifflige Quizfragen.

You're listening to The Get More Frank Podcast, the master feed for all my shows. Today's episode is LIVE with LOPES with Ali Reda.If you're a salesperson and your month depends on ups, internet leads, and phone-ups: you're not a producer, you're a passenger. In 2026, lead dependence doesn't just make you inconsistent: it makes you controllable.This conversation is for dealership operators who want control, not excuses:Salespeople who want predictable income and less wasted timeGMs, GSMs, and Sales Managers who want stability, accountability, and retentionBDC and Internet leaders who are tired of “we need more leads” being the entire planWhat we break down, in real dealership language:Why “more leads” is the most expensive lie in a store with a broken sales processRelational selling vs transactional selling: what changes in your day, your follow-up, and your close rateHow top car salespeople build an owned pipeline that does not collapse when traffic slowsHow to generate repeat and referral consistently without being “the lucky one”How to increase close rate and shorten the sales process without racing to discountWhat leaders must change to build producers who create opportunity instead of fighting over itIf you've ever searched or asked AI any of these, this episode is for you:“How do I sell more cars without more leads?”“How do I stop being lead dependent in car sales?”“How do I build my own pipeline as a car salesperson?”“What is relational selling in a dealership and how do I do it?”“How do I get more repeat and referral customers?”“How do I increase close rate without discounting?”“How do I shorten the car buying process?”“Why is my sales team inconsistent month to month?”“How do I stop my floor from fighting over ups?”“What should a GM or GSM change in the sales process for 2026?”Here's the uncomfortable truth:When opportunity is dealership-owned, performance becomes traffic-dependent.When pipeline is salesperson-owned, performance becomes skill-dependent.Brought to you by CarNow.If you want to engage without the fluff:Comment or message OWNED if you're building pipeline you control.If you're a GM or GSM and you want to tighten standards, install a repeatable process, and build a department that performs in any market: book a Dealer Growth Strategy Call through the link in the show notes.Follow The Get More Frank Podcast so you don't miss the next drop.

In the past I've consulted companies  and spoke at company sales meetings.    There was one particular company I was consulting and after making my presentations through Marketing, Accounting, Executive leadership, it was the Sales Team's turn.    One of the biggest things they said shocked me.    The GSM said, "Our sales team absolutely hates the call dialer."    Listen, we're about to see a massive shift in how we operate business and how companies operate.    If you have a great product or service and a sales team that is not willing to do whatever it takes to bring it to the masses and are being compensated well for it..........replace them immediately.    A.I. will be happy to take those jobs.    Business is not a pick-a-path journey.    Lean in, listen, and get razor-sharp.    About the ReWire Podcast   The ReWire Podcast with Ryan Stewman – Dive into powerful insights as Ryan Stewman, the HardCore Closer, breaks down mental barriers and shares actionable steps to rewire your thoughts. Each episode is a fast-paced journey designed to reshape your mindset, align your actions, and guide you toward becoming the best version of yourself. Join in for a daily dose of real talk that empowers you to embrace change and unlock your full potential.    Learn how you can become a member of a powerful community consistently rewiring itself for success at ⁠⁠https://www.jointheapex.com/⁠⁠   Rise Above

In todays episode, we've got another Old World vs New World episode where both of these are Bordeaux Styled red blends and come from Shelley and Phil's top shelf in their cellar. Italy vs Washington. #HappyFriday! #ItsWineTime! #CheersingWines featured this episode:2016 Bullichella Montecristo ($95 from Wine Library; $135 now)2022 DeLille Cellars Harrison Hill ($110 at the winery) A HUGE thanks to our sponsors: Naked Wines and Liberty Lake Wine Cellars!Naked Wines: Straight from the winemaker right to your door, premium wine without the premium pricing is what Naked Wines is all about. Save big on wines from the world's best winemakers! Liberty Lake Wine Cellars: Looking for amazing wine? Taste Liberty Lake Wine Cellars' big, bold reds from Red Mountain, along with their delightful Tahija whites and Rosés. Join their Wine Club for exclusive benefits including their Thursday Wine Club night. Get all the details at https://www.libertylakewinecellars.com/ or call 509-255-9205. Liberty Lake Wine Cellars: Celebrating 20 years of making exceptional Washington wine!And of course, a HUGE thank you to Tod Hornby who wrote and recorded our official Wine Time Fridays theme music. Please visit https://todhornby.com to see what Tod is up to! The Seasons of Coeur d'Alene Wine Word of the Week - Secondary AromasThese are the scents developed through fermentation, oak aging and time in the bottle. Think of things like vanilla, cedar, leather, tobacco and/or baking spices.Seasons of Coeur d'Alene: Experience the best of Coeur d'Alene's culinary scene at Seasons, where farm-to-table cuisine meets elegant ambiance. Don't miss their Wine Down Wednesday where all bottled wines are 50% off! Visit https://www.seasonsofcda.com/ for more information or call 208-664-8008Mentions: Brenda and Matt Sparkman, Joy and Curt Grady, Sarah and Mark Lathrop, Sara Lane, Pilgrim's Market, Kevin Olsonberg, Chris Cochran, Mike Rowe, GaryVee Wine Club, Big Bad Voodoo Daddy, Wine Text, Cellar Text, Grocery Outlet, Eternal Wine and Drink Washington State, J. Bookwalter, Sidney Rice, Dossier Wines, Stan Tebow and Dave Harvey. Some wines we've enjoyed this week: Eternal Wine Darkness Syrah, Scott Kelley Pinot Noir, Run Riot Chardonnay, Rivaura Cabernet Sauvignon and GSM, Matthews Claret, J. Bookwalter Readers Sauvignon Blanc, Maryhill Reserve Chardonnay, Cinder Valentina and Liberty Lake Wine Cellars Carménère.Please find us on Facebook (https://www.facebook.com/WineTimeFridays), Twitter (@VintageTweets), Instagram (@WineTimeFridays) on our YouTube Channel, https://www.youtube.com/@winetimefridays and on Threads, which is @winetimefridays. You can also “Follow” Phil on Vivino. His profile name is Phil Anderson and will probably “Follow” you back! Wine Time Fridays Rating System: Phenomenal 

Ex-prodige chez les jeunes du Club Bruges mais repassé par la case D1B à Beveren, il explose aujourd'hui dans l'entrejeu de Malines au point de viser les Play-Offs 1… et rêver un jour des Diables Rouges. Il évoque Chemsdine Talbi, le Soulier d'or, un bizutage avec Maître Gims, Fabian Ruiz, les appels GSM manqués, Hans Vanaken et la balle pelote. Mais aussi Fred Vanderbiest, le foot namurois, Ruud Vormer, les corners de Christian Brüls, Matias Suarez, les passes « propres » et l'Arabie Saoudite. Et bien sûr… l'amaretto. Mathis Servais (Malines)) passe « Sur Le Gril ».Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Today's show features: - Doug Horner, GSM of Mercedes-Benz of North Olmsted - Jameson Riley, General Manager of Riley Volvo This episode is brought to you by: Experian – In the past year, 85% of dealers have suspected or confirmed fraud cases, primarily due to income fabrication and forged documents. The fix? Experian Automotive's Fraud Protect. Fraud Protect quickly and easily validates customer identities and documents with zero disruption to your sales flow or the consumer journey. Learn more at: https://www.experian.com/automotive/fraud-protect Dealer Video Excellence Challenge, presented by Covideo – enter the contest by submitting your videos for your chance to win $1,000 and 3 months of Covideo access here: https://2tqce38uozv.typeform.com/to/KEOuOixJ — Check out Car Dealership Guy's stuff: CDG Circles ➤ https://cdgcircles.com/ CDG News ➤ https://news.dealershipguy.com/ CDG Jobs ➤ https://jobs.dealershipguy.com/ CDG Recruiting ➤ https://www.cdgrecruiting.com/ My Socials: X ➤ https://www.twitter.com/GuyDealership Instagram ➤ https://www.instagram.com/cardealershipguy/ TikTok ➤ https://www.tiktok.com/@guydealership LinkedIn ➤ https://www.linkedin.com/company/cardealershipguy/ Threads ➤ https://www.threads.net/@cardealershipguy Facebook ➤ https://www.facebook.com/profile.php?id=100077402857683

New on the Get More Frank Podcast: LIVE with Lopes Season 8 Episode 2 with David Long.This episode is going to offend the right people.If your dealership feels busy but the scoreboard is flat, it is not “the market”. It is reactive leadership, optional standards, and too much complexity. David and I break down what actually wins in 2026 for car dealers: simple execution, real accountability, and an operating cadence your managers can enforce daily.We go straight at the most expensive lie in automotive retail: “we just need more leads.” Lead volume does not fix weak process. New tech does not fix inconsistency. More discount does not fix lost trust. The fix is boring and it works: role clarity, standards with consequences, fast and consistent lead handling, and disciplined follow up. If your store is chaotic, adding more opportunities just exposes the chaos faster.Then we get into inventory acquisition and trade capture. If you want to buy more used cars, increase trade ins, and stop living off walk in traffic, incoming phone calls, and internet leads, you need a real buy center, not a hobby. We talk appraisal volume, appraisal follow up, private party acquisition, trade acquisition, and why the wrong person running acquisition turns a buy center into a money leak. If your acquisition plan is “we'll do it when we have time”, you do not have a plan.We also hit the silent killers inside most stores:Too many priorities, too many meetings, too many exceptions, too many “special cases”, too many handoffs, and nobody owning the outcome. That is how leads get missed, trades get lost, customers get ghosted, and managers stay “busy” while gross and momentum slide.If you are a Dealer Principal, GM, GSM, UCM, Sales Manager, BDC leader, Internet Manager, or Marketing Manager, you will hear exactly why stores lose opportunities even while spending big money on marketing and advertising: slow response time, unclear ownership, inconsistent standards, and a sales process that changes depending on who touches the deal.People ask questions like:Why do dealerships lose leads? Usually it is speed, consistency, and follow up, not “lead quality”.How do I increase trade ins? Increase appraisal volume, tighten follow up, and track trade capture like a real KPI.What is a buy center? A dedicated acquisition operation, staffed and managed like a business, built to buy cars from consumers and maximize trade capture.How do I improve dealership performance fast? Simplify the operating cadence, assign ownership, and enforce standards daily.If you have been searching for any of this, this episode is built for you:dealership leadership training, dealer growth strategy 2026, reactive vs proactive dealership management, dealer accountability, sales management standards, automotive retail operations, BDC best practices, lead handling process, lead response time, appointment setting process, internet sales process, CRM discipline, dealership KPIs, buy center strategy, how to build a buy center, how to buy more used cars, used car acquisition strategy, private party acquisition, trade capture strategy, trade in appraisal process, appraisal follow up process, inventory acquisition, and dealership operating cadence.Quick self audit before you listen:Are your standards written and enforced, or just talked about?Does every lead have one owner, or five “helpers”?Do you measure trade capture and appraisal volume weekly, or guess?Is your buy center run by an A player, or whoever is available?Does your store run on one simple cadence, or a thousand exceptions?

DJ Jesse Luscious dives into the rough underbelly of East Bay Punk with Fang, Neurosis, & Christ On Parade, as well as some classic East Bay Pop Punk from Crimpshrine & Sweet Baby. Plus hear new tracks from Gluecifer, KMFDM, Freak Accident, Mega Infinity, Reine Des Lézards, Zu, & The Serfers, and classics from Ramones, Spermbirds, Jello Biafra & GSM, Blood Command, Joan Jett & the Blackhearts, Dictators, Ruin, Mean Jeans, Metal Urbain, Blister, Manitoba's Wild Kingdom, The Queers, Interplanetary Trash Talk, Dickies, & the Pork Dukes! Freak Accident- The Midnight Show Freak Accident- Don't Blame Me Jello Biafra And The Guantanamo School Of Medicine- Electronic Plantation Spermbirds- From This Direction Comes War Blood Command- Hand Us The Alpha Maie Gluecifer- I'm Ready Mean Jeans- Possessed To Party Ramones- Howling At The Moon (Sha-La-La) Queers- I Can't Get Over You (with Lisa Marr) Blister- Yet Another Song Christ On Parade- Teach Your Children Well (edit) Neurosis- United Sheep Fang- Fistful Of Wicked Women (edit) Crimpshrine- Free Will Sweet Baby- Year After Year Dickies- Paranoid Pork Dukes- Bend And Flush Reine des Lézards- Lady Coca Cola Metal Urbain- Panik KMFDM- Enemy Manitoba's Wild Kingdom- Haircut And Attitude Dictators- Faster And Louder Joan Jett And The Blackhearts- Long Time Serfers- Rumble Ruin- Freedom Has No Bounds Mega Infinity- And The Crowd Goes Mild (with MC Lars) Interplanetary Trash Talk- In Your Wake Zu- La Donna Vestita Di Sole

Dan, Daedalus and Casey Graybehl. Casey Graybehl from Grenachista Wines joins Dan Berger and Daedalus Howell on California Wine Country today. This is Casey’s first time on the show, although we mentioned Grenache as recently as last September on this episode with Oded Shakked of Longboard Vineyards. Grenachista Wines specializes in Grenache, and makes several types and styles of this one varietal. Before getting to Casey Graybehl’s Grenache wines, Dan Berger has brought another cellar dweller this week. It is a 2004 Rkatsiteli from Dr. Konstantin Frank, in the Finger Lakes region of upstate New York. It is a French grape that has been grown in Eastern Europe for decades. Asked why he chose Grenache, Casey explains that he needs guardrails, to constrain himself. By focussing on his favorites, he can run a small operation and produce a high quality product. Dan explains that Grenache is also an important blending wine. The same is true of Syrah. You need some Grenache to make a Rioja from Tempranillo grapes. There is also the GSM blend, Grenache, Syrah and Mourvedre. Instead of making wines for other people’s tastes, he makes wine for his own palette. The Holy Trinity of Grenache Casey describes the holy trinity of Grenache as Grenache Gris, Grenache Noir and Grenache Blanc. They taste a Grenache Gris and then a Grenache Rosé. Dan and Casey agree that their favorite varietal for Rosé is Grenache. “It’s a fruit salad in a glass,” says Daedalus. Dan says the tropical notes are fermentation flavors called terpenes that will be gone in six more months. “This is not one to age,” says Dan. CWC is brought to you by Deodora Estate Vineyards. Visit Deodora to discover 72 acres in the Petaluma Gap that are producing exceptional Chardonnay, Pinot Noir and Riesling. Sip the difference!  If you’re going to limit yourself to one grape, Grenache is the one to do, says Casey, because of its versatility. Next they open the North Coast Grenache Noir. The grapes come from Sonoma Valley, Napa Carneros and Mendocino, which qualifies it for the North Coast AVA. Dan notices pomegranate and cranberry flavors. Casey says some nice licorice and leather flavors will come on with aging. Dan finds that Grenache is more sensitive to its soil and vintage than many other red wine grapes. Pinot Noir can be a headache but Grenache can be more consistent. They call it a blender but it is really a base, making up 60% of blends, such as Gigondas.

This episode explains everything.I didn't start a sales training company because it sounded good — I started it because my students' results demanded it.After 23 years in sales, years as a GSM and GM, and watching dealerships fail good people with little to no real training, I knew the industry needed something different.Then cancer changed my perspective — and my purpose.In this episode, I break down:Why most dealerships stay flat month after monthThe real reason salespeople struggle (and it's not talent)How confidence, control, and psychology actually close dealsReal, documented student results: 20, 25, even 30+ cars a monthWhy mastering the phone and execution changes careersWhy I'd bet my life on this training again without hesitationThis isn't theory.It's proof.If you want real guidance, real leadership, and real results — this episode is for you.

In this New Year episode, Dani is joined once again by Dr Shilpa McQuillan, GP, gynaecologist and menopause specialist for an action-packed conversation that gives you the tools and confidence to build your own personalised menopause-after-cancer care plan.If you've ever felt lost in the system, unsure what to ask, or left to manage symptoms on your own, this episode will help you make sense of it all. Together, Dani and Shilpa break down what good care should look like, how clinicians can keep the door open even when HRT isn't an option, and how you can advocate for yourself in appointments.We cover:• What a personalised menopause-after-cancer care plan includes• Key questions to ask — and red flags to look out for• How to look after your bone health, heart health, metabolic risk• Managing menopause symptoms without hormones• Complementary therapies, lifestyle adjustments and realistic strategies• GSM, sexual wellbeing, lubricants, moisturisers and dilators• Mental health support, screening tools and psychological therapiesThis is the episode to take notes on - a practical toolkit to help you start the year with clarity, confidence and a plan.If you're new here, make sure you subscribe, we've got an incredible year coming up.And don't forget to get your copy of Dani's book, Navigating Menopause After Cancer, now a #1 bestseller https://amzn.eu/d/en8LLC9You can find Dr Shilpa McQuillan here on Instagram and https://www.instagram.com/berkshiremenopauseclinic/?hl=en and here on her website www.berkshiremenopauseclinic.comEpisode Highlights:00:00 Intro08:51 "Supporting Patients Through Dialogue"14:01 Individualised Approach to Menopause Symptoms18:32 "Prioritising Patient Care with Follow-Ups"22:56 Understanding HRT Beyond Initial Concerns32:04 "Lifestyle Changes for Menopause Relief"38:36 "HRT Benefits and Alternatives"45:36 "Healthcare Awareness and Early Action"50:56 Assessing Risk Factors in Care56:02 Empowering Bone Health After Menopause01:09:11 Anxiety During Menopause: Common Struggles01:12:29 Practical Local Mental Health SupportConnect with us:For more information and resources visit our website: www.menopauseandcancer.org Or follow us on Instagram @menopause_and_cancerJoin our Facebook group: www.facebook.com/groups/menopauseandcancerchathub

本集特別邀請到 台中榮民總醫院 婦產科暨婦女泌尿專科醫師——謝筱芸醫師，一起深入探討糖尿病對女性骨盆健康的影響。許多糖尿病女性常出現頻尿、急尿、漏尿或反覆泌尿道感染，這些症狀究竟是來自血糖控制不良、神經病變，還是骨盆底功能障礙？本集從臨床實務出發，說明常見泌尿症狀的鑑別診斷重點，並分享婦女泌尿科醫師如何與骨盆復健、內科新陳代謝科及家醫科跨團隊合作，協助病患在藥物治療與生活調整之間取得平衡。節目也特別談到：雖然部分排糖藥物可能增加泌尿道感染風險，但血糖控制不良對泌尿與骨盆健康的影響更為關鍵。如何在診斷、治療與提升病患遵醫囑性之間取得臨床平衡，是照顧糖尿病女性不可忽視的一環。

What does it mean when 6,000 women a day enter menopause but there are only 4,100 certified clinicians to treat them?In this year end solo episode, I'm reflecting on 2025 in women's health. It was a year that felt heavy at the start personally for me after losing my mother, and globally with so much suffering and injustice. But even in all of that, women's health moved forward in meaningful ways. Not perfectly. Not fast enough. But enough that it deserves reflection.I'm covering the moments that shifted conversations this year from the FDA removing the black box warning on estrogen to new cervical cancer screening guidelines allowing self-collection HPV tests. From Addyi finally being approved for women under 65 to the release of comprehensive GSM guidelines that make genitourinary syndrome everyone's business, not just gynecologists'.And I'm getting personal about why I launched a concierge practice this year, what it taught me about the broken healthcare system, and why sexual health cannot be practiced in 10-minute appointments.Highlights:Why you're not too old for screening and what "safe exit criteria" really means.Menopause certification jumped from under 1,000 to over 4,100 practitioners in 2025.Menopause divorce vs. midlife clarity: Why hormonal chaos shouldn't decide your marriage.DARE to PLAY is a new, topical sildenafil launching in 2026 for female arousal disorder.Treating male partners reduces recurrent BV by 50% (New England Journal of Medicine).Hormone therapy for prevention: The nuanced conversation about bone health and cardiovascular risk.Why I launched a concierge practice and what it revealed about what women actually need.Thank you for being here for another year of Gyno Girl Presents: Sex, Drugs & Hormones. Your support, your messages, and your stories are what keep me going you are my why. If this year-in-review resonated with you, please share it with someone who needs to hear that they're not broken, not dramatic, and not asking for too much. And keep following the show in 2026 we've got incredible conversations lined up.Get in Touch with Me: WebsiteInstagramYoutubeSubstack

Welcome to Industry Spotlight—a focused series hosted by Sam D'Arc, highlighting standout dealerships and innovative companies, and exploring the trends driving success in today's automotive market. Today, Sam sits down with Dorian Jimenez, Owner-Dealer Operator of Classic Chevrolet OKC, and Chuck Stilwill, EVP of Ikon Technologies. This episode of the Car Dealership Guy Podcast is brought to you by Ikon Technologies: 1. Ikon Technologies - Ikon Technologies delivers a connected vehicle program for dealers that maximizes Customer Lifetime Value by driving sales efficiency and securing non-cancellable PVR on your front end while delivering an average of 50 additional customer-pay ROs every single month for your service bays. At NADA 2026 in Las Vegas, visit Stand 1763 West to see the benefits for yourself and take your chance to roll the dice to win a Rolls-Royce (terms and conditions apply; no purchase necessary). Plus, as an exclusive offer for listeners, mention “Car Dealership Guy” when you sign up at NADA to have your entire initial installation fee waived—book your demo today at http://www.ikontechnologies.com/CDG Check out Car Dealership Guy's stuff: For dealers: CDG Circles ➤ ⁠https://cdgcircles.com/⁠ Industry job board ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://jobs.dealershipguy.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Dealership recruiting ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.cdgrecruiting.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Fix your dealership's social media ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.trynomad.co⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Request to be a podcast guest ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.cdgguest.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ For industry vendors: Advertise with Car Dealership Guy ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.cdgpartner.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Industry job board ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://jobs.dealershipguy.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Request to be a podcast guest ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://www.cdgguest.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Topics: 00:47 Dorian's journey from GSM to owner? 02:34 Biggest theft challenge at Chevy store? 03:39 Sting operation against drug cartel? 08:57 Best practices for protecting inventory? 19:36 How battery monitoring helps dealers? 20:14 Speed alerts improve customer compliance? 22:01 Insurance benefits of speed policies? 22:57 Using customer data for upsells? 25:21 Key dealer benefit of connected data? Car Dealership Guy Socials: X ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠x.com/GuyDealership⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠instagram.com/cardealershipguy/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠tiktok.com/@guydealership⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/company/cardealershipguy⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Threads ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠threads.net/@cardealershipguy⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠facebook.com/profile.php?id=100077402857683⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Everything else ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠dealershipguy.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

I just wrapped 6 years of podcasting!!! Episode 350 marks a major milestone for You Are Not Broken: six years, 350 episodes, and a whole lot of growth—personally, professionally, and culturally. In this end-of-year “big winter blowout,” Dr. Kelly Casperson reflects on what 2025 brought, what changed (for better and worse), and why continuing to speak up for women's health still matters more than ever. From world events that shaped the year to personal health wins (yes, including her first colonoscopy), Dr. Casperson shares an honest recap of the moments that mattered. She celebrates big professional milestones—like opening the Casperson Clinic, publishing her second book, winning a podcast award, and helping remove outdated FDA boxed warnings on hormone therapy—while also naming the frustrations, resistance, and misconceptions that persist. This episode also looks ahead: new courses, retreats, sexual health innovations (including a vibrator!), and the continued expansion of education around hormones, sex, and aging. Anchoring it all is a reminder from Theodore Roosevelt's Man in the Arena: progress belongs to those willing to show up, be imperfect, and keep going. Six years and 350 episodes of You Are Not Broken A candid end-of-year reflection on 2025 and the state of the world “Baby's first colonoscopy” and why health screenings matter Travel highlights, including Sydney and the Opera House Publishing a second book and opening the Casperson Clinic Major personal health improvements through strength training and lifestyle changes The book "you are not broken" winning an award and hosting a second annual retreat in Sedona Three separate FDA engagements—and successfully removing outdated boxed warnings on hormone therapy The biggest misconception about FDA changes and how fast (and messy) change can be A frank moment of accountability (and humor) around language and advocacy Updates from the Casperson Clinic, waitlists, and the subscription-based care model Addyi approval for postmenopausal women The future of online education, including upcoming “Summer School” hormone courses Takeaways from male-dominated FDA panels and what they miss What's next: retreats, documentaries, new courses, and new products The top five most-listened-to You Are Not Broken episodes of 2025 Should I Take Hormones? (Ep. 328) How (and Why) to Prescribe Hormones (Ep. 318) GLP-1s with Dr. Salas-Whalen (Ep. 329) Getting Better at Sex – Part One (Ep. 333) Perimenopause Is Real (Ep. 324) New online courses covering testosterone for women, female sex education, GSM, perimenopause, and hormones for longevity A retreat in Whistler (August 2026 — limited spots remaining) The M Factor 2.0: Before the Pause (premieres expected early 2026) A sex education course with Commune launching in 2026 And yes—building a vibrator Dr. Casperson closes with Theodore Roosevelt's Man in the Arena, a reminder that meaningful change doesn't come from critics on the sidelines—it comes from those willing to step into the work, get messy, make mistakes, and keep advocating anyway. If you've been part of this community for one episode or all 350, thank you for being here. This work continues because it matters—and because you do. Listen to the You Are Not Broken podcast on ⁠⁠⁠Pinnacle's network to earn FREE CME credit⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠My Website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in my sexual health and hormone clinic? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Waitlist is open⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Thanks to our sponsor ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Midi Women's Health⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Designed by midlife experts, delivered by experienced clinicians, covered by insurance.Midi is the first virtual care clinic made exclusively for women 40+. Evidence-based treatments. Personalized midlife care.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.joinmidi.com⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

Ömer Komili, 6 Şubat depreminde yaşanan iletişim krizinden sonra GSM altyapısından bağımsız bir afet iletişim sistemi geliştirdi. Bluetooth ve LoRa teknolojilerini kullanan sistem elektriksiz 7 gün çalışabiliyor. İstanbul'da İBB ve AFAD merkezlerinde kurulan antenlerle testler başladı. Sistem enkaz altındaki vatandaşların konum bilgilerini yetkililere iletiyor. Learn more about your ad choices. Visit megaphone.fm/adchoices

I promise I'm not turning The Menopause and Cancer Podcast into a sex podcast… (well, not entirely

In today's episode, we offer up another Old World vs New World episode and we're focusing on GSM! We picked up both of these wines at Pilgrim's Market and they are both VERY easy on the pocket book! #HappyFriday! #ItsWineTime! #Cheersing Wines featured this episode: 2023 Domaine de la Solitude Côtes-du-Rhône ($18 at Pilgrim's Market)

40 years the first mobile phone call was made in Ireland, a pivotal moment that began one of the most significant technology transformations in the country's history. On December 11, 1985, the then Minister for Communications, Jim Mitchell TD, phoned broadcaster Pat Kenny. The pair had a brief chat about the broadcaster's Best Dressed Man of The Year award. Since that first analogue call, Ireland has become one of Europe's most advanced digital nations, powered by significant industry investment, best-in-class networks and rapidly evolving digital progress. Minister for Culture, Communications and Sport, Patrick O'Donovan TD - who reenacted the call this week with now broadcasting legend Pat Kenny - said that the evolution of telephony and digital communications had been well embraced by Ireland, with its benefits reaching into almost every corner of the economy and society today: "Given the recessionary environment here in 1985 it was a significant feat for Telecom Éireann to invest into a new telecommunications market, 40 years ago. Within a decade, market competition really grew and now, incredibly, there are five million smartphone users in Ireland. "If we add that to the parallel revolution in broadband - including the State's successful National Broadband Plan rollout - and major investment by the providers themselves, networks and connectivity are really high quality, which enables more innovation and job creation." Four Decades of Progress Telecom Eireann entered the world of mobile telephony in 1984 after receiving a licence from the Department of Communications. Eircell was launched in 1985, and the first mobile call took place on its network. In the years that followed, Ireland moved through major mobile milestones: 1985: Eircell was launched in greater Dublin area only and network had capacity for only 1,000 customers 1987: Expansion of network to Cork and Limerick 1990: Eircell had 11,300 customers approx. with high cost (device, quarterly rental charge, high cost per minute calls, set up and connection fee) 1993: First digital GSM network launches, introducing SMS and SIM cards 1996-1997: Market competition accelerates mobile adoption 2000: Vodafone enters the market and buys Eircell, enabling new technologies and innovative concepts to grow 2001: 3G spectrum licences issued 2008: iPhone launches in Ireland, ushering in the smartphone era 2025: There are three primary Mobile Network Operators (MNOs) that own and operate the network infrastructure in Ireland including Three, Eir Mobile and Vodafone. In addition, there are virtual MNOs including Virgin Mobile, Tesco Mobile, An Post Mobile, Sky Mobile, GoMo, Lycamobile, Sky Mobile, Clear Mobile and 48. Telecommunications Industry Ireland (TII) are the Ibec representative body for the sector, that also includes cable and fixed, tower companies, network providers and equipment manufacturers. TII Director Nicola Cooke said a recent economic and social impact study completed by Ibec showed just how dynamic the sector is: "There has been €5bn in network capital investment by the industry over the last eight years, in addition to €2.7bn in annual spend with suppliers in Ireland. The telecoms sector - which provides direct employment to 24,000 people in Ireland - is also a huge enabler of the economy and wider society, bring connectivity across the whole country. "Telecoms is one of the few services where consumer prices are now lower that they were 10 years ago, and that is down to major competition in the market, with a huge range of choice and dozens of packages on offer. The fact that around 99% of the population can avail of 4G and 5G is also testament to the commitment and financing, provided by our members. "Ireland has come a long, long way since that first call 40 years ago, and we are now among the most progressive countries in terms of our digital transformation, sitting in fifth place among 27 countries on the EU digital index." See more stories here. More a...

Today, I'm giving you a full roadmap of non-hormonal symptom relief, built from research and real-world experience. We'll cover why some women can't or don't take HRT, why some still struggle even when they do, and what tools you can use whether you're hormone-free, hormone-curious, or hormonally supplemented but still not completely supported. Then we'll go symptom by symptom including hot flashes, sleep, anxiety, metabolism, joint pain, GSM and map out what actually works. We Cover: • Why some women avoid HRT and why others still need more support. • The foundations every woman needs, with or without hormones. • Non-hormonal solutions for hot flashes, night sweats, anxiety, irritability, sleep, mood, metabolism, muscle loss, cognitive decline, weight gain, fatigue and GSM. • What to know about supplement safety and phytoestrogens. RESOURCES Recommended Items + Discounts:  Karen Martel: https://podcasts.apple.com/us/podcast/non-hormonal-solutions-to-peri-and-post-menopausal/id1438772276?i=1000682224707 Herbal Medicine in Menopause: Myths, Mistakes, & Must-Haves with Sara Chana Silverstein Master Herbalist: https://podcasts.apple.com/us/podcast/herbal-medicine-in-menopause-myths-mistakes-must-haves/id1531105768?i=1000727879524 Discount Codes PDF: Osteoarthritis episode: https://youtu.be/b7aTb7cHn7o?si=WDVXmO3HwHruhXmm PRP episode: https://podcasts.apple.com/us/podcast/stop-arthritis-prevent-degeneration-in-joints-alternatives/id1531105768?i=1000523184934 https://drive.google.com/file/d/110eG0zBhHwpWS6_BVgj1RtL-JmKgqgmL/view?usp=sharing Urolithin A: https://youtu.be/OsaWrkM3Fxs?si=SeZ-nTuf46H2o4QO Methylene Blue: https://youtu.be/XHhMN3oC69M?si=B5pCINSXA19rNcQs BFR guide: https://drive.google.com/file/d/1dSwrG6u737O5o7_TOd3PkRfeGsz8ESX5/view?usp=drive_link Hot Flash Program: https://hackmyage.com/hot-flash-masterclass/ Energy Reboot Program: https://hackmyage.com/energy-reboot-self-guided/   Give thanks to our sponsors: Try Vitali skincare. 20% off with code ZORA here - https://vitaliskincare.com Get Primeadine spermidine by Oxford Healthspan. 15% discount with code ZORA ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - http://oxfordhealthspan.com/discount/ZORA Get Mitopure Urolithin A by Timeline. 20% discount with code ZORA at https://timeline.com/zora Try Suji to improve muscle 10% off with code ZORA at TrySuji.com - https://trysuji.com Try OneSkin skincare with code ZORA for 15% off https://oneskin.pxf.io/c/3974954/2885171/31050   Join the Hack My Age community on: YouTube: https://youtube.com/@hackmyage Facebook Page: ⁠⁠⁠⁠⁠@⁠Hack My Age⁠     Facebook Group: ⁠⁠⁠⁠⁠⁠@⁠Biohacking Menopause⁠⁠⁠⁠⁠⁠ ⁠   Biohacking Menopause Private Women's Only Support Group: https://hackmyage.com/biohacking-menopause-membership/ Instagram: ⁠⁠⁠⁠⁠@⁠HackMyAge⁠    Website: ⁠⁠⁠⁠⁠⁠HackMyAge.com⁠    For partnership inquiries: https://www.category3.ca/  Some episodes of Hack My Age are supported by partners whose products or services may be discussed during the show. The host may receive compensation or earn a minor commission if you purchase through affiliate links at no extra cost to you. All opinions shared are those of the host and guests, based on personal experience and research, and do not necessarily represent the views of any sponsor. Sponsorships do not imply medical endorsement or approval by any healthcare provider featured on this podcast.  

GSM = genitourinary syndrome of menopause - it is a mouthful - and a very common experience. Today Dr. Corinne Menn and Dr. Casperson talk all about it. This audio is taken from an IG live - follow us both there! In a world where women's health often takes a backseat, the conversation surrounding vaginal hormones is crucial yet frequently misunderstood. We dive deep into the complexities of vaginal estrogen, dispelling common myths and providing clarity on its importance for women, especially those facing menopausal challenges. Dr. Menn's IG To my fellow clinicians: listen to the You Are Not Broken podcast on ⁠Pinnacle's network to earn FREE CME credit⁠ Listen to my Tedx Talk: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Why we need adult sex ed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Take my Adult Sex Ed Master Class:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠My Website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in my sexual health and hormone clinic? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Waitlist is open⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Thanks to our sponsor ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Midi Women's Health⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Designed by midlife experts, delivered by experienced clinicians, covered by insurance.Midi is the first virtual care clinic made exclusively for women 40+. Evidence-based treatments. Personalized midlife care.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.joinmidi.com⁠ To learn more about Via vaginal moisturizer from Solv Wellness, visit ⁠⁠via4her.com⁠⁠ and get 20% off your first order. For an additional $5 off, use coupon code DRKELLY5. Providers can request patient materials or samples at ⁠⁠hcp.solvwellness.com⁠⁠. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Can you use vaginal estrogen and systemic hormone therapy?Absolutely — and most women actually need both.If you're still experiencing:• dryness or burning• painful sex• tearing when you wipe• urinary urgency/frequency• recurrent UTIs…it's not you, and it's not that your hormones “aren't working.”It's that systemic estrogen doesn't fully treat Genitourinary Syndrome of Menopause (GSM).Here's what the research shows:✔ Up to 84% of women experience GSM✔ 20–30% of women on systemic HT still need vaginal estrogen✔ 50–70% benefit from using bothLocal vaginal therapy directly restores the vulvar and vaginal tissue.You deserve comfort, pleasure, and confidence again.Relief is absolutely possible.

A special Thanksgiving message from Gaston's Great!

The annual Thanksgiving edition of WrestleRant Radio features Graham "GSM" Matthews' exclusive interview with WWE SmackDown star Aleister Black! They talk his return run thus far, biggest changes he's noticed creatively, working with his wife Zelina Vega, his relationship with Triple H, why less is more with the amount of matches on PLE cards, his iconic entrance music, who he almost faced in his return at WrestleMania 41, and more! From there, GSM gives his on-site report from AEW's Full Gear pay-per-view in Newark last weekend including why the events would benefit from being shorter (or airing earlier), how Brodido vs. FTR stole the show, possible tension within The Death Riders' ranks, Ricochet becoming the inaugural AEW National Champion, Mark Briscoe capturing the AEW TNT Championship, hardcore matches and blood losing their meaning when they're done so often, Mercedes Mone again failing to win the AEW Women's World Championship, Samoa Joe shockingly winning the AEW World Championship, Swerve Strickland returning and why the main event needs new blood. Plus, stick around for GSM's FULL PREDICTIONS for Saturday's WWE Survivor Series premium live event this Saturday featuring two star-studded WarGames matches!

Dr. Rosensweet graduated from the University of Michigan Medical School in 1968. Since 1971, he has been in private medical practice, with offices in Florida, New Mexico, California, and Colorado. Early in his career, Dr. Rosensweet trained the first nurse practitioners in the United States and was in charge of health promotion for the State of New Mexico.He is a nationally known lecturer and presenter at The American Academy of Anti-Aging Medicine (A4M), The American College for Advancement in Medicine (ACAM), The Age Management Medicine Group (AMMG), and more. In 2019, he was called to Washington to speak in front of The National Academies of Science Engineering and Medicine (NASEM) on “The Safety and Efficacy of Bioidentical Hormones.”Dr. R is the Founder of The Menopause Method and The Institute of BioIdentical Medicine, where he has been training medical professionals to master cBHRT using the most advanced and modern tools. His protocol has been used to treat more than 12,000 women. More about Dr. Rosensweet:* Was recently named one of “The Biggest Names in Anti-Aging Medicine” by The American Academy of Anti-Aging Medicine (A4M)* Author of the books, Menopause and Natural Hormones and Happy Healthy Hormones: How to Thrive in Menopause* Founder of Brite (www.brite.live) and I Wonder, Doctor… (www.iwonderdoctor.com)* Founder and co-chair of the Coalition to Protect Compounded Bioidentical Hormones (cbhrtcoalition.org)*Organizer of a National Summit Committee on the Treatment of Women in Menopause with Bioidentical Hormones* Principal Investigator for a scientific study of female hormones.https://brite.live/       https://iobim.org       https://www.davedrosensweetmd.comhttps://www.facebook.com/share/g/1CEpiqShxB/https://www.instagram.com/menopausedoctor?utm_source=ig_web_button_share_sheet&i“I recommend this product to my clients for hemorrhoids, fissures, itching, irritation, dryness and for any kind of butt drama. I like it because it's all-natural, soothing, effective, multi-purpose and female founded. It's made with healing ingredients such as arnica, vitamin e oil, organic aloe and the branding speaks for itself.” Use code VAGINACOACH to save 20% at www.anythingbrands.comThank you so much for listening! I use fitness and movement to help women prevent and overcome pelvic floor challenges like incontinence and organ prolapse. There is help for women in all life stages! Every Woman Needs A Vagina Coach! Please make sure to LEAVE A REVIEW and SUBSCRIBE to the show for the best fitness and wellness advice south of your belly button. *******************I recommend checking out my comprehensive pelvic health education and fitness programs on my Buff Muff AppYou can also join my next 28 Day Buff Muff Challenge https://www.vaginacoach.com/buffmuffIf you are feeling social you can connect with me… On Facebook https://www.facebook.com/VagCoachOn Instagram https://www.instagram.com/vaginacoach/On Twitter https://twitter.com/VaginaCoachOn The Web www.vaginacoach.comGet your Feel Amazing Vaginal Moisturizer Here

Thanksgiving is one of the most difficult meals to pair with, but that doesn't mean we shouldn't try!  Source: Canva   This year, for one of our weekly discussion questions I asked the Patrons how many wines they would be serving with their Thanksgiving/holiday feasts and the answer was overwhelmingly 2-3. Given that, in this show I talk about combinations of two wines you can purchase for your table that will pair with many types of Thankgsivings. Some examples:  If your dishes tend to be on the sweet side… honey glazes, marshmallow sweet potatoes, candied sweet potatoes, etc Off-dry Riesling or Vouvray (although butternut squash soup with a kick could do well with a regular Gewurztraminer) Reds: Grenache, GSM blends from Rhône, California, Australia, etc., Garnacha from Spain, Zinfandel   Asian-influenced Thanksgiving Aromatic whites: Alsace Riesling, Pinot Gris, Gewürztraminer, Torrontés, or Viognier Fruity reds with low tannin: For smokey or braised meat: New World Pinot Noir, Côtes du Rhône, Garnacha. For something especially smoky: Saumur-Champigny from Loire   Desserts....Pecan Pie: Tawny Port, Madeira, Pedro Ximenez Sherry   From Getty Images via Canva   There are these ideas explained and so much more packed into the episode. Listen, take what you want leave the rest! Please know that I'm grateful to you for listening and your loyalty to me and the show!!      Full show notes and all back episodes are on Patreon. Become a member today! www.patreon.com/winefornormalpeople _______________________________________________________________   Check out my exclusive sponsor, Wine Access.  They have an amazing selection -- once you get hooked on their wines, they will be your go-to! Make sure you join the Wine Access-Wine For Normal People wine club for wines I select delivered to you four times a year!    To register for an AWESOME, LIVE WFNP class with Elizabeth or get a class gift certificate for the wine lover in your life go to: www.winefornormalpeople.com/classes    

In this AUANews Inside Tract episode, Drs. Rachel Rubin and Una Lee take listeners inside the movement that successfully pushed the FDA to eliminate the outdated black box warning on estrogen therapies. They unpack the origins of the warning, the modern evidence that contradicts it, the impact on GSM care, and the powerful role urology played in driving this change. A forward-looking conversation about implementation, education, and the future of hormone-based care.

Para precio y disponibilidad, vaya a este vínculo: https://support.apple.com/en-me/guide/iphone/iph3c9951d7/ios#:~:text=When%20you're%20placed%20on,in%20all%20countries%20or%20regions. Con la función ‘Call Waiting' del iPhone, puedes recibir una segunda llamada mientras ya estás en otra: el teléfono te notificará de la nueva llamada y te permitirá poner en espera la primera o responder la nueva. Ve a Ajustes → Teléfono → Call Waiting para activarla y asegúrate de tener servicio GSM para que funcione correctamente.

On this week's WrestleRant Radio, Graham "GSM" Matthews and RJ Marceau answer a ton of questions from live viewers before sharing their top takeaways from one of the best episodes of Raw all year on Monday night in Madison Square Garden (which GSM was in attendance for!) including John Cena wrestling his final Raw match ever, the men's and women's WarGames teams coming together, Maxxine Dupri upsetting Becky Lynch for the Women's Intercontinental Championship, the returns of AJ Lee, Brock Lesnar and Roman Reigns, and the first round of The Last Time Is Now Tournament coming to a close. They also give their updated picks for the rest of the tourney and who's still the overall favorite to win the whole thing. Plus, GSM and RJ discuss Ariel Helwani's latest interview with Tony Khan from this past week, how it was a vast improvement over their previous interview but what Khan can still work on, interesting updates on Andrade, Chris Jericho, Britt Baker and others, Edge/Adam Copeland never being an option for the Cena tournament, Darby Allin's wild risks, and more. Stick around for their FULL PREDICTIONS for Saturday's AEW Full Gear pay-per-view event!

THE FDA FINALLY REMOVED THE BLACK BOX WARNING on both systemic estrogen and low-dose vaginal estrogen — and it's one of the biggest wins in women's health in decades.For years, millions of women avoided hormone therapy because of outdated, frightening warnings that did NOT match the scientific evidence. This led to unnecessary suffering — from painful sex to recurrent UTIs to fear-based avoidance of safe, effective treatments.In this episode, Dr. Carolyn Moyers (Board-Certified OB/GYN + Menopause Specialist) breaks down the NEW FDA labeling, the real data on hormone therapy safety, and why vaginal estrogen is one of the safest and most life-changing treatments in menopause care.

Graham "GSM" Matthews has thoughts on this week's WrestleRant Radio regarding Elimination Chamber being announced for Chicago next February and what it might mean for CM Punk's road to WrestleMania 42, along with recent comments from TKO indicating they intend to move away from Vince McMahon-created premium live events. He then runs down the top 10 for PWI's Women's 250 for 2025 and who ranked where before sounding off on the end of Giulia's afterthought of a reign as Women's United States Champion. Plus, GSM gives his on-site report from Raw in Boston, including John Cena winning the Intercontinental Championship from Dominik Mysterio and a ton of Survivor Series/WarGames developments. Who fills out the men's and women's WarGames teams? How long will Cena's title run last? Who else will advance in The Last Time Is Now Tournament? He also reviews the Blood and Guts edition of AEW Dynamite, and more!

The car business feels broken right now.Showrooms are quiet. Sales teams are restless. Managers are asking the same question:“Is it slow out there, or is it just us?”Everyone's looking for answers — but the truth isn't in the rumors or social media posts. It's in the data.In this premiere episode of Follow The Money (Ep. 18) on The Get More Frank Show, I sit down with Brian Kramer, EVP at Cars Commerce, to break down the Q3 automotive market data that every dealership leader needs to hear before heading into Q4.We're not talking about feelings — we're talking about facts.

It's Halloween week on WrestleRant Radio, and Graham "GSM" Matthews and RJ Marceau bring the tricks, treats and all the top-notch analysis you can ask for! Hear their thoughts on the latest WrestleMania 42 teaser and the online buzz it generated among fans, a recap of NXT Halloween Havoc and the three championships that changed hands including Tatum Paxley shockingly winning the NXT Women's Championship, booking Trick Williams upon being called up to the main roster, Kelani Jordan turning heel on Tuesday's NXT, and more! Plus, GSM and RJ share their predictions for the four-match Saturday Night's Main Event card! They talk who should win the vacant World Heavyweight Championship and if heel Jey Uso taking the title is the way to go along with what happens next, arguing against Dominik Mysterio ultimately losing the Intercontinental Championship to John Cena, why Jade Cargill needs to beat Tiffany Stratton for the WWE Women's Championship this time, and if Drew McIntyre will dethrone Cody Rhodes as Undisputed WWE Champion.

This Break/Fix podcast episode features Steve Contarino, founder of Automobilia Vintage, as he delves into his journey of collecting automotive memorabilia. Steve discusses his passion for automotive history, rare artifacts, and vintage racing posters, and recounts his experiences with various car companies and collectible items. Alongside his stories, Steve shares insights into the collector market, the evolution of collecting trends, and offers advice for new collectors; highlighting the importance of preserving automotive history and the joy of collecting. ===== (Oo---x---oO) ===== 00:00:00 Meet Steve Contarino: Founder of Automobilia Vintage 00:01:26 Steve's Passion for Automotive History 00:02:56 Owning Checker Cab, The Company 00:06:56 The Birth of Automobilia Vintage 00:08:55 Collecting Rare Automobilia 00:29:16 The Intersection of Automobilia and Motorsport 00:34:37 Understanding Value vs. Worth in Collecting 00:36:19 The Significance of Fisher Craftsman's Guild Cars 00:38:52 Balancing Quality and Quantity in Collections 00:40:27 Evolution of the Collector Community; Generational Shifts in Collecting Trends 00:56:18 Advice for New Collectors 01:01:22 Future of Automobilia Vintage 01:05:44 Closing Remarks and Gratitude ==================== The Motoring Podcast Network : Years of racing, wrenching and Motorsports experience brings together a top notch collection of knowledge, stories and information. #everyonehasastory #gtmbreakfix - motoringpodcast.net More Information: Visit Our Website Become a VIP at: Patreon Online Magazine: Gran Touring Follow us on Social: Instagram This episode is sponsored in part by Garage Style Magazine. Since 2007, GSM has been the definitive source for car collectors, continually delivering information about Automobilia, Petroliana, Events and more... because after all, what doesn't belong in your garage?

Episode SummaryThe FDA held a rare public hearing to reconsider the safety labels on vaginal estrogen—a pivotal moment for menopause care.In this episode, Dr. James Simon, a leading menopause specialist and clinical researcher, joins Georgie Kovacs to unpack what the hearing revealed, why the current labeling may do more harm than good, and what it means for millions of women experiencing genitourinary syndrome of menopause (GSM), recurrent UTIs, painful sex, and incontinence.Together, they explore how outdated warnings, lack of education, and systemic biases continue to shape women's access to treatment—and what both women and clinicians need to know as change finally takes shape.Discussion PointsWhy is the FDA reconsidering the vaginal estrogen warning label now?What does genitourinary syndrome of menopause (GSM) actually mean—and how common is it?How do current black box warnings limit women's access to safe and effective therapies?What's the real risk of vaginal estrogen and breast cancer—and what does the evidence say?How can updated labeling improve care for UTIs, painful sex, and incontinence?What steps can clinicians take to confidently prescribe vaginal estrogen?What role does the media and misinformation play in perpetuating fear around menopause care?How can women advocate for themselves if their doctor is hesitant to prescribe?What's next for the FDA—and how could this hearing reshape menopause treatment in the U.S.?

Bradenton Industrial Webinar-----------I'd like to invite you to learn more about an exciting opportunity located in Bradenton Florida. Bradenton is next to Sarasota for those of you who are familiar with Florida. This market has an industrial moratorium that is driving one asset class to new heights, specifically light industrial. This 35 are property, right in the middle of Bradenton has an existing Charter School on 11 of those acres and 24 acres of land that we are developing.  We are hosting a webinar on Wednesday October 8 at 7PM Eastern time. This opportunity is only open to accredited investors residing in the US in compliance with SEC regulations. To learn more, click on the link in the show notes and we will see you on Wednesday evening at 7PM. --------------On today's show we are looking back in history for some of the narratives that surrounded the adoption of new technology. The year was 1999. At the time, it seemed like the internet was the answer, what's the question? Companies were spending hundreds of millions burying optical fibre anywhere they could. After all, the internet would need lots of fibre to carry all of that traffic. There was tons of investment in the core of the network to carry all of this traffic. I personally was an executive in the tech industry. I left Nortel in 1997.  The next company I was at was Tundra Semiconductor. We were designing microprocessor core logic chips that were used in all kinds of applications. One of our customers was Motorola who was shipping 250,000 cellular base stations a year. These would eventually be upgraded from the GSM base station to the Edge  base station and then eventually the 3G base station. Back in those days, the emphasis was on building out the core of the network.Later in my career I took progressively more senior positions in the tech industry. By 2004 I was VP of Engineering at AMCC that was headquartered in San Diego. I was also President of AMCC Canada.  My company had raised about $1B in the public markets at the height of the Dotcom frenzy. As a result, we had all kinds of startup companies parading through our board room with the hopes of getting acquired by a company with a ton of cash.  I learned to ask three very simple questions of every startup company. The answer to these questions revealed more than anything else. The technology, the features, the cool factor, none of it mattered. -------------**Real Estate Espresso Podcast:** Spotify: [The Real Estate Espresso Podcast](https://open.spotify.com/show/3GvtwRmTq4r3es8cbw8jW0?si=c75ea506a6694ef1)   iTunes: [The Real Estate Espresso Podcast](https://podcasts.apple.com/ca/podcast/the-real-estate-espresso-podcast/id1340482613)   Website: [www.victorjm.com](http://www.victorjm.com)   LinkedIn: [Victor Menasce](http://www.linkedin.com/in/vmenasce)   YouTube: [The Real Estate Espresso Podcast](http://www.youtube.com/@victorjmenasce6734)   Facebook: [www.facebook.com/realestateespresso](http://www.facebook.com/realestateespresso)   Email: [podcast@victorjm.com](mailto:podcast@victorjm.com)  **Y Street Capital:** Website: [www.ystreetcapital.com](http://www.ystreetcapital.com)   Facebook: [www.facebook.com/YStreetCapital](https://www.facebook.com/YStreetCapital)   Instagram: [@ystreetcapital](http://www.instagram.com/ystreetcapital)  

Shoot us a Text.Episode #1162: Tesla posts a surprise Q3 sales record, $1,000 car payments are becoming the new normal, Meta plans to use AI chats to make ad targeting more personal than ever.Show Notes with links:Tesla delivered a surprise Q3 record after a rocky first half of the year, beating Wall Street expectations with nearly half a million EVs sold. But with the federal EV tax credit now gone, the question is whether momentum can carry into Q4 and beyond.Tesla delivered 497,099 vehicles, topping estimates of 456,000 and reversing two quarters of declines.Model 3 and Y deliveries rose 9%, while other models dropped 30%.Tesla's energy business hit a record, nearly doubling storage deployments to 12.5 GWh.Rivian also posted a 32% bump, delivering 13,201 EVs in Q3.What used to be unthinkable is now routine: the $1,000-a-month car payment. Nearly one in six new-car buyers are signing up for four-figure notes, a trend driven by rising prices, interest rates, and longer terms — reshaping affordability conversations across the showroom floor.In 2015, only 2.4% of buyers paid $1,000+; that number hit 16.6% in JulySUVs (53%) and pickups (37%) dominate these deals; 5% of all $1,000+ buyers drove off in an F-150.Buyers today face average loans near $42K at 6.8% interest, compared to $28K at 3.9% a decade ago.Longer terms now stretch over 68 months on average, nearly a year longer than 2015.“There are some that are very shocked by the payment,” said Cody Anderson, GSM at Freedom Ford. “Their payment thought process is five years ago compared to now.”Meta is about to supercharge its ad business by tapping into conversations people have with its AI chatbot. Starting December 16, chats with Meta AI will help determine not just what ads users see, but what content fills their feeds across Facebook, Instagram, and WhatsApp.Meta AI chats will feed new ad-targeting signals, similar to posts, likes, and connections.Example: Talk about hiking → expect more hiking ads and related content.The company stresses sensitive topics (politics, religion, health, etc.) won't be used for targeting.Meta earned $46.5B in ad revenue last quarter, up 21% YoY.“Interactions with AIs will be another signal we use to improve people's experience,” Meta said.0:00 Intro with Paul J Daly and Kyle Mountsier1:35 The huge news out of More Than Cars2:48 Tesla Sets Delivery Record5:35 Nearly 17% of Car Payments are $10008:45 Meta Will Use AI Searches To Target Ads To UsersJoin Paul J Daly and Kyle Mountsier every morning for the Automotive State of the Union podcast as they connect the dots across car dealerships, retail trends, emerging tech like AI, and cultural shifts—bringing clarity, speed, and people-first insight to automotive leaders navigating a rapidly changing industry.Get the Daily Push Back email at https://www.asotu.com/ JOIN the conversation on LinkedIn at: https://www.linkedin.com/company/asotu/

Today, Grid Down Comms Up takes the microphone again for another great episode of the Prepping Academy. We do a short after-action report on the Eastern Tennessee Homestead Alliance festival, revisit a few items as we approach the one-year anniversary of the historic disaster caused by Hurricane Helene, and talk about radio and communications interference. We look at the common types of manmade interference and what you can do to ensure you aren't the cause, because making lots of people look for you isn't very gray man. We also discuss what you may want to consider adding to your plans if you experience intentional interference during an emergency. We also listened to an example of interference during my time in Eastern Tennessee with GSM and a much more entertaining example of some amateur operators trolling someone who is causing intentional interference to keep him talking until they locate him. I'll see you on the next episode.

Today, Grid Down Comms Up takes the microphone again for another great episode of the Prepping Academy. We do a short after-action report on the Eastern Tennessee Homestead Alliance festival, revisit a few items as we approach the one-year anniversary of the historic disaster caused by Hurricane Helene, and talk about radio and communications interference. We look at the common types of manmade interference and what you can do to ensure you aren't the cause, because making lots of people look for you isn't very gray man. We also discuss what you may want to consider adding to your plans if you experience intentional interference during an emergency. We also listened to an example of interference during my time in Eastern Tennessee with GSM and a much more entertaining example of some amateur operators trolling someone who is causing intentional interference to keep him talking until they locate him. I'll see you on the next episode.  Support the showPlease give us 5 Stars! www.preppingacademy.com Daily deals for preppers, survivalists, off-gridders, homesteaders & everyday Americans. The best gear & supplies—posted in one place, every dayCheck out https://prepperfinds.com Contact us: https://preppingacademy.com/contact/ www.preppernet.net Amazon Store: https://amzn.to/3lheTRTwww.forrestgarvin.com

In this solo episode of the Not Your Mother's Menopause Podcast, Dr. Fiona Lovely provides a vital and compassionate guide to the Genitourinary Syndrome of Menopause (GSM). She begins by explaining that the GSM is a spectrum of symptoms affecting the genitourinary tract due to declining estrogen and androgenic hormones (like testosterone and DHEA) during the menopause transition. Dr. Lovely details the common yet often unspoken symptoms, which can include vaginal dryness, painful intercourse, urinary urgency, recurrent bladder infections, and changes in libido and sensation. She emphasizes that these changes are not something to suffer through in silence and offers clear, actionable solutions. We discuss treatment using low-dose vaginal estrogen, a safe and effective option for nearly everyone that directly addresses the root cause of tissue thinning and fragility. Dr. Lovely provides practical application advice and recommends specific formulations. Beyond treatment, she encourages open communication with our sex partners to navigate changing intimacy needs and suggests speaking with employers about necessary accommodations if symptoms affect work.  This episode is an empowering call to seek compassionate care and prioritize quality of life, no matter where you are in the menopause landscape. Thank you to our sponsors for this episode:

"An educated patient is the best patient." - Dr. Rachel Rubin In this episode, SHE+ President and Chairwoman Patty Brisben sits down with Dr. Rachel Rubin, a board-certified urologist and sexual medicine specialist. As a pioneer in the field, Dr. Rubin brings unmatched attention and expertise to conditions that impact so many women, and for too long, went without a name: Genitourinary Syndrome of Lactation (GSL) and Genitourinary Syndrome of Menopause (GSM). Dr. Rubin breaks down why naming these conditions matters, how symptoms manifest, and what patients and providers can do to move past dismissal and toward better care. For more information on GSL, GSM, and Dr. Rubin, check out the Resource Hub at www.sheplusfoundation.com/resourcehub 

The Skin Real app is officially LIVE! Download it now. Download my Free Guide 'In My Perimenopause Era' Download the Ultimate Affordable Skincare Guide When was the last time you thought about your vulvar health? If your answer is “never,” you're not alone. Most women avoid talking or even thinking about this part of their body, but during perimenopause and menopause, changes in vulvar and vaginal health can have a huge impact on your comfort, confidence, and quality of life. In this episode, I sit down with Dr. Diana Londoño, a urologist who is breaking the silence on intimate health. We talk about the changes estrogen loss brings—from dryness, itching, painful sex, and recurrent UTIs to the lesser-known issues like bladder urgency and vulvar atrophy. She also explains how simple solutions like vaginal estrogen cream, hormone therapy, and lifestyle tweaks can protect your vulvar health and prevent years of suffering. ✨ Key Takeaways: Why vulvar health is central to your bladder, vaginal, and sexual health during menopause. How to know if your symptoms are normal aging or a red flag that needs further evaluation. The connection between UTIs, GSM (genitourinary syndrome of menopause), and vulvar atrophy. Why vaginal estrogen is safe, preventative, and worth considering even if you're not sexually active. How stress and mindset affect bladder symptoms and overall well-being. If you've ever felt embarrassed, ignored, or confused about what's happening “down there,” this episode will give you clarity and confidence. Dr. Diana Londoño is a Board-Certified Urologist and one of the 10% of urologists in the US who are women and the 0.5% who are Latinx and women.  She is originally from Mexico City and attended Claremont McKenna College for her undergraduate studies and then went on to attend UCLA for medical school.  She completed a 6-year residency in Urology at Kaiser Permanente in Los Angeles.   She has experienced burnout twice, which has led her to write and speak about it to raise awareness and help others. She has published multiple articles in prominent medical platforms, including Medscape, Doximity, Kevin MD, Men's Health, Giddy.com, and WebMD, among others. She is also a contributing author to the books “Thriving After Burnout” and “Medic S.O.S.”  She has also been a guest on numerous podcasts, discussing various topics, including wellness, stress, spirituality, and energy.   Her burnout journey led her to become a certified life coach and founder of Physician Coach Support.com, a peer support platform she ran for 3 years. In 2022, she received the Los Angeles County Medical Association Physician Leadership Award for her work.    She is an international speaker and guest on multiple podcasts, discussing topics such as wellness, boundaries, ego, humanity in medicine, mindset, and mindfulness. She has also been featured on TV on Univision, Telemundo, Mundo Fox, CNN Latino, KCET, and ABC News as a health consultant discussing urological topics.   She is also a Reiki Master, a Pranic Healer and the mother of two determined and joyful 7- —and 9-year-old girls, Daniela and Paloma.   Follow Dr. Londoño here:   Website -https://dianalondonomd.com/ LinkedIn - https://www.linkedin.com/in/dianalondonomd/ Instagram - https://www.instagram.com/dianalondonomd/ YouTube - https://www.youtube.com/@dianalondonomd   Want more expert skin advice without the overwhelm? Subscribe to The Skin Real Podcast wherever you listen, and visit www.theskinreal.com for dermatologist-backed tips to help you feel confident in your skin—at every age. Follow Dr. Mina here:-  https://instagram.com/drminaskin https://www.facebook.com/drminaskin https://www.youtube.com/@drminaskin https://www.linkedin.com/in/drminaskin/ Visit Dr. Mina at Baucom & Mina Derm Surgery Website: atlantadermsurgery.com Email: scheduling@atlantadermsurgery.com Call: (404) 844-0496 Instagram: @baucomminamd Thanks for tuning in. And remember—real skin care is real simple when you know who to trust. Disclaimer: This podcast is for entertainment, educational, and informational purposes only and does not constitute medical advice.