Podcasts about flamingos

How much does a black bear eat each day? Do flamingos fly? And why does a giraffe have hooves? Curious questions from you listeners guide us into God's wild and wonderful world on this special kid-made episode!Episode Links:Explore Apologia's award-winning science courses and curriculum at: https://www.apologia.com/Episodes Mentioned:Lesson 23: Bears Don't Hibernate — 4 Cool Ways God Designed Creatures to Rest: https://player.captivate.fm/episode/3511d4f1-617f-4742-b3f9-ab6c55f1ea50/Lesson 9: Are All Black Bears Black?: https://player.captivate.fm/episode/78e0351c-b224-40f7-9041-7627d528eef2/Lesson 31: What Is The Difference Between A Turtle And Tortoise?: https://player.captivate.fm/episode/dccb26a1-9dde-498f-8d6d-573f1478e243/Why Do Giraffes Have Spots? Lesson 105: https://player.captivate.fm/episode/57dc20eb-2e33-4b58-9765-fa726c5ba736/Why And How Do Leaves Change Colors? Lesson 49: https://player.captivate.fm/episode/38cf2f29-1a36-4c54-81ba-becb473615a2/Can a Narwhal Get a Brain Freeze? Lesson 102: https://player.captivate.fm/episode/5beea601-57de-4eaf-a8d7-9f95a80f57cf/This podcast contains paid advertisements.This podcast uses the following third-party services for analysis: Podder - https://www.podderapp.com/privacy-policy

Jeffrey destaca una noticia y unos lanzamientos que les encantarán.¿Qué estoy usando hoy?:SchonDSGN Black Ultem (M) Pelikan Edelstein JadeTinteríasMadrid Pen Show (13 y 14 de noviembre)Barcelona Pen Show Gris TrencadísLamy AL-star Flamingo y PinePelikan Classic M200 Festivales mundiales Cherry BlossomPlatinum #3776 Century 2.0 Demonstrator GTPlatinum BISOTintería del capítulo: ARMONÍA

"Bekannt durch Film, Funk, Fernsehen" Selten war dieser Spruch zutreffender als bei der exaltierten R&B-Showtruppe The Treniers. Nur ihre Platten liefen in all‘ den Jahrzehnten nicht besonders. Götz Alsmann stellt ihr Live-Album des Jahres 1970 vor. Von Götz Alsmann.

2026-02-22 | UPDATES #138 | Massive blow for Russia — the Votkinsk strike.A bold, deep-strike into the heart of Russia's missile industry — Ukraine has hit the Votkinsk Machine Building Plant. It's a long-range, high-value operation that — if confirmed — could blunt Moscow's ability to replenish ballistic and cruise missiles.On the night of 20–21 February 2026 — Ukrainian forces say they struck the JSC Votkinsk Machine Building Plant in the Udmurt Republic, a core site that manufactures engines and components for systems including Iskander, Oreshnik and some larger strategic missiles. Kyiv's General Staff publicly stated the strike and confirmed some details, saying it used domestically produced FP-5 “Flamingo” ground-launched cruise missiles. (ukrinform.net)----------SUPPORT THE CHANNEL:https://www.buymeacoffee.com/siliconcurtainhttps://www.patreon.com/siliconcurtainhttps://www.gofundme.com/f/scaling-up-campaign-to-fight-authoritarian-disinformation----------A REQUEST FOR HELP!I'm heading back to Kyiv this week, to film, do research and conduct interviews. The logistics and need for equipment and clothing are a little higher than for my previous trips. It will be cold, and may be dark also. If you can, please assist to ensure I can make this trip a success. My commitment to the audience of the channel, will be to bring back compelling interviews conducted in Ukraine, and to use the experience to improve the quality of the channel, it's insights and impact. Let Ukraine and democracy prevail! https://buymeacoffee.com/siliconcurtain/extrashttps://www.patreon.com/siliconcurtainhttps://www.gofundme.com/f/scaling-up-campaign-to-fight-authoritarian-disinformationNONE OF THIS CAN HAPPEN WITHOUT YOU!So what's next? We're going to Kyiv in January 2026 to film on the ground, and will record interviews with some huge guests. We'll be creating opportunities for new interviews, and to connect you with the reality of a European city under escalating winter attack, from an imperialist, genocidal power. PLEASE HELP ME ME TO GROW SILICON CURTAINWe are planning our events for 2026, and to do more and have a greater impact. After achieving more than 12 events in 2025, we will aim to double that! 24 events and interviews on the ground in Ukraine, to push back against weaponized information, toxic propaganda and corrosive disinformation. Please help us make it happen!----------SOURCES: Reuters: governor of Udmurtia says Ukrainian drones damaged site.Kyiv Independent: report on Flamingo strike, eyewitness videos and social posts. Defence-blog / Military analysis: satellite imagery and workshop damage assessment. UNN / local Ukrainian reporting: photos showing extensive damage to workshops. Pravda / Ukrainian defence reporting: FP-5 Flamingo missile claimed by Kyiv. UPI & LA Times: international wire coverage and context on reach and significance. Militantnyi / regional defence outlet: open-source satellite imagery analysis reporting specific workshop damage (No. 22, No. 36). ----------

Send a textAs it's been a while; a new Q&A!!This week I answer questions on protein-levels for postpartum recovery (for athletes and non-athletes) and I explain why I think most people DO NOT need a Personal Trainer that specialises/has experience with a particular condition (AFTER rehab is completed, of course, because I'm not a maniac)As always; HPNB only has 5 billing cycles. So this means that you not only get 3 months FREE access, no obligation! BUT, if you decide you want to do the rest of the program, after only 5 months of paying $10/£8 a month you now get FREE LIFE TIME ACCESS! That's $50 max spend, in case you were wondering. Though I'm not terribly active on  Instagram and Facebook you can follow us there. I am however active on Threads so find me there! And, of course, you can always find us on our YouTube channel if you like your podcast in video form :) Visit healthypostnatalbody.com and get 3 months completely FREE access. No sales, no commitment, no BS. Email peter@healthypostnatalbody.com if you have any questions, comments or want to suggest a guest/topic       Playing us out "Dresden the Flamingo".

Or: Spanish Gold. A treasure hunt intrigues Shannon and Saylor, even though King Moses suggests it's bogus. Regardless, it should be a fun job… right? Or is there danger lurking…

Your host Andy, Ant and Dun take a look at Saturday's Horror Show vs Blackburn Rovers, and also have the pleasure of speaking with the voice of the R's, Tyler Morris about his career in commentary to date, and does he need a bungee cord for his own safety?- Now Sam Field has gone on loan, who is the new scapegoat?- It's the team, it's the whole damn team - Dun goes full Jason Kelce minus the gravelly voice, and mummers outfit...- After three away games where they put their case forward as one of the best units in the league, The defense rests... - Disaster averted - Both Madsen and Kone come out relatively unscathed -The cavalry arrives. JCs on the bench, chair, Varane - Sam Field IS better off on loan...Not sure about Morrison though- The Nourry Q&A...Took a strange turn...by 90 degrees- New York warms up, as does the Winter Olympics- The Tyler Morris interview- The return of Blighty Bulletin - which is just 'fine'.- Ant delves deep into his kitbag to find Hull and Southampton kits. Does Yellow and Teal pair well with a tinfoil hat?- ...and are there Flamingos in Flamingo Land - Just how bold will our predictions be for the away games at Hull and Southampton? - Jacob goes as dark as Ant's Originals gorgeous blackout shirt- Lovely Stuff - Joy in repetition, Bones and Meat in a box for Valentines Day and the awarding of inaugural QPR NYC Peace Prize.- No meet up at the Factory this weekend, back for the SaintsRate, review, share, follow, listen, stream, download, and check out QPR NYC on Big Cartel to check out our merch including Ant's Originals...

Aujourd'hui, je vous propose de repartir explorer les zones humides. 

Today we've got a wide variety of topics covering Elon Musk's Boring Company being tied to Epic Universe, a MASSIVE manatee rescue, and your Q&A!

Florida's feathered fight continues, as the effort to change the state bird finally passes through the House. But will it take flight in the Senate? Ryan, Dana, and Chris Trenkmann talk about this latest effort and the person who should be getting credit for it. See omnystudio.com/listener for privacy information.

Florida's feathered fight continues, as the effort to change the state bird finally passes through the House. But will it take flight in the Senate? Ryan, Dana, and Chris Trenkmann talk about this latest effort and the person who should be getting credit for it.

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]:

Donald Trump kündigte Ende 2025 eine massive Aufrüstung der US-Marine an: neue, gigantische Schlachtschiffe, eine eigene "Trump-Klasse" mit maximaler Feuerkraft. Er nennt sie die "goldene Flotte". Doch sind solche Stahlkolosse im Zeitalter von Drohnen und Hyperschallwaffen überhaupt noch zeitgemäß? In der aktuellen Folge spricht Stefan Niemann mit dem Admiral a.D. Manfred Nielson über Trumps Pläne und die Frage, wie eine Seemacht im 21. Jahrhundert aussehen sollte. Nielson ist der Meinung: "Moderne Einheiten müssen heute nicht über Größe überzeugen, sondern durch Technologie". Dabei geht es auch um den strategischen Wettbewerb mit China und um die Rolle der US-Navy in den kommenden Jahrzehnten. Ein historischer Rückblick zeigt, dass die Debatte über militärische Stärke nicht neu ist. Schon im US-Wahlkampf 2012 reagierte Barack Obama auf Kritik an der schrumpfenden amerikanischen Flotte mit dem Hinweis, dass moderne militärische Fähigkeiten nicht durch bloßes Zählen von Schiffen zu erfassen seien - ein Argument, das heute aktueller denn je erscheint.Zuvor blickt Kai Küstner wie gewohnt auf den russischen Krieg gegen die Ukraine. Nachdem die Ukraine im vergangenen Jahr mit der Produktion von Drohnen in Deutschland begonnen hatte, erwartet Präsident Selenskij nun eine erste Auslieferung bereits in den nächsten Tagen. Überhaupt verzahnen sich die Rüstungsindustrien der Ukraine und ihrer europäischen Partner zunehmend: Kyjiw vollzieht einen Strategiewechsel und will künftig Waffen exportieren. Außerdem geht es um den Test einer deutsch-britischen Hyperschallrakete und um die Frage, ob der anfangs so gepriesene ukrainischen Marschflugkörper “Flamingo" hinter den Erwartungen zurückblieb. Auch die Lage an den Fronten und die anhaltenden russischen Luftangriffe auf die ukrainische Energieinfrastruktur werden eingeordnet.Lob und Kritik, alles bitte per Mail an streitkraefte@ndr.de Interview mit Admiral a.D. Manfred Nielson https://www.ndr.de/nachrichten/info/audio-412636.html Flamingo - erfüllt Erwartungen bislang nicht: https://kyivindependent.com/ukraine-strikes-russias-oreshnik-launch-site-in-kapustin-yar-with-flamingo-missiles-general-staff-says/ https://www.tagesspiegel.de/internationales/da-stimmt-was-nicht-ist-der-gross-beworbene-ukrainische-marschflugkorper-flamingo-ein-flop-15207640.html Trump kündigt Bau neuer Kriegsschiffe an: https://www.tagesschau.de/ausland/amerika/trump-kriegsschiffe-100.html Alle Folgen von "Streitkräfte und Strategien" https://www.ndr.de/nachrichten/info/podcast2998.html Podcast-Tipp: 15 Minuten. Der tagesschau-Podcast am Morgen https://www.ardaudiothek.de/sendung/urn:ard:show:b84b465ae5abcd64/

En el monstruo vibrante y a veces abrumador de la Ciudad de México, Rosa Flamingo busca un refugio sonoro. En este episodio, Diego Yines nos habla de su nuevo sencillo "Año Nuevo", una canción bailable que nace de la incertidumbre global y personal, pero que encuentra un giro esperanzador. Conversamos sobre cómo la ciudad inspira y agobia sus letras, su evolución hacia una paleta visual más luminosa, y sus planes de transitar hacia un sonido más experimental cercano al jazz.https://open.spotify.com/intl-es/track/6m7t44UQq8Elsl74B2F35Xhttps://www.instagram.com/rosaflaming0/

Aujourd'hui, je vous propose de partir explorer les zones humides. Ces trésors de biodiversité sont de véritables écosystèmes refuges pour de nombreuses espèces, en plus d'avoir un sacré pouvoir régulateur du climat.

In dieser Folge geht es um eine Vogelart, die für so viele Dinge steht, dass eine Folge kaum ausreicht - und dazu passt er perfekt in diesen grauen Februar, denn er beamt uns direkt weg nach Florida, in die Camargue oder nach Albanien. Es geht um den Rosaflamingo! Warum steht er im Zoo immer direkt am Eingang, warum ist er eigentlich rosa? Was löst dieser Vogel in so vielen Menschen aus, auch in Antonia und Philipp? Und warum gilt er sogar in Deutschland inzwischen als heimischer Brutvogel? Er ist Stil-Ikone, Gartendeko, Popmotiv und Symbol zwischen Kitsch und Queerness. Und ist ganz nebenbei auch noch ein Wunder der Natur - in rosa, weiß und schwarz - Schöner geht es kaum!

Save 10% on a Las Vegas Advisor 2026 membership and book with code MTM.  https://www.lasvegasadvisor.com/shop/products/lva-membership-platinum/ Episode Description This week the visitor and gaming numbers came in for December, 2025 giving us a picture for the year as a whole. While many metrics were down significantly in 2025, what can we take away from the year and how damaging will it be to the future of Las Vegas. Can the city bring back the everyman and why is gaming revenue not falling as quickly? In other news Four Queens has arrived with the perfect Year of the Horse gift. We also discuss: Caesars garage in ruin, Luger's secret salad, reimagining Flamingo's garden, Nevada Landing's fake website, saving with Las Vegas Advisor, the Hard Rock glass and why Flamingo's 1996 commercial gives us nostalgia. Episode Guide 0:00 Caesars garage an actual ruin? 0:30 Mirage/Hard Rock glass update 1:43 Peter Luger's "secret salad" 2:42 Zoox recovers from mysterious shutdown 4:07 Losing a $450K sidebet 5:25 Harrah's Laughlin Legionnaires 6:44 Flamingo Hilton 1996 ad 7:35 The perfect tiki bar space for Vegas? 8:39 Four Queens insane Year of the Horse gift 10:03 Las Vegas Advisor 10% off - 2026 books now available 11:25 Nevada Landing's retro website 14:10 Vegas 2025 year end numbers 15:31 Visitors, occupancy & room rates down for 2025 16:40 Can Vegas bring back the everyman? 19:00 Looking forward to 2026? Each week tens of thousands of people tune into our MtM Vegas news shows at http://www.YouTube.com/milestomemories. We do two news shows weekly on YouTube with this being the audio version. Never miss out on the latest happenings in and around Las Vegas! Enjoying the podcast? Please consider leaving us a positive review on your favorite podcast platform! You can also connect with us anytime at podcast@milestomemories.com.  You can subscribe on Apple Podcasts, Google Podcasts, Spotify or by searching "MtM Vegas" or "Miles to Memories" in your favorite podcast app. Don't forget to check out our travel/miles/points podcast as well!

Here's your local news for Thursday, January 29, 2026:We explain what would change if Governor Evers approves an update to the state's drinking water standards,Learn what's behind the nationwide shortage of teachers for the visually impaired,Debunk state Republicans' claims that an annual crane hunt would reduce crop damage,Share an update on an open records lawsuit against the state's Department of Justice,Find out what's biting under the ice,Take a closer look at the Flamingos' roster turnover ahead of the 2026 season,And much more.

Air Week: January 26-February 1, 2026 George Goldner, Pt. 3 – Gone & End Records It’s part 3 of our 3 part series on record man, George Goldner. He is said to have had the “golden ear” for hit records and songwriter Jerry Leiber even complimented his talent for picking hit songs by saying that Goldner had, “the musical taste of a fourteen-year-old-girl.” Born to Jewish immigrants in 1919, Goldner’s first love was Latino dance music and he began his career by opening night clubs and starting Tico Records, a Latino label in 1948. By 1953, he was interested in Rhythm & Blues and began releasing records under the Rama subsidiary. In early 1954, he set up Gee Records and scored a huge hit in early ’56 with The Teenagers, “Why Do Fools Fall In Love.” By mid-’57, due to his gambling debts, Goldner sold Tico, Rama and Gee to alleged mobster Morris Levy. This week, we will take a close look at Goldner’s last R&B labels that he would run independently: Gone & End Records. Both new labels did well with Gone scoring hits with NY vocal group, the Dubs and Goldner-arranged instrumental “7-11 (Mambo No. 5)” by the Gone All Stars featuring Buddy Lucas on tenor sax. End soon followed with million-sellers from The Chantels, The Imperials and The Flamingos. Both labels proved that Goldner still had the magic ear for picking the music teenagers wanted to hear and buy, but eventually both labels would face the same fate as Goldner’s early record companies. You’ll get the full story of Gone and End Records and the finale of George Goldner on this week’s “Juke In The Back.” LISTEN BELOW

Send us a textKick-off those worn-out shoes, let your hair down and pour yourself some lite roast, because the Bo-Hosts welcome you to: Sammy Sunday Mornings! The "BONUS OTIS" mini-episodes are bite-size and focus on the mellower side of the RedRocker's catalog!This time, we feature Sammy's first solo retail single, Flamingos Fly, from his first solo album, 1976's Nine on a Ten Scale.Flamingos Fly was "gifted" to Sammy from Van Morrison, as Hagar and Morrison were both recording at the same studio (The Record Plant) at the same time. Morrison liked Hagar's voice and essentially "gifted" him the song.  While Van Morrison wrote the lyrics, Sammy's choice to record them as his first solo statement feels prophetic in that the song is drenched in imagery of the Cabo Wabo lifestyle long before it existed, almost like this song was the subconscious - or conscious- blueprint for his "Billionaire Beach Bum" persona. Come to think of it, did Sammy hit upon this lifestyle vibe before Buffet? Buffet only became recognized for his "island escapism" persona" following the release of the song "Margaritaville" in 1977, a full year after 1976's Nine on a Ten Scale! Also, if you listen to Van Morrison's version (released in 1977), it sounds wayyyy different!Ponder that as you enjoy a lazy, hazy Sammy Sunday!All songs available for purchase on iTunes! We bought it- so should you!"What is understood...NEED be discussed"Facebook:https://www.facebook.com/profile.php?id=100085582159917Instagram: https://www.instagram.com/thebogusotisshow/?hl=enConnect with the Bo-Hosts:bogusotisshow@gmail.com

This week, Megan tells Dave and Evan about H IS FOR HAWK (2:48), based on Helen MacDonald's memoir about losing their beloved father and, while grieving, adopting a stubborn hawk. Then all three of them talk about DOOBA DOOBA (11:31). This found footage horror film rubbed them all the wrong way. Some of them really wrong. Finally, Megan and Evan discuss THE MYSTERIOUS GAZE OF THE FLAMINGO (36:46). Both Evan and Megan were moved by this movie's embrace of queer joy while it's simultaneously an AIDS allegory. Over on Patreon, we watch the Safdie Brothers' 2017 film GOOD TIME

Dan, Daedalus and Julie Julie Pedroncelli from Pedroncelli Winery is back on California Wine Country with Dan Berger and Daedalus Howell. She has been on the show before, the last time was this episode of last January. Dan describes the current slowdown in the wine business. The other times that the wine market went soft, there were one or two causes, but today there are several causes. But the benefit to the consumer is, the longer it takes to sell the wine, the more the wine improves. The Pedroncelli family has owned the property for almost 100 years. The vineyards are very carefully farmed and they take great care making their portfolio of wines. “Four generations and still going strong,” says Julie. Her grandparents put down roots in Dry Creek Valley outside of Geyserville. They bought a property in 1927 that had a vineyard and a shuttered winery. The previous owners were making wine as far back as the early 1900s. Their winemaker Montse Reese just completed her 18th harvest at Pedroncelli. They produce mostly Zinfandel, Cabernet Sauvignon, Sauvignon Blanc and a few red wine blends. Her father is 94 and retired just a few years ago. 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!  Sauvignon Blanc and Zinfandel Julie has brought the “block party” today, with single-vineyard wines that represent certain blocks in the Pedroncelli vineyards. She has brought a Sauvignon Blanc, which they will start with, before moving on to the Zinfandel and others. “White wine is always good to start with.” This Block 11 reserve Sauvignon Blanc, vintage 2024, is not their regular production. It is a special designation reserve. Block 11 showcases the grassier, herbaceous side of SV and Montse Reese thought the neutral oak would bring out that side of SV without overdoing it. Daedalus detects a bit of Pez candy flavor, which Dan thinks is like green mint. Next is a Block 13 Zinfandel from 2023 which was a cool year. This is a little spicy, with a bit of black pepper flavors, and a raspberry note that Dan says is a characteristic of Dry Creek Valley Zins. Block 13 has had Zinfandel grown on it for over 100 years. It is the third generation of Zinfandel vines on the property. Some of the vines are 100 years old but they have replanted twice. It was Zinfandel, then Petit Syrah, then back to Zin. Montse found that this block stands out and merits a single-vineyard bottling. They used bud wood from the Rockpile vineyard for the planting and Montse also uses a yeast that was developed at Rockpile. Julie describes its character as feminine, not high in alcohol, very delicate in its fruit, “…it doesn’t hit you over the head, it’s not a fruit bomb, it’s more like a light spice bomb,” says Julie. Cabernet Sauvignon and Syrah The Cabernet Sauvignon needs to breathe, decanted it would be a little more open. The long finish is not oak, though, it’s all the flavors you want in Cab without the other flavors like too much Oak. Dan Berger will be opening a 1966 Louis Martini Barbera next week for a special occasion. There are two Masters of Wine candidates from Taiwan who are taking a course at the Flamingo. Dan has met them and they expressed interest in an old California wine. Dan has one of two remaining bottles. He will open one for the students at Ca’ Bianca in Santa Rosa, along with two or three other wines from the era. The fourth wine they taste today is the Pedroncelli Syrah. Dan says he has never heard of anything like this before. It is a lower-alcohol Syrah, about 12%. This one was earlier harvested, to make a lighter style of red with lower alcohol. They picked two or three weeks before they otherwise would have.

Save 10% on a Las Vegas Advisor 2026 membership and book with code MTM.  https://www.lasvegasadvisor.com/shop/products/lva-membership-platinum/ Episode Description This week the Sphere Entertainment Company announced firm plans for their second Sphere. While plans for Dubai, London and elsewhere have fallen through, the first "mini Sphere" will be built in National Harbor. With only 6,000 seats will this measure up to the OG and will it take away from the Vegas Sphere? Plus how will it differ? In other news Rio and Golden Gate both celebrated birthdays this week. How the times have changed! We also discuss: Caesars new presidential villas, goodbye to the Mirage Arch, battles against clever bettors, the death of the Atari Hotel and how Las Vegas is struggling with the influencers it hires to promote the city. Episode Guide 0:00 Vegas dinner and a show? 0:30 Downtown LV Events Center is going away 2:21 Rio's 36 birthday 3:35 Golden Gate turns 120 + BBQ suite? 4:48 Caesars sexy renovated Presidential Villas 6:18 The Mirage arch is no more 7:23 Goodbye gold 7:58 Carlos N Charlie's Update at Flamingo 9:31 The battle to stop clever people betting 11:15 Limiting sports betters = limiting card counters? 13:20 Vegas Atari Hotel is officially canceled 14:41 Las Vegas influencer struggles 17:40 The Sphere replicates - Mini Sphere coming 18:44 Will the mini Sphere take away from Vegas? Each week tens of thousands of people tune into our MtM Vegas news shows at http://www.YouTube.com/milestomemories. We do two news shows weekly on YouTube with this being the audio version. Never miss out on the latest happenings in and around Las Vegas! Enjoying the podcast? Please consider leaving us a positive review on your favorite podcast platform! You can also connect with us anytime at podcast@milestomemories.com.  You can subscribe on Apple Podcasts, Google Podcasts, Spotify or by searching "MtM Vegas" or "Miles to Memories" in your favorite podcast app. Don't forget to check out our travel/miles/points podcast as well!

Gemini is back!  She and Al are hosting together, in person. They're at the Las Vegas Distillery, where they get the lowdown on the place's history, products, vibe, distribution and more from Cody Fredrickson and Jonny Verplank. We also have interviews with Jason Vega of The Flamingo (talking Salt & Straw), Rio's Andi Van Willigan (talking Kitchen Table), Bellagio's Warren Richards (talking the Mayfair Supper Club) and Gyu+'s Freddie Paloma (talking about the new location), and restaurant reports from Ada's, Big B's Texas BBQ, St. Felix Sin City, With Love Always and Union Biscuit.

Here's your local news for Thursday, January 15, 2026:We explain why city-county tensions boiled over at a public hearing on Wednesday,Get the details on MMSD's budget outlook for 2026-2027,Describe the transparency concerns that emerged after WisconsinEye went dark,Tell you the best spots to cast your fishing line,Meet the Flamingos' newest midfielder,And much more.

Welcome back legends, The Two Flogs return for Season 3 so strap in!The boys breakdown their holidays and Kirbs receives the keys to the city as a Bruns local now. The kirbys break had it all caravans, gelato, storms, gelato, chips, gelato, Spiderbait, gelato, keying cars and did we mention gelato.The Gibsons jet off to NZ and Gibbo fills us in on bareback horseriding, gelato, saunas, gelato, beers, gelato and gambling but not gambling.Kirb isn't a fan of Flamingos. Let's watch them fight and....... Hosted on Acast. See acast.com/privacy for more information.

Our very first merch line is here! Get it now at http://mtmvegas.shop Want more MTM Vegas? Check out our Patreon for access to our exclusive weekly aftershow! patreon.com/mtmvegas Want to work with us? Reach out! inquiries at mtmvegas dot com Save 10% on a Las Vegas Advisor 2026 membership and book with code MTM. https://www.lasvegasadvisor.com/shop/products/lva-membership-platinum/ Episode Description This week Mark stayed at Venetian as part of a New Year's Eve comp. On this show we review the newly renovated Venetian King Room, discuss how the new decor works and doesn't work plus discuss the one huge downside of his stay. Does Venetian have the best standard rooms in Vegas and why is the resort so good? In other news as 2026 rolls on remember not to forget your losses from last year. We have some good advice for you. We also discuss: Joe's at Forum Shoppes, our favorite bars revisisted, deconstruction of Eastside Cannery, Vegas 2025 weather, Flamingo's new carpet, the death of mid-tier hospitality & a crazy sports bet gone wrong. Episode Guide 0:00 Budweiser Clydesdale in a casino? 0:35 2025 Vegas weather recap 1:55 Las Vegas Athletics trademark denied! 3:16 Eastside Cannery deconstruction process 4:27 Main Street Station's rooms are decent 5:49 Crazy sports bet gone wrong 6:57 Mid-tier hospitality is dying? 9:32 Flamingo new carpet - Sexy ending! 10:54 Gambler advice - Look at your win/loss statements 12:20 Joe's at Forum Shoppes and F1 Arcade surprise 13:32 Silver Stamp & Red Dwarf are still so good 15:28 Pedestrian bridge screens are an improvement? 16:33 Mark's Venetian room review & check-in experience 17:58 The Venetian room refresh is solid 19:11 Does Venetian have the best standard room in Vegas? 20:12 Hair in the tub & cleanliness of Vegas hotel bathrooms Each week tens of thousands of people tune into our MtM Vegas news shows at http://www.YouTube.com/milestomemories. We do two news shows weekly on YouTube with this being the audio version. Never miss out on the latest happenings in and around Las Vegas! Enjoying the podcast? Please consider leaving us a positive review on your favorite podcast platform! You can also connect with us anytime at podcast@milestomemories.com.  You can subscribe on Apple Podcasts, Google Podcasts, Spotify or by searching "MtM Vegas" or "Miles to Memories" in your favorite podcast app. Don't forget to check out our travel/miles/points podcast as well!

This playlist is 67% vinyl friendly. Poor. ‘In the year 2021, few turntables have captured the essence of that time the way that the Old Future Turntable and Speaker has. The silhouette of the player itself pays homage to ’70s architecture in Seoul. Slanted roofs were common during that era, and when you place the turntable and speaker side-by-side, they look like part of a ’70s city skyline. Some vinyl-lovers may not like this product initially because a flat, horizontal turntable would be preferred for sound quality. For those who care about audio quality over aesthetics, the turntable's legs are adjustable, so you can make the table more even‘. – yankodesign.com Looks like it could more readily blow out hot and cold air than music. Any track marked * has been given either a tiny or a slightly larger 41 Rooms tweak/edit/chop and the occasional tune might sound a bit dodgy, quality-wise. On top of that, the switch between different decades and production values never helps in the mix here. Lyric of Playlist 147 ‘Observational, not sexist‘ noted Jean-Jacques Burnel. 00.00 (Intro) THE FLAMINGOS – Stars (Edit) – Unreleased demo – 1983. Episode #1 for info. 00.41 NEW ORDER – Face Up – Low-life, LP – Factory – 1985 Face Up? More like Jump up, given the NO audience reaction when the chorus kicks in. 05.25 THE YOUNG GODS – Mes Yeux De Tous – Appear Disappear, 2LP – Two Gentlemen Records – 2025 Given that in the mid ’80s they were initially loosely bandied around in the same ‘industrial’ bag as my ‘charges’ Click Click I’ve heard very little of their lengthy and regular catalogue. This though is a mighty tune. 09.19 BLAWAN – Toast – Dismantled Into Juice, 12″ EP – XL Recordings – 2023 Nicely odd, with a ‘chorus’ that makes me smile. 11.56 BASETANK – Got Some Skills – The New Breed, v/artists promo only CD – Detonation – 1999 If it wasn’t for the 35 year age gap I might have said the vocalist on Got Some Skills and the modern day Microwave Man and his electric dirt bike online ‘Let Me Tell You Something, right?‘ words of wisdom sketches were of the same family. 15.44 9 LAZY 9: Turn Me Loose; UP, BUSTLE & OUT: Nightwalk; DJ FOOD: Klutes Groove; DJ TOOLZ: Rusty Goes GaGa; FUNKI PORCINI: It’s A Long Road – The Morning After The Night Before (one half of a CD free with DJ magazine – 1994 Old skool mixing from Cold Cut. ‘It’s (been) a long road… ‘ indeed. 22.00 HERCULES & LOVE AFFAIR (feat HIPS & LIPS) – Someone Else Is Calling, 12″ EP – Stratasonic – 2025 The sort of hypnotic beats that could have fitted in with a Winkles, Bedford set list back in the ’80s… although someone might point a sound or two here didn’t exist back then. 26.12 FUNKY GREEN DOGS – The Way * – 12″ – Twisted – 1997 ‘Anyone who ever liked That Sound from the Murk camp will like this just as much, even if they do whinge that it’s very similar to their previous favourite. The original is the heart-winner with its quivering vocal and acid-meets-funk groove, but the mixes are all attractive in their respective ways – Farley & Heller plod sweetly, Canadian Crash Productions do something along the same lines. Dirty White Boy rock and roll and Club 69 kick up a noisy messy rumpus. The best track of all is possibly the excellent FGD way-out beats. **** Daisy & Havoc, Record Mirror (Music Week), 10.5.97 Tough, liquidy goings on from the Murk camp. I was a fan. 32.51 JOY – Fragile Space – 7″ – DB-Low – 2000 ‘Isaac Hayes meets Led Zeppelin‘ ran the press release for Joy Jones’ one-off (at the time) dip into music, and there’s something sad about a single of substance that didn’t do enough for the artist to build on. 35.52 TALKING HEADS – Listening Wind – Remain In Light, LP – Sire – 1980 Just checked. This is the last in a near complete run of the album’s tracks making it to 41 Rooms. So, that must make Remain In Light a bit of a classic to my ears. 39.36 THE SOULSAVERS – Rumblefish – Beginning To See The Dark 12″ – Ghost Ride Music – 2002 The sort of drifting beats thing that would crop up late night/early morning on KISS FM or maybe Ross Allen radio shows of the time. 43.28 BETHANY & RUFUS – 900 Miles – 900 Miles, CD only – Little Monster Records – 2005 With Bethany being the daughter of ’60s folk group, Peter Paul and Mary’s Pete Yarrow, an ancient tune reworked. And it’s a cello apparently, not a double bass. 46.48 STARGARD – (Theme Song From) Which Way Is Up – 7″ – MCA -1977 As funky now as the day it was delivered. I was 20… and this would have been hitting someone’s decks at Bedford’s Nite Spot, Spectrum, Pilgrims, The Anglers’ Laird bar and elsewhere out of town I went. 51.29 THE STAPLE SINGERS – I’ll Take You There – 7″ – Stax – 1972 They never let on where exactly ‘there’ is but with their gospel background we can maybe guess. Good luck with it. 55.30 CARLA THOMAS – Things Ya Make Me Do (Summer Mix) – 12″ – Ruff Justice – 1994 Not the ’60s Stax label Carla Thomas but it’d be nice to think this CT’s parents had that lady in mind when naming this lady. What might have been tagged ‘street soul’… and possibly out of Manchester, UK. 59.06 THOMAS DYBDAHL – All’s Not Lost – That Great October Sound, CD only – Checkpoint Charlie Audio Productions – 2001 Delicate sounds from Norway. 01.04.06 PURESSENCE – Don’t Know Any Better – 7″ – Reaction Records – 2008 James Mudriczki’s vocal… 01.07.22 DAVID SYLVIAN – Nostalgia – Brilliant Trees, LP – Virgin – 1994 Post Japan, his first solo album making its mark in fine style. It was unlikely to do otherwise. 01.12.57 JOY DIVISION – Insight – The Peel Sessions, 12″EP – Strange Fruit – 1986 Peel sessions sort of mimicked a live gig recording at its crystal clear best… this one included. 01.16.50 SIOUXSIE and THE BANSHEES – Christine (Warner Chappell demo) – Kaleidoscope, CD only – Polydor – 2006 With everyone and everything here ‘battling’ to be the most understated – and with it all quite possibly recorded in a rehearsal room, on the likes of (say) a 4-track Portastudio – this is as demo’ish as a major act’s demo could sound back then. 01.19.32 THE FLAMINGOS – Shone Like The Sun #3 (unreleased demo) – 1984 With Cliff (Peacock) in Scott Walker-mode there were a few versions of this tune and to these ears it sounds even better now than it did back then. In effect the above might have doubled as my 27th birthday party and Shone Like The Sun was very likely on the Flamingos set list. 01.24.12 THE WAKE – Make You Understand – Here Comes Everybody, CD only comp – Factory Benelux – 2015 Recorded for a Feb ’84 BBC Radio 1 session for David/Kid Jensen and played live but the former only ever surfaced on the above. Stephen, Mac, Carolyn and Caesar: Winkles, Bedford, 13.11.83 Photo credit/copyright: Dec Hickey 01.26.33 LITTLE NEMO – Bed In Summer – La Cassette Froide, split cassette (with Rain Culture) – self released – 1986 Not the first European band in the mid to late ’80s to have sounded as if heavily influenced by UK bands of the early ’80s and with a track only to be found on this cassette. I won’t be acquiring one any time soon. 01.30.27 THE STRANGLERS – London Lady – 7″ – United Artists – 1977 Yep, Jean-Jacques Burnel with a lyric or two (well, one in particular) that we youth most probably laughed through back in the day but now would maybe slightly wince at. The times… 01.32.52 ELVIS COSTELLO – (I Don’t Want To Go To) Chelsea – 7″ – Radar – 1978 I had a brief moment with Declan’s singles around this time… and you had to admire the balls of a musician who wanted to strike out as an Elvis. 01.35.53 THE SUB ENSEMBLE – Faster Than The Sun (Domu Remix) – Download only – 2008 Very short-lived crew aided and abetted here by acclaimed Bedford-based (then, anyway) DJ, producer, remixer, Domu. 01.41.01 JAZZ THE GLASS – 16 Seconds – Download only, Soundcloud – 2019 A 41 Rooms regular, with another winner. Dave reminded me it uses a sample from a US boy band. I think it was a white label 12″ with anonymous writing (just initials?), that I took a punt on at no more than 20p. 01.44.47 FPI PROJECT – Come On (And Do It) (TC Funky Mix) – 12″ – Synthetic Records – 1993 ‘Tremendous funky Italian offering as ever from those talented members of Ital’s most consistent band. Chocca full of good alternative mixes, including a wicked guitar ladened TC Funky mix… ‘ – Kenny Grogan, Mixmag Update, 28.4.93 ‘Everything you love/hate about Italian records in double helpings. Very FPI Project but bang on time with its riffing flamenco-style guitar and a bundle of irresistibly funked up mixes firm Mother-man Lee Fisher, which include a wonderful reinvention of the bouncy original into a big booming beast of a track‘. Matthew Cole, Record Mirror (Music Week), 10.7.93 ‘Girl chanted powerful Hamilton Bohannon-ish happy party pounder’s original Gypsy Kings-like guitars strummed 125bpm Official, 125.2bpm Gipsy, TC 1993 wukka-wukked 125.2bpm TC Funky Mixes, new sax squawked jerkily percussive building 124.9bpm Mothers At Work Remix and Dub‘. – James Hamilton, Record Mirror (Music Week), 7.8.93 As mentioned on the show, a one and a bit trick pony but sometimes a funky break and some vocal snippets is all you need for some head nodding bizniz. 01.49.36 CHEZ DAMIER & CO-INSIDE – Give A Little Love (Made In Detroit Mix) – The United States EP, 12″ – t:ime – 1993 ‘Nottingham’s Sine boys have always (worn) worthier US influences on their sleeves. This time they make a more permanent connection with two excellent transatlantic collaborations. Their Made In Detroit Mix of Give A Little Love by Chez Damier & Co-Inside is a supremely cool organ-washed garage groove with the repeated title line drifting in and out of the mix. It becomes less mellow and more dubby as it progresses…‘ – Andy Beevers, Record Mirror (Music Week), 4.9.93 And yet another tune with a minimal use of different lyrics… and here you’re not getting the full nine minutes they’re spread over on the 12″. Still, jaunty US house with a bit of a UK ’90s garage’y feel… and I think I edited out some frogs sounds. 01.53.15 THE REESE PROJECT – The Colour of Love (Groove Corporation Trance Mix) * – 12″ – Network – 1992 ‘Even by his own standards, Kevin Saunderson is having a good year. With Inner City firmly re-established as one of the world’s leading dance acts, he now takes the limelight under his alter-ego for what will be one of the biggest tunes of the year. The Deep Reese mix (featured on the Network ‘Elixir Vitae’ double pack extravaganza that some might say is their apology for KWS) is still the one for me. The coffee table intro soaring into a menacing groove with the hookiest of vocal samples… bliss. But wait, let us not forget Groove (aka Electribe 101) Corporation’s four mixes full of true British grit and trance appeal… ‘ – Dave Seaman ****1/2 Mixmag Update, July 9, ’92. ‘Kevin ‘Master Reese’ Saunderson’s powerful Rachel Kapp wailed Club Chart topper is now out on one single in brilliant swimming bass pulsed wriggly warbling 121.6bpm Magic Juan Atkins Mix, gospelishly started hypnotically chugging (0-)123.4bpm Deep Reese Mix, piano plonked 121.6-121.5bpm Underground Resistance Mix, repetitively stuttered Kym Sims-ish 122-122.1bpm MK Deep Dub, good insistently rolling 114.7-114.8bpm Groove Corporation Trance Mix and bass bubbled blippy 114.9-114.8bpm Groove Corporation Wobble Dub, while the first of the separate promos had its throbbing somehow then steady 115bpm Vocal (Trance Mix) and 115-114.9bpm Acid Revival (Wobble Dub), plus alternative swirlingly chugging 115bpm Plae Blue Mix and Instrumental Groove Corporation 021 Remixes‘. – James Hamilton, Record Mirror (Music Week), 1.8.92 Slinky Saunderson… and albeit it the same theme, a thousand miles from Terry Callier’s What Colour Is Love in every other way. Show 148 should surface here Feb 1. Dec x The post Post Punk Plus Podcast Playlist 147 – Original upload 4.1.26 appeared first on 41Rooms.

Donnie is the host of The Deep Dive with Donnie Flamingo podcast. https://patreon.com/DeepDivewithDonFlamingo?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=creatorshare_fan&utm_content=join_linkSubscribe to Patreon:https://www.patreon.com/c/snakepitstudiosFollow Breaking Hyman with Morgan and Friends :https://www.instagram.com/breakinghymanpod/Follow The Patriot and The Rattlesnake Podcast : https://www.instagram.com/thepatriotandrattlesnakepod/

LAST Crashing The Party for the year! New  Crashing The Party airs Tues Dec 30 9AM at www.wpkn.org 89.5 fm and on your app- download it! Dig Bo Diddley's vocal groups- the Moonglows Carnations, Flamingos, and Marquees plus the Stars, the 5 Jets, Eldoradoes, Calvaes, the Meadowlarks, and so much more vocal group excellence --  three hours of tuff stuff with your hosts Marc and Miriam! Giant thanks to all the fans of the show and the heavy hitters who deliver da goods at WPKN, at Luxuria Music and at the HoundNYC. Much love and gratitude from the "group" here at Crashington Arms - Marc, Matt, and Miriam!

The Creeps are still on the Holiday nonsense but aren't as festive this week. However, they're feeling better and are back with sassy intros again. This last episode of 2025 brings you a viral trend that we once again had to catch up on by the time y'all have already had it. That's right, we're trying weird Swedish candy! We have Bubs soft foam tutti fruitti sour Diamonds or Rhombs (I guess because they're sort of rhombus shaped?) And there was even a single banana caramel we found snuck in the bottom of the back like a stowaway. It didn't taste nearly as great as these. The hype was real, you absolutely have to track these down. After that, they wash those down with the crispness of new fallen snow. For our Holiday Season Fest that will never die, they also tried the new seasonal Vanilla Frost Sprite!Then, Kelsey takes us to a zoo in Cornwall, where Frankie flamingo flies the coop, cage, roost, nest, what do flamingos live in? On? Around? Certainly not the cold cement of England. Not this flamingo.The Creeps also talk about sniffles, candy shop scoops, Spotify Wrapped listening age, slippin tastes, animal cuteness, shared music interests, and Grandma stuff. Shoutout to Poppin Candy for Britte's obsession.

In Bethlehem, the Christmas story comes to life with a tree lighting in manger square, but empty streets reveal a holy city struggling without pilgrims or tourists. Twenty years after a family's self-published elf on the shelf, now a Christmas icon from school roofs to Macy's parade and SNL skits. Flamingos are returning to Florida's everglades, hopeful signs that these rare sightings in the wild could signal a comeback for the pink birds. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

National cookie exchange day. Entertainment from 2003. 1st cloned cat, Shoe bomber Richard Reid caught, Perferatted toilet paper invented. Todays birthdays - Barbara Billingsly, Hacksaw Hawkins, Gene Rayburn, Hector Elizondo, Robin & Maurice Gibb, Luther Campbell, Ralph Fiennes, Meghan Trainor. Joe Cocker died.    (2022)

Send us a textWhat does it take to turn a late-night hustle into a stadium-ready brand? We sit down with Steve Phillips, owner of Mr. Fries Man Las Vegas, to pull back the curtain on a journey powered by street marketing, credit smarts, and unapologetic belief. From LA catering to a Flamingo storefront to a coveted concession at Allegiant Stadium, Steve shows how grit and quality can beat perfect timing—and how one pitcher of Kool-Aid can win a room full of decision-makers.We get honest about the real math of stadium deals: why section placement is pure real estate, how event mix affects margin, and the inventory traps that can push a small operator into the red if crowds get shuffled to lower levels. Steve walks through his game-day prep, the 7:30 a.m. starts, and the variable staffing that keeps service tight when doors open. Then we tackle delivery. Fries don't travel well, so he enforces a three-mile radius to protect quality and reviews. Not all money is good money; sometimes the best marketing is saying no to orders that hurt the brand.The conversation widens to life insurance and family security. Steve lays out practical guidance on term coverage for young parents, when an IUL makes sense, and why he refuses to sell policies that clients can't sustain. We also explore Vegas nightlife from the inside—late headline sets, free-entry shifts, and how clubs lean on bar revenue. Through it all, Steve's theme is consistent: believe to a “delusional” degree, set clear boundaries when hiring friends, and stack small operational edges until they become momentum.We close with what's next: a sports bar concept that pairs fries, wings, and screens; and a nonprofit plan that connects at-risk teens to paid kitchen work and trade certifications in HVAC, plumbing, and electrical. It's business with a backbone—profitable, community-forward, and built to last. If this story moves you, follow, share with a friend who needs a push, and leave a review to help more builders find the show.

In this episode, Swamiji continues his discourse on Bhagavad Gita Chapter 7, Verse 9, Part 2, reflecting on how the laws of nature themselves point to the existence of a Divine lawmaker. He explains that while science discovers and applies these laws, it cannot account for their origin. The very presence of order in creation implies a Creator who designed it.  Swamiji illustrates this truth with striking examples:  Fish live their entire lives in water without drowning, while humans can drown in a pool—God has designed them differently.  Birds fly effortlessly because their bones are hollow, a design that even modern aviation studies to replicate.  Flamingos migrate instinctively to Siberia, a land they have never seen, guided by divine programming.  He emphasizes that if there are laws, there must be a law maker, and that law maker is God. The glory of God is so vast that saints and poets have admitted defeat in trying to describe it. Swamiji cites Homer's insight that even thousands of musicians playing for thousands of years could not complete the narration of God's glories. Similarly, Vedic literature describes Anant Shesh, the thousand-hooded serpent, endlessly singing God's praises since the beginning of creation, yet never reaching completion.  Quoting Vyasadev, Swamiji explains that anyone who thinks they can count God's virtues has a childish intellect—just as a child imagines the mountain or ocean to be “this big.” The infinite glories of God cannot be measured. Even St. Augustine realized this when a child told him it was impossible to fit the ocean into a hole, just as it was impossible to capture God's glories in a book.  This episode inspires listeners to recognize God's infinite greatness, trust in His plan, and surrender with faith and devotion. The narration closes with the reminder that God's glory is boundless, and His love for us is eternal.  About Swami Mukundananda: Swami Mukundananda is a renowned spiritual leader, Vedic scholar, Bhakti saint, best-selling author, and an international authority on the subject of mind management. He is the founder of the unique yogic system called JKYog. Swamiji holds distinguished degrees in Engineering and Management from two of India's most prestigious institutions—IIT and IIM. Having taken the renounced order of life (sanyas), he is the senior disciple of Jagadguru Shree Kripaluji Maharaj, and has been sharing Vedic wisdom across the globe for decades.  

Ingmar Stadelmann & Andreas O. Loff fassen die Ausgabe 223 des Podcasts Lanz & Precht zusammen.Ticket für die Show "Stadelmann liest Höcke": https://www.ingmarstadelmann.de/stadelmannliesthoecke/Links zu unseren Partnern: https://linktr.ee/richardwoerreicheichdichDas Buch "Das geht nicht mehr weg" von Andreas: https://amzn.to/49CqQZsWunderbare Sprecherin der Rubriken: Franziska Weisz Coverdesign: Hands of God Folgt uns auf InstagramIngmar Stadelmann: https://www.instagram.com/ingmarstadelmann/ Andreas Loff: https://www.instagram.com/andreas.loff/ Hosted on Acast. See acast.com/privacy for more information.

The Juicys did Vegas… and then they did Disney. Strap in, because this week we're recapping one of the wildest two-week stretches Juice has pulled off yet.We kick things off with a full Las Vegas deep dive: • How the Strip felt in mid-November after months of “Vegas is dying” headlines • Craps at the Flamingo, Ultimate X runs, downtown 3:2 blackjack, and whether Knockout 52 actually exists • Lisa's O'Sheas experience, the disco show, and the best food of the trip • And of course—Bravocon: celebrity sightings, the best panel, favorite giveaways, and what surprised Juice the mostThen we pivot to the happiest (and sometimes most confusing) place on earth: Disney World. Juice breaks down: • Best park moments (including the Christmas Party) • What absolutely sucked (hi, Tiana's Bayou Adventure) • Best rides, best food, waterpark fun, and real tips for first-timers • And the eternal question: Why do adults do this to themselves?Finally, we close with a first look at the end-of-season results for the Mucho Dinero Answer Key: • 293–251–3 on the year (+2.46% ROI) • A monster November (+6.33% ROI) • Surprising market breakdowns • And our kiosk-stuffing adventures the last two weekendsWe'll have a full Answer Key deep dive next episode — but for now, enjoy the recap and the chaos. Good juju to your bets, everyone.Support the showFollow along on Twitter or Instagram @goodjujubets.goodjujubets.net - All Things Good JuJu

  Our very first merch line is here! Get it now at http://mtmvegas.shop Want more MTM Vegas? Check out our Patreon for access to our exclusive weekly aftershow! patreon.com/mtmvegas Want to work with us? Reach out! inquiries at mtmvegas dot com Episode Description This week Venetian's head went before Nevada regulators for his gaming license. During his time he spoke about the successes of the property following $1 billion in investment from owner's Apollo and VICI. Has private equity improved this iconic resort, how have they done it differently and why is Venetian one of the most complete resorts in the world? In other news the A's have released the completion date for their ballpark, but they also seemingly have a tariff problem. We also discuss: the opening date for Eggslut at Rio, Voodoo's Christmas popup, the top restaurant in Vegas, top 5 Vegas tacos, Excel World Championships, Flamingo's new carpet and what will replace Sand Dollar over at Plaza. Episode Guide 0:00 Excel World Championships return to Vegas! 0:40 Flamingo's renovations have started plus new carpet 2:10 Eggslut at Rio announces opening date 3:11 Voodoo Lounge transformer for Christmas 4:25 Restaurants for an enemy follow up 6:37 Top 1K restaurants in the world - 4 in Vegas 7:57 Applied Analysis new contract - Conflicts of interest? 9:44 A's construction schedule released - Tariff problems? 11:12 Plaza's Sand Dollar replacement - Hogs & Heifers! 12:12 Top 5 tacos in Las Vegas? 13:03 Disco Show saying goodbye - Closing permanently 14:43 Venetian's best year ever? 16:45 How Apollo has infused fresh life into Venetian 17:57 Why the Sphere was such a score for the Venetian Each week tens of thousands of people tune into our MtM Vegas news shows at http://www.YouTube.com/milestomemories. We do two news shows weekly on YouTube with this being the audio version. Never miss out on the latest happenings in and around Las Vegas! Enjoying the podcast? Please consider leaving us a positive review on your favorite podcast platform! You can also connect with us anytime at podcast@milestomemories.com.  You can subscribe on Apple Podcasts, Google Podcasts, Spotify or by searching "MtM Vegas" or "Miles to Memories" in your favorite podcast app. Don't forget to check out our travel/miles/points podcast as well!

This week we've got a seriously fun one for you. We are joined by the wonderful Ellen Weatherford from the podcast Just the Zoo of Us.John and Shannon are guests on her show later this month, so we asked Ellen to come chat with us about the bird of her choice. She came back with flamingos—and boy, were we thrilled.It turned into such a bright, lively, surprisingly deep conversation about one of the most iconic birds out there. From their wild social lives to their unbelievable color and biology, this episode is just pure joy from start to finish.Ellen joins John Bates, Shannon Hackett, RJ Pole, and Amanda Pole.Here are links to our social and YouTube pages, give us a follow: YouTube Instagram TikTok BlueSky

Buying a home or thinking about refinancing? Talk to Gregg Shaft with Barrett Financial Group. He makes the process smooth, fast, and stress-free. http://barrettfinancial.com/gshaft Our very first merch line is here! Get it now at http://mtmvegas.shop Want more MTM Vegas? Check out our Patreon for access to our exclusive weekly aftershow! patreon.com/mtmvegas Want to work with us? Reach out! inquiries at mtmvegas dot com Episode Description This week October's numbers came in and it was yet another tough month for the Las Vegas market. While Strip gaming revenue was the lone brightspot due to high-end baccarat, the city received less visitors and lower overall rates. Will this fall recovery actually happen or will it be more of the same? In other news Caesars Entertainment announced even more renovations coming to Flamingo for its 80th anniversary. A new lobby, renovated bars and more bring the classic Flamingo tropical vibe into the 21st century. In other #news Jason Aldean's may have new management, Metro Pizza is closing their flagship location, Morimoto is stunning at MGM Grand, Mark Wahlberg isn't giving up on Hollywood 2.0, how Vegas Loop is planning airport rides in 2026 and why we have the best firefighters. Episode Guide 0:00 Firefighters keeping Vegas Loop safe 0:37 Flamingo gets big renovation for 80th anniversary 2:11 Follow up on history of IP and Quad name change 4:30 How Caesars has renovated most of their properties in the last few years 4:58 Watching F1 via traffic cameras? 6:26 Vegas Loop airport rides coming Q1 2026? 9:19 Mark Wahlberg isn't giving up on Hollywood 2.0 10:10 Plaza's non smoking casino struggling & mystery multiplier 11:46 The new Morimoto at MGM Grand is here 12:55 Metro Pizza closing "flagship" location 13:53 Jason Aldean's shakeup - Country bar struggling 16:10 Category 10 Update - Margaritaville replacement construction 16:55 October stats are in - Vegas numbers down with one bright spot 18:30 Losing almost 8% of visitors for the year? 20:05 Is the Fall in Vegas better as predicted? Each week tens of thousands of people tune into our MtM Vegas news shows at http://www.YouTube.com/milestomemories. We do two news shows weekly on YouTube with this being the audio version. Never miss out on the latest happenings in and around Las Vegas! Enjoying the podcast? Please consider leaving us a positive review on your favorite podcast platform! You can also connect with us anytime at podcast@milestomemories.com.  You can subscribe on Apple Podcasts, Google Podcasts, Spotify or by searching "MtM Vegas" or "Miles to Memories" in your favorite podcast app. Don't forget to check out our travel/miles/points podcast as well!

As told by Tony and Rum.  

Topics: Paul Confession, Hockey/Dentist, Breaking Animal News, Hamster Travel, Memory/Brains/Spirit, Jesus Is Lord,  Feelings, Christmas Kids/Screens BONUS CONTENT: Senior Dance, Going To The Doctor     Quotes: "Most leaders are not honest about their current struggles." "I think we're spiritual." "Jesus is the Maestro." "These excuses aren't gonna make themselves." "I didn't know we could take hamsters on planes." . . . Holy Ghost Mama Pre-Order! Want more of the Oddcast? Check out our website! Watch our YouTube videos here. Connect with us on Facebook!

We cover the credits of the crew.

This is a free preview of a paid episode. To hear more, visit meetthemess.substack.comMove over, Meet the Press—it's time to MEET THE MESS!This week on the podcast, Jen and Karyn are back! The House voted to release the Epstein files, but will it actually happen? Meanwhile, Trump delivers a completely bananas speech to McDonald's franchise owners and tells a female reporter, “Quiet, piggy.”Also on deck: a behavioral scientist breaks down what it means when someone does (or doesn't) return their cart. Plus, Kim Kardashian fails the bar exam, the FDA removes the black box warning from menopause hormone therapy, and a flamingo escapes a British zoo and decides to retire on a beach in the south of France. And finally, do you have a preferred font?On Meet the Mess, bestselling authors Jen Lancaster & Karyn Bosnak dive into the messiest news stories and hottest topics of the week to give a fresh and entertaining take on current events and life in general. An extended video version with the “Hot Mess of the Week” is available to paid Substack subscribers. Visit meetthemess.substack.com for more.Meet the Merch:• https://www.etsy.com/shop/MeetTheMessConnect with us on Instagram:• https://www.instagram.com/meetthemesspod• https://www.instagram.com/jennsylvania• https://www.instagram.com/karynbosnakConnect with us on TikTok:• https://www.tiktok.com/@meetthemess• https://www.tiktok.com/@karynbosnak

2025-11-14 | Silicon Wafers 055 | Flamingo Over Novorossiysk: Ukraine's Long-Range Energy War. In the early hours of November 14, Russia's biggest Black Sea oil hub, Novorossiysk, went dark in a way that brings a thrill to pro-Ukraine activists on social media. Giant fireballs, and mobile phone footage of Russian's exclaiming and swearing in the entertaining we have become familiar with. But is the strike of strategic importance, and does it tell us anything about how Ukraine's deep strike capabilities are changing? Drones over the water, fire at the Sheskharis terminal, and something Moscow really hates to see, which is tankers not loading oil.The next night, hundreds of kilometres to the north, explosions roll across the Russian city of Oryol. Local Telegram channels film glowing debris raining into courtyards. And for the first time, Ukraine officially says it's using its new home-grown deep-strike weapon: the Flamingo cruise missile. Tonight, we're going to connect those two things — the burning oil terminal and the cute-sounding missile with a 3,000-kilometre reach — and ask: How is Ukraine's second front of the war unfolding — not the territorial war, but the one against Russian energy, logistics, and the regime's balance sheet?----------SOURCES: Kyiv Post – “Novorossiysk Oil Shipments Suspended After Ukraine Drone Strike Sets Key Terminal Ablaze” (Nov. 14, 2025)Kyiv Post – “Explosions Rock Russia's Oryol – Ukraine's Use of ‘Flamingo' Long-Range Missiles Confirmed” (Nov. 13, 2025)Kyiv Post – “ANALYSIS: Ukraine's Bombardment of Russia – Not Just Oil Refineries, Warships Any More” by Stefan Korshak (Nov. 5, 2025)The Kyiv Independent – “Ukraine confirms use of Flamingo missiles in strikes on Crimea, Zaporizhzhia, targets inside Russia” (Nov. 13, 2025)Reuters – “Storms, drone attacks and record oil exports pile pressure on Russian port of Novorossiisk” (Oct. 15, 2025)Reuters – coverage on Novorossiysk oil export suspension after attack (Nov. 14, 2025) AP News – “Ukraine's long-range strikes cut Russia's oil refining capacity by 20%, Zelenskyy says” (late Oct. 2025)Chatham House – “Ukraine's best defence against Putin's energy war is more attacks on Russia's oil refining sector” (2025)Foundation for Defense of Democracies – “Ukraine conducts strikes on Russian targets using domestically produced missiles and drones” (Nov. 2025)The Guardian – “Ukraine war briefing: Flamingo flies into battle, Zelenskyy defers to commanders over Pokrovsk” (Nov. 14, 2025) ----------SILICON CURTAIN FILM FUNDRAISERA project to make a documentary film in Ukraine, to raise awareness of Ukraine's struggle and in supporting a team running aid convoys to Ukraine's front-line towns.https://buymeacoffee.com/siliconcurtain/extras----------SILICON CURTAIN LIVE EVENTS - FUNDRAISER CAMPAIGN Events in 2025 - Advocacy for a Ukrainian victory with Silicon Curtainhttps://buymeacoffee.com/siliconcurtain/extrasOur events of the first half of the year in Lviv, Kyiv and Odesa were a huge success. Now we need to maintain this momentum, and change the tide towards a Ukrainian victory. The Silicon Curtain Roadshow is an ambitious campaign to run a minimum of 12 events in 2025, and potentially many more. Any support you can provide for the fundraising campaign would be gratefully appreciated. https://buymeacoffee.com/siliconcurtain/extras----------SUPPORT THE CHANNEL:https://www.buymeacoffee.com/siliconcurtainhttps://www.patreon.com/siliconcurtain----------

Episode: 2533 On Table Legs and Flat Surfaces.  Today, the power of three.

Silicon Bites Ep269 | News Update - Day 1,357 - 2025-11-11 | Robots at the Pier: Ukraine's Sea War Moves Ashore. Tonight: Ukrainian robot boats slam into Russia's Black Sea energy lifeline. We'll break down what got hit, why Tuapse matters, and how Sea Baby and Magura drones, plus the Flamingo long-range missile program, fit into a winter of attrition against the Kremlin's oil cashflow and logistics.What happened last night in the Black Sea? Overnight, multiple Ukrainian unmanned surface vessels—call them USVs, call them robot boats — surged into Tuapse, one of Russia's critical Black Sea oil export hubs. Local videos showed at least two heavy blasts and fires inside the harbour area. The Kyiv Independent reports: “Russia's port town of Tuapse… was rocked by explosions… local Telegram channels reported an attack by Ukrainian sea drones,” adding that regional authorities later confirmed an attack and claimed they destroyed four USVs. They also admitted: “One of the unmanned boats detonated near the shoreline… the shock wave damaged the second-floor windows… a garage and a boat shed.” Though we suspect that much more stuff than this went boom. (Nov. 10, The Kyiv Independent)----------SOURCES: https://kyivindependent.com/explosion-in-russian-black-sea-port-of-tuapse-amid-reported-sea-drone-attack/ - "Explosion in Russian Black Sea port of Tuapse amid reported sea drone attack"https://www.kyivpost.com/post/63972 - "Ukrainian Robot Boats Hit Russian Black Sea Oil Terminal"https://www.reuters.com/business/energy/russias-tuapse-halted-fuel-exports-after-drone-attacks-refinery-stopped-sources-2025-11-05/ - "Russia's Tuapse halted fuel exports after drone attacks ..."https://www.themoscowtimes.com/2025/11/04/spill-discovered-in-black-sea-after-ukrainian-strike-on-tuapse-oil-terminal-bbc-a91030 - "Spill Discovered in Black Sea After Ukrainian Strike on ..."https://www.bloomberg.com/news/articles/2025-09-25/oil-loadings-resume-at-russian-black-sea-terminals-after-attacks - "Oil Loadings Resume at Black Sea Terminals After Attacks"https://theins.ru/en/news/285238 - "Two terminals at Russia's key Black Sea port of Novorossiysk ..."https://en.wikipedia.org/wiki/Tuapse_oil_terminal - "Tuapse oil terminal"https://www.reuters.com/world/europe/ukraines-sea-baby-drones-are-growing-up-with-longer-range-bigger-payload-2025-10-22/ "Ukraine's 'sea baby' drones are growing up with longer range, bigger payload | Reuters"https://www.navalnews.com/naval-news/2025/10/ukraine-unveils-sea-baby-usv-armed-with-rockets-and-machine-gun/ - "Ukraine unveils Sea Baby USV armed with rockets and ..."https://apnews.com/article/0719211dd0314f2b9d15422e81ca66e3 - "Ukraine unveils upgraded sea drone it says can strike anywhere in the Black Sea"https://www.usni.org/magazines/proceedings/2025/september/ukraines-magura-naval-drones-black-sea-equalizers - "Ukraine's Magura Naval Drones: Black Sea Equalizers"https://kyivindependent.com/sbu-releases-new-baby-sea-drones-confirms-it-was-used-in-an-attack-on-sea-bridge/ - "SBU reveals next-gen Sea Baby naval drones, confirms ..."----------SILICON CURTAIN LIVE EVENTS - FUNDRAISER CAMPAIGN Events in 2025 - Advocacy for a Ukrainian victory with Silicon Curtainhttps://buymeacoffee.com/siliconcurtain/extrasOur events of the first half of the year in Lviv, Kyiv and Odesa were a huge success. Now we need to maintain this momentum, and change the tide towards a Ukrainian victory. The Silicon Curtain Roadshow is an ambitious campaign to run a minimum of 12 events in 2025, and potentially many more. Any support you can provide for the fundraising campaign would be gratefully appreciated. https://buymeacoffee.com/siliconcurtain/extras----------SUPPORT THE CHANNEL:https://www.buymeacoffee.com/siliconcurtainhttps://www.patreon.com/siliconcurtain----------

Want more MTM Vegas? Check out our Patreon for access to our exclusive weekly aftershow! patreon.com/mtmvegas Want to work with us? Reach out! inquiries at mtmvegas dot com Episode Description This week VICI properties bought yet another huge Vegas Strip casino. This time they purchased Strat from Golden Entertainment along with 6 other Southern Nevada properties. With VICI now owning the land under 5 different Strip operators' casinos, is it time to call them the landlord of Las Vegas and what does this mean for the future? In other news Durango has officially opened their modest phase 2 expansion of their casino as they ramp up for the much bigger phase 3. We also discuss: Penn's debacle with ESPN Bet, how Caesars is adding slots to busy walkways, what is happening at Mayfair Supper Club, why more giant screens coming to the Strip, FAA cutting flights to Vegas, Golden Steer NYC and an F1 car made out of whiskey barrels. Episode Guide 0:00 Jack Daniels Whiskey Barrel F1 car 0:30 Durango's phase 2 expansion is now open 1:29 Mayfair Supper Club shakeup - Entertainment is out 3:15 Bally's funding issues & pumpkin A's stadium 4:55 Another giant screen coming to the Strip 5:25 Bojangles coming close to the Vegas Strip 6:10 Golden Steer exported to NYC 7:23 Is Golden Steer worthy of its lofty reputation? 8:36 New poll - Park MGM vs. Planet Hollywood 10:24 Caesars Palace poker room returns home 11:07 Flamingo adds slots to busy walkway 12:22 Caesars CEO - Most insane quote ever? 13:27 FAA cutting flights to Vegas by 10% 15:04 Penn loses huge ESPN Bet partnership 16:57 Strat land sold to "The Landlord of Vegas" 18:35 How VICI is taking over Strip land ownership Each week tens of thousands of people tune into our MtM Vegas news shows at http://www.YouTube.com/milestomemories. We do two news shows weekly on YouTube with this being the audio version. Never miss out on the latest happenings in and around Las Vegas! Enjoying the podcast? Please consider leaving us a positive review on your favorite podcast platform! You can also connect with us anytime at podcast@milestomemories.com.  You can subscribe on Apple Podcasts, Google Podcasts, Spotify or by searching "MtM Vegas" or "Miles to Memories" in your favorite podcast app. Don't forget to check out our travel/miles/points podcast as well!

Common Man Hour 3 --Mark Rosen Joins --Wolves Defense --Out-Coached? --Flamingos