Podcasts about SRAM

While I was out in New Zealand, I caught up with Amaury Pierron to unpack the highs and lows of the past couple of seasons. From those incredible wet-weather wins at the Val di Sole and Les Gets World Cups, to breaking his collarbone in La Thuile and then coming back to take the win in Lenzerheide later that season. Then we're joined by SRAM Race Tech Craig Miller and RockShox long-travel product manager Jason Blodgett to go deeper into how they work alongside Amaury and the Commencal Muc-Off Team to find World Cup-winning pace. We get into some of the latest RockShox tech and explore how a close athlete/engineer relationship not only benefits those at the top of the sport, but also us everyday riders. This is a rare insight into the relationship between athlete and engineer at the sharpest end of the sport. So sit back, hit play and check out this episode with Amaury Pierron, Craig Miller and Jason Blodgett. You can also watch this episode on YouTube here. You can follow Amaury on Instagram @amaurypierron4 and over on YouTube here. You can find RockShox on Instagram @rockshock and SRAM @srammtb and also over at sram.com. Podcast Stuff Sponsoring Partners This episode is a paid partnership with SRAM and RockShox, you can check out their new DH suspension and drivetrain, plus the updated Mavens over at sram.com. Patreon I would love it if you were able to support the podcast via a regular Patreon donation. Donations start from as little as £3 per month. That's less than £1 per episode and less than the price of a take away coffee. Every little counts and these donations will really help me keep the podcast going and hopefully take it to the next level. To help out, head here. Merch If you want to support the podcast and represent, then my webstore is the place to head. All products are 100% organic, shipped without plastics, and made with a supply chain that's using renewable energy. We now also have local manufacture for most products in the US as well as the UK. So check it out now over at downtimepodcast.com/shop. Newsletter If you want a bit more Downtime in your life, then you can join my newsletter where I'll provide you with a bit of behind the scenes info on the podcast, interesting bits and pieces from around the mountain bike world, some mini-reviews of products that I've been using and like, partner offers and more. You can do that over at downtimepodcast.com/newsletter. Follow Us Give us a follow on Instagram @downtimepodcast or Facebook @downtimepodcast to keep up to date and chat in the comments. For everything video, including riding videos, bike checks and more, subscribe over at youtube.com/downtimemountainbikepodcast. Are you enjoying the podcast? If so, then don't forget to follow it. Episodes will get delivered to your device as soon as it's available and it's totally free. You'll find all the links you need at downtimepodcast.com/follow. You can find us on Apple Podcast, Spotify, Google and most of the podcast apps out there. Our back catalogue of amazing episodes is available at downtimepodcast.com/episodes Photo – Sven Martin

Kurzes Trikot, blauer Himmel, lange Touren – wir geraten ins Schwärmen Partner dieser Folge ist Sram. Mit dem Code "ROADBIKE-HRM" erhältst du beim Kauf eines Hammerhead Karoo einen Herzfrequenzsensor samt Brustgurt kostenlos dazu. Bestellbar unter: https://www.eu.hammerhead.io/ Du willst ROADBIKE zwei Monate gratis lesen? Dann gehe auf: https://www.roadbike.de/podcast-abo (nur für Empfänger in Deutschland) ROADBIKE ist Faszination Rennrad! Tests, News, Tipps, Interviews, Reise- und Szene-Reportagen rund um deine Leidenschaft Rennradfahren findest du bei uns auf allen Kanälen. Monatlich erscheint eine neue gedruckte ROADBIKE-Ausgabe - erhältlich am Kiosk, digital als ePaper oder ganz praktisch als Abonnement. https://shop.motorpresse.de/zeitschriften/sport-freizeit/roadbike/abo-print.html Auch mit unserer Website ...Dieser Podcast wird vermarktet von der Podcastbude.www.podcastbu.de - Full-Service-Podcast-Agentur - Konzeption, Produktion, Vermarktung, Distribution und Hosting.Du möchtest deinen Podcast auch kostenlos hosten und damit Geld verdienen?Dann schaue auf www.kostenlos-hosten.de und informiere dich.Dort erhältst du alle Informationen zu unseren kostenlosen Podcast-Hosting-Angeboten. kostenlos-hosten.de ist ein Produkt der Podcastbude.

It's common for everyone to chase the simple answer for what the best product is. In this week's Geek Warning, Dave and Ronan discuss why those chasing the best of something may be landing upon fiction.   Additionally, you'll hear about the differing paths in aero road wheel design between the latest from Princeton Carbon Works and Cadex. Of course, there's a PSA. And our Good Thing segment returns.   As with every week, members of Escape Collective get access to our Ask a Wrench segment. This week, Zach Edwards joins Dave to discuss greasing principles, whether to lube electronic derailleurs, mixing-and-matching SRAM brakes, and more. If you like the show, then please consider leaving a review. It's much appreciated.   Happy geeking! Time stamps:   1:20 - Princeton Carbon Works' research into rim widths 10:50 - Cadex's new aero combo  20:00 - SRAM's new gravity MTB products 22:55 - Ibis has a new XC bike  24:00 - On our mind: There is no best  36:50 - PSA: Know when you don't know   46:00 - Good thing: Sync Ergonomics Aerobar Three 51:00 - Ask a Wrench (Members only) 51:30 - Do you lube the pivots of electronic derailleurs?  59:20 - Shimano MTB vs Road on an indoor trainer  1:04:00 - Mixing SRAM dropbar shifters with four-piston calipers  1:09:30 - Applying grease to a thread and how much to use 

Patrick and Benji recap the past week in the world of cycling and preview the upcoming races.*Exclusive deals from our trusted partners*

Brendog's back in studio and this episode is stacked! On this episode of The Ride Companion Brendan Fairclough pulls back the curtain on his new SRAM deal and why it really happened, the new parts he's running (Boxxer / Maven / XX DH) and what it's like bolting fresh parts on… then riding a brand new custom downhill track in South Africa. Brendog also reveals details of his new travel series and teases which major platform could stream it, the real reason Monster Energy became a title sponsor of the World Cup Downhill series, watching A1 with Troy Lee, Danny MacAskill's mind blowing bridge ride, downhill bike set-up tips and much more! Episode Sponsors:- - SRAM: https://www.sram.com/en/sram - Want an easy way to tick your daily nutritional needs? Support the show and get 15% OFF HUEL products with code 'RIDE' at https://huel.com/. Unlock a healthier, easier way to eat with Huel — nutritionally complete meals in minutes, so you can focus on what really matters… biking. - Mudhugger → Get 10% off with code ridecompanion10 at themudhugger.co.uk - Kecks → 10% off with code THERIDECOMPANION at https://kecks.co.uk Get early access & ad-free episodes → https://www.patreon.com/theridecompanion You can also support our long term partners: Marin Bikes → marinbikes.com/gb Focus Bikes → focus-bikes.com SRAM: sram.com/en/sram adidas FiveTen: adidas.co.uk/five_ten invisiFrame: 15% off with code REFRESHANDRIDE at invisiframe.co.uk Troy Lee Designs → 10% off with code theridecompanion at saddleback.avln.me/c/OzduCWvjtcOr Manta Sleep → 10% off with code theridecompanion tinyurl.com/theridecompanion HUEL → 15% off with code RIDE: huel.com/ Mudhugger → Get 10% off with code ridecompanion10 at themudhugger.co.uk Compex → 20% off with code THERIDECOMPANION: compex.com/uk/ Igloo → igloocoolers.com/ Kecks → https://kecks.co.uk use code THERIDECOMPANION for 10% off Feedback Sports: feedbacksports.com WORX → 15% off with code THERIDECOMPANION at uk.worx.com HKT Products → 10% off with code PODCAST at hktproducts.co.uk Follow The Ride Companion Instagram @theridecompanion YouTube @TheRideCompanion Olly Wilkins Instagram @odub_23 YouTube @owilkins23 YouTube clips and BTS channel @moreridecompanion Get official Ride Companion merch, find old episodes and more theridecompanion.co.uk

Kada žene progovore o nasilju i zloupotrebi moći, gotovo uvijek se prvo ispituje njih: zašto su šutjele? Mnogo rjeđe se pita kako to da je nasilje bilo moguće? Koji mehanizmi su pomogli da tako dugo traje? Zašto je muški glas "vjerodostojan", a onaj drugi, ženski, “problematičan”? U razgovoru s Ljiljanom Todorović Jovanović (FemIn Kolektiv, Srbija) i kolegom Sašom Bojićem voditeljica Maja Marić polazi od bazičnog pitanja: zašto još uvijek ne slušamo žene kada govore o nasilju i zloupotrebi moći? Von Maja Maric.

Thanks to Garmin for supporting the podcast!  New CADE merch alert: https://wearethewildones.com/en-gbp/collections/all 00:00 Ad: Garmin data FTW 00:50 postcard   09:34 Pog's pissed off   15:13 Pog's power numbers are insane   18:10 SRAM backs down   21:42 32-inch wheels   32:19 New ‘affordable' handmade-in-italy steel bike   37:06 Dream cycle lanes   42:00 some very sad news   43:01 chalk paint fluff up   44:53 Most likely to… overpack on bikepacking trip   47:05 Most likely to… show up late for group ride   48:07 Most likely to… not finish an event   49:44 Most likely to… eat something questionable mid-ride   50:35 Most likely to… say ‘this is why I ride gravel'   50:53 Most likely to… say it's fine when it's not fine   51:02 Most likely to… start the Whatsapp group   51:41 Most likely to… forget to load the route   53:46 Unpopular Opinion: everything's been done   57:15 ‘the guy bros had it right the whole time' You can check out the video versions of the podcast, plus more videos from Cade Media here: https://www.youtube.com/@Cade_Media/videos If you'd like us to send in a question, story, some good news, things you'd like us to discuss or anything else, email us at wildonespodcast@cademedia.co.uk Thanks and see you next time. Or you can send us a voice note on Whatsapp: +44 7860 860 213 Our address: CADE, PO Box 790, Durham, DH1 9TH, UK (Unfortunately we can't guarantee anything you send will be featured, and are unable to return anything you send us) Learn more about your ad choices. Visit podcastchoices.com/adchoices

It's just Davi and Olly in the studio this week and.. it goes exactly how you'd expect. Davi has a new bike day and brings his new Marin Alcatraz dirt jump bike to work (nothing hits like a fresh hardtail), which turns into a bigger conversation about why you don't actually need the best bike to have the most fun. The lads talk about Hardline Tasmania being cancelled, the Super Bowl let down and end up debating UK geology, winter riding, and whether mountain bike culture is slowly losing its soul. We also experiment with a pair of Oakley x Meta glasses in the studio (which gets weird), talk about wether pov is the future of podcasting and answer your listener questions! Episode Sponsors:- - Cadence turns your phone or Apple Watch into a fully-featured bike computer, but with way more flexibility than most head units. You get unlimited custom screens, over 200 metrics, full Bluetooth sensor support, proper navigation, offline maps, structured training, and Strava Live Segments. Head to https://getcadence.app/trc Try it for a month for FREE and see if it replaces your bike computer! - Manta Sleep: Get 10% off with code theridecompanion at https://tinyurl.com/theridecompanion - WORX Tools → 15% off the full range with code THERIDECOMPANION: https://uk.worx.com - Kecks → 10% off with code THERIDECOMPANION at https://kecks.co.uk Get early access & ad-free episodes → https://www.patreon.com/theridecompanion You can also support our long term partners: Marin Bikes → marinbikes.com/gb Focus Bikes → focus-bikes.com SRAM: sram.com/en/sram adidas FiveTen: adidas.co.uk/five_ten invisiFrame: 15% off with code REFRESHANDRIDE at invisiframe.co.uk Troy Lee Designs → 10% off with code theridecompanion at saddleback.avln.me/c/OzduCWvjtcOr Manta Sleep → 10% off with code theridecompanion tinyurl.com/theridecompanion HUEL → 15% off with code RIDE: huel.com/ Mudhugger → Get 10% off with code ridecompanion10 at themudhugger.co.uk Compex → 20% off with code THERIDECOMPANION: compex.com/uk/ Igloo → igloocoolers.com/ Kecks → https://kecks.co.uk use code THERIDECOMPANION for 10% off Feedback Sports: feedbacksports.com WORX → 15% off with code THERIDECOMPANION at uk.worx.com HKT Products → 10% off with code PODCAST at hktproducts.co.uk Follow The Ride Companion Instagram @theridecompanion YouTube @TheRideCompanion Olly Wilkins Instagram @odub_23 YouTube @owilkins23 YouTube clips and BTS channel @moreridecompanion Get official Ride Companion merch, find old episodes and more theridecompanion.co.uk

In this episode of Bicycle Retail Radio, SRAM Midwest Regional Field Guide Manager Dan Jennings and Technical Field Guide Sean Owen guide retailers through four key areas to focus on this spring: assessing service profitability, building a smarter service inventory, refining service menu pricing and value, and optimizing physical service space and workflow.Packed with practical tips and actionable insights, the conversation gives shops the tools they need to strengthen their service operations and drive profitability.Support the show

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]:

In this episode of The Ride Companion, mountain bike legend Martyn Ashton joins us for one of our most honest, funny and important conversations yet. We talk about Martyn's latest project, Rideable Now and why he believes mountain bikers need to show up for mountain biking. During the episode Martyn gives the usual dose of self deprecation to powerful reflections on injury, identity and access. Rideable Now exists to help riders with life changing injuries and disabilities get back on the trails through adaptive mountain bikes, community support and real opportunities to ride again. Learn more and consider supporting rideable now here https://www.rideablenow.com Episode Sponsors:- - Want an easy way to tick your daily nutritional needs? Support the show and get 15% OFF HUEL products with code 'RIDE' at https://huel.com/. Unlock a healthier, easier way to eat with Huel — nutritionally complete meals in minutes, so you can focus on what really matters… biking. - Fan of quality tools and want to geek out? Feedback Sports is available from your local bike shop, online retailers such as Saddleback and Bike 24, plus directly from https://feedbacksports.com - Looking for a new car or van and don't want to deal with dodgy dealers? Check out https://www.cargurus.co.uk - Kecks → 10% off with code THERIDECOMPANION at https://kecks.co.uk Get early access & ad-free episodes → https://www.patreon.com/theridecompanion You can also support our long term partners: Marin Bikes → marinbikes.com/gb Focus Bikes → focus-bikes.com SRAM: sram.com/en/sram adidas FiveTen: adidas.co.uk/five_ten invisiFrame: 15% off with code REFRESHANDRIDE at invisiframe.co.uk Troy Lee Designs → 10% off with code theridecompanion at saddleback.avln.me/c/OzduCWvjtcOr Manta Sleep → 10% off with code theridecompanion tinyurl.com/theridecompanion HUEL → 15% off with code RIDE: huel.com/ Mudhugger → Get 10% off with code ridecompanion10 at themudhugger.co.uk Compex → 20% off with code THERIDECOMPANION: compex.com/uk/ Igloo → igloocoolers.com/ Kecks → https://kecks.co.uk use code THERIDECOMPANION for 10% off Feedback Sports: feedbacksports.com WORX → 15% off with code THERIDECOMPANION at uk.worx.com HKT Products → 10% off with code PODCAST at hktproducts.co.uk Follow The Ride Companion Instagram @theridecompanion YouTube @TheRideCompanion Olly Wilkins Instagram @odub_23 YouTube @owilkins23 YouTube clips and BTS channel @moreridecompanion Get official Ride Companion merch, find old episodes and more theridecompanion.co.uk

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Red Bull Hardline Tasmania is just days away. Let's go bench racing and make some fun predictions! Olly and Davi break down the full rider list including Jackson Goldstone, Asa Vermette, Bernard Kerr, Troy Brosnan, Sam Hill, Aaron Gwin, Roger Vieira, Gracey Hemstreet and more plus, who's missing, who's under the most pressure and, why Hardline might be changing. The lads also make Hardline Tasmania podium predictions, unpack winter riding guilt and survival strategies, share what they've been watching and listening to and somehow end up deep in Lunch & Learn chaos. Get involved in the comments on YouTube with your podium picks and you could win a Focus frame and more! Red Bull Hardline Tasmania goes down February 7–8, live on Red Bull TV. Episode Sponsors:- - Mudhugger: Get 10% off with code ridecompanion10 at https://www.themudhugger.co.uk - Manta Sleep: Get 10% off with code theridecompanion at https://tinyurl.com/theridecompanion - Cadence turns your phone or Apple Watch into a fully-featured bike computer, but with way more flexibility than most head units. You get unlimited custom screens, over 200 metrics, full Bluetooth sensor support, proper turn-by-turn navigation, offline maps, structured training, and Strava Live Segments. Head to https://getcadence.app/trc Try it for a month for FREE and see if it replaces your bike computer! - Kecks → 10% off with code THERIDECOMPANION at https://kecks.co.uk Get early access & ad-free episodes → https://www.patreon.com/theridecompanion You can also support our long term partners: Marin Bikes → marinbikes.com/gb Focus Bikes → focus-bikes.com SRAM: sram.com/en/sram adidas FiveTen: adidas.co.uk/five_ten invisiFrame: 15% off with code REFRESHANDRIDE at invisiframe.co.uk Troy Lee Designs → 10% off with code theridecompanion at saddleback.avln.me/c/OzduCWvjtcOr Manta Sleep → 10% off with code theridecompanion tinyurl.com/theridecompanion HUEL → 15% off with code RIDE: huel.com/ Mudhugger → Get 10% off with code ridecompanion10 at themudhugger.co.uk Compex → 20% off with code THERIDECOMPANION: compex.com/uk/ Igloo → igloocoolers.com/ Kecks → https://kecks.co.uk use code THERIDECOMPANION for 10% off Feedback Sports: feedbacksports.com WORX → 15% off with code THERIDECOMPANION at uk.worx.com HKT Products → 10% off with code PODCAST at hktproducts.co.uk Follow The Ride Companion Instagram @theridecompanion YouTube @TheRideCompanion Olly Wilkins Instagram @odub_23 YouTube @owilkins23 YouTube clips and BTS channel @moreridecompanion Get official Ride Companion merch, find old episodes and more theridecompanion.co.uk

Wir diskutieren, wie viele Räder Radsportler wirklich brauchen. Partner dieser Folge ist Sram. Mit dem Code "ROADBIKE-HRM" erhältst du beim Kauf eines Hammerhead Karoo einen Herzfrequenzsensor samt Brustgurt kostenlos dazu. Bestellbar unter: https://www.eu.hammerhead.io/ Du willst ROADBIKE zwei Monate gratis lesen? Dann gehe auf: https://www.roadbike.de/podcast-abo (nur für Empfänger in Deutschland) ROADBIKE ist Faszination Rennrad! Tests, News, Tipps, Interviews, Reise- und Szene-Reportagen rund um deine Leidenschaft Rennradfahren findest du bei uns auf allen Kanälen. Monatlich erscheint eine neue gedruckte ROADBIKE-Ausgabe - erhältlich am Kiosk, digital als ePaper oder ganz praktisch als Abonnement. https://shop.motorpresse.de/zeitschriften/sport-freizeit/roadbike/abo-print.html Auch mit unserer Website https://www.bike-x.de/rennrad/ oder über ...Dieser Podcast wird vermarktet von der Podcastbude.www.podcastbu.de - Full-Service-Podcast-Agentur - Konzeption, Produktion, Vermarktung, Distribution und Hosting.Du möchtest deinen Podcast auch kostenlos hosten und damit Geld verdienen?Dann schaue auf www.kostenlos-hosten.de und informiere dich.Dort erhältst du alle Informationen zu unseren kostenlosen Podcast-Hosting-Angeboten. kostenlos-hosten.de ist ein Produkt der Podcastbude.

Sua ligação com a bicicleta começou cedo em sua vida. Pedalava com os amigos pelas ruas do bairro e viveu a mania do BMX no Brasil. Na escola, jogou voleibol, mas destacou-se mesmo em matemática e física, desenvolvendo interesse por eletrônica e computação. Cursou Engenharia Naval na Escola Politécnica da USP e, nesse período, praticou mergulho livre e autônomo, além de ginástica olímpica recreativa. Iniciou a vida profissional estagiando e depois trabalhando no marketing de multinacionais, ao mesmo tempo em que passou a se aventurar no mountain bike, que dava seus primeiros passos no Brasil. A partir do início dos anos 1990, passou a se envolver de forma mais intensa com a bicicleta. Com Daniel Aliperti, um amigo de infância, fundou a loja Pedal Power, que logo se tornou referência no mercado. Algum tempo depois, deu início à importação de marcas icônicas do segmento, como Ritchey, Santa Cruz e Rocky Mountain. Profissionalmente e pessoalmente, sua ligação com a bicicleta só aumentava. Abriu uma loja em Campos do Jordão e aproveitou para explorar as oportunidades locais no mountain bike, trekking e aventuras ao ar livre. Em 1997, começou a participar de enduros a pé e das primeiras corridas de aventura realizadas no Brasil. No ano seguinte, ao lado da esposa, integrou a melhor equipe brasileira na primeira edição da Expedição Mata Atlântica e participou da lendária Southern Traverse, na Nova Zelândia. Ao longo dos anos seguintes, competiu em mais algumas corridas de aventura até se voltar novamente ao mountain bike, geralmente em dupla com sua esposa. Em 1999, sua importadora, a Proparts, passou a representar  outras marcas fortes, como RockShox e SRAM. Nos anos seguintes, ao lado de Giancarlo Clini, idealizou o que viria a se tornar a Aliança Bike, associação que presidiu por dois mandatos consecutivos. Depois, conquistou a representação da Specialized e implantou a subsidiária da marca no Brasil. Quase uma década depois, conquistou a Mavic, a Zipp e, posteriormente, a gigante Garmin. Em 2018, decidiu então mudar-se com a família para o Canadá e, no ano seguinte, deixou a Pedal Power, focando exclusivamente no fortalecimento e crescimento das marcas que representa e em agregar novas marcas ao seu portfólio, como Vittoria, Shokz e Orbea. Conosco aqui, o engenheiro naval com pós-graduação em administração, empreendedor que tem um portfólio com 14 marcas representadas no Brasil, um dos melhores corredores de aventura brasileiros do final dos anos 1990, um amante do ciclismo e da vida ao ar livre, o paulistano Marcelo de Barros Dantas Maciel. Inspire-se! Um oferecimento @2peaksbikes A 2 Peaks Bikes é a importadora e distribuidora oficial no Brasil da Factor Bikes, Santa Cruz Bikes e de diversas outras marcas e conta com três lojas: Rio de Janeiro, São Paulo e Los Angeles. Lá, ninguém vende o que não conhece: todo produto é testado por quem realmente pedala.  A 2 Peaks Bikes foi pensada e criada para resolver os desafios de quem leva o pedal a sério — seja no asfalto, na terra ou na trilha. Mas também acolhe o ciclista urbano, o iniciante e até a criança que está começando a brincar de pedalar. Para a 2 Peaks, todo ciclista é bem-vindo.  Conheça a 2 Peaks Bikes, distribuidora oficial da Factor, da Santa Cruz e da Yeti no Brasil. @2peaksbikesla SIGA e COMPARTILHE o Endörfina no Youtube ou através do seu app preferido de podcasts. Contribua também com este projeto através do Apoia.se. SIGA e COMPARTILHE o Endörfina no Youtube ou através do seu app preferido de podcasts. Contribua também com este projeto através do Apoia.se.    

The first Ride Companion catch up of 2026… and we're straight back into it! We kick things off talking winter illnesses, whether 'man flu' is actually a thing, and why the placebo effect might be more powerful than people think, along with a few honest Christmas confessions (including why we're both useless at gifts). Lunch & Learn is back and we look at motorway lane rules what the rules actually say. We also cover winning Pinkbike Comment of the Year, New Year goal setting vs streak culture, Davi's newest hobby obsession, the idea of finally doing our bike test, and a huge 2026 partnership announcement for the podcast. Episode Sponsors:- - Want an easy way to tick your daily nutritional needs? Support the show and get 15% OFF HUEL products with code 'RIDE' at https://huel.com/. Unlock a healthier, easier way to eat with Huel — nutritionally complete meals in minutes, so you can focus on what really matters… biking. - Cadence turns your phone or Apple Watch into a fully-featured bike computer, but with way more flexibility than most head units. You get unlimited custom screens, over 200 metrics, full Bluetooth sensor support, proper turn-by-turn navigation, offline maps, structured training, and Strava Live Segments. Head to https://getcadence.app/trc Try it for a month for FREE and see if it replaces your bike computer! Get early access & ad-free episodes → https://www.patreon.com/theridecompanion You can also support our long term partners: Marin Bikes → marinbikes.com/gb Focus Bikes → focus-bikes.com SRAM: sram.com/en/sram adidas FiveTen: adidas.co.uk/five_ten invisiFrame: 15% off with code REFRESHANDRIDE at invisiframe.co.uk Troy Lee Designs → 10% off with code theridecompanion at saddleback.avln.me/c/OzduCWvjtcOr Manta Sleep → 10% off with code theridecompanion tinyurl.com/theridecompanion HUEL → 15% off with code RIDE: huel.com/ Mudhugger → Get 10% off with code ridecompanion10 at themudhugger.co.uk Compex → 20% off with code THERIDECOMPANION: compex.com/uk/ Igloo → igloocoolers.com/ Kecks → https://kecks.co.uk use code THERIDECOMPANION for 10% off Feedback Sports: feedbacksports.com WORX → 15% off with code THERIDECOMPANION at uk.worx.com HKT Products → 10% off with code PODCAST at hktproducts.co.uk Follow The Ride Companion Instagram @theridecompanion YouTube @TheRideCompanion Olly Wilkins Instagram @odub_23 YouTube @owilkins23 YouTube clips and BTS channel @moreridecompanion Get official Ride Companion merch, find old episodes and more theridecompanion.co.uk

Fahrräder und Fahrradkunden erzählen unendlich viele Geschichten! So auch dieses CURVE Kevin, dass extra zum Pendeln mit dem Hund aufgebaut wurde. Der Pudel kommt dann sogar noch live zu uns in die Sendung :-) Herrchen und Hund freuen sich schon auf viele gemeinsame Kilometer auf diesem coolen Rad! Dazu viele News und Dinge der letzten Woche: Wir haben alle Cornflakes getestet und Frosties endlich bei Edeka gefunden. Van Rysel baut komische TT-E-Bikes und SRAM hat auch was neues rausgebracht. Freut Euch auf eine abwechslungsreiche Sendung mit vielen tollen Themen. ## LÖWENANTEIL BIO-GERICHTE bis 30% RABATT ## Code: BIKE Holt Euch Löwenanteil mit 5% Extra-Rabatt zum Januar-Rabatt! https://cutt.ly/zr45mYdU ## LINKS ZUR SENDUNG ## INGO BEI INSTAGRAM: https://www.instagram.com/quendler/ ANDRÉ BEI INSTAGRAM: https://www.instagram.com/tofukind/ World Bicycle Relief Spendenseite:
https://fund.worldbicyclerelief.org/de-DE/project/erlebnisstattergebnis-2025?tab=ubersicht Hochfunktionale Depression: Jan Ullrich bei Terra XPLORE: https://youtu.be/Q_tqj7bGPLM?si=1Y1dV9NHFu8kBjUB Sellpy 2nd Verkaufsplattform: https://www.sellpy.de/ Bluebrixx Mobiles Labor: https://www.bluebrixx.com/de/prod/107469/mobiles-labor/ ## INHALT ## 00:00:00 Das Pudelfahrrad 00:28:03 Bio-Gerichte für nach der Sendung 00:32:54 World Bycicle Relief: 11.000 neue Räder Ende 2025 00:36:31 Cornflakes & Frosties Erfahrungen und beste Marken 00:53:30 MSR 300! Das war ein krasses Event. Und super! 01:05:17 Kariesgefahr durch zu viel Zucker beim Sport? 01:10:49 Neuheiten bei SRAM: USB-Ladegerät unser Gamechanger! 01:21:26 WOB Cross Duathlon: Wie zeichnet man einen Duathlon auf? 01:40:03 Beklopptes Concept Bike von Van Rysel, Swiss Side mit Male Motor? 02:01:04 Gravaa Reifendruck-System insolvent 02:10:43 PICKS 02:30:41 Unterbrechung: Der Pudel kommt! 02:35:45 Weiter geht es mit den Klemmbausteinen 02:53:44 Serien von Vince Giligan richtig gucken! 03:00:47 Pre-Show: Ernährungspyramide in den USA

サイクリング関連の気になる機材を紹介するHOT TECH

Thanks to Garmin for supporting the podcast!  00:00 Ads 01:00 A man set his dog on us 06:43 Francis got scammed on AliExpress? 11:50 150 and 155mm SRAM cranks 23:37 Pinarello's new gravel bike is sus 29:01 Canyon cuts 320 jobs 32:06 Five Finger Death Punch + airbag bib shorts 34:26 FUOTW 35:40 Unpopular Opinion: indoor > outdoor riding 39:16 Unpopular Opinion: is carbon fibre really eco-unfriendly? 40:49 Unpopular Opinion: small brands are the best 45:52 Send us your Unpopular Opinions and Questions! 46:20 Other obsessive hobbies… 48:40 Bald Guy Chat: God Tier SPF and Caps You can check out the video versions of the podcast, plus more videos from Cade Media here: https://www.youtube.com/@Cade_Media/videos If you'd like us to send in a question, story, some good news, things you'd like us to discuss or anything else, email us at wildonespodcast@cademedia.co.uk Thanks and see you next time. Or you can send us a voice note on Whatsapp: +44 7860 860 213 Our address: CADE, PO Box 790, Durham, DH1 9TH, UK (Unfortunately we can't guarantee anything you send will be featured, and are unable to return anything you send us) Learn more about your ad choices. Visit podcastchoices.com/adchoices

Matt Jones and Jono Jones join The Ride Companion to break down MAVRIX ahead of console release on January 22nd. During this episode Matt and Jono give Davi and Olly a tour of the virtual world they've help create, the boys explain how it was built, what makes it feel so real and why it could bring a whole new wave of riders into mountain biking. Matt and Jono also share the future plans for the game, the intricacies of real world physics, the secret challenges Matt has never talked about and much more! Learn more about MAVRIX here: https://mavrix.game Episode Sponsors:- - Invisiframe → 15% off kits, decals & more with code REFRESHANDRIDE: https://www.invisiframe.co.uk - Live Better Longer and kick 2026 off the right way! BUBS is running a HUGE New Year New You sale PLUS for a limited time only, our listeners are getting 20% OFF at BUBS Naturals by using code RIDE at checkout. https://www.bubsnaturals.com - Manta Sleep: Get 10% off with code theridecompanion at https://tinyurl.com/theridecompanion Get early access & ad-free episodes → https://www.patreon.com/theridecompanion You can also support our long term partners: Marin Bikes → marinbikes.com/gb Focus Bikes → focus-bikes.com SRAM: sram.com/en/sram adidas FiveTen: adidas.co.uk/five_ten invisiFrame: 15% off with code REFRESHANDRIDE at invisiframe.co.uk Troy Lee Designs → 10% off with code theridecompanion at saddleback.avln.me/c/OzduCWvjtcOr Manta Sleep → 10% off with code theridecompanion tinyurl.com/theridecompanion HUEL → 15% off with code RIDE: huel.com/ Mudhugger → Get 10% off with code ridecompanion10 at themudhugger.co.uk Compex → 20% off with code THERIDECOMPANION: compex.com/uk/ Igloo: → igloocoolers.com/ Kecks → https://kecks.co.uk use code THERIDECOMPANION for 10% off Feedback Sports → feedbacksports.com WORX → 15% off with code THERIDECOMPANION at uk.worx.com HKT Products → 10% off with code PODCAST at hktproducts.co.uk Follow The Ride Companion Instagram @theridecompanion YouTube @TheRideCompanion Olly Wilkins Instagram @odub_23 YouTube @owilkins23 YouTube clips and BTS channel @moreridecompanion Get official Ride Companion merch, find old episodes and more theridecompanion.co.uk

328 - In this episode of Full Spectrum Cycling, Greg engages in a lively conversation with JK and Tony that is filled with humor and camaraderie. The episode kicks off with a light-hearted discussion about the weather, transitioning into stories about cycling experiences and the challenges of riding in colder temperatures. Greg shares his thoughts on various biking gear, including a malfunctioning light and the benefits of short cranks in cycling. The conversation flows into personal stories about holiday gatherings, food experiments, and the joys of cooking, particularly focusing on a unique ham dish that JK prepared with leftover holiday ham. The hosts delve into cycling culture, upcoming events, and the importance of supporting local businesses in the cycling community. (yes, this is Riverside.fm's description of this episode! Edited for accuracy!) https://youtu.be/vPLHuRtnhh0 The Milwaukee Minute (or 5) Milwaukee Bike Bazaar - March 14, 2026, at Riverside High School. Parting out bikes Glorioso's reopens ToAD dates Can we talk about pizza bread for a moment? Please Greg's 30 Video Series Talkin' Schmack  RIP Jim Blackburn and Joe Montgomery  ARC Light Pro Flat pedals with Smart lights - https://redshiftsports.com/products/arclight-pro-flat-pedals  Big Quill Pigs and 27.5 wheels Gear Page at FB.c - https://fat-bike.com/2025/12/fat-bike-gear-recommendations/  And subscribe to the Weekly Dose of Fat newsletter - https://news.fat-bike.com  Short crank arms? SRAM now goes down to 150 RAD bankruptcy  Park Tool's Director of Education Calvin Jones is Retiring Tuscobia 160 had snow this year - https://www.apg-wi.com/price_county_review/snow-brings-luster-back-to-tuscobia-winter-ultra/article_501233c8-26c0-42e0-ac06-2e5697b1a66a.html  Punk and Hardcore Flyers in the Wall Art Book - https://heavymusicartwork.com/products/punk-hardcore-flyers-on-the-wall-vol-1  Show Beer - Grateful Dead Juicy Pale Ale Bob Weir was quite the cyclist - Had a 1990 Teesdale that was painted by official GD painter Prairie Prince If you like this show PLEASE Subscribe in Apple Podcast - https://podcasts.apple.com/us/podcast/full-spectrum-cycling/id1569662493   Stuff for sale on Facebook Marketplace Shit Worth Doing February 20th, 2026 - Flat Out Friday - Fiserv Forum - Milwaukee, WI Just announced - https://pitchfork.com/news/sunn-o-don-their-cloaks-for-new-album-song-and-tour/ Bikes! Large Schlick Cycles 29+ Custom Build - Black Medium Schlick Cycles 29+ Custom Build - Orange Large Schlick Cycles Tatanka, Orange. 29+ Schlick Cycles frames for custom builds Contact info@everydaycycles.com =============================Equipment we use during the production of Full Spectrum Cycling:============================= Cameras Mevo Core - https://amzn.to/3VpGzmJ - (Amazon) Mevo Start - https://amzn.to/3ZG2B7y - (Amazon) Panasonic 25mm 1.7 lens - https://amzn.to/3OH8Ph0 - (Amazon) Olympus 12mm-42mm lens - https://amzn.to/4iiEyCO - (Amazon) Audio Rode Podcaster Pro II - https://amzn.to/3xKbRfI  (Amazon) Microphones Earthworks Ethos Microphone - https://amzn.to/4eR6kEC  (Amazon) MXL BCD-1 Dynamic Microphone - https://amzn.to/3Yigjx9  (Amazon) Rode Wireless Go II - https://amzn.to/3Su114D  (Amazon) Audio Technica BPHS1 Headset Mics - https://amzn.to/4cXebi2  (Amazon) Blue Compass Boom Arm - https://amzn.to/4cClJr1  (Amazon) Accessories Ulanzi Crab Tripod - https://amzn.to/3WIxWVk  (Amazon) Neewer Camera Desk Mount with Overhead Camera Mounting Arm and 1/4" Ball Head, 17" - 41" Adjustable Tabletop Light Stand with C Clamp - https://amzn.to/3Wuo5Bc  (Amazon) =============================Disclosure: Some of the links on this page may be affiliate links. Clicking these and making a purchase will directly support Full Spectrum Cycling. Thanks!=============================

Welcome to a new year of Geek Warning, the cycling tech-focused podcast from Escape Collective.This week, Ronan and Dave get back into the swing of things by covering some fresh products from Cane Creek, SRAM, BBB, and more.It seems Ronan and Dave both had PSAs on their minds, so we have two this week. One is about watching wear on what are common wear items. The other is about how to perform a basic safety check on any bike.Unfortunately, we hit audio issues with the member-only Ask a Wrench that only showed up during editing. For some reason, the recording device decided to give Dave a lisp and mute him off-and-on. Sorry.Time stamps:4:00 - Cane Creek answered Dave's wish15:30 - BBB's CoreCap19:20 - SRAM goes shorter in cranks22:30 - Tailfin's bottle cage mount where you want it24:30 - Garbaruk breaks SRAM's rules28:00 - RIP Joe Montgomery29:00 - Mechanic crystal balling is on Dave's Mind38:30 - A basic safety check procedure43:00 - Ask a Wrench with Brad Copeland (Members Only, with some audio issues, sorry!)43:30 - SRAM eTap rim brake shifters50:30 - Draining a bike and warding off corrosion1:04:30 - Can 3D printing save me from a new Wahoo speed sensor?1:08:00 - How to make toe spikes last longer

In this week's BikeRadar podcast, Jack Luke and Simon von Bromley are back again to discuss the hottest tech stories in cycling.  Following last week's episode about the leaked Cannondale CAAD14, we received a tip off about an amazing value Giant road bike that's not for sale in the UK or US. Luckily, the internet knows no bounds and we've got all the details you need to know about it.  The two then discuss SRAM's new range of Red AXS cranks, which come in lengths down to a tiny 150mm – and whether that's something anyone should be interested in.  Likewise, should you care about Presta valve alternatives? Simon did some testing with BBB's new CoreCap valves, and has some thoughts to share.  Lastly, Jack and Simon discuss the reaction to the leaked Cannondale CAAD14 and why aluminium race bikes can make sense for a lot of riders, even if they're technically not as ‘optimised' as carbon bikes can be. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Memory shortages are all the rage in 2026. How should you play the AI data center supply crunch?We discussed this back in 2025, and now it is here: Memory shortages are hitting the AI data center supply chain across the board. But is this an AI bubble, or just a normal cyclical growth cycle? In this video, we break down the entire memory hierarchy—from ultra-fast on-chip SRAM to HBM and long-term storage—and give you the basket of companies to watch for each layer.We also discuss why Pure Storage is our top bet for secondary storage and how equipment suppliers like Lam Research could benefit as manufacturers race to expand capacity.Join us on Discord with Semiconductor Insider, sign up on our website: www.chipstockinvestor.com/membershipSupercharge your analysis with AI! Get 15% of your membership with our special link here: https://fiscal.ai/csi/Sign Up For Our Newsletter: https://mailchi.mp/b1228c12f284/sign-up-landing-page-short-formChapters:00:00 – Memory Shortages: Bubble vs. Cyclical Growth 02:13 – The AI Memory Hierarchy Explained (SRAM, DRAM, NAND) 04:59 – SRAM Stocks: Nvidia, AMD, & Synopsys 06:50 – Embedded Memory: Weebit Nano & MRAM players 07:46 – DRAM & HBM Leaders: SK Hynix, Micron, Samsung 09:00 – The NAND & HDD Resurgence (Seagate & WD) 11:00 – Why Pure Storage is a Top Bet 14:00 – The Fab Five & Lam Research OpportunityIf you found this video useful, please make sure to like and subscribe!*********************************************************Affiliate links that are sprinkled in throughout this video. If something catches your eye and you decide to buy it, we might earn a little coffee money. Thanks for helping us (Kasey) fuel our caffeine addiction!Content in this video is for general information or entertainment only and is not specific or individual investment advice. Forecasts and information presented may not develop as predicted and there is no guarantee any strategies presented will be successful. All investing involves risk, and you could lose some or all of your principal. #semiconductors #chips #investing #stocks #finance #financeeducation #silicon #artificialintelligence #ai #financeeducation #chipstocks #finance #stocks #investing #investor #financeeducation #stockmarket #chipstockinvestor #fablesschipdesign #chipmanufacturing #semiconductormanufacturing #semiconductorstocks Nick and Kasey own shares of Nvidia, Micron, Pure Storage, Sk hynix, Kioxia, Lam Research

Blake Samson (aka Zimblake) is back on The Ride Companion to talk about what happens after you take the leap. In this episode, Blake breaks down launching his new YouTube channel, building his first major project from scratch, why he's choosing the slow, proper route, and how trust, planning, and passion are shaping what comes next. We also dive into burnout, long-form content, attention spans, building without cutting corners and why fun still matters when your passion becomes your work. This is the next chapter of Blake's journey and it's only just getting started. Episode Sponsors:- - Mudhugger: Get 10% off with code ridecompanion10 at https://www.themudhugger.co.uk - HUEL: Support the show and get 15% OFF HUEL products with code 'RIDE' at https://huel.com/. Unlock a healthier, easier way to eat with Huel — nutritionally complete meals in minutes, so you can focus on what really matters… biking. - Fan of quality tools and want to geek out? Feedback Sports is available from your local bike shop, online retailers such as Saddleback and Bike 24, plus directly from https://feedbacksports.com Get early access & ad-free episodes → https://www.patreon.com/theridecompanion You can also support our long term partners: Marin Bikes → marinbikes.com/gb Focus Bikes → focus-bikes.com SRAM: sram.com/en/sram adidas FiveTen: adidas.co.uk/five_ten invisiFrame: 15% off with code REFRESHANDRIDE at invisiframe.co.uk Troy Lee Designs → 10% off with code theridecompanion at saddleback.avln.me/c/OzduCWvjtcOr Manta Sleep → 10% off with code theridecompanion tinyurl.com/theridecompanion HUEL → 15% off with code RIDE: huel.com/ Mudhugger → Get 10% off with code ridecompanion10 at themudhugger.co.uk Compex → 20% off with code THERIDECOMPANION: compex.com/uk/ Igloo: igloocoolers.com/ Feedback Sports: feedbacksports.com WORX → 15% off with code THERIDECOMPANION at uk.worx.com HKT Products → 10% off with code PODCAST at hktproducts.co.uk Follow The Ride Companion Instagram @theridecompanion YouTube @TheRideCompanion Olly Wilkins Instagram @odub_23 YouTube @owilkins23 YouTube clips and BTS channel @moreridecompanion Get official Ride Companion merch, find old episodes and more theridecompanion.co.uk

Esta semana Canyon acaba de anunciar la vuelta de la Spectral:ON y las Torque:ON con una nueva batería de 800 Wh, reforzada, más segura y moderna (además de con nuevas celdas con una mejor gestión de la carga) y unos precios súper atractivos. En el podcast analizamos este regreso y todo lo que pasó con estas e-bikes y la reacción y respuesta que hubo por parte de Canyon. De hecho, esta vuelta también nos sirve para disertar un poco sobre motores, ya que no ocultamos el hecho de que el Shimano EP801 no es el motor más potente y moderno de los que hay actualmente en el mercado pero sí uno de los más fiables y que mejor gestiona la autonomía de la batería. Siguiendo al filo de la navaja de la polémica analizamos la Pivot LES SL Drop Bar, una MTB rígida de XC con horquilla de 100 mm y ruedas de 29x2,25” con manillar de gravel ¿por qué? Pues hablando de ello vemos como en mercados más allá del español tiene bastante sentido e incluso lo tiene en el nuestro, aunque a mucha gente le cueste entender algo el concepto. Sin salirnos del MTB y volviendo a las e-bikes hablamos sobre la presentación de la Mondraker Scree, que también nos sirve para disertar un poco sobre el precio de las e-bikes y una tendencia que estamos viendo con las novedades que llegan con e-bikes con precios algo más asequibles rompiendo, por debajo, la barrera de los 5.000 €. También de una estabilización del recorrido de suspensiones en las e-bikes, recuperando el concepto trail, en lugar de tener que “cargar” con recorridos de suspensiones y geometrías excesivamente largos y agresivos para nuestras necesidades. En la ensalada de novedades tenemos el nuevo sillín PRO Stealth elaborado con tecnología 3D, el nuevo casco Lazer Sphere KinetiCore y las novedades de SRAM en manetas para TT y en medidas de bielas. Enlaces de interés: El regreso de las Canyon Spectral:ON y Torque:ON https://www.mtbpro.es/actualidad/canyon-spectralon-y-torqueon-regresan-con-nuevas-baterias-de-800-wh-desde-3999-euros Pivot LES SL Drop Bar https://www.pivotcycles.com/en-us/bikes/les-sl-drop-bar Mondraker Scree: la nueva e-trail ‘full power' de la marca https://www.mtbpro.es/actualidad/mondraker-scree-llega-la-nueva-e-trail-full-power-de-la-marca Sillín PRO Stealth en 3D https://www.maillotmag.com/actualidad/la-impresion-3d-llega-al-sillin-pro-stealth-con-dos-opciones-de-anchura Lazer Sphere KinetiCore https://www.maillotmag.com/actualidad/nuevo-lazer-sphere-kineticore-versatilidad-y-gran-relacion-calidad-precio SRAM: manetas aero y bielas más cortas https://www.maillotmag.com/actualidad/sram-presenta-manetas-especificas-para-triatlon-y-contrarreloj-y-bielas-mas-cortas-para

Jono Jones is back for the first Ride Companion episode of 2026 and, Matt Jones joins the chat too! On this episode Jono talks about his return to Saracen Bikes, his early World Cup years, racing as a junior, learning to manage pressure and why stepping away from results chasing has changed how he rides. Jono also shares insights on his role at MAVRIX, what he actually does, why he left banking to work with Matt, how realistic physics shape the gameplay, and why gaming skills are starting to translate into real world riding. There are also stories from A Slice Of British Pie, North vs South, early career mistakes, kite surfing in South Africa, and Matt Jones joins the episode for some classic stories and brotherly banter! We hope you enjoy the episode. Don't forget to hit like, subscribe and leave a comment! Learn about MAVRIX: https://mavrix.game Episode Sponsors:- - Mudhugger: Get 10% off with code ridecompanion10 at https://www.themudhugger.co.uk - HUEL: Support the show and get 15% OFF HUEL products with code 'RIDE' at https://huel.com/. Unlock a healthier, easier way to eat with Huel — nutritionally complete meals in minutes, so you can focus on what really matters… biking. - Manta Sleep: Get 10% off with code theridecompanion at https://tinyurl.com/theridecompanion Get early access & ad-free episodes → https://www.patreon.com/theridecompanion You can also support our long term partners: Marin Bikes → marinbikes.com/gb Focus Bikes → focus-bikes.com SRAM: sram.com/en/sram adidas FiveTen: adidas.co.uk/five_ten invisiFrame: 15% off with code REFRESHANDRIDE at invisiframe.co.uk Troy Lee Designs → 10% off with code theridecompanion at saddleback.avln.me/c/OzduCWvjtcOr Manta Sleep → 10% off with code theridecompanion tinyurl.com/theridecompanion HUEL → 15% off with code RIDE: huel.com/ Mudhugger → Get 10% off with code ridecompanion10 at themudhugger.co.uk Compex → 20% off with code THERIDECOMPANION: compex.com/uk/ Igloo: igloocoolers.com/ Feedback Sports: feedbacksports.com WORX → 15% off with code THERIDECOMPANION at uk.worx.com HKT Products → 10% off with code PODCAST at hktproducts.co.uk Follow The Ride Companion Instagram @theridecompanion YouTube @TheRideCompanion Olly Wilkins Instagram @odub_23 YouTube @owilkins23 YouTube clips and BTS channel @moreridecompanion Get official Ride Companion merch, find old episodes and more theridecompanion.co.uk

Frohes Neues Jahr! Wir blicken zurück auf 2025. Was haben wir erwartet, was ist eingetroffen und was hat uns begeistert?
Wir blicken zurück auf tolle Events, Social Rides und persönliche Highlights. Wir schauen aber auch in die Zukunft. Was erwarten wir von Sram, Shimano und den Bikeherstellern? Wo wird sich die Branche in Sachen Reifenfreiheit hinbewegen? Freut Euch auf eine extrem abwechslungsreiche Sendung mit vielen kleinen Sidekicks. André und ich hatten jedenfalls viel Spaß und viel zu lachen. ## BLACKROLL RECOVERY PILLOW ## Code: ENJOY10 Damit gibt es 10% auf alle nicht reduzierten Produkte im deutschen und schweizer Onlineshop! https://blackroll.com/ ## LINKS ZUR SENDUNG ## INGO BEI INSTAGRAM: https://www.instagram.com/quendler/ ANDRÉ BEI INSTAGRAM: https://www.instagram.com/tofukind/ ZWIFT MSR 300 Minuten Event am Samstag:
https://www.zwift.com/eu-de/events/view/5333176 Wildkaffee 2-Way-Cup https://wild-kaffee.com/products/2-waycup-tassen-refined-version ## INHALT ## 00:00:00 Winter, Kalte Finger, Glätte 00:09:34 Ingos Testfahrten mit dem TIME Rädern 00:18:56 Ingos Festive 500 & ZWIFT Races 00:28:11 EYB X MSR 300 Minutes of Happiness ZWIFT Event am Samstag 10.1. 00:37:19 Blackroll Recovery Pillow 00:45:01 Laufen & Cross Duathlon WOB 01:01:17 Gegen inneren Schweinehund: „Lieber 10 km fahren, als 50 km nicht gefahren zu sein!“ 01:03:15 Social Rides, ZWIFT Communities: alles bringt viel Freude 01:13:47 Mehr Carbs in 2025 & Cornflakes-Geheim-Tipp 01:32:32 Fahrräder bei EYB: Bike-Boutique vs. günstige Komplettbikes 01:51:19 SRAM bringt überraschend neue Hebel in die Force/Rival 01:55:49 Was bringt SRAM 2026 heraus? 02:03:18 Schwalbe Click Valve: Gamechanger (aber nur für Ingo?) 02:13:12 ZWIFT Cog: Besser Indoor Fahren! 02:16:38 Karoo 4? Edge 1060? Oder bleibt alles beim Alten? 02:25:49 Ingo fährt wieder viel Rannrad! Enve Melee 02:28:33 Strava-Jahresrückblick: Ingos zu viele Kilometer 02:34:31 Recovery-Steuerung: Ingos Wearables + wichitger Überlastungs-Tipp 02:38:42 Shimanos neue Dura Ace in 2026? 02:46:42 André hat sein eigenes Rad gebaut! 02:50:36 Unser Showroom & Werkstatt-Umbau im letzten Jahr 02:55:11 OPEN Day 2025 und Vorschau OPEN Day 2026 03:02:45 CylcingWorld Düsseldorf die bessere Eurobike? 03:05:23 Rad-Event-Tipps: MSR 300, North 2 Peak, RDCR 03:08:00 2026: die letzte Eurobike? 03:16:14 RDCR war auch ein cooles Event! 03:17:33 Ceramicspeed-Besuch, Film kommt in 1,5 Wochen 03:21:57 Unsere Ziele und Ausblick auf 2026 03:27:17 Reifenbreiten-Trends für 2026 03:35:15 Outro… Ääääääh… da war doch noch was: PICKS! 03:54:58 Outro jetzt aber! 03:56:22 PreShow: Studio-Umbau

This week Gaby is on the pod to talk about a deck swap we're doing with our local friends. Like a Secret Santa, we were each given a mission, and needed to build a deck to that spec. After finishing our decks, we couldn't help but think of what we'd do with some custom magic energy.  Join us as we each design a commander, first for "Mischievous Chaos" and then for "If you've got the money honey i've got your disease".  Join Beacon of Creation's Discord: https://discord.gg/t88Vpwh Show Notes and Images: https://beaconofcreation.com Intro music by Dee Culp

Thanks to Garmin for supporting the podcast!  00:00 Garmin ad: Jimmi's Rookie Error 01:00 Gym gains & Greggs pub 05:50 SRAM battery hack you should NEVER try 08:57 Mid-range bikes that are cool? 12:36 Campagnolo promises mid-tier groupset amidst mass lay-offs 25:40 People are angry at Specialized 31:56 Tour de France stage winner caught 5 times over drink-driving limit 32:39 4 ways we make winter fitness more enjoyable 46:56 Jimmi's £200 DIY Fluff up 49:34 Unpopular Opinion: e-shifting is for people who suck at basic maintenance 53:26 Send us your Unpopular Opinions and Questions! 53:39 FINALLY answering your important questions You can check out the video versions of the podcast, plus more videos from Cade Media here: https://www.youtube.com/@Cade_Media/videos If you'd like us to send in a question, story, some good news, things you'd like us to discuss or anything else, email us at wildonespodcast@cademedia.co.uk Thanks and see you next time. Hosted on Acast. See acast.com/privacy for more information. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Big wheels, a product recall, and another rant about poor bike design – oh yes, it's time for Geek Warning.This week Dave and Ronan return to your digital radios. On the list is a sneaky disappearance of a liked SRAM product, news from UCI regarding 32in wheels, and a recall from Trek. Some time on the tools has Dave ranting about a particularly bad example of internal cable routing, and of course, there's a PSA.Members of Escape Collective get access to the full episode, which includes our popular Ask a Wrench segment (this week with pro race mechanic Brad Copeland). Just a note that we had an audio issue, and so Ask a Wrench this week has us answering two questions rather than the usual three. Still, there's plenty of ground covered.Happy geeking!Time stamps:4:00 - SRAM quietly discontinues older AXS rim brake options7:20 - UCI leaves the door open to 32in wheels in MTB12:00 - Trek's big recall of little things17:10 - Rant time from Dave24:00 - PSA for another place to look for a creak29:00 - Ask a Wrench (Members Only)32:00 - Corrections Corner for the previous Ask a Wrench34:00 - Can a chainring wear out before a chain?41:00 - Shimano 105 shifter levers not engaging

Thanks to Garmin for supporting the podcast! 00:00 Garmin ad: Jimmi's rookie error01:00 Get back…04:53 Snow + winter socks reviews09:34 Canyon's £34m sales problem: are less people buying bikes?22:46 IPT becomes NSN30:58 Tadej's WILD VO2 max result reveal37:37 Hack for flat SRAM shifters (FUOTW)43:59 Unpopular Opinions on bar tape50:33 Unpopular Opinions on mudguards57:16 Send us your Unpopular Opinions!57:29 UK vs US ridingYou can check out the video versions of the podcast, plus more videos from Cade Media here:https://www.youtube.com/@Cade_Media/videosIf you'd like us to send in a question, story, some good news, things you'd like us to discuss or anything else, email us at wildonespodcast@cademedia.co.ukThanks and see you next time. Hosted on Acast. See acast.com/privacy for more information.

Thanks to Garmin for supporting the podcast! 00:00 Garmin ad: Jimmi's rookie error01:00 hello again 06:25 we tried TikTok bike hacks 08:43 proof cycling (& therapy) can save your life 12:59 new cycling documentary to watch 14:30 Gorewear is shutting down?! 18:54 what's Shimano up to now? 25:12 we need to talk about Merida's new gravel bike… 29:07 What is this new Cinelli? 32:41 Lezyne and Peloton recalls 35:58 dodgy editing (FUOTW) 37:36 Garmin ad refresh coming soon (but not today)39:35 Unpopular Opinion: if steel were invented today…41.50 Unpopular Opinion: bikes shouldn't come with saddles45:30 Send us your Questions, Unpopular Opinions etc…45:44 The weight loss through exercise dilemmaCheck out Chris' documentary: https://youtu.be/FLGuamHY2pcYou can check out the video versions of the podcast, plus more videos from Cade Media here:https://www.youtube.com/@Cade_Media/videosIf you'd like us to send in a question, story, some good news, things you'd like us to discuss or anything else, email us at wildonespodcast@cademedia.co.ukThanks and see you next time. Hosted on Acast. See acast.com/privacy for more information.

Madrone Cycles got its start making parts to repair a variety of SRAM mountain bike derailleurs, but now they're taking on a much more ambitious project: building their own derailleur from the ground up. Third-party derailleurs had a brief run in the earlier days of mountain biking, but SRAM and Shimano have completely dominated the market for decades now. So why is Madrone taking on the big players now, and what sets their new Jab derailleur apart? We sat down with Madrone founder, Aaron Bland, to discuss all that and more.RELATED LINKS:Blister Mountain Bike Buyer's GuideGet Our Free Newsletter & Gear GiveawaysBLISTER+ Get Yourself CoveredTOPICS & TIMES:Founding Madrone & starting with SRAM derailleur rebuilds (1:57)Electronic shifting & the resurgence of third-party mechanical derailleurs (4:02)The hardest details to get right (12:57)Evolution of the Jab design (17:25)Serviceability & Madrone's rebuild service (22:58)Clutch & pivot designs (27:43)Modularity (32:26)Jab options (41:34)What's next from Madrone? (47:37)CHECK OUT OUR OTHER PODCASTS:Blister CinematicCRAFTEDGEAR:30Blister Podcast Hosted on Acast. See acast.com/privacy for more information.

How different is European and North American gravel racing? Velo was at the Gravel World Championships in the Netherlands to inspect the bikes used by Marianne Vos, Tom Pidcock, and many others, with the relatively fast and smooth course necessitating some interesting equipment choices. Integrated air pressure adjustment systems, huge (for gravel) gearing, and wildly different tire combinations were all spotted, as well as more than a few custom paint jobs for the special occasion. Alvin, Josh, and Levy also delve into Suunto's legal action against Garmin and why it differs from the Strava debacle all while Levy attempts to convince the crew that social media is a psy-op while denying being addicted to Strava. We also dig into the latest 3T Racemax2 gravel bike, its background, Josh's early impressions from riding the bike, and ultimately, why the bike is so dang interesting. Finally, we talk about SRAM winning its suit with the UCI and the perfectly passive-aggressive response the UCI offered as a response. Further reading Custom Bikes and Unreleased Tech at UCI Gravel Worlds from Pidcock, Vermeersch and More The Sorta-Gravel Tech Pro Roadies Used at UCI Gravel Worlds Opinion: Suunto's Lawsuit Against Garmin Only Makes Strava Look Worse Third Time Lucky: Florian Vermeersch Takes Gravel World Championship with Stomping Day-Long Effort UCI Gravel World Championships: Lorena Wiebes Defeats Marianne Vos in Gripping Finale 3T Overhauls the Racemax 2 Italia for More Clearance, More Storage, and More Aero See prior episodes of the Velo Podcast here. 

Today on the podcast, the guys discuss some epic recent rides as well as Liam's unfortunate injury before delving into the latest exciting products from KETL & Trail One. We then jump into a classic set of listener questions ranging from drivetrain compatibility, to an age old debate of Shimano vs. SRAM and everything in between. Tune in! Our YouTube channel: www.youtube.com/channel/UCczlFdoHUMcFJuHUeZf9b_Q Worldwide Cyclery YouTube Channel: www.youtube.com/channel/UCxZoC1sIG-vVtLsJDSbeYyw Worldwide Cyclery Instagram: www.instagram.com/worldwidecyclery/ MTB Podcast Instagram: www.instagram.com/mtbpodcast/ Submit any and all questions to podcast@worldwidecyclery.com Join us on epic mountain bike trips that you will never forget in locations like Tasmania, Italy & Nepal. Grab $250 off any All Mountain Rides trip by just mentioning WWC: https://worldwidecyclery.com/blogs/worldwide-cyclery-blog/all-mountain-rides-all-inclusive-mountain-bike-guided-trips-w-worldwide-cyclery-crew

Emergency episode time!Caley and Ronan dive into a story that started with chainrings and cogs, and ended up in court. The BCA has ordered the UCI to suspend its new Maximum Gear Ratio Standard, siding with SRAM in a dispute that could reshape how cycling's rules are made. What began as a “safety test” for rider speed has become a battle over who really governs the sport, and whether the UCI is still above competition law.

Thanks to Garmin for supporting the podcast! 00:00 Garmin Ad: Jimmi's rookie error01:00 More on the Elves Falath EXP + Roasting11:14 Giant has a big problem14:02 Make Steel Bikes Cool Again21:16 Oakley's new AI glasses29:33 Cool one-piece cockpit mod32:38 Trek's new gravel bike is wild39:32 UCI u-turn on handlebars42:42 Emily ruined Jimmi & Nic's date night (FUOTW)46:56 Unpopular Opinion: Gravel is a Big Bike con48:41 Unpopular Opinion: 1x only exists cos SRAM is crap50:24 Overrated/Underrated: chain catchers56:24 Send us your Unpopular Opinion and questions!56:44 Am I still a cyclist?You can check out the video versions of the podcast, plus more videos from Cade Media here:https://www.youtube.com/@Cade_Media/videosIf you'd like us to send in a question, story, some good news, things you'd like us to discuss or anything else, email us at wildonespodcast@cademedia.co.ukThanks and see you next time. Hosted on Acast. See acast.com/privacy for more information.

Will lower gearing make for safer racing? The UCI thinks so, but it could also make all of SRAM's 1X drivetrains verboten at the highest level of competition without affecting Shimano or Campagnolo. SRAM disagrees, obviously, and has begun legal action in Europe, citing reputational damage and EU competition laws. And as you'd expect, the UCI responded yet again. That and more on this week's episode of Velo Podcast. Velo Tech Editor Josh Ross and host Mike Levy dig into the details of the UCI's potential 10.46-meter rollout rule and why SRAM's 10-tooth cog doesn't comply, safer courses versus lower gearing, and the UCI's near silence when it comes to so many issues. We also discuss the UCI president's social media post congratulating Tadej Pogacar on his TT World Champs victory in Rwanda, despite Remco being the actual victor, and we look at Alvin's first impressions of Factor's new Aluto gravel bike.

On this week's episode of the BikeRadar news podcast, Jack Luke is joined by Simon von Bromley to discuss the biggest tech stories in cycling this week. Leading with the news that Giant bikes are being held at the US border due to allegations of forced labour and “undercutting American businesses”, Jack and Simon discuss the biggest tech tidbits from the Sea Otter Europe trade show and explain why SRAM has taken legal action against the UCI.  Wrapping things up, Jack and Simon discuss last week's top news story – as voted by your clicks – and Jack explains why, in his opinion, loud freehubs are an abomination. Trump administration bars Giant Bicycles imports to the US, citing forced labour allegations Why is SRAM taking legal action against the UCI? This tiny brand could challenge SRAM's UDH dominance with new direct-mount derailleur One of road cycling's most iconic shoes has finally been updated Dangerholm's mind-bending gravel bike weighs only 7.19kg – but its components are even more interes… I've just found Pogačar's 2018 race bike – it's mismatched, beat up, and cooler than an… Why don't bike manufacturers adopt a universal system for aligning the stem in the correct position? Loud freehubs are a crime against good manners – and there's a better way Learn more about your ad choices. Visit podcastchoices.com/adchoices

This week on Geek Warning, Ronan Mc Laughlin and Dave Rome discuss why SRAM's battle with the UCI may also prove positive for Shimano.Ronan asks Dave what his workshop wish is, which leads to an unexpected tangent about bike washing. There's, of course, a PSA, which ends up being a conversation about how to align dropbar shifters. And the geeks summarise a bunch of the latest and biggest new tech.Lastly, Zach Edwards (Boulder Groupetto) joins the pod to answer some questions from members in the Ask a Wrench segment. As a reminder, only members of Escape Collective get access to this section of the podcast.Happy geeking!Time stamps:1:00 - A hypothetical question5:30 - SRAM taking the UCI to court, explained (plus a big rumour)10:15 - Waiting on an update to the UCI's handlebar ruling (now out of date since recording)13:00 - Ronan ponders Dave's dream workshop wish 25:00 - PSA that alignment markings on bars may be fictional plus an explainer on road shifter alignment38:00 - Rotor returns to the groupset game, sorta40:15 - Further update on SRAM's Transmission firmware update41:40 - Trek's CheckOut, a full suspension gravel bike43:15 - RockShox's matching Rudy XL45:00 - RLS helmet safety system and Canyon growing the full-service side of the business48:30 - Lezyne enters the rear Radar game50:00 - Ask a Wrench (Escape members only)52:30 - Shimano Di2 rear shifting woes58:30 - Adding shifters to SRAM AXS1:04:00 - Greasing posts and cleaning seat tubes

Thanks to Garmin for supporting the podcast! 00:00 Garmin ad: Jimmi's rookie error01:00 100th show + how many bikes does it take to pull a car?04:49 Benny's first video + bts08:43 SRAM is suing…13:52 Cube's 2026 bikes revealed19:12 Vuelta chaos23:42 Everesting World Championships28:25 Bad behaviour at Badlands35:12 100th Show Big Quiz Special!55:35 Jimmi almost ran himself over (FUOTW)57:01 In defense of ShokzIf you'd like us to send in a question, story, some good news, things you'd like us to discuss or anything else, email us at wildonespodcast@cademedia.co.ukThanks and see you next time.You can check out the video versions of the podcast, plus more videos from Cade Media here:https://www.youtube.com/@Cade_Media/videoshttps://www.cube.eu/2026If you'd like us to send in a question, story, some good news, things you'd like us to discuss or anything else, email us at wildonespodcast@cademedia.co.ukThanks and see you next time. Hosted on Acast. See acast.com/privacy for more information.

SRAM take the UCI to court, Factor threaten to pull their sponsorship and we get all the gossip from Chris' Australian National Masters Criterium Championship win over the weekend.

Today on the show: Remco made it three in a row as he passed Pogačar by, SRAM is suing the UCI and we have an update on the Israel-Premier Tech situation.

When Escape Collective first launched, Ronan Mc Laughlin announced plans for an aero leaderboard. The goal was to test race bikes in real-world conditions using the latest testing tools to determine which bikes are the fastest. That obviously didn't come to fruition, and this week, Dave and Ronan discuss why. The conversation leads to the two discussing how they approach reviews and the anxieties that surround them.The geeks also have a PSA, Dave attempts to explain pedal kickback in mountain bikes to Ronan, and there's chat of SRAM's speed update for Transmission.Members of Escape Collective (via the member podcast feed) get the full episode, which contains Ask a Wrench. This week Dave answers a handful of member-submitted questions, which this week relate to bottom brackets, servicing hydraulic disc brakes, and cassette wear.Time stamps:2:00 - Why bike reviews can't have conclusive answers26:00 - PSA related to replacing disc brake pads31:00 - DT Swiss' new DF system and pedal kickback explained39:30 - A quiet release from SRAM that speeds up Transmission shift speed50:00 - Ask a Wrench (member-only feed)50:00 - Specialized's OSBB explained56:30 - Cassette wear and how to measure for it1:04:00 - SRAM Road Hydro lever service1:11:00 - Buying a bike with a BB86 bottom bracket

This week, Ronan and Dave ponder how they would equip an entry-level road bike, and while chatting, realise that maybe the industry has backed itself into a costly corner.Of course, there's a PSA and a bunch of tech news to discuss. Meanwhile, members of Escape Collective get access to Ask a Wrench, where this week Dave and Zach Edwards answer four member-submitted technical questions.Enjoy!Time stamps:3:15 - How would we spec an entry-level road race bike22:30 - A PSA to grease your axles27:20 - Cervelo's new R530:40 - Castelli's PFAS-free poor weather jacket37:00 - SRAM's 1987 Limited Edition Silver group38:00 - What's coming up, plus Escape gets a much-wanted feature42:00 - Ask a Wrench (member's only)44:00 - Suspension service intervals and not riding50:00 - Breaking chainring bolts56:00 - Using a shorter fork on a modern XC bike1:01:30 - Why are aero wheels not a thing in fast MTB races?

Today’s episode is all about high-performance memory in switches. We dig into the differences among TCAM, SRAM, DRAM, and HBM, and all the complex tradeoffs that go into allocating memory resources to networking functions. If you've ever had to select a Switching Database Manager template or done similar operations on a switch, this is your... Read more »

Today’s episode is all about high-performance memory in switches. We dig into the differences among TCAM, SRAM, DRAM, and HBM, and all the complex tradeoffs that go into allocating memory resources to networking functions. If you've ever had to select a Switching Database Manager template or done similar operations on a switch, this is your... Read more »

Today’s episode is all about high-performance memory in switches. We dig into the differences among TCAM, SRAM, DRAM, and HBM, and all the complex tradeoffs that go into allocating memory resources to networking functions. If you've ever had to select a Switching Database Manager template or done similar operations on a switch, this is your... Read more »