Podcasts about hydroplaning

Hydroplaning through an intersection with zero control. That's the picture that opened this episode — and honestly? It's a little too relatable.LINKS:Download How to Pray God's Word for Your ChildrenFollow Everyday Prayers @MillionPrayingMoms A Prayer to Surrender Control to the True Vine by Doris Swift As moms, we carry so much. And sometimes what looks like nurturing or serving is actually just... control in disguise.Jesus offers us the truth that control was never our job. We are the branches. He is the vine. And apart from Him, we can do nothing — which means we were never meant to do it all anyway.Surrender isn't waving a white flag in defeat. It's the moment victory actually begins. Reference: John 15:5 Prayer: Dear Lord, thank you for reminding us that you are the vine and we are the branches. Help us to abide in you daily, and to remember it’s not our role to control, and apart from you, we can do nothing. May we bear much fruit, be a light that shines brightly, say yes when the yes is meant for us, and allow your joy in us to overflow and pour out over our families, as we surrender all to you. In Jesus’ name, amen. Discover more Christian podcasts at lifeaudio.com and inquire about advertising opportunities at lifeaudio.com/contact-us.

From building Applied Intuition from YC-era autonomy tooling into a $15B physical AI company, Qasar Younis and Peter Ludwig have spent the last decade living through the full arc of autonomy: from simulation and data infrastructure for robotaxi companies, to operating systems for safety-critical machines, to deploying AI onto cars, trucks, mining equipment, construction vehicles, agriculture, defense systems, and driverless L4 trucks running in Japan today. They join us to explain why “physical AI” is not just LLMs on wheels, why the real bottleneck is no longer model intelligence but deployment onto constrained hardware, and why the future of autonomy may look less like one-off demos and more like Android for every moving machine.We discuss:* Applied Intuition's mission: building physical AI for a safer, more prosperous world, powering cars, trucks, construction and mining equipment, agriculture, defense, and other moving machines* Why physical AI is different from screen-based AI: learned systems can make mistakes in chat or coding, but safety-critical machines like driverless trucks, autonomous vehicles, and robots need much higher reliability* The evolution from autonomy tooling to a broad physical AI platform: starting with simulation and data infrastructure for robotaxi companies, then expanding into 30+ products across simulation, operating systems, autonomy, and AI models* Why tooling companies came back into fashion: Qasar on why developer tooling looked unfashionable in 2016, why Applied Intuition still bet on it, and how the AI boom made workflows and tools central again* The three core buckets of Applied Intuition's technology: simulation and RL infrastructure, true operating systems for vehicles and machines, and fundamental AI models for autonomy and world understanding* Why vehicles need a real AI operating system: real-time control, sensor streaming, latency, memory management, fail-safes, reliable updates, and why “bricking a car” is much worse than bricking an iPad* Physical machines as “phones before Android and iOS”: Peter explains why today's vehicle and machine software stack is fragmented across many operating systems, and why Applied Intuition wants to consolidate the platform layer* Coding agents inside Applied Intuition: Cursor, Claude Code, internal adoption leaderboards, and how AI tools are changing engineering workflows even in embedded systems and safety-critical software* Verification and validation for physical AI: why evals get harder as models improve, how end-to-end autonomy changes simulation requirements, and why neural simulation has to be fast and cheap enough to make RL practical* From deterministic tests to statistical safety: why autonomy validation is shifting from binary pass/fail requirements toward “how many nines” of reliability and mean time between failures* Cruise, Waymo, and public trust: Qasar and Peter discuss why autonomy failures are not just technical issues, how companies interact with regulators, and why Waymo is setting a high bar for the industry* Simulation vs. reality: why no simulator perfectly represents the real world, how sim-to-real validation works, and why real-world testing will never disappear* World models for physical AI: hydroplaning, construction equipment, visual cues, cause-and-effect learning, and where world models help versus where they are not enough* Onboard vs. offboard AI: why data-center models can be huge and slow, but onboard vehicle models need millisecond-level latency, low power, small size, and distillation-like efficiency* Why physical AI is not constrained by model intelligence alone: the hard part is deploying models onto real hardware, under safety, latency, power, cost, and reliability constraints* Legacy autonomy vs. intelligent autonomy: RTK GPS in mining and agriculture, why hand-coded path-following worked for decades, and why modern systems need perception and dynamic intelligence* Planning for physical systems: how “plan mode” applies to robotaxis, mining, defense, and multi-step physical tasks where actions change the state of the world* Why robotics demos are not production: the brittle last 1%, humanoid reliability, DARPA Grand Challenge-style prize policy, and the advanced engineering gap between research and deployment* Applied Intuition's hard-earned lessons: after nearly a decade, Peter says they can look at a robotics demo and predict the next 20 problems the company will hit* Qasar's advice to founders: constrain the commercial problem, avoid copying mature-company strategies too early, and remember that compounding technology only matters if you survive long enough to see it compound* Why 2014 YC advice may not apply in 2026: capital markets, AI company dynamics, and the difference between building in stealth with a deep network versus building as a new founder today* What Applied is hiring for: operating systems, autonomy, dev tooling, model performance, evals, safety-critical systems, hardware/software boundaries, and engineers with deep curiosity about how things workApplied Intuition:* YouTube: https://www.youtube.com/@AppliedIntuitionInc* X: https://x.com/AppliedInt* LinkedIn: https://www.linkedin.com/company/applied-intuition-incQasar Younis:* X: https://x.com/qasar* LinkedIn: https://www.linkedin.com/in/qasar/Peter Ludwig:* LinkedIn: https://www.linkedin.com/in/peterwludwig/Timestamps00:00:00 Introduction: Applied Intuition, Physical AI, and 10 Years of Building00:01:37 Physical AI vs. Screen AI: Why Safety-Critical Changes Everything00:02:51 The Origin Story: Tooling, YC, and the Scale AI Comparison00:05:41 The Three Buckets: Simulation, Operating Systems, and Autonomy Models00:11:10 Hardware, Sensors, and the LiDAR Question00:14:26 The Operating System Layer: Why Vehicles Are Like Pre-Android Phones00:19:13 Customers, Licensing, and the Better-Together Stack00:21:19 AI Coding Adoption: Cursor, Claude Code, and the Bimodal Engineer00:26:41 Verifiable Rewards, Evals, and Neural Simulation00:31:04 Statistical Validation, Regulators, and the Cruise Lesson00:40:25 World Models, Hydroplaning, and Cause-Effect Learning00:43:34 Onboard vs. Offboard: Latency, Embedded ML, and Distillation00:50:57 Plan Mode for Physical Systems and Next-Token Prediction Universally00:53:04 Productionization: The 20 Problems Every Robotics Demo Will Hit00:58:00 Founder Advice: Constraints, Compounding Tech, and Mature-Company Mimicry01:05:41 Hiring Philosophy: Hardware/Software Boundary and Engineering Mindset01:08:50 General Motors Institute, Education, and the Curiosity MindsetTranscriptIntroduction: Applied Intuition, Physical AI, and 10 Years of BuildingAlessio [00:00:00]: 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.Swyx [00:00:10]: And today we're very honored to have the founders of Applied Intuition, Qasar and Peter. Welcome.Qasar [00:00:17]: You guys really know how to turn it on to podcast mode. That was, you guys are real pros at this.Qasar [00:00:23]: They were just joking around right before this, and then they flipped it pretty quick.Alessio [00:00:29]: Oh, yeah, it's good to have you guys. Maybe you just wanna introduce yourself so people know the voice on the mic and they'll know what they're hearing.Peter [00:00:33]: Oh, sure. Yeah, I'm Peter Ludwig. I'm the co-founder and CTO of Applied Intuition.Qasar [00:00:38]: And my name is Qasar Younis. I am the CEO and co-founder with Peter.Alessio [00:00:42]: Nice. Can you guys give the high-level overview of what Applied Intuition is? And I was reading through some of the Congress files, when you went out there, Peter, and eighteen of the top twenty global non-Chinese automakers, you two guys, you have customers in agriculture, defense, construction. I think most people have heard of Applied Intuition tied to YC when it was first started, and then you were kinda in stealth for a long time, so maybe just give people the high-level overview of what it is today, and then we'll dive into the different pieces.Peter [00:01:10]: Yeah. So at Applied Intuition, our mission is to build physical AI for a safer, more prosperous world. And so we work on physical AI for all different types of moving systems, everything from cars to trucks to construction and mining equipment, to defense technologies. And we're a true technology company, so we build and sell the technology, and we sell it to the companies that make the machines. We sell it to the government, really anyone that wants to buy a technology to make machines smart.Physical AI vs. Screen AI: Why Safety-Critical Changes EverythingQasar [00:01:38]: Yeah. And I think in the broader AI landscape, a lot of the focus, rightfully so in the last, three years has been on large language models, and so everything fits in a screen. Like, whether it's code complete products or things like that. And what's different about us is we're deploying intelligence onto a lot of things that don't have screens. they're physical machines. There are sometimes screens within the cabin or for example of a car or a truck or something like that, but most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learn systems can make mistakes if you're asking for, like, some, so something like, “Tell me about these podcast hostsQasar [00:02:28]: that I'm about to go meet.” But you can't do that obviously when you run, like, as an example, we run driverless trucks in Japan right now, as we speak. We can't have errors. Those are L4 trucks. Yeah.Alessio [00:02:40]: Yeah. Was that always the mission? I remember initially, I think people put you and Scale AI very similarly for some things about being kinda like on the data infrastructure side of things. What was the evolution of the company?The Origin Story: Tooling, YC, and the Scale AI ComparisonPeter [00:02:51]: Well, from the very beginning, we always wanted to, really be a technology company that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robotaxi companies. And we started off doing a lot of work in simulation and data infrastructure. And then over the years, we've expanded our portfolios. Now we have, over thirty products, and it's a pretty broad technology play within the landscape of physical AI.Qasar [00:03:19]: Yeah, I think the Scale reason is because we're all YC Universe companies. But it was a very different company. Scale, was, is more of a services company, data labeling company fundamentally. We started and still are, do a lot of tooling. So like, you think developer tooling is now in vogue again, thanks to the AI boom. But honestly, ten years ago, it was out of vogue. It w Like, doing a tooling company in 2016, 2017 was not, like, the thing to do because, I don't know if you remember, the VCs generally, their views was that toolings are They're just workflows, and workflows ultimately are not really interesting. And we've gone and come, full circle with that. But when we started the company, our kind of it's kinda like in the periphery of what the company wants to be. It was like, from our earliest days, like, we wanna deploy software on physical machines, like on cars and on trucks and things like that. And obviously, we didn't know that the transformer boom was gonna happen. We didn't know that autonomy systems would become end-to-end. Those things we didn't know. And why that's important when autonomy systems become end-to-end, it is just now those models can be generalized to, multiple form factors. And so back nine, ten years ago, tooling was a great way, and still is a great way to, build the technology and sell technology to our end customers, a lot of them who wanna build this stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part of a development suite of tools all the way to buying the full thing. The way to think about the company, or at least the way we think about the company is, as Peter said, a technology provider. It's kinda like, what NVIDIA does or what an AMD, but we just don't do chips.Qasar [00:05:06]: We don't do silicon. But we're a technology provider fundamentally. And I think even, we used to joke when we started the company, like, we're not the guys to build, like, Instagram. Like that was just towards That's not our That's just not us in a most fundamental way. IAlessio [00:05:20]: You have thoughts.Qasar [00:05:21]: Yes.Qasar [00:05:22]: Well, it's, it's I mean, I think it's just like what And I mean, we worked on Maps and stuff, Google Maps. Consumer products are extremely difficult for a lot of different reasons. It just, I think doesn't scratch the itch. I think we're like Michigan guys who are kind of more of that traditional engineering kind of a realm, or lineage. we used to jokeThe Three Buckets: Simulation, Operating Systems, and Autonomy ModelsPeter [00:05:41]: I gotta say, though, what was clear ten years ago was that there was so much more that was possible with software and AI in vehiclesPeter [00:05:47]: and that was generally the space that we started in ten years ago.Peter [00:05:51]: And the precise path that we've taken over the years, I think we've been strategic, and we've adjusted to make sure that we're actually building stuff that's valuable to the market. And like, the technology has changed so much. Like our own technology stack has completely changed, I would say, roughly every two years. And so now we've probably done, let's say, four complete evolutions of our own technology stack. And I sort of see that cadence roughly keeping up.Peter [00:06:13]: And so the way even we think about engineering is almost on this two-year horizon, we're preparing ourselves that, hey, like, we wanna invest the appropriate amount, but then also be very dynamic as the research gets published and as our research team figures out new advancements and adapting to that.Qasar [00:06:27]: Yeah. One thing that has been consistent is the type of people we've, we've recruited. It's engineers who are fall into the sometimes very traditional, like, GoogleQasar [00:06:38]: -gen suite, but way different from, other companies. We are hiring folks who really know the intersection of hardware and software, who know really low-level systems. Obviously, traditional ML researchers and folks who've, actually, put ML systems into production. That's been pretty consistent. I think that, like, you look at the mix of our engineering, eighty-three percent of the company is engineering, so it's, like, a giant list.Qasar [00:07:05]: A lot of engineers.Alessio [00:07:06]: Which, by the way, a thousand engineersQasar [00:07:07]: Yeah. A thousand engineers.Alessio [00:07:08]: that's on your website, so I imagine it's up to date.Qasar [00:07:11]: It is, it is up to date, yes. Yes.Alessio [00:07:12]: okay. And then forty-plus founders.Qasar [00:07:15]: Yeah. We would tend to also, This was more luck than strategy. But we've recruited a lot of ex-founders. It's been a great place for founders, YC and non, ‘cause obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people.Qasar [00:07:33]: For them to exercise both their technical and non-technical skills because, we're, we're, we're on the applied side. We have a research team that we do fundamental research, we publish, and we've, we've had great traction there. But fundamentally, the business wants to take this intelligence and deploy it into production and there's, like, a certain type of person that's more interested in that.Alessio [00:07:54]: Yeah. You mentioned the tech stack, Peter, so I just wanted to give you some rein to just go into it. I'm interested in where Wayve Nutrition, starts and ends in some sense, what won't you do? What, do you do that's common among all the verticals that you cover?Peter [00:08:10]: There's a few buckets of work that we do, and we've been at this for almost ten years now, so the technology's pretty broad. But we got startedQasar [00:08:17]: Yeah, with a thousand engineers, like, you could work on lots of things.Peter [00:08:19]: There's lots of stuff, yeah, espe-especially with AI tools to help.Peter [00:08:22]: So we got our start in simulation and simulation tooling and infrastructure. And so generally, if you're trying to build a very complex software system that involves moving machines, you need to test that, and the best way to test it is it's a combination of virtual developments, a simulation, and then also obviously real world testing.Peter [00:08:39]: And then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation's a very deep topic.Peter [00:08:49]: We have a whole suite of products in that, and we could talk for many hours about that specifically. But that is one part of what we do as a company. Reinforcement learning as a subpart of that is also super critical. I think a lot of the a lot of the best advancements happening in a lot of these AI systems right now in some way relate to reinforcement learning, and with now we have lots of compute, and you can do tons of interesting things for reinforcement learning. The second bucket of work that we do is on operating systems technology. true operating systems. Like, think about, schedulers and memory management and middleware and message passing and highly reliable networking and data links. Like, the reality is, if you want to deploy AI onto vehicles, you need a really good operating system. And when we were getting deeper into that space, there wasn't really anything that we were happy with.Peter [00:09:39]: Like, things existed, absolutely, and we were using what was available in the market, and as an engineering organization, we roughly realized these things aren't great. We think we can do this better, and so let's, let's build something. And that was then the that was the moment of inspiration that started our operating systems business, which is now a very real business for us. And in order to write and run great AI, you need a great operating system, and so that-that's what got us into that. And then the third bucket that we work on, it's, it's true fundamental AI technology. Models, we do a lot of work in, as mentioned, the foundational research, but then the also the world models and the actual autonomy models that are running on these physical machines, and that's across cars, trucks, mining, construction, agriculture, and defense, and so that's both land, air, and sea.Qasar [00:10:31]: And also, a smaller subsector of that third bucket is the interaction of humans with those machines.Qasar [00:10:38]: So that's a multimodal, experience. Historically, if you're moving a dirt mover or any of these machines, there are, like, buttons you press, whether they're actual physical tactile buttons or something like a touch screen. That's just That fundamentally is changing to where you're just talking to the machine and the machine and you're teaming with the machine.Alessio [00:10:58]: Voice?Qasar [00:10:59]: Yeah, voice, absolutely, yeah.Alessio [00:11:00]: Oh.Qasar [00:11:00]: And also the machine just being aware of who is in the cabin, what their state is. you can think from a safety systems perspective, the most simple version of this is, like, the driver is tired, right? They're, they're if you get those alerts when you're driving your car and saysHardware, Sensors, and the LiDAR QuestionQasar [00:11:15]: -maybe take a coffee break, that take that times, a couple of order of magnitudes up. But this concept of teaming man and machine is important. When you think about running agents or just running, different instances of, Claude and doing work for you in the background, you can take that analogy out, almost copy and paste and put it into, like, a farm, where you have a farmer who's running a number of machines. So where they interact with the machine is where there's maybe a critical decision or a disengagement or something like that, but generally speaking, the agent on the physical machine is running and making decisions on the behalf of the farmer until there's something maybe critical. And that's also what we work on. So that's not pure autonomy. It's a little bit of a mix, but it falls under, autonomy. In the automotive sense, that's typically defined in SAE levels as an L2++ systemQasar [00:12:05]: -with a human in the loop. But just take that idea, to other verticals.Alessio [00:12:09]: Yeah. You've not mentioned hardware at all, like sensors or obviously we you mentioned you don't do chips. I think even in AV there's, like, a big, cameras versus lidars. Like, what are, like, in your space maybe some of those design decisions that you made, and are they driven by the OEM's ability to put things on the machinery? And like, how much influence do you guys have on co-designing those?Peter [00:12:32]: Yeah. So we don't make sensors. Like, we're, we're not a manufacturer. Obviously, we use a lot of sensors in our autonomy products. in terms of what actually goes on the vehicles, we have a preferred set of sensors that we, let's say fully support, and then our customers, they can sort of choose from those. And obviously if there's a very strong opinion on supporting something else, we'll add that to the platform as well. And the lidar question is at this point sort of the age-old,Peter [00:12:59]: topic in autonomy, and the state of the industry right now is lidar is hands down a useful sensor, specifically for data collection and the R&D phase of autonomy development. if you see, for example, a Tesla R&D vehicle, it actually has lidar on itPeter [00:13:17]: to this day, right? In the Bay Area we see these. you'll see, like, Model Ys or Cybercab that have lidars on them just driving around. So it's, it's useful because it gives you per pixel depth information. So if you can pair a lidar with a camerand you can say that, well, this camera's looking this direction, this lidar's looking this direction, and now for each pixel of the camera I can see how far away is that pixel. you can actually then use that as a part of your model training, and then the that depth information then becomes a learned, a learned state of the camera data. And then when you're doing the production system, you can now remove the lidarPeter [00:13:52]: and now you can actually get depth with just the camera. And so that difference between, like, a highly sensored R&D vehicle and then the down-costed production vehicle, we use that across our whole portfolio of products. And of course the end goal is you want super low cost and super reliable.Peter [00:14:08]: And then in certain use cases you have some more, bespoke things. Like in defense as an example, you do things at night oftentimes, and so you care about sensors like infrared, more so than And you don't, you don't wanna be putting energy out, so you don't wanna use lidar or radar.Peter [00:14:23]: but you still need to be able to see at nighttime. So yeah, we work the whole gamut.The Operating System Layer: Why Vehicles Are Like Pre-Android PhonesAlessio [00:14:27]: Cool. So that's kinda like on the hardware level. Then on the OS level, how does that look like? What is, like, unique? my drive- I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks.Alessio [00:14:38]: It's on, like, cheap Android tablet. It's like, it's laggy and all of that. What does the OS of, like, the autonomy future look like?Peter [00:14:46]: When most people, it's really what you just described. When you think about operating system in a vehicle, you're thinking about the HMI, right? The human machine interface, and absolutely that's a an important part of it, but that's actually only one thin layer on top. So when we talk about operating systems for, like, AI in vehicles, there's many layers that go deep into the CPU critical realm and embedded systems, and you're talking about the real time control ofPeter [00:15:13]: let's say the electric motors or the engine and the actuators, and you have different redundancies for different, let's say, the steering actuation in the vehicle. And all of these things, need very core support in the in the operating system. And then of course for autonomy you have real time sensor data that's streaming in, and the latencies there are really important, right? If you try to Imagine you try to run Microsoft WindowsPeter [00:15:35]: like streaming your sensor data in or controlling the vehicle. Like, the latencies are gonna be absurd. Like, you can never do that. And so what's special about what we do is we really have this system level thinking, right? So we're looking at, we care about every performance characteristics of the entire system, and then we also, because we're doing a lot of the software or all of that software, we can fine-tune and control all of those things. So we can very carefully tune in the latencies for every aspect of the system. We can carefully tune in the memory management. We can have the right, fail-safes and fallbacks, for different things. ‘Cause you have to account for what if, what if there is a critical failure? What if there's a cosmic ray that flipsPeter [00:16:14]: a bit in the middle of the processor that causes some, malfunction? And you have to have a fail-safe to all of that, and so the core operating system is a part of that. And then the one last thing, which is a lot less exciting but is, actually a very big topic, is reliability of updates.Peter [00:16:30]: so the I have a Tesla and you get updates fairly frequently, right?Peter [00:16:36]: Once a month. Most companies that are making vehiclesPeter [00:16:40]: are basically never doing updates, and they're And even if they are doing updates, they're usually only updating maybe one module. Maybe they're updating the HMI module. But they're not able to update, let's say, the CPU critical parts of the system.Peter [00:16:51]: You have to go into the dealer for that. And so with our operating system now we can actually enable highly reliable updates of any system in the vehicle, and that's way easier said than done. Like, there's lots of technical, technically deep stuff, in the tech stack to do that in a way that you're not going to accidentally brick a vehicle.Peter [00:17:08]: And right? If, imagine yourAlessio [00:17:10]: That would be bad.Alessio [00:17:11]: Bad.Peter [00:17:11]: Bricking a car is a very expensivePeter [00:17:13]: and honestly, like across the industry maybe one of the most just pure impactful things that we've done is we've just, we're, we're now enabling the industry to actually do software updates.Alessio [00:17:22]: Just to clarify as well, who is the customer for this? Like, I assume a lot of hardware manufacturers have their own firmware, and I'm sure some of them would just have you write it for them because you're experts. And others would have their own. Like, who pays for this? Who invites you into the house? Is it, is it the end user, or is it, is it the manufacturer?Peter [00:17:41]: Yeah. So let me make an analogy firstly on the on the fragmentation of software. So physical machines today are more akin to the state of the phone market before Android and iOS existed, right? So I worked on Android at Google by the way many years ago, and part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones, and they bought all of these phones from the industry, and it turned out they had like 50 different operating systems on these phones. And it was virtually impossiblePeter [00:18:17]: for Google to make their app run on all 50 devices equally well. And so the solution was, well, actually what if, what if they created-A really great operating system and made it attractive to all of these phone makers, and that was sort of the genesis for what Android was and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of the physical, industry right now, it's a little bit like that. Like, there's yes, these companies have firmware, but they have so many different operating systems, it's so fragmented, and to actually get a modern AI application to run on these vehicles, you actually, you first have to consolidate the operating system, and so that's, that's why we've done that. And then, your specific question was who are our customers? It's, it's, generally it's the companies that are making these machines.Peter [00:19:06]: And we're, we're, we're selling our technology to them to really simplify the architecture and then enable these AI applications to run on them.Customers, Licensing, and the Better-Together StackSwyx [00:19:13]: How much is reusable across? Like, do you have, like, one OS that is just configured for everything, or is there some more customization that is needed?Peter [00:19:22]: Yeah, highly reusable. So the fundamental technology is quite universal, right? So things that we do have to think about though are, like, chipset support. And so if you're, if you're coding, let's say, an LLM and you have start with an assumption that, “Hey, oh, I'm gonna, I'm gonna use CUDA, and I'm gonna run this, on an NVIDIA chip,” then you don't really have to think about the hardware in that sense. Like, you're just, “Okay, I'm just I'm in the CUDA/NVIDIA ecosystem, and I'm, I'm going to use that.” But the hardware, especially in safety critical systems, it's a lot more diverse. There's not one or one or two players. There's a bunch of different chipsets that we have to support. And so our operating system doesn't just run on, like, the equivalent of X86. It has to, it has to run on a number of different architectures from chips from a bunch of different companies. But again, we've been working on this for a long time now, so we have, we have support for all of those chipsets. And then when you want to then run the AI applications, we can then do that reliably across now a variety of providers.Qasar [00:20:19]: And I think that is, like, heavily inspired by Android, right? Android has a huge suite of testing and it's a reliable operating system that runs on thousands of devices. And we think we can, we can do the same in all these physical moving machines, with the difference that we're really in a safety critical realm. Android isn't.Alessio [00:20:40]: So on Android, I don't need to use Gmail, I can use Superhuman. Like, what about your machinery? Like, can people bring somebody else's automation to it, or is it kinda like all-in-one?Qasar [00:20:50]: You have to use us. No. Yeah. we're If, Yeah. Yeah, it's totally open. Yeah.Peter [00:20:56]: Yeah. our philosophy is that we are a technology company, and so we license our technology to customers to use how they want. And so if a customer wants to If they wanna license our autonomy tech and our operating system, then great, we'll license those. If they just wanna license the operating system and then use different autonomy tech, that's fine also, and we have great documentation andSwyx [00:21:17]: Or if they wanna use developer tooling.Peter [00:21:18]: Yeah, exactly.AI Coding Adoption: Cursor, Claude Code, and the Bimodal EngineerSwyx [00:21:19]: It's, like, a better together if, obviously, if you, if they work together. Is it all C++ I assume is with different compile targets?Peter [00:21:27]: We use a lot of C++.Peter [00:21:28]: Rust is sort of a hot, the new hot kid on the blockPeter [00:21:32]: for a bunch of things as well. But yeah, the lower level you get, especially when you get to real-time constraints, you hit C++ at some point, and at some point maybe you work your way into assembly when needed.Swyx [00:21:44]: Oh, damn.Alessio [00:21:46]: I'm curious about the coding agent adoption, just, like, since you're mentioning more esoteric languages. Like, what's the adoption internally? What have you learned?Peter [00:21:55]: Yeah. We use everything. So Cursor was, I think the hottest tool in the company for a good while. Now Claude Code, I think has taken the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoptionPeter [00:22:09]: with-within the company. And yeah, it's, they're phenomenally useful. it's, Honestly, we take inspiration from some of those tools also in how we're adapting some of that mindset of thinking to the physical realm. Like if it's so easy to build an app for this or that thing that lives just on a screen, we can We're taking now a lot of the same ideas and applying that to, “Okay, well, if you wanted a physical machine to do something, how easy can we make that, using our own tooling and platform as well?”Alessio [00:22:40]: Are you changing any of, like, the OS architecture, kinda like the way you expose services to, like, be more AI friendly or?Peter [00:22:48]: Yeah, absolutely. The in the early days of our tools infrastructure work, it was a lot about, You had engineers that were experts in certain topics, but the things that you're dealing with, they're oftentimes more mathematical or more abstract, where actually GUI tools are very useful for certain things. Like as an example, we have a product we call Sensor Studio, which is, it helps you design the sensor suite for your autonomous vehicle, whether, again, it could be a car, it could be a drone, could be a mining equipment, could be a robot. And you place sensors in different places. You There's different, There's a library. You can understand what are the trade-offs that you're making in the design of that system, and that was, like, a very, a very GUI intensive, thing ‘cause it's a little more like a CAD tool in that senseSwyx [00:23:37]: YepPeter [00:23:37]: if you've seen CAD tools. Nowadays, though, right, we expose all of the underlying APIs for that and now using, AI agents, you can actually configure a sensor suite with just text and likely reach a better result than you could've through the GUI in the past, and we're taking that thinking now through the whole product portfolio.Swyx [00:23:57]: Another thing I was thinking about is just in terms of, like, AI, adoption, does it change your hiring at least a little bit, or how do you, how do you sort of manage engineers, differently?Peter [00:24:08]: Yeah. absolutely, it does. we, I think like every company in the Valley right now, are evolving our hiring practicesPeter [00:24:16]: because the skills required to be effective are changing so fast, right? you used to really select for just rote implementation ability and now it is more the AI engineer skill set, right? Where it's like, yeah, how to implement, but actually-Just banging out code is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the interviews that we give now I think are way harder than they've ever been.Peter [00:24:46]: But we also allow, right, selective use of AI tools to solve the problems. And I think in that you start to see more of a bimodal distribution of engineers, right? You start to see like wow, there's, there's this subset of people that they really get it. Like they're, they're all in and they've, they've clearly invested the hours needed to learn these tools and how to be effective.Peter [00:25:09]: And then there's sort of the group of people that haven't done that, and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really into this.Qasar [00:25:20]: I first wrote the my AI engineer piece three years ago, and when I first wrote about it, I was like, “Actually, not everyone should be an AI engineer,” ‘cause I think there's a there's an extremist stance where well, every software is an engineer is an AI engineer. And my actual example of people who should not be adopting AI was embedded systems and operating systems, and database people. Are they adopting AI?Peter [00:25:41]: I think it's the classic bitter lesson, topic, which is the Six months ago I would've said the same thing, but it's, it's becoming super useful for every domain.Qasar [00:25:53]: I'm sure.Peter [00:25:54]: Right? Like,Peter [00:25:56]: there was, I think six months ago, or maybe a year ago, if you tried to use, let's say the latest Claude model for writing shaders, GPU shaders, the results were probably underwhelming. And if you use the latest model now to do that kind of task, you're a little bit blown away, like, “Wow, that actually worked. That's amazing.” And we see the same thing in the embedded realm. No question though, especially when you get into safety critical systems, the human validation isPeter [00:26:25]: is 100% key. Like I You're not gonna trust your life to a an AI written software that's, that's not been very carefully, checked by humans. And so I think now the really the challenge is about that appropriate level of human validation for these safety critical systems.Verifiable Rewards, Evals, and Neural SimulationAlessio [00:26:41]: How do you think about, yeah, touching on the simulation side, I think verifiable reward and reinforcement learning is, like, the hottest thing. What have you done internally to build around that? And like, what gives you What makes you sleep at night? Like, if somebody's like, just web coding something or likeAlessio [00:26:57]: wants to try something new, you have like a good enough system. Because I think the opposite is also true, is like if it's super easy to write anythingAlessio [00:27:04]: then it puts a lot of work on like the verifiableAlessio [00:27:07]: side of it. Like, what does that look like for people?Peter [00:27:10]: Yeah. So verifiability, a broader bucket of like evaluations, right? Like how do you evaluate the results that you're, you're getting? I think this is probably the hardest problem right now, because the As the models get better, it can be harder and harder to find the faults on the system.Peter [00:27:29]: And so like the problem of doing proper eval to find those faults, like that problem also keeps getting harder as the models get better. But it's no less important than it's ever been, right? You still there are still going to be edge cases that are not met and whatnot. And so it's, it's a big area of investment for us. On the reinforcement learning topic, the key thing is there's all these new requirements that come to be in the latest generation of these technologies. So for example, end-to-end is the big thing right now in autonomy and physical AI, which is you can now train these models that can effectively take sensor data in and then put control signals out, and get really good results out of that. But the way that you train and improve those models is really different from the previous generations. And so to do reinforcement learning on an end-to-end model, you now need to actually simulate all the sensor data, right? So then this becomes a we call our, work in this neural simulation, but it'sPeter [00:28:26]: think of it like a hybrid of Gaussian, splatting and diffusion methods, and where you really care about performance. Like performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile, in the end. It also gets to a lot of our work in embedded systems, which is like performance critical work, and that performance optimization, performance criticality, it carries over to a lot of the model training work. because, like, the only way to make it affordable is it has to be really fast.Qasar [00:28:58]: I think it's worth a few minutes talking about our own, evolving thoughts on verification and validation withinQasar [00:29:05]: kind of, traditional simulators, which are, you can think of like vehicle dynamics or something like that, which you're just taking textbooks and taking those formulasQasar [00:29:13]: and putting them into software, to like now this neural sim/world model universe. I think that's an interesting topic.Peter [00:29:20]: Yeah. So in more traditional development, right, you oftentimes would have, more black-and-white answers to questions.Peter [00:29:28]: And so the in Europe as an example, there's, a regulatory, system, it's called Euro NCAP. It's the European New Car Assessment Program, and as part of that, the vehicles have to pass a bunch of tests, and those tests actually, include, safety systems. So automatic emergency braking for a child that runs in front of a carPeter [00:29:51]: or let's say an occluded child that runs out and you hit it. And so you have You end up with sort of these binary answers of like, well, did the car under test pass this specific test? And there's a very well-known set of test casesPeter [00:30:05]: that the vehicle has to pass. And that was how the industry worked, let's say, until 10-ish years ago. But what's changed now is with these models, everything is statistics, right? Like you no longer have a black-and-white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can I get in the system, and how can I, how can I prove that to be true? And the big unlock honestly for physical AI as an industry is that these models are just becoming much more reliable. Right? Things like things actually work a lot better. It's like the number of nines you can get out of these systems are now good enough that it actually becomes cost effective to really deploy these things. And so the big shift in, so verification and validation has been from a little bit more of a Again the past it was strictly requirements, and are you meeting or not? And now it's more of a statistical, verification and validation case where it's all about how many nines of reliability and meantime between failures, that sort of thing.Statistical Validation, Regulators, and the Cruise LessonSwyx [00:31:04]: And is the target audience regulators or even the customers are yeah, if you I imagine the customers are bought in, and it's mostly regulators that need to be satisfied.Peter [00:31:15]: We do work with the US government, we do work of course with the European governments and the government of Japan, and the government is not like an AI lab by any means.Peter [00:31:25]: So Swyx [00:31:26]: They just care about the outcome.Peter [00:31:27]: They care about the outcome.Peter [00:31:28]: And so we do education, in that regard, and like so sort of teaching about, “Hey, this is how we think validation should be done, and this is an approach that we think is reasonable,” and how to think about like when is a driverless system actually safe enough to go on the roads and that sort of thing. But I wouldn't say that the government is asking for it. It's like we're more teaching the government in that, in that sense. It's honestly, it's more so for our own, our own comfort, right? Like, we want to build very safe systems, and then of course our customers care deeply about that as well. But in that context we're also typically educating our customers.Qasar [00:32:01]: Yeah. Our first, our first core value is on round safety. So I think we can't underline enough that, us also verifying and validating that the systems that we're deploying are safe to us is probably as important as, like, some regulator or a customer saying,Swyx [00:32:19]: Of course. Okay. Yeah.Swyx [00:32:20]: You have to satisfy yourselves.Peter [00:32:22]: As I say, as a whole across the world, regulation oftentimes it's like a almost lowest common denominator. But like, you really have to substantially exceed what the regulators are expecting to make good products.Swyx [00:32:33]: Yeah. One thing I often talk about, I think and I try to make this relatable to the audience also, is Cruise, where they had an accident that basically ended the company. I wonder if people overreact to single incidents, because incidents are going to happen regardless, right? ‘Cause it's a statistical thing, but as long I don't know if regulators understand that, you cannot extrapolate from a single incident, but we do because that's all we have to go on. And your sample sizes are necessarily gonna be lower than, I don't knowSwyx [00:33:00]: consumer driving.Qasar [00:33:01]: Yeah. I think the Cruise example wasn't a technology failure. there was The real, compounding issue there was just how did the company talk to the regulators and what was their kind of behavior, and I think that became more of the issue. If you look,Peter [00:33:19]: It isn't It definitely was a technology failure, but it was made much worse by theSwyx [00:33:23]: Put the car back on the woman.Qasar [00:33:25]: Yeah. And let me put it another way. There is a version where Cruise still exists.Swyx [00:33:29]: right. Right.Qasar [00:33:30]: Right. It'sSwyx [00:33:30]: It was like the last strawQasar [00:33:31]: ItSwyx [00:33:31]: in like a long chain ofSwyx [00:33:33]: like issues.Qasar [00:33:33]: So do you feel like ATG had that horrific accident or someone actually dying, because, that was a homeless person crossing the street? So yeah, I think we can't understate enough that ultimately, like, statistical validation of something, that's one part of it, but it's not the only part of it. Like, consumer and let's say, mainstream adoption of these technologies is also gonna be part of that conversation. I think companies like Waymo are doing a lot of service positively to the industry in the sense of they're, they're setting a high benchmark and they're showing, kind of in a very responsible way how to, how to deal with these. There have been Waymo incidences as well. They've just not been as significant as the Cruise one that you mentioned. But yeah, so I think you'll just continue to see that. I think probably the long term question is really gonna be, again, around Like it is very clear humans are way worse drivers statistically.Qasar [00:34:29]: Like, there's no, there's no debate. And so at what point But we're emotional animals.Swyx [00:34:34]: Yeah. So my thing is, like, we have to get to a point as a society where we accept horrific accidents that would never happen by a human because statistically we understand that it is safer overall. In the same way that planes, they're safer, than I think they're the safest mode of transport that we have.Qasar [00:34:50]: Yeah. it's more dangerous to drive to the airport than it is to get on a flight.Qasar [00:34:53]: So if you're everQasar [00:34:54]: if you're ever getting nervous about getting on a plane, just think “I just gotta get to the airport.”Swyx [00:34:58]: Yes, we're flying.Qasar [00:34:59]: If I get to the airportQasar [00:35:00]: I'll be good.Swyx [00:35:00]: But then it's, planes also concentrate the tail risk if planesQasar [00:35:03]: Yeah. AndPeter [00:35:04]: And I was, I don't think we honestly have to worry about there ever being, accidents from these systems that are like much worse than what humans would cause, ‘cause humans do terrible things.Peter [00:35:14]: Like, people fall asleep at the wheel all the time.Swyx [00:35:16]: I have.Swyx [00:35:17]: Like, I'll call, I've been a drowsy driver.Peter [00:35:19]: Kinda drunk drivers, and that'sPeter [00:35:20]: that's the extreme end of the example. But these AI systems, you have redundancies, you have fallbacks. Like, there's many things have to go wrong for there to actually be a something catastrophic because there's, there's so many, fallbacks that these systems have.Alessio [00:35:36]: your simulation is like so vast because there's so many use cases. What are, like, maybe things that worked in a simulation and then you put it out and it's like, “F**k, this isAlessio [00:35:45]: this just did not work at all?”Peter [00:35:47]: Yes.Alessio [00:35:47]: IsPeter [00:35:47]: That's maybe a bit of a misconception, about simulation there. So let me go a little bit, more technical on this. So at first go, no simulation is going to represent the real world. There's always a process of this, sim to real matchingPeter [00:36:02]: where you actually, you need the real world feedback to basically feed into the parameters that are being used in the simulator, and you have to do that, it's like this validation flow, a number of times until you can get some confidence that, like I think the simulator is now accurately representingPeter [00:36:19]: what's gonna happen in the real world. Now, if you have a situation where you've done that full validation and you thought that it was accurate and then there's something different, those are much trickier cases, and that's, that absolutely can happen, but really I think the validation process is a really important part. You can never skip the simulation validation process, like where you're actually ensuring that, hey, the actual, my sim to real gap here is small enough that I can trust these simulation results. And there's, there's so many fun things that you can do when you get into it. Like, I'll, I'll give one fun example that came up recently is like in these humanoid robotics, systemsOverheating actuators is a real problem, right? So obviously phenomenal demos. IPeter [00:37:01]: The most amazingAlessio [00:37:02]: For 10 minutes.Peter [00:37:03]: The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? If, like, And one of the ways you can use simulation though is you can actually have that, the temperature of those actuators be one of the parameters that's representedPeter [00:37:18]: in the simulation. And if you're doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that, oh, it knows that as it's moving, it's actually beginning to overheat this motor. But if you didn't have that parameter of, let's say, the heat of that motor represented in the simulation initially, then your RL policy might It will disregard that. And now you run that on the robot and the robot will overheat and fail.Alessio [00:37:43]: I guess the question is, like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like, temperature is like a great example, right? WellAlessio [00:37:53]: why did you make my robot worse when it runs in like a freezer?Alessio [00:37:57]: So it actually shouldn't worry about that. it's like, yeah, how do you design these simulations?Peter [00:38:02]: This is honestly the This is what makes simulation so hard, right? it's because you Simulation is fundamentally about you're trying to optimize the development of a system, right? Like, how can I build this system faster and better and cheaper and what are all the levers that I have to actually accomplish that? And because simulation's just a software program, you can, you can change it a lot more easily than you can hardware systems. And then what's particularly awesome about the let's say, world models and using that as a part of simulation is now the simulation doesn't just scale with, let's say, adding new math equations inPeter [00:38:36]: but we can actually scale the simulation environment now with additional real world data and that also unlocks a whole new field of robotics.Qasar [00:38:46]: There is a meniscus line where you cross where still doing real world testing is better. there's, in this, sim-to-real gap, you can reproduce reality at exceedingly expensive costs and this So nothing is free. So really you have to you're finding that line where you're getting great performance, you're getting great feedback, whether it's on the training side or on the eval side, but it's way cheaper than doing it in the real world. At some point it, that doesn't make sense. And so even, from our earliest days in autonomy, our view was you're still gonna do real world testing. You There's, there's not, there's not this, magical land where you're not gonna do that. And maybe even like a more nuanced version of this in like traditional software development is, most of your testing for software in a vehicle, 95% of that can be like traditional CI/CD kind of, flows that you would have in traditional web development. But once you have Now you, let's say you have a truck. Well, you can do like 4% of those in like a rig which has all the components, the electrical and electronics of a truck, but doesn't have, it doesn't have the tires and it doesn't have the And then you have the 1%, which is actually the vehicle. There's something There's a similar analogy in terms of using simulation for intelligent systems. You can do a lot in a simulator, but in using world models, but ultimately it's, it's physical AI. So you're gonna deploy it on physical machines andQasar [00:40:17]: the freezer example comes to, comes to light.Alessio [00:40:20]: The world model thing has been to me the hardest thing toAlessio [00:40:22]: wrap my head around. Like we have Faith Eliyon on the podcast.World Models, Hydroplaning, and Cause-Effect LearningQasar [00:40:25]: We've been doing a small series with like another Intuition company, General Intuition as well.Qasar [00:40:31]: yeah, and I mean, lots of, lots of coverage on NeRFs and yes.Alessio [00:40:34]: Yeah. It feels like we talk with about, the heliocentric system, right? It's like in a world model, if you just feed visual data, the model might learn that the sun spins around the Earth. It makes sense, right? And it's like, well, not really. And I think what are like some of these other things that like hydroplaning is one thing I think about, is like can a world model understand hydroplaning and like what amount of water like causes it to happen? And it's like, yeah, to me it's like I don't understand how you guys do it. I guess it's like the real thing is like when you're doing both cars and the highway in Japan versus the excavator in a mine in,Qasar [00:41:13]: ArizonaAlessio [00:41:13]: wherever you're Arizona, wherever you're deploying them.Alessio [00:41:15]: How much of it are you relying on the world models to like generate the simulations for you and then try and close the gap after versus like giving the world models as a tool to your engineers to like curate the simulations if that makes sense?Peter [00:41:28]: Yeah, totally. So yeah, I can say at a pure engineering level, I think if you're hoping to do real world deploys and you're purely relying on a world model approach, you probably won't get to something that works, before you go bankrupt. So there is just a very practical mindset of like, world models are amazing and they're extremely useful for a lot of use cases, but there are a lot of other things that you need to do to actually get something started and something deployed and working. most fundamentally, world models are all about It's understanding the world, but also understanding what's going to happen. It's like the cause-effect relationship.Peter [00:42:01]: Right? And so like it, right, if you have a take some sort of construction tool, and that construction tool is gonna be doing some work on the Earth in some way, it's gonna be moving earth, the world model needs to understand that cause-effect relationship. Like, okay, when I, when I take this material from here and put it over there and now I have things that are over here and not over there anymore and that cause-effect, relationship. data obviously is a is a big problem. The hydroplaningPeter [00:42:26]: one is actually a really great example because it's actually quite non-obvious sometimes. Right? It's like, well, it's, it's raining and well this road, has, let's say the appropriate curvature to it so the water is running off the road and cars are driving faster here and then you approach a road that's very flat and water is now puddling on that road and all of a sudden cars are driving slower because when they were driving faster they were starting to lose control. And there are a lot of visual nuance, very nuanced visual cues in the scene and so I do think in the world model concept there's a good chance that the model actually would learn that you should just drive slower when these visual cues exist, and that's obviously the beautiful-The beauty of, these kinds of models where they just, they learn these non-obvious things.Swyx [00:43:14]: It doesn't need to know about hydroplaning to know that it needs to drive slower.Peter [00:43:17]: Yes.Swyx [00:43:17]: I guess it's Yeah. I wanna ask questions about, also deploying models. I presume, like, you use a lot of these world models for training data and simulation, but what about deploying it onto the systems in production? Presumably you have you have, like, GPUs on deviceOnboard vs. Offboard: Latency, Embedded ML, and DistillationSwyx [00:43:36]: but they're I keep saying on device. What's the what's the right term for that?Peter [00:43:40]: On machine.Swyx [00:43:41]: On machine.Peter [00:43:41]: Or embedded, yeah.Swyx [00:43:42]: Yeah. What is the embedded world like? because for people who are not used to that world, this is very alien.Peter [00:43:49]: Yeah. So it's actually We call it onboard and off board.Peter [00:43:52]: So like, onboard software and off board software.Peter [00:43:54]: And the great thing about off board software is you don't have to care about time, and you can run really large models, right? So you can, you can say, “Well, this model, I don't care if it takes one second for it to give me a result or 10 seconds for it to give me a result, because we have time.” And the models can be really big, and they can run, in a data center or on a on a huge GPU and you can obviously have distribute to compute, et cetera. But onboard you don't have any of those benefits. You're like, “Well, I need I have this many milliseconds where I need an answer from this model.” And so a lot more of the energy then is about, think of it more like distillation and it's like truly efficiency and like, literally every fraction of a millisecond counts. And you can't have a situation where the model takes too long because then the vehicle can't actually function.Peter [00:44:42]: And so you can, you can still use a lot of the same techniques, and the models themselves you can think of as like a derivative of larger models that you can run offline, and then you're, you're trying to just get a model that is still performs really well but it's, it's a it's smaller, small enough version that you can then run on this embedded system where you care about latency and power.Qasar [00:45:03]: Yeah. And I think like, the broader point I think which, maybe is not obvious but it's worth saying is in physical AI world, we're not really constrained right now by, like, the intelligence of the models. It's actually what Peter's talking about, it's actually deploying them inSwyx [00:45:19]: The hardware they give you.Qasar [00:45:21]: Yeah. On the hardware you give you.Qasar [00:45:22]: And so And there's just a reality is of safety critical systems. So those end up being the your limiting factorsQasar [00:45:29]: rather than, let's say, a limiting factor for, a foundation model companyQasar [00:45:34]: is gonna be just capital maybe or researchers.Qasar [00:45:38]: So we're, we're in that way dealing with, for us as people who kind of come in that realm with like a very interesting Those constraints force creativity.Swyx [00:45:47]: And I imagine, nobody was deploying or giving you the hardware for transformers back in 2018, whatever, but now they are. What's the evolution like? just peel back the curtains a little bit.Peter [00:45:59]: Yeah. Transformers first off, I think the paper was originally published in 2017.Swyx [00:46:02]: 2017.Swyx [00:46:02]: So there's no time.Peter [00:46:04]: And ISwyx [00:46:05]: But I'm just saying I guess I'm saying, like, embedded ML systems usually, like, a lot less parameters, a lot less compute, and now, like, orders of magnitude more.Peter [00:46:14]: Yeah. absolutely. what I was gonna say though was I think in the in the original paper in 2017, maybe it's in the last paragraph, somewhere in the paper they talk about, like, “Oh, by the way, this technique might be useful for, like, images and videos as well.”Peter [00:46:30]: These last subjects.Peter [00:46:31]: And it took a few years for that impact to really hit. But like, now, we're seeing transformers are everywhere.Swyx [00:46:39]: Yeah. Vision transformers.Peter [00:46:40]: And then then the compute just keeps getting better and better. But you do have this fundamental trade-off, right? It's like you have power, you have cost, and performance and like, getting the right, getting the right mix of those things in an embedded package that can also be, like, shaken and baked in all thePeter [00:47:00]: conditions that these things have to have to operate in. But yeah, I think that they're only going to keep getting better and so we also try to plan our strategy understanding that, we know the rate of improvements of these systems.Swyx [00:47:11]: Yeah. So like, Google just released the Gemma 2B modelSwyx [00:47:15]: that effective 2B model. Is that useful to you guys or is that too big?Peter [00:47:18]: You can run that model on an embedded system, definitely.Peter [00:47:21]: the So yes, it's, it's useful in that regard. The bigger question is, like, what do you use it for in an embedded system? Like, you actually need to customize it quite a bit to make it useful for something. But yeah, you could run a two billion parameter model, definitely.Swyx [00:47:35]: It also interesting, like, what percent is a custom ML model that only does that thing versus a generalist LLMSwyx [00:47:41]: which probably is not that useful actually for your context.Peter [00:47:46]: Like, you, like, you can imagine different use cases, right?Peter [00:47:48]: So theSwyx [00:47:49]: The voice stuff, yes.Peter [00:47:49]: Yeah, the voice test. Totally, yes.Peter [00:47:51]: So for the actual, autonomy elements, that's 100% in-house. We do every bit of that, the data simulation, the model, everything. But when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these more generalist models like Gemma actually can be quite, can be quite useful.Swyx [00:48:09]: Yeah. And then there's also obviously a trade-off between, like, what percent must you do on machine, versus just call home.Peter [00:48:16]: Yeah. It's all about latency.Swyx [00:48:17]: Latency.Peter [00:48:17]: It's all about latency. Yeah.Swyx [00:48:18]: Yeah. Well, like, I think actually in a lot of contexts, especially in the US, you can just have a connection to the web.Qasar [00:48:26]: Yeah. I think though most of our universe is everything has to be fairly, embedded and local because just the nature of Even in the US there's a lot of likeSwyx [00:48:39]: PatchinessQasar [00:48:40]: don't haveQasar [00:48:41]: have coverage, right? And if you look at, like, the old world of autonomy within mining, which is, like, long before transformers and kind of, neural networks, in the like CNN and kind of a universe, they were really just hand-coded, systems. They were just like, this machine is gonna run to that place with thisPeter [00:49:03]: That was our GPS, like very accurate GPS.Qasar [00:49:05]: Yeah. And so that worked, and that worked for 20 years, so why would we actually need to use transformers or kind of more modern end-to-end systems? Mainly because you can only really run a path and run backwards. That provided a lot of value, but m-Not as much as you get when the machine is actually intelligent. It's, it's seeing, it's perceiving, it's acting in a dynamic world.Alessio [00:49:28]: I looked up RTK, real-time kinematic, one to two-centimeter accuracy.Qasar [00:49:32]: Yeah. Fantastic. But the and fantastic in faraway lands where there's not gonna be cell phone coverage.Peter [00:49:39]: Yeah, so it's widely used on the legacy mining and agricultural autonomy systems today. So like, for example, a combine that can be precise within one or two centimeters as it's driving down the field, they use RTK.Qasar [00:49:53]: Yes.Peter [00:49:53]: But it's, it's expensive.Qasar [00:49:54]: Yeah. And it's, it's, it's autonomy, but it's not intelligent in the way that I think all of usQasar [00:49:58]: if in twenty-six we'd be talking about intelligence.Alessio [00:50:00]: In one of your blog posts, you mentioned research on large scale transformers that are similar to those doing modern generative AI. What are, like, the big differences other than, “You're absolutely right. I should steer the car, so you probably wanna remove that?”Peter [00:50:14]: We have a diversified bet strategy internally, and the reason we've done that is because we operate in now a bunch of industries, a bunch of geographies, and each of the approaches has, obviously a different risk to them.Peter [00:50:27]: And so like, we're not going to put all of our eggs in a single basket for a single approach because that approach may no

Consumer Reports tests dozens of tire models every year across all types of weather and road conditions. Our expert evaluations cover key performance factors like grip, handling, braking, comfort, and tread life. In this episode, we explain everything you need to know before buying new tires—when to replace them, how to choose the best tires, and whether electric vehicles need special tires. You'll learn how climate and driving style affect tire performance, and get insider insight into how our engineers test tires for safety and durability. We'll also answer your top tire questions, from whether small rocks in the tread really matter to how all-wheel drive impacts winter traction.   Join CR at https://CR.org/joinviaYT to access our comprehensive ratings for items you use every day. CR is a mission-driven, independent, nonprofit organization.     SHOW NOTES ----------------------------------- 00:00 - Introduction 00:36 - When to Buy Tires 02:29 - How to Inspect Tires 04:10 - All-Weather vs. All-Season Tires 08:47 - EV Tires 12:19 - Best Tire Shopping Advice 17:36 - How CR Works With Tire Manufacturers 18:31 - How We Test and Rate Tires 23:15 - 2026 Tire Top Picks 24:31 - What Are We Testing Next? 26:54 - Question #1: Do small rocks lodged in tire treads affect performance? 29:52 - Question #2: How do AWD systems affect tire performance?     ----------------------------------   CR Tire Ratings https://www.consumerreports.org/cars/tires/?EXTKEY=YSOCIAL_YT   Choose the Right Tires for Your Vehicle https://www.consumerreports.org/cars/tires/buying-guide/?EXTKEY=YSOCIAL_YT   Top Pick Tires: The Best Car, SUV, and Truck Tires https://www.consumerreports.org/cars/tires/top-pick-tires-best-car-suv-truck-tires-a5778937779/?EXTKEY=YSOCIAL_YT   Best All-Weather Tires https://www.consumerreports.org/cars/tires/best-all-weather-tires-a8621335540/?EXTKEY=YSOCIAL_YT   Best Tire Brands of 2026 https://www.consumerreports.org/cars/tires/best-tire-brands-a2990346660/?EXTKEY=YSOCIAL_YT   Best Car Tires of 2026 https://www.consumerreports.org/cars/tires/best-car-tires-of-the-year-a1101679070/?EXTKEY=YSOCIAL_YT   Best SUV and Truck Tires if 2026 https://www.consumerreports.org/cars/tires/best-suv-and-truck-tires-a3436694441/?EXTKEY=YSOCIAL_YT   Best All-Season Tires https://www.consumerreports.org/cars/tires/best-all-season-tires-a9794563815/?EXTKEY=YSOCIAL_YT   Best Winter/Snow Tires https://www.consumerreports.org/cars/tires/best-winter-snow-tires-a1191260310/?EXTKEY=YSOCIAL_YT   Most and Least Satisfying Tire Retailers https://www.consumerreports.org/cars/tire-stores/most-and-least-satisfying-tire-retailers-a8618669903/?EXTKEY=YSOCIAL_YT   How to Save Money When Buying Tires https://www.consumerreports.org/cars/tire-buying-maintenance/how-to-save-money-when-buying-replacement-tires-a6799675738/?EXTKEY=YSOCIAL_YT   How to Read a Tire Sidewall https://www.consumerreports.org/cars/tires/buying-guide/#how-to-read-a-tire-sidewall-and-what-can-it-tell-me/?EXTKEY=YSOCIAL_YT   When to Replace Your Tires https://www.consumerreports.org/cars/tires/when-to-replace-your-tires-a3107469842/?EXTKEY=YSOCIAL_YT   Do Electric Vehicles Need Special Tires? https://www.consumerreports.org/cars/tires/do-electric-vehicles-need-special-tires-a4689725362/?EXTKEY=YSOCIAL_YT

Consumer Reports tests dozens of tire models every year across all types of weather and road conditions. Our expert evaluations cover key performance factors like grip, handling, braking, comfort, and tread life. In this episode, we explain everything you need to know before buying new tires—when to replace them, how to choose the best tires, and whether electric vehicles need special tires. You'll learn how climate and driving style affect tire performance, and get insider insight into how our engineers test tires for safety and durability. We'll also answer your top tire questions, from whether small rocks in the tread really matter to how all-wheel drive impacts winter traction.   Join CR at https://CR.org/joinviaYT to access our comprehensive ratings for items you use every day. CR is a mission-driven, independent, nonprofit organization.     SHOW NOTES ----------------------------------- 00:00 - Introduction 00:36 - When to Buy Tires 02:29 - How to Inspect Tires 04:10 - All-Weather vs. All-Season Tires 08:47 - EV Tires 12:19 - Best Tire Shopping Advice 17:36 - How CR Works With Tire Manufacturers 18:31 - How We Test and Rate Tires 23:15 - 2026 Tire Top Picks 24:31 - What Are We Testing Next? 26:54 - Question #1: Do small rocks lodged in tire treads affect performance? 29:52 - Question #2: How do AWD systems affect tire performance?     ----------------------------------   CR Tire Ratings https://www.consumerreports.org/cars/tires/?EXTKEY=YSOCIAL_YT   Choose the Right Tires for Your Vehicle https://www.consumerreports.org/cars/tires/buying-guide/?EXTKEY=YSOCIAL_YT   Top Pick Tires: The Best Car, SUV, and Truck Tires https://www.consumerreports.org/cars/tires/top-pick-tires-best-car-suv-truck-tires-a5778937779/?EXTKEY=YSOCIAL_YT   Best All-Weather Tires https://www.consumerreports.org/cars/tires/best-all-weather-tires-a8621335540/?EXTKEY=YSOCIAL_YT   Best Tire Brands of 2026 https://www.consumerreports.org/cars/tires/best-tire-brands-a2990346660/?EXTKEY=YSOCIAL_YT   Best Car Tires of 2026 https://www.consumerreports.org/cars/tires/best-car-tires-of-the-year-a1101679070/?EXTKEY=YSOCIAL_YT   Best SUV and Truck Tires if 2026 https://www.consumerreports.org/cars/tires/best-suv-and-truck-tires-a3436694441/?EXTKEY=YSOCIAL_YT   Best All-Season Tires https://www.consumerreports.org/cars/tires/best-all-season-tires-a9794563815/?EXTKEY=YSOCIAL_YT   Best Winter/Snow Tires https://www.consumerreports.org/cars/tires/best-winter-snow-tires-a1191260310/?EXTKEY=YSOCIAL_YT   Most and Least Satisfying Tire Retailers https://www.consumerreports.org/cars/tire-stores/most-and-least-satisfying-tire-retailers-a8618669903/?EXTKEY=YSOCIAL_YT   How to Save Money When Buying Tires https://www.consumerreports.org/cars/tire-buying-maintenance/how-to-save-money-when-buying-replacement-tires-a6799675738/?EXTKEY=YSOCIAL_YT   How to Read a Tire Sidewall https://www.consumerreports.org/cars/tires/buying-guide/#how-to-read-a-tire-sidewall-and-what-can-it-tell-me/?EXTKEY=YSOCIAL_YT   When to Replace Your Tires https://www.consumerreports.org/cars/tires/when-to-replace-your-tires-a3107469842/?EXTKEY=YSOCIAL_YT   Do Electric Vehicles Need Special Tires? https://www.consumerreports.org/cars/tires/do-electric-vehicles-need-special-tires-a4689725362/?EXTKEY=YSOCIAL_YT

Consumer Reports tests dozens of tire models every year across all types of weather and road conditions. Our expert evaluations cover key performance factors like grip, handling, braking, comfort, and tread life. In this episode, we explain everything you need to know before buying new tires—when to replace them, how to choose the best tires, and whether electric vehicles need special tires. You'll learn how climate and driving style affect tire performance, and get insider insight into how our engineers test tires for safety and durability. We'll also answer your top tire questions, from whether small rocks in the tread really matter to how all-wheel drive impacts winter traction.   Join CR at https://CR.org/joinviaYT to access our comprehensive ratings for items you use every day. CR is a mission-driven, independent, nonprofit organization.     SHOW NOTES ----------------------------------- 00:00 - Introduction 00:36 - When to Buy Tires 02:29 - How to Inspect Tires 04:10 - All-Weather vs. All-Season Tires 08:47 - EV Tires 12:19 - Best Tire Shopping Advice 17:36 - How CR Works With Tire Manufacturers 18:31 - How We Test and Rate Tires 23:15 - 2026 Tire Top Picks 24:31 - What Are We Testing Next? 26:54 - Question #1: Do small rocks lodged in tire treads affect performance? 29:52 - Question #2: How do AWD systems affect tire performance?     ----------------------------------   CR Tire Ratings https://www.consumerreports.org/cars/tires/?EXTKEY=YSOCIAL_YT   Choose the Right Tires for Your Vehicle https://www.consumerreports.org/cars/tires/buying-guide/?EXTKEY=YSOCIAL_YT   Top Pick Tires: The Best Car, SUV, and Truck Tires https://www.consumerreports.org/cars/tires/top-pick-tires-best-car-suv-truck-tires-a5778937779/?EXTKEY=YSOCIAL_YT   Best All-Weather Tires https://www.consumerreports.org/cars/tires/best-all-weather-tires-a8621335540/?EXTKEY=YSOCIAL_YT   Best Tire Brands of 2026 https://www.consumerreports.org/cars/tires/best-tire-brands-a2990346660/?EXTKEY=YSOCIAL_YT   Best Car Tires of 2026 https://www.consumerreports.org/cars/tires/best-car-tires-of-the-year-a1101679070/?EXTKEY=YSOCIAL_YT   Best SUV and Truck Tires if 2026 https://www.consumerreports.org/cars/tires/best-suv-and-truck-tires-a3436694441/?EXTKEY=YSOCIAL_YT   Best All-Season Tires https://www.consumerreports.org/cars/tires/best-all-season-tires-a9794563815/?EXTKEY=YSOCIAL_YT   Best Winter/Snow Tires https://www.consumerreports.org/cars/tires/best-winter-snow-tires-a1191260310/?EXTKEY=YSOCIAL_YT   Most and Least Satisfying Tire Retailers https://www.consumerreports.org/cars/tire-stores/most-and-least-satisfying-tire-retailers-a8618669903/?EXTKEY=YSOCIAL_YT   How to Save Money When Buying Tires https://www.consumerreports.org/cars/tire-buying-maintenance/how-to-save-money-when-buying-replacement-tires-a6799675738/?EXTKEY=YSOCIAL_YT   How to Read a Tire Sidewall https://www.consumerreports.org/cars/tires/buying-guide/#how-to-read-a-tire-sidewall-and-what-can-it-tell-me/?EXTKEY=YSOCIAL_YT   When to Replace Your Tires https://www.consumerreports.org/cars/tires/when-to-replace-your-tires-a3107469842/?EXTKEY=YSOCIAL_YT   Do Electric Vehicles Need Special Tires? https://www.consumerreports.org/cars/tires/do-electric-vehicles-need-special-tires-a4689725362/?EXTKEY=YSOCIAL_YT

In this clip which originally aired 3/14/24, Rusty Wallace shares a classic NASCAR story about testing at Talladega without restrictor plates and going 242 mph in the mid nineties. This video was edited down for content with improved audio quality!#nascar #racing #kennywallace #rustywallace #talladega Brought to you by JEGS! Click here: http://jegs.ork2.net/rQ9Oy5***thumbnail photos by Getty Images courtesy of NASCAR MediaJEGS has been in business since 1960.Racers selling to racers.Focusing on American Muscle – but also big product line of automotive tools, garage gear & other performance parts.JEGS is well established with racers of all kinds, including the NHRA, bracket racing, circle track & more!Free shipping on orders over $199.Unrivaled expertise from techs.Millions of parts for every car person's needs.Sign up for their email for exclusive deals!

Gear up, Jeep lovers! Hi, I'm Tony, and welcome to the Jeep Talk Show where we celebrate our 15th year of bringing you the best Jeep content out there! We're a crew of passionate Jeepers - not assassins, although that would be cool - dedicated to keeping the Jeep spirit alive. Whether you see us on camera or behind the scenes, we're all about organizing epic off-road events and delivering content that's both entertaining and educational. Join our community where the real magic happens. Here, you'll find your tribe, share the love for Jeeps, and hit the trails together. This Episode's Highlights: Jeep News Spotlight: Explore the latest on new Jeep models and delve into the history with insights on the first Jeep Jamboree from 1953, where the true adventure began on the Rubicon Trail. Fabricating Frenzy: Led by Larry, we dive into the world of Jeep diagnostics. Learn about the importance of code readers, from basic OBD2 scanners to advanced tools like Top Don, V-gate, and Gear Wrench. Larry shares his personal ordeal with a Steer Smarts install, giving you practical tips on maintaining your Jeep, especially when it comes to those pesky codes that even your radio can throw at you! Jeep Life Lessons: We discuss Jeep safety, particularly the universal issue of hydroplaning, offering tips on how to deal with it. Hear firsthand experiences about how tire choice and vehicle setup can impact your driving in wet conditions. Market Insights: We analyze Jeep's market trends, discussing the shift towards higher-priced models and what it means for the average Jeep enthusiast. Could we see a return to more affordable, rugged Jeeps? We speculate on future models and engine choices, including the much-loved 4.0 and the newer Hurricane engines. Tools Talk: Whether you're a seasoned mechanic or just starting, we explore the essential tools every Jeep owner should have. From budget-friendly options to those "buy once, cry once" investments, we cover it all to help you maintain your Jeep like a pro. Don't miss our entertaining anecdotes, like the saga of Henry the chicken, and insights into Jeep culture that only long-time enthusiasts like us can provide. Subscribe to our channel for more Jeep adventures, and hit the like button if you're as excited as we are! For more content, visit JeepTalkShow.com/YT. Let's keep the Jeep love rolling! Keywords: Jeep, Jeep Community, Jeep Maintenance, Jeep Performance, Off-Roading, Jeep Jamboree, Jeep Models, Diagnostic Tools, Hydroplaning, Jeep History. The Jeep Talk Show has been in publication for 15 years! We have a large group of team members and hosts. We publish five episodes a week. One episode, Chic Chat, is a women only hosted episode for women that feel more comfortable watching women talk about Jeeps and off road. We hope you give us a try and if you like the show please subscribe! Our website is https://jeeptalkshow.com. We do both video and audio only so you can watch or listen which ever is more conveinant depending on where you are and what you are doing. Driving to and from work, mowing the grass, or working out at the gym. Let the Jeep Talk Show 1000+ episodes make your day better and more entertaining! Join the Jeep Talk Show family! (chat server) https://jeeptalkshow.com/discord Patreon! https://www.patreon.com/jeeptalkshow (subscribe for commercial free episodes!) Round Table recording Tuesday's 7:30pm CT (Zoom meeting) https://jeeptalkshow.com/roundtable pass jeep Visit our website! https://jeeptalkshow.com Sign up for our newsletter! https://jeeptalkshow.com/newsletter Instagram @jeeptalkshow https://instagram.com/jeeptalkshow

Airplanes are mysterious things: unimaginably heavy metal giants soaring through the sky, jam-packed not only with people but also all kinds of electronic gizmos. But look at the airplane's tires compared to the plane's body – they're tiny! How can they withstand all that weight and speed? Let's check out what's happening when a plane is preparing to land on the runway. All 300 tons of your airplane touch the ground while still going 170 mph. Imagine your typical 2-story house, fully furnished, dropping to the ground as fast as a flying arrow - that must be quite the impact! And still, those tiny little tires don't pop under the tremendous pressure? Other videos you might like: Why No One Should Swap Seats on a Plane    • Why No One Should Swap Seats on a Plane   Pilots Reveal 16 Nuances That Make Your Flight Safe    • Pilots Reveal 16 Nuances That Make Yo...   What Happens When a Bird Flies Into a Plane Engine    • What Happens When a Bird Flies Into a...   TIMESTAMPS: Why pilots point the aircraft nose toward the sky before landing 1:17 What's inside airplane tires? 2:21 And what makes them so strong? 3:54 What happens when one of the tires fails 4:20 Why plane tires are so tiny and skinny 5:34 Hydroplaning 7:20 #planes #aviation #brightside SUMMARY: Pilots must point the aircraft nose toward the sky just before landing so that they don't stall the machine. To make an airplane tire, producers use a mix of different kinds of synthetic rubber. It makes aircraft tires so strong that each of them can easily deal with a 38-ton load. The gas that fills aircraft tires is nitrogen. It doesn't react with rubber, which makes it a much safer choice for airplanes. The pressure in plane tires is 6 times bigger than the pressure in your car tires. It's 200 psi, and that's what you'd feel if you took a dive 450 ft underwater. There can be plenty of wheels on large airplanes, like an Airbus A380 or Boeing 747. And if one of the tires fails, the plane will still land safely. When a plane touches the ground, its tires aren't rolling at first - they're just skidding. In other words, the plane is dragging them along the runway until it slows down enough for the wheels to be able to rotate. Larger tires would be pointless since they wouldn't make landing more effective or safer. After plane tires are produced, they always get tested. Manufacturers create computer simulations that check how tires will behave if they're pushed past their speed limit or get overloaded. And still, airplane tires aren't entirely safe from bursting. Accidents did happen in the past, and planes did skid off runways because of one of the tires exploding mid-landing. Hydroplaning is a situation that occurs when the runway is covered with a layer of water preventing a landing plane from braking and stopping in time. The aircraft just keeps skidding forward, unable to find traction. That's why some airports have grooves on the runway. Water flows into these grooves, making the surface not so slippery and dropping the chances of hydroplaning. Music by Epidemic Sound https://www.epidemicsound.com/ Subscribe to Bright Side : https://goo.gl/rQTJZz ---------------------------------------------------------------------------------------- Our Social Media: Facebook:   / brightside   Instagram:   / brightgram   5-Minute Crafts Youtube: https://www.goo.gl/8JVmuC Stock materials (photos, footages and other): https://www.depositphotos.com https://www.shutterstock.com https://www.eastnews.ru ---------------------------------------------------------------------------------------- For more videos and articles visit: http://www.brightside.me/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Today on LIVE! Daily News, we have all the details on how you can help Kolter Grey, who was involved in a crash with a semi-truck. Another crash sends one to the hospital from a dangerous intersection, a meet-and-greet with Mike "Burrito" Hernandez, and how to sign up for Mutton Bustin' at the 2024 San Angelo Rodeo. Also, Donna Brosh with the Girl Scouts of Central Texas visit the LIVE! Studio and talks about Girl Scout Cookies with LIVE!'s Matt Cutrer.

Hydroplaning our way into your hearts + Another Edition of "What would you Do?"

James here and I am back with another Powerworks Podcast short, and this time, my co-host Glenn Power and I are diving into the fascinating and dangerous world of hydroplaning!

James here and I am back with another Powerworks Podcast short, and this time, my co-host Glenn Power and I are diving into the fascinating and dangerous world of hydroplaning!

Dave's Hydroplaning Days Are Over

It's Tuesday and bug surprise, we're still alive! Eddie and Kevin recap the end of times during the holidays and Eddie gives us some new characters. Enjoy! This weeks episode is sponsored by True Classic T-Shirts Go to https://bluechew.com/ and use promo code: "Pep" for your 1st month Free! Check out the video release today on Patreon and catch up on all our videos a week later at: https://www.youtube.com/channel/UCY6rV2LpSsOUczZ7IyNoGlA For additional content support Eddie on Patreon: www.patreon.com/eddiepepitone  Write us a review on iTunes https://tinyurl.com/mv57us2d Send emails to: EddiePepPodcast@gmail.com Follow Eddie Twitter: @EddiePepitone Instagram: @EddiePep Follow Kevin @KevinTienken Go to www.eddiepepitone.com for show dates and all things Eddie Thank you to Allen Mezquida for our beautiful artwork

In this episode, special guest Grumm makes an appearance. They talk rutabegas, they talk apples, they talk math and most importantly they don't talk about the thing that they brought Grumm on for in the first place in true unexpected fashion.   www.boomdachik.com  

Hydroplaning sideways down the highway into pot holes.  Also, what's some of those pronouns we got, I found a friend,  Howard Stern ain't fixing the pot holes, ridiculous Kyrie Irving fined $50,000, birds aren't real, happy Johnny Depp has a chubby face and much more! Sponsored by: Hello Fresh - Go to HelloFresh dot com slash opie16 and use code opie16 for up to 16 free meals AND 3 free gifts! Blue Chew - Try BlueChew FREE BlueChew dot com promo code OPIE at checkout--just pay $5 shipping. Join the Private Facebook Group - click "subscribe" on my www.facebook.com/opieradiofans Instagram and Tik Tok - OpieRadio Merch - www.opieradio.com   See omnystudio.com/listener for privacy information.

Water on the road can be just as dangerous as ice. We talk with our vehicle maintenance expert about what to do if your car starts to hydroplane. We also talk about going through high water, when to get new tires, and how to find the best windshield wipers.

Today every state in the Nation has miles of grooved pavement to enhance the safety of our roadways.

To many people, the Medical marijuana is just an easy way hippies convinced the government to sell a legal high - and it can be hard to tell what is what from the outside perspective.  Stigma makes many people feel that the medical info is an excuse to wake and bake...So is it possible to decipher intent from action??  Much like a car shop, is there a basic way to identify if someone is using cannabis is for necessary repairs or superfluous upgrades?Join the Professor and guest LA - Cannabis Professional and Connoisseur - as we discuss the intimate differences in the outcomes concerning cannabis use.   

Hydroplaning In the aftermath of Henri here in New England, I thought I'd lead off with the topic of  Hydroplaning and the role tires play. Hydroplaning happens when one or more tires are lifted from the road by a wedge of water that gets trapped in front of and under a tire as the vehicle drives through the water. Hydroplaning most frequently occurs during heavy rainstorms when water creates puddles on the highway. In addition to the accompanying splash and scaring the heck out of the driver, hydroplaning typically causes the steering wheel to jerk. In addition to hydroplaning, drivers need to be extra careful during heavy rains and tropical storms, be wary of potential deep standing water and other road hazards.  Mercedes' launches Pothole Speed Bump detection in select models Pothole damage costs U.S. drivers $3 billion per year, according to a study from the AAA.  Some of the more common damage is a flat tire, bent or damaged rims, suspension damage, steering damage, and even damage to the car's body. Potholes can even knock your car out of alignment.  Clearly, potholes are more than just a pain in the neck — they are a real safety hazard for drivers. According to Pothole.info, out of approximately 33,000 traffic fatalities each year, one-third involve poor road conditions. Now Mercedes-Benz is doing something to help alleviate both the dangers and the cost of damage caused by potholes.  Vehicle Crashes Remain Leading Employee Death Cause According to the Bureau of Labor Statistics, more than 38% of workplace fatalities are from vehicle accidents, and total motor-vehicle injury costs were estimated at $463.0 billion. Costs include wage and productivity losses, medical expenses, administrative expenses, motor-vehicle property damage, and employer costs. Even though traffic volume decreased significantly in 2020, our roadways have been deadlier. Last year 42,060 people died in crash-related incidents—the highest in 13 years. These highway fatalities represent a 24 percent spike compared to 2019, which was the highest fatality rate in 96 years since 1924.  The fact is that the time an executive spends in their vehicle is without a doubt the highest risk period of their day. From a safety standpoint, this is borne out by the latest statistics on fatal vehicle crashes from the National Highway Traffic Safety Administration (NHTSA).  It is important express that a Security Driver is also trained to supply safe driving. At times the Principal may not understand the secure driving is also safe driving. A good security driver operating the vehicle proactively can prevent accidents. The safety and security of the vehicle occupants during this most dangerous period of time has been and is the responsibility of the security driver. Are You Zoning out Behind the Wheel? A great article on the Axiom website about the Safety Systems in Vehicles: as a Security Driver or supplier of Secure Transportation, you are aware of how many driving tasks are now automated — speed control, braking, lane-keeping, and even changing lanes. It seems never-ending. Carmakers keep adding more automated features in the name of safety. But now, the government wants to find out if assisted-driving technology itself is dangerous by making it too easy for people to misuse. The more sophisticated the assisted-driving system, the more complacent drivers can become, abdicating their own responsibility for operating the car. This can lead to avoidable crashes and dangerous incidents that undermine public confidence in automated driving. Even with the latest technology, drivers still need to watch where they're going and be prepared to take the wheel; fully autonomous vehicles are years from widespread deployment.

Welcome to the Instant Trivia podcast episode 148, where we ask the best trivia on the Internet. Welcome to the Instant Trivia podcast episode 148, where we ask the best trivia on the Internet. Round 1. Category: To Build A Flower 1: A fib plus French for "lake". a lilac. 2: Any automobile plus any country. a carnation. 3: Word preceding "and proper" plus "Charmed" actress McGowan. a primrose. 4: An English fop plus the king of beasts. a dandelion. 5: A numeric pair plus a part of the mouth. a tulip. Round 2. Category: Book Sequels? 1: Arthur Dimmesdale returns (difficult, as he died in the first book) in this 19th c. author's "The Chartreuse Number". Hawthorne. 2: Guy Montag is back, and those books are gonna pay in this author's "Celsius 232.78". Ray Bradbury. 3: Natty Bumppo discovers there's "The Penultimate Iroquois" in this man's 1820s sequel. James Fenimore Cooper. 4: Edmond Dantes gets a promotion in this author's 19th c. sequel "The Fresh Prince of Monte Cristo". Alexandre Dumas. 5: Jose Arcadio Buendia is back a-buildin' in this author's 1960s book "25 More Years of Me-Time". Gabriel García Márquez. Round 3. Category: Driving 1: This type of transmission has been making stop-and-go driving easier since 1904. Automatic transmission. 2: If you can read this clue, you can name this station wagon part that means to follow too closely. Tailgate. 3: In September 2000, Congress held hearings on this company's product found on Fords. Firestone. 4: Experts disagree on whether 10 and 2 o'clock or 9 and 3 is better for this; no one thinks much of the old wrist drape. steering wheel position. 5: Term for the type of skidding in which a car rides on a film of water, losing traction. Hydroplaning. Round 4. Category: Shoot The Moon 1: This lunar "sea" was the landing site of Apollo 11's Eagle module. Sea of Tranquility. 2: This term used for an Apollo mission's return to Earth tells you it landed in water. Splashdown. 3: On the Apollo 14 mission, Alan Shepard introduced this sport to the moon. Golf. 4: A NASA doctor declined this president's dinner invitation to the Apollo 11 crew because of risk of infections. Richard M. Nixon. 5: Apollo 15, 16 and 17 used these 4-wheeled vehicles to fetch rock samples. Lunar rovers. Round 5. Category: Oh, The Humanities! 1: Last name of Lucian, a certain psychoanalyst's grandson; his '50s paintings keyed on realistic nudes, for some reason. Freud. 2: He's hosted the Tony Awards and in 2014 won one for "Hedwig and the Angry Inch". Neil Patrick Harris. 3: His "Book of Marvels" was about his 13th C. trip to the East with his father and uncle (who'd been there before). Marco Polo. 4: Keep incredibly calm and carry on; Britannica says "tranquillity of mind" is key to this ancient Greek philosophy. stoicism. 5: Keep incredibly calm and carry on; Britannica says "tranquillity of mind" is key to this ancient Greek philosophy. stoicism. Thanks for listening! Come back tomorrow for more exciting trivia!

EP173 - Hydroplaning Conundrum by Vent Lab

- Nissan Makes Big Investment in the UK- All-New Nissan Qashqai Debuts- Natural Gas Shortage in Mexico Hitting Production- Quantumscape the Lone Bright Spot in Auto Stocks- Lordstown is Taking Its EV Skateboard Racing- New System Can Prevent Hydroplaning- Ford Improves On-Road Safety of Its Vans- Tesla Cuts Model 3 and Y Base Price- Tesla Model Y Launched in South Korea- Bosch Adopts Microsoft Azure to Develop Car Software- Honda HR-V Gets Bold New Looks- Old Wreck is a Austin Healey

- Nissan Makes Big Investment in the UK - All-New Nissan Qashqai Debuts - Natural Gas Shortage in Mexico Hitting Production - Quantumscape the Lone Bright Spot in Auto Stocks - Lordstown is Taking Its EV Skateboard Racing - New System Can Prevent Hydroplaning - Ford Improves On-Road Safety of Its Vans - Tesla Cuts Model 3 and Y Base Price - Tesla Model Y Launched in South Korea - Bosch Adopts Microsoft Azure to Develop Car Software - Honda HR-V Gets Bold New Looks - Old Wreck is a Austin Healey

Selamat datang di season 2 kali ini sya akana membahas tentang bahaya dari aquaplaning yang sering terjadi pada saat musim hujan dan ini sangat menyangkut dengan keselamatan berkendara stay tune #otomotifpodcast #ensiklomotifpodcast #otomotifindonesia --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/ensiklomotif/support

We look back at Letterman and hilarious guest appearances including Richard Simmons and Tom Brokaw. Alexis shared a baby-related AbFab and told us about a tone tracker. Rob is filling in for Dawn today.

Tires and wheels: we here at the BMW podcast feel they’re underappreciated und underrated. But when it comes to performance and security, few parts of your car can make such a big difference. Which is why we want to dedicate this episode of “Changing Lanes” to your car’s tires, and dive into all the relevant topics and the most asked questions surrounding wheels. For example: the difference between summer tires and winter tires, and why you should switch tires and when. Hydroplaning and how to avoid it. The right tread depth… And so much more!We can promise you one thing: after listening to this episode you’ll never underappreciate your tires again! 2:10 Do I need winter tires? 5:25 When should I change tires? 6:30 When do I need new tires? 10:03 What types of tires are there? 14:18 What is the correct air pressure? 16:41 What is a tread pattern? 17:33 What’s the correct way to store tires? 18:30 What does tire balancing actually mean? 19:34 What is hydroplaning and what can I do about it? And if you want to read more about tires and wheels, go to BMW.com: https://www.bmw.com/en/automotive-life/wheels-and-tires.html “Changing Lanes” is the official podcast of BMW. Subscribe for new episodes each week, in which our hosts take you on an exciting journey and talk about innovative technologies, lifestyle, design and more.

Why is hydroplaning so dangerous? When people realize they're hydroplaning, they panic. In this podcast, I'll go over some tips to avoid or better handle hydroplaning.

Wes is our host this episode as the group discusses news, we also remind the audience how Spencer became The Sausage King. The conversation then turns into how powerful cinema, film and the visual medium is as it relates to bringing people together and creating relationships.Then for some reason, Spencer suggests Nickelodeon has had more of an impact than Disney. He then must defend this argument against the others in our segment called, Hydroplaning.We end it, as always with our 0 to 60 segment, where each cast member has 60 seconds to say what ever they want. There's a lot of Will Smith references in this one.Like us on Facebook https://www.facebook.com/offhighwaycast/Follow us on Twitter @OffHighwayCastCheck out our Website https://www.freeway.productionsA Freeway Production

Well, we're back, but we're not quite completely with it yet, as our discussion was railroaded with complete memory loss about halfway through. That's ok, though, because it was good to be back and talking about our one true love: Michael and KITT.0:00 - 13:36 - Intro., Welcome, Chit Chat13:39 - 49:25 - Knight Rider Season 2 Episode 6 Discussion49:28 - 57:31 - Who's More Likely?, Next Week, Close--Follow us on Facebook: https://www.facebook.com/ciampaklein/Instagram: @ciampakleinTwitter: @CiampaKleinEmail us: letusblowyourmind@gmail.comCall our hotline: (207)835-1954Rate and review us on iTunes!

Well, we're back, but we're not quite completely with it yet, as our discussion was railroaded with complete memory loss about halfway through. That's ok, though, because it was good to be back and talking about our one true love: Michael and KITT.0:00 - 13:36 - Intro., Welcome, Chit Chat13:39 - 49:25 - Knight Rider Season 2 Episode 6 Discussion49:28 - 57:31 - Who's More Likely?, Next Week, Close--Follow us on Facebook: https://www.facebook.com/ciampaklein/Instagram: @ciampakleinTwitter: @CiampaKleinEmail us: letusblowyourmind@gmail.comCall our hotline: (207)835-1954Rate and review us on iTunes!

Wes takes Chazz, Sumner and Spencer on conversation detailing what's to come in the next year. Also, there's a rabbit trail where Sam Raimi's Spider-Man is discussed in comparison to the MCU.Chapter 1. Intro/NewsChapter 2. Hydroplaning (9:30)Chapter 3: 2019 (26:50)Chapter 4: 0-60 (50:28)

Chapter 1: News (1:17)Chapter 2: Hydroplaning (12:45)Chapter 3: 2018 (23:16)Chapter 4: 0-60 (44:31)

Hydroplaning creates immediate danger of sliding out of your lane.

Dynamic Hydroplaning: Water on the runways reduces the friction between the tires and the ground and can reduce braking effectiveness. The ability to brake can be completely lost when the tires are hydroplaning because a layer of water separates the tires from the runway surface. This is also true of braking effectiveness when runways are covered in ice. When the runway is wet, the pilot may be confronted with dynamic hydroplaning. Dynamic hydroplaning is a condition in which the aircraft tires ride on a thin sheet of water rather than on the runway’s surface. Because hydroplaning wheels are not touching the runway, braking and directional control are almost nil. To help minimize dynamic hydroplaning, some runways are grooved to help drain off water; most runways are not. Tire pressure is a factor in dynamic hydroplaning. Using the simple formula of 8.6 times the square root of the tire pressure in p.s.i., a pilot can calculate the minimum speed, in knots, at which hydroplaning begins. In plain language, the minimum hydroplaning speed is determined by multiplying the square root of the main gear tire pressure in psi by nine. For example, if the main gear tire pressure is at 36 psi, the aircraft would begin hydroplaning at 54 knots. Landing at higher than recommended touchdown speeds exposes the aircraft to a greater potential for hydroplaning. And once hydroplaning starts, it can continue well below the minimum initial hydroplaning speed. On wet runways, directional control can be maximized by landing into the wind. Abrupt control inputs should be avoided. When the runway is wet, anticipate braking problems well before landing and be prepared for hydroplaning. Opt for a suitable runway most aligned with the wind. Mechanical braking may be ineffective, so aerodynamic braking should be used to its fullest advantage. Viscous Hydroplaning: Slippery surfaces can cause tires to slip. One of the most common factors is rubber build-up on the runway, generally in the touchdown zone. From Wikipedia: Viscous aquaplaning is due to the viscous properties of water. A thin film of fluid no more than 0.025 mm in depth is all that is needed. The tire cannot penetrate the fluid and the tire rolls on top of the film. This can occur at a much lower speed than dynamic aquaplane, but requires a smooth or smooth-acting surface such as asphalt or a touchdown area coated with the accumulated rubber of past landings. Such a surface can have the same friction coefficient as wet ice. From Wikipedia: Reverted Rubber Hydroplaning: Reverted rubber (steam) aquaplaning occurs during heavy braking that results in a prolonged locked-wheel skid. Only a thin film of water on the runway is required to facilitate this type of aquaplaning. The tire skidding generates enough heat to change the water film into a cushion of steam which keeps the tire off the runway. A side effect of the heat is it causes the rubber in contact with the runway to revert to its original uncured state. Indications of an aircraft having experienced reverted rubber aquaplaning, are distinctive 'steam-cleaned' marks on the runway surface and a patch of reverted rubber on the tire. Reverted rubber aquaplaning frequently follows an encounter with dynamic aquaplaning, during which time the pilot may have the brakes locked in an attempt to slow the aircraft. Eventually the aircraft slows enough to where the tires make contact with the runway surface and the aircraft begins to skid. The remedy for this type of aquaplane is for the pilot to release the brakes and allow the wheels to spin up and apply moderate braking. Reverted rubber aquaplaning is insidious in that the pilot may not know when it begins, and it can persist to very slow groundspeeds (20 knots or less).

Episode 33: Lori Cook, Traffic Safety Advisor for AAA East Central NEO gives us a few tips on how to prevent hydroplaning! This episode would make a great tool box talk! For more information about the Portage County Safety Council, please visit our website today!

- VW Truck and Hino Form Partnership - Volvo Truck Readies First Full EV - Big Truck Sales Continue to Climb - Tesla Model Y Production Details - Continental Develops Hydroplaning Detection System - Automakers Too Focused on Short Term

- VW Truck and Hino Form Partnership- Volvo Truck Readies First Full EV- Big Truck Sales Continue to Climb- Tesla Model Y Production Details- Continental Develops Hydroplaning Detection System- Automakers Too Focused on Short Term