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Raoul Pal and GMI's head of macro research Julien Bittel, CFA, open their biweekly "Shooting the Shit" episode, which is normally exclusive to Real Vision Alpha members and above, to everyone. It's a sneak peek into how the guys brainstorm, interpret charts and look for opportunities through the macro lens. In this episode, they break down the forces driving markets right now, from global liquidity and crypto regulation to AI, compute, energy, stablecoins, and they explain why the old business cycle framework may be losing power as the exponential age accelerates. Today's sponsor is Plus500 US. Take your trading to the next level with cross-market contracts, from precious metals to key indices, and more. Whether you're a seasoned trader in the Futures arena or brand new, Plus500's user-friendly trading platform offers you the advanced tools, market insights, and quick execution you've been looking for. Get started with Plus500 for as little as $100 at https://us.plus500.com. Trading in futures involves the risk of loss. Timestamps: 0:00 - Introduction: The Exponential Age & Universal Code Thesis 1:40 - Trump, AI & Crypto: The Political Acceleration 4:07 - The US-China Grand Bargain: Trade, Taiwan & Nvidia 6:16 - Are We Mid-Cycle? The Case for a Supercycle 9:09 - Inflation, Compute Demand & Anthropic's Explosive Growth 11:37 - Crypto & Equity Chart Rundown (BTC, ETH, Circle, Tesla, Solar) 21:51 - Dollar, Rates & Copper: What to Watch 24:35 - Global Liquidity vs. Bitcoin: The Dominant Framework 30:02 - Portfolio Performance & Why You Shouldn't Trade Crypto 34:05 - The Buildout Has Only Begun: AI, Robots & the CapEx Supercycle 37:26 - Compute & Energy: The New GDP Formula 39:14 - The Economic Singularity & Why Old Macro Frameworks Are Broken 44:44 - Closing: Adapt Your Framework or Get Left Behind Learn more about your ad choices. Visit podcastchoices.com/adchoices

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

$850 billion. That's what retail and e-commerce returns will cost in 2026, generating 8.4 billion pounds of landfill waste — and a surprising share of it involves products that worked perfectly. They just didn't look the way people expected. About 22% of consumers return items because the product looked different in person than it did online, and for home goods and textiles, that number climbs higher. The culprit has a name: metamerism — the way colors shift under different light sources, so the navy sectional and the matching throw pillow that looked identical on your screen clash under your living room LEDs. Don Carli, founder of Nima Hunter and Senior Research Fellow at the Institute for Sustainable Communication, joins Sustainability In Your Ear to explain why this keeps happening and what it would take to stop it.The fix isn't a moonshot. The relevant standards — glTF for digital rendering and ICC Max for physical material appearance — already exist and were designed to be connected. Digital textile printing already makes it possible to produce fabrics with pigment recipes that match under any lighting condition, not just one. What's missing is coordination: brands putting spectral consistency requirements into their supplier purchase orders, the same way the GMI certification transformed packaging quality once Target and Home Depot required it. The Khronos 3D Commerce Working Group has already standardized how products look across digital screens — the next step is bridging that standard to the physical object.When we get this right, a sofa stays in the home it was ordered for instead of traveling a thousand miles back to a distribution center and ending up in a landfill. That's what circularity looks like when it's applied to the seam between the digital world and the physical one.Follow Don's work at WhatTheyThink.com and on X at @DCarli.Subscribe to Sustainability In Your Ear on iTunesFollow Sustainability In Your Ear on Spreaker, iHeartRadio, or YouTube

The Exponential Age is here, and it's accelerating — bringing developments humanity is simply not ready for. In a special crossover episode of The Journey Man and the Exponentialist, Raoul Pal sits down with David Mattin, technology lead at GMI, for a deep dive into the world of AI, tech, crypto, consciousness, and what humans can learn from sharks and crocodiles.Right now is the best time to join the Exponentialist, an independent research service co-created by Raoul and David. Markets are moving fast, with Big Tech reshaping the world as you know it. You can't stop it — but you can prepare, position, and profit. Through March 9, get 15% OFF 6 months or 25% OFF 12 months of the Exponentialist.Everything you need to understand the shift and supercharge your trading. Sign up at realvision.com/exponentialist Learn more about your ad choices. Visit podcastchoices.com/adchoices

We're answering real diabetes questions straight from social media in this episode of the Your Diabetes Insider Podcast - and yeah… some of them are insightful, some are wildly confusing, and a few left me absolutely speechless! We're talking net carbs vs total carbs, whether caffeine actually spikes blood sugar, what happens if your phone controls your insulin pump (and you lose it

In this episode, we're pulling back the curtain to give you an exclusive peek inside a live workshop from our holistic healing program, the Ending Body Burnout Method. You'll experience firsthand how we create a transformative healing space in our weekly workshops, designed to support our clients in between their 1:1 functional medicine consults & root-cause coaching sessions. If you're feeling inspired by today's “brain rewiring” episode, you won't want to miss our upcoming free 3-day event 'Rewire Your Brain To Heal Body Burnout'. And, if you'd love to work with us, doors open to our Ending Body Burnout Method NEXT MONTH!! Jump on the waitlist to secure exclusive waitlist bonuses worth over $500! In today's episode, Chris & Filly talk about: A powerful evidence-based brain rewiring technique called Graded Motor Imagery (GMI) GMI is used by many people, including world class athletes, to improve how their brains and bodies communicate with one another In people with chronic health conditions, GMI helps rewire the brain's response to tasks/triggers The HUGE amount of peer-reviewed studies showing the effectiveness of GMI in improving chronic muscle/joint pain How we have also seen GMI techniques improve other symptoms like gut pain, headaches, food and chemical sensitivities, dizziness, fatigue, anxiety and abnormal stress response to triggers How to rewire your response to triggers and turn off unwanted sensations in your body and mind Show Note Links: Register for our FREE 3-Day Live Coaching Event: Rewire The Brain To Heal Body Burnout Join the waitlist for our Ending Body Burnout Method program to save up to get first dibs on a spot (limited spaces as the program includes 1:1) & $500+ early-bird bonuses - doors open to waitlisters 9 Feb! [CLIENT ACCESS ONLY] - Go here to watch the full Graded Motor Imagery Workshop inside the Ending Body Burnout Method portal Take Chris & Filly's Ending Body Burnout Assessment here Check out how you can work with Chris & Filly Functional Medicine here Disclaimer: This Ending Body Burnout Show podcast and any information, advice, opinions or statements within it do not constitute medical, health care or other professional advice, and are provided for general information purposes only. All care is taken in the preparation of the information in this Podcast. Chris & Filly Functional Medicine does not make any representations or give any warranties about its accuracy, reliability, completeness or suitability for any particular purpose. This Podcast and any information, advice, opinions or statements within it are not to be used as a substitute for professional medical, psychology, psychiatric or other mental health care or natural medicine health care. Chris & Filly Functional Medicine recommends you seek the advice of your doctor or other qualified health providers with any questions you may have regarding a medical condition. Inform your doctor of any changes you may make to your lifestyle and discuss these with your doctor. Do not disregard medical advice or delay visiting a medical professional because of something you hear in this Podcast. To the extent permissible by law Chris & Filly Functional Medicine and the Ending Body Burnout Show Podcast will not be liable for any expenses, losses, damages (including indirect or consequential damages) or costs which might be incurred as a result of the information being inaccurate or incomplete in any way and for any reason. No part of this Podcast can be reproduced, redistributed, published, copied or duplicated in any form without the prior permission of Chris & Filly Functional Medicine.

N415 - ADA 2025 - Hemoglobina glicada ou GMI: qual métrica usar? - Karla Melo e Márcio Krakauer by SBD

This episode covers a decision from the RV Industry Association to postpone the GMI device requirement for new RVs. We delve into a 15% drop in those planning to winter in the U.S., Campspot's new AI-powered dynamic pricing model aims to change campground reservations, and Oregon State Parks' upcoming fee hikes due to a $14 million budget shortfall. All that and more! Get free shipping on orders over $99 at Etrailer: https://www.etrailer.com/vehicle-finder.aspx?etam=p0001 Get your first month of Mile Marker Membership FREE at https://rvmiles.memberful.com/checkout?plan=96363 with code RVMILES.  Subscribe to the RV Miles Podcast Channel: https://www.youtube.com/RVMilesPodcast.  ****************************** Connect with RV Miles:  RV Miles Facebook Group: https://www.facebook.com/groups/rvmiles Shop the RV Miles Amazon Store: https://www.amazon.com/shop/rvmiles RV Miles Mailing List: https://rvmiles.com/mailinglist Mile Marker Membership: https://rvmiles.com/milemarkers 00:00 Introduction 00:05 Understanding the New GMI Requirement 02:10 Canadian Snowbirds' Travel Trends 02:55 Dynamic Pricing in Campgrounds 04:21 Oregon State Parks Budget Shortfall 05:19 Mississippi's Largest RV Resort 06:10 KOA's Campground of the Year 06:53 Thanksgiving Travel Predictions 08:25 Conclusion

The CPG Guys are joined in this episode by Fuad Hannon, VP of New Business Verticals at DoorDash. In this role, he leads a team responsible for building new operating capabilities and delivery channels at DoorDash to expand our local commerce platform to new growth areas. Prior to his current role, Fuad was Head of New Business Verticals at DoorDash, previously a General Manager, and was part of the founding team for DoorDash Drive, the company's white-label fulfillment platform. Prior to DoorDash, Fuad worked at Google leading their Google Express operations and began his career as a strategy consultant at McKinsey & Company. Fuad holds a B.S. from the University of Minnesota and an MBA from the Harvard Business School.Follow Fuad Hannon on LinkedIn at: https://www.linkedin.com/in/fuadhannon/Follow Yoobi on LinkedIn at: https://www.linkedin.com/company/doordash/Follow NIQ online at: https://www.doordash.com/This episode is sponsored by DoorDash.Here's what we discussed:Grocery growth: Grocery has been a big area of investment for DoorDash. What progress have you made, and how do you see DoorDash's role evolving in this space?Consumer behavior: How does consumer behavior in grocery differ from other categories in on-demand delivery, and what opportunities does this create for grocery brands? Category momentum: What types of grocery categories or products are seeing the most success on on-demand platforms, and why?We learned at GS that you have just expanded your partner list - can you take us through what's new and exciting with your partners?Convenience & necessity mix: How does the mix of convenience (meal delivery) and necessity (grocery) create differentiated opportunities for brands? Best-in-class partnership: Can you share an example of a partnership with a grocery-focused CPGthat illustrates best-in-class use of DoorDash?Grocery & DoorDash Ads: How does the growth of the grocery vertical unlock new opportunities within DoorDash Ads, and what makes DoorDash a differentiated platform for grocery brands that want to advertise?Dashmart - I am very familiar with this offering through my days at GMI, even have walked through the NYC one, what's new and exciting about who are leveraging this?Advice to brands: What advice would you give to grocery brands looking to scale their presence on digital platforms like DoorDash?Future of Grocery on DoorDash: What message are you hoping to leave the industry about DoorDash's leadership in grocery and new verticals?CPG Guys Website: http://CPGguys.comFMCG Guys Website: http://FMCGguys.comRhea Raj's Website: http://rhearaj.comLara Raj in Katseye: https://www.katseye.world/DISCLAIMER: The content in this podcast episode is provided for general informational purposes only. By listening to our episode, you understand that no information contained in this episode should be construed as advice from CPGGUYS, LLC or the individual author, hosts, or guests, nor is it intended to be a substitute for research on any subject matter. Reference to any specific product or entity does not constitute an endorsement or recommendation by CPGGUYS, LLC. The views expressed by guests are their own and their appearance on the program does not imply an endorsement of the CPGGUYS LLC expressly disclaims any and all liability or responsibility for any direct, indirect, incidental, special, consequential or other damages arising out of any individual's use of, reference to, or inability to use this podcast or the information we presented in this podcast.

In this episode of Quality Matters, Dr. Richard Bergenstal, Executive Director of the International Diabetes Center, joins host Andy Reynolds to explore the evolving landscape of diabetes care—from the legacy of A1C to the promise of continuous glucose monitoring (CGM).Rich shares lessons from decades of clinical leadership, research and quality improvement, including his advisory role in NCQA's updated Diabetes Recognition Program. He explains how CGM, new metrics like the Glucose Management Indicator and a focus on patient experience are reshaping how we define and deliver high-quality diabetes care.Listen to this episode to discover:Why the A1C Era Was Just the Beginning. Learn how A1C transformed diabetes care—and why it's no longer enough. Richl explains why A1C is a measurement tool, not a management tool, and how CGM fills that gap.The Rise of CGM and What It Means for Quality. Understand why CGM use has surged sixfold in two years, and how it empowers patients with real-time data, alerts and confidence. Discover how CGM metrics like Time in Range and GMI are becoming the new standard.The Power of Visualizing Glucose Data. Explore how tools like the Ambulatory Glucose Profile (AGP) help clinicians and patients detect patterns, personalize care and move from data to action.Why Quality Measures Must Evolve. Learn how NCQA's updated Diabetes Recognition Program adds HEDIS measures to reflect contemporary care standards and whole-person health.What's Next in Diabetes Care. Hear Dr. Rich's optimistic, collaborative vision for  the next decade of helping people who have diabetes live better lives.This conversation is essential for quality leaders, clinicians and health plan professionals who want to stay ahead of the curve in diabetes care, digital health and patient-centered quality improvement.Key Quote:The A1C set up the need for the next technology. Why did finger stick glucose come about? Because the average A1C said, “You need to do better.” But nobody wants to poke their finger multiple times a day. So A1C led to finger sticks. Finger sticks led to CGM and now CGM has changed the dialogue. The A1C era had its role, but you look for the next thing to get to the next level. That's what I see CGM as. -Rich Bergenstal, MDTime Stamps:(02:14) The Era of A1C(04:22) What is Continuous Glucose Monitoring (CGM)(06:43) Bridging the A1C and CGM Eras(10:45) Addressing Skepticism and Myths about CGM(18:37) The Future of Diabetes Care Dive Deeper:NCQA's Diabetes Recognition ProgramRecent Quality News About DiabetesConnect with Rich Bergenstal 

This week on GMO, the crew looks ahead to Memorial Day adventures—Alex Burkett is planning a trip to Cuyahoga Valley National Park, while Matt Whitermore recently visited Ferncrest Promised Land for a franchise discovery day (shoutout to Ferncrest and Brian Linton!). The team explores how wellness tourism continues to shape RV travel, with more parks offering yoga, meditation, and guided nature walks. A new Pilot/Campspot survey shows campers are favoring shorter trips to national parks and small towns within 200 miles. We also unpack how the 2026 GMI requirements on new RVs are pressuring campgrounds to upgrade their infrastructure. Plus, we highlight Electric Outdoors's off-grid glamping canopy, USA TODAY's 10 Best campgrounds, and the booming overlanding trend showcased at Overland Expo West. ---- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Good Morning Hospitality⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ is part of the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Hospitality.FM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Multi-Media Network and is a Hospitality.FM Original The hospitality industry is constantly growing, changing, and innovating! This podcast brings you the top news and topics from industry experts across different hospitality fields. Good Morning Hospitality publishes three thirty-minute weekly episodes: every Monday and Wednesday at 7 a.m. PST / 10 a.m. EST and every Tuesday at 8 a.m. CET for our European and UK-focused content. Make sure to tune in during our live show on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn page⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ every week and join the conversation live! Explore everything Good Morning Hospitality has to offer: • Well & Good Morning Coffee: Enjoy our signature roast—⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠order here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • Retreats: Join us at one of our exclusive retreats—learn more and register your interest ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • Episodes & More: Find all episodes and additional info at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠GoodMorningHospitality.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Thank you to all of the Hospitality.FM Partners that help make this show possible. If you have any press you want to be covered during the show, email us at goodmorning@hospitality.fm Learn more about your ad choices. Visit megaphone.fm/adchoices

This week on GMO, the crew looks ahead to Memorial Day adventures—Alex Burkett is planning a trip to Cuyahoga Valley National Park, while Matt Whitermore recently visited Ferncrest Promised Land for a franchise discovery day (shoutout to Ferncrest and Brian Linton!). The team explores how wellness tourism continues to shape RV travel, with more parks offering yoga, meditation, and guided nature walks. A new Pilot/Campspot survey shows campers are favoring shorter trips to national parks and small towns within 200 miles. We also unpack how the 2026 GMI requirements on new RVs are pressuring campgrounds to upgrade their infrastructure. Plus, we highlight Electric Outdoors's off-grid glamping canopy, USA TODAY's 10 Best campgrounds, and the booming overlanding trend showcased at Overland Expo West. ---- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Good Morning Hospitality⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ is part of the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Hospitality.FM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Multi-Media Network and is a Hospitality.FM Original The hospitality industry is constantly growing, changing, and innovating! This podcast brings you the top news and topics from industry experts across different hospitality fields. Good Morning Hospitality publishes three thirty-minute weekly episodes: every Monday and Wednesday at 7 a.m. PST / 10 a.m. EST and every Tuesday at 8 a.m. CET for our European and UK-focused content. Make sure to tune in during our live show on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn page⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ every week and join the conversation live! Explore everything Good Morning Hospitality has to offer: • Well & Good Morning Coffee: Enjoy our signature roast—⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠order here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • Retreats: Join us at one of our exclusive retreats—learn more and register your interest ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • Episodes & More: Find all episodes and additional info at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠GoodMorningHospitality.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Thank you to all of the Hospitality.FM Partners that help make this show possible. If you have any press you want to be covered during the show, email us at goodmorning@hospitality.fm Learn more about your ad choices. Visit megaphone.fm/adchoices

Video Version Only on HCPLive! In this episode of Diabetes Dialogue, hosts Diana Isaacs, PharmD, an endocrine clinical pharmacist, director of Education and Training in Diabetes Technology, and co-director of Endocrine Disorders in Pregnancy at the Cleveland Clinic, and Natalie Bellini, DNP, program director of Diabetes Technology at University Hospitals Diabetes and Metabolic Care Center, sit down with Steven Russell, MD, PhD, Chief Medical Officer at Beta Bionics, to discuss the latest real-world outcomes from the iLet Bionic Pancreas—an autonomous insulin delivery (AID) system cleared by the US Food and Drug Administration (FDA) in 2023. Russell outlines the iLet's fully automated design, which sets it apart from conventional AID systems by requiring no manual settings, carb ratios, or correction factors. The system determines 100% of insulin dosing, adapting continuously to glycemic trends without relying on user engagement. This autonomy makes the iLet particularly effective for individuals with suboptimal diabetes self-management or limited access to endocrinology care. New real-world data, covering 3,300 users from the first year of commercial rollout, reveal a mean baseline A1c of 8.5%—higher than the 7.8% in the pivotal trial and reflective of the broader U.S. type 1 diabetes (T1D) population. The iLet reduced glucose management indicator to 7.3%, yielding an average A1c reduction of 1.2%, more than double that seen in the pivotal study. Outcomes were most pronounced among those with severe hyperglycemia: users starting with A1c >14% saw average reductions of 7%, with low rates of diabetic ketoacidosis (DKA) and minimal increases in hypoglycemia (median time

Gehört der Wolf zu Deutschland? Ja, meint Dr. Wolfgang Epple und begründet es ausführlich. Er greift dabei zurück auf seine eigenen Erfahrungen im Umgang mit Wölfen, Schafen, Hühnern, Bibern und Greifvögeln und auf seine Vorbilder Konrad Lorenz und insbesondere Hans Jonas. Epple spricht sich vehement gegen die Tötung „unschuldiger Wildtiere“ aus, schließlich wurden Tiere – im Unterschied zum Menschen – nicht aus dem Paradies vertrieben. Deshalb stellt er sich gegen eine Politik, wie sie von Söder und Aiwanger vertreten wird, spricht von „Ehrfurcht“ (zusammengesetzt aus „Ehre“ und „Furcht“) und erklärt, was GMI bedeutet, nämlich: grundlegende moralische Intuition. Auf die sollten wir hören.

HAPPY BIRTHDAY SPECIALS THRU OCTOBER 31! -->BDAY25 - 25% OFF Full Price Courses & Supplements --> BDAY15 - 15% OFF Sale Price Supplements Join Our VIP Mastermind Tribe -->DianeKazer.com/VIP Become a New Patient -->DianeKazer.com/CALL Get My FREE 11 Essentials to THRIVE -->DianeKazer.com/THRIVE I know it's halloween month and all…which means that trickery is part of the vibe, but seriously, I've got a topic you must learn to discern if you've truly been tricked. And that trick my friends, is about the LYME LIE. And this LYME LIE is exactly what Dr Jack and I are breaking down today on our CHI Podcast at 10am PT / 1pm ET so join us live here. Why? Because everyone ‘has' Lyme in their body, however very few realize it and even fewer get tested for it, then beyond THAT, those who DO get tested, receive results that are inaccurate and/or incomplete due to poor lab testing + a big dose of WHITE COAT SYNDROME. So once you learn if you've been TRICKED then it's time to TREAT. There is a Toxic Truth around this stealth pathogen that yields a clue as to the possible near term consequences of what we have been sold as ‘the C0vid Veyerus' that made us sick. What if I told you that these are BUG BORN WEAPONS that you can have inside your body even if you have not been bitten by a tick because: “Lyme disease came about because of a bioweapons research program?” Which started 50+ years ago…and history is repeating itself! So join us as we share why LYME disease is exponentially on the rise and how our parasite cleanses play a vital role in managing and potentially overcoming this debilitating condition. WE WILL COVER What are GMI's? (and as always why should YOU care?) What Lyme & Covid have in Common (HINT: Covid was NOT the first BioWeapon) Symptoms of Lyme… What's ‘Long Covid' really? What if there's no such thing (and the root cause is being hidden from you) The history of Lyme & why it's a root cause factor for ALL of us (including Parasites) The Lyme / Parasite Connection Got (or had) Breast Implants? … Chances are you've been diagnosed with ‘Lyme' if you were tested for it (but was it accurate?) The myth of ‘I've never Been Bit By a Tick, so I couldn't possibly have Lyme' Want to join in on our Afterparty Tribe with SOLUTIONS from our weekly Podcasts? Directly following our Warrior Wednesday CHI podcast we dive straight into our VIP Tribe Mastermind Call (where I'll be guiding all of you on HOW to take action on the topics we'll share in the show). Join our VIP Mastermind Tribe Ministry HERE for only $1 to: Talk to Dr Jack and I directly about Lyme Disease, Coinfection, Parasites and how we treat this in our patients. Get VIP only discounts on supplements, DIY courses and more (this ALONE pays for your VIP membership itself) Gain exclusive access to Solutions that will help you treat LYME and the actual root causes! I love to hear from you! Drop your comments and questions in the live chat and share with others who are sick and suffering and sick OF suffering, hopeless because they're desperate for answers! Here for you warrior!

Sony has announced the FE 85mm F1.4 GM II (SEL85F14GM2) a premium lens offering a supreme blend of high resolution and stunning bokeh with fast autofocus (AF) in a compact body. This is a second-generation lens that inherits the optical design that achieved a high-level fusion of bokeh and resolution that was highly praised in the first-generation model, and generously incorporates the latest technology. When paired with the latest Sony's Alpha series cameras, it is an ideal option for a wide variety of creative uses in both photography and videography, including portraiture, wedding, travel, and cinematic video. "In 2016 we launched our G Master lineup and the first generation 85 mm F1.4 lens was one of the three lenses part of that pivotable moment for Sony. Our goal with our G Master line is to design glass that delivers stunning resolution and beautiful bokeh, while incorporating the highest level of technology available at the time, and we are proud to continue that message with the next-generation FE 85mm F1.4 GM II we are announcing today," said Yann Salmon Legagneur, Head of IP&S Marketing, Sony Europe. "We know how many photographers and videographers rely on the large aperture 85 mm focal length. We are excited to offer an updated version packed with our latest lens technologies, all while maintaining a lightweight lens and extremely compact in size." High Optical Performance & Beautiful Bokeh The advanced and refined optical design and elements of the FE 85mm F1.4 GM II bring together appealing bokeh and enhanced high resolution, making it ideal for portrait photography. Packed with two XA (extreme aspherical) and two ED (extra-low dispersion) lenses, the state-of-the-art optical design effectively suppresses aberrations. This combination offers corner-to-corner resolution and rendering, resulting in outstanding image quality. Moreover, Sony's original Nano AR Coating II technology effectively subdues flare and ghosting for clear, crisp images. The 11-blade circular aperture and optimisation of spherical aberration during the design and manufacturing process provide the beautiful, soft bokeh that is the hallmark of G Master series. Fast, Precise, Quiet Autofocus and High Tracking Performance The FE 85mm F1.4 GM II's AF is agile, maximising the speed advantage of advanced camera bodies. For stills, it is up to 3 times fastericompared to the previous modelii, tracking moving subjects with high precision. It keeps up flawlessly with the Alpha 9 III's 120fpsiiicontinuous shooting, and subject tracking performance is improved by as much as 7 timesii. Lightweight and Stylish Design without Compromises Compared to the current FE 85mm F1.4 GMi, the weight of the FE 85mm F1.4 GM II (642 grams, 22.7 oz.) is approximately 20% lighterivand 13% smaller in volume, which is achieved through the latest mechanical technology and optical design. The FE 85mm F1.4 GM II has a filter diameter of ?77 mm, diameter of 84.7 mm (about 3-3/8 in) and length of 107.3 mm (about 4-1/4 in). Effective Cinematic Creation Features For smooth movie creation at F1.4, the FE 85mm F1.4 GM II has an XD linear motor. Focus breathing is minimal, and in-body focus breathing compensationv is supported. It has two customisable focus hold buttons and an AF/MF switch offer flexible operability, while an independent aperture ring and Linear Response MF focus control provide the kind of intuitive control that creative shooting demands. Crafted for Durability The front lens element features a fluorine coating, which makes it easy to remove fingerprints, dust, oil, and other contaminants from the lens surface. The dust and moisture resistant designvi provides extra reliability for outdoor use in challenging conditions. Pricing and Availability The FE 85mm F1.4 GM II will be available in September 2024 for approximately €2,100 EUR at a variety of Sony's authorised dealers. A product video on the new FE 85mm F1.4 GM II can be viewed here. For detailed product infor...

I don't talk to many young people — so many of them wear headphones or earbuds and they look stressed out. I'm guessing the music they're listening to is narcissist pop about Me, Myself and I, my need for more Me time, my exorbitant rent, boring job, bad boss, crowded bike paths, long wait times at climbing walls, the fear of arterial plaque caused by foods containing GMI and DMU, and if I smile at them, they'll take me for a privileged white male and give me the middle finger. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit garrisonkeillor.substack.com/subscribe

Today we're taking a no BS look at the world of customer insights, leadership and business visioning with the President of software company Zappi, the ever-passionate and transparent leader, Ryan Barry.Having led Zappi for over four years, Ryan shares his no holds barred take on company leadership and people leadership, and it's all anchored on authenticity. Years ago, Ryan says, he was called out by his wife on having a ‘work voice' and realized he shouldn't be wearing 50 different masks depending on who he's speaking with. He says this new transparent approach to dealing with people and stakeholders alike makes his life easier, and ensures his leadership style is more human. Ryan is also committed to communication, or even over-communication, dropping casual and informal voice messages and videos as news evolves in his company regularly, to ensure no one has to second guess what is happening, and that business updates are intentional and impactful. To negate a possible consequence of this – dominance and over-assertion - Ryan says he leans into curiosity, asking questions openly on things he is not knowledgeable about and deferring to the experts. “Your job sometimes is just to listen,” he says.Jo and Ryan also discuss the future of the workplace, particularly as more remote, flexible and virtual working styles are implemented, and spontaneous moments to ‘break bread with people' in a physical office become fewer. Nearly 70% of Ryan's staff are on the other side of the planet to him. He's cognizant of the necessity to build and nurture relationships and believes that cannot be replaced by physically being in the same place together. Trust, collaboration and understanding are best built in real life, Ryan says, as are new ideas generated from sporadic and unplanned moments ‘bumping into' people. Whatever new technological automation and outsourcing the future holds for the workplace, this ability to connect, human to human, won't be replaced.Holding a personal brand of his own, supported by his podcast Inside Insights and newsletter Ryan's Rants, Ryan regularly shares reflections on the industry he operates in and where customer strategies go wrong. More and more companies are driven by two polarized objectives: short term earnings vs long term goals, awards and accolades vs ads that actually drive sales, political stances vs internal policies. Ryan highlights the importance of being frank with yourself on why business decisions are made, of taking input from your staff (whether you like what they say or not), of building principles that create consistency for future scenarios, and of ensuring your business actions match what you say. “People's bullshit meter is way up”, and Ryan says that's a good thing. >>>To look outside, Ryan goes outside to get lost in nature and mountains, usually with his dog. Getting out from the physical reality he's surrounded by allows him to tap into another level of ideas in his subconscious.>>>Ryan Barry oversees Zappi's global business operation leading the companies growth and impact internally and externally and serves as a member of the firm's executive leadership team. Prior to Zappi, Ryan worked for GMI, an online survey solution provider which was eventually acquired by Kantar and also started a dog care company which was sold in 2015 and a non profit aimed at raising money for less fortunate children.In addition to his work at Zappi, Ryan serves on Michigan State's advisory board for their MR program. Ryan lives outside of Boston with his wife, two sons, daughter and his two hound dogs and loves the great outdoors and Boston sports.Follow Ryan on LinkedInSubscribe to

Recorded at the 2024 Phoenix Live Podcast Tour at GMI on April 1st to 3rd.Featuring:Ryan Murray, Deputy Director and Chief Information Security Officer, State of ArizonaSee Ryan's past episodes here: https://podcast.techtables.com/guests/ryan-murray/Ralph Johnson, Chief Information Security Officer, State of WashingtonOwen Zorge, Chief Information Security Officer, City of ChandlerCátia Pereira, Manager - Sales Engineering at KiteworksIn this episode you'll learn:Building Statewide Cybersecurity Programs: How the State of Arizona has implemented its Cyber Readiness Program to support local governments with advanced cybersecurity tools, improving overall security posture and resource allocation.Fostering Collaboration in Cybersecurity: Insights into how the State of Washington is overcoming mistrust between state and local entities through collaborative initiatives and grant programs to enhance cybersecurity measures across jurisdictions.Effective Cybersecurity Partnerships: Real-world examples from the City of Chandler on the benefits of collaborative purchasing and support systems, and how working with state programs can lead to significant improvements in local cybersecurity defenses.Public-Private Sector Partnerships: The role of companies like Kiteworks in facilitating secure communication and data sharing within public sector organizations, highlighting successful implementations and scalable solutions.Addressing Workforce Challenges: Creative strategies from Arizona and Washington on how to tackle the cybersecurity talent gap, including eliminating degree requirements, providing on-the-job training, and engaging with educational institutions to build a future-ready workforce.Harnessing Emerging Technologies: Practical applications of AI and machine learning in cybersecurity for anomaly detection and autonomous vulnerability management, and the importance of balancing innovative tech adoption with fundamental security practices.Leadership and Strategic Insights: Perspectives from top CISOs on managing cybersecurity in state and local governments, the importance of trust and collaboration, and strategies for continuous improvement in an evolving threat landscape.Full transcript and show notesRyan's LinkedInRalph's LinkedInOwen's LinkedInCátia's LinkedIn***RECOMMENDED EPISODES→ #173: Doug Ducey, 23rd Governor of Arizona→ #174: Jack McCain & Tim Roemer - National Security from State to Local Governments→ #175: Ralph Johnson, Allen Ohanian, Martha Goodwin, and Dr. Muriel Reid – The Human Firewall: Cybersecurity's Next Frontier→ #176: Lester Godsey, Christian Taillon, Tina Carkhuff, and Gary Depreta - Weaving the Cybersecurity Tapestry: The Art of Public Private Collaboration***WHEN YOU'RE READY

Recorded at the 2024 Phoenix Live Podcast Tour at GMI on April 1st to 3rd.Lester Godsey, CISO, Maricopa CountyLester serves as the Chief Information Security Officer for Maricopa County, the fourth largest county in the United States by population. With extensive experience in managing cybersecurity risks and implementing robust security protocols, Lester plays a crucial role in safeguarding the county's digital infrastructure against various cyber threats.Tina Carkhuff, Industry Executive Advisor, SplunkTina is an Industry Executive Advisor at Splunk and the former CIO for the City of Houston. With a deep passion for data analytics, Tina brings valuable insights from her work with Splunk's platform in detecting and mitigating fraud. Her experience spans across various public sector roles, where she has consistently leveraged data to drive significant improvements in cybersecurity and operational efficiency.Gary DePreta, Senior Vice President US Public Sector, CiscoGary is the Senior Vice President of US Public Sector at Cisco. Having joined Cisco in 2005 and with a brief stint at Splunk, Gary has a unique perspective on the integration of advanced cybersecurity solutions. He is dedicated to addressing the complex challenges faced by public sector entities, particularly in enhancing cybersecurity through innovative technologies and strategic partnerships.Christian Taillon, Threat Response Engineer, Grand Canyon EducationChristian is a Threat Response Engineer at Grand Canyon Education. He is committed to protecting student and institutional data while training the next generation of cybersecurity professionals. Christian's work involves utilizing advanced security technologies and fostering public-private partnerships to bolster cyber defenses within the educational sector.In this episode you'll learn:How Maricopa County combats misinformation and disinformation, particularly during election cycles, and the importance of incident response protocols in addressing physical and digital threats.The role of data analytics in fraud detection, exemplified by Splunk's success in helping states like New Jersey save billions in fraudulent unemployment claims.The impact of Cisco's acquisition of Splunk on enhancing cybersecurity through the integration of data analytics and threat intelligence.The strategic importance of public-private partnerships in improving cybersecurity defenses, with real-world examples from Grand Canyon Education.The challenges and solutions associated with using AI and machine learning in cybersecurity, including maintaining data privacy and automating threat response.How trust in government can be restored and improved through effective public-private collaborations and innovative use of technology.Full transcript and show notesLester's LinkedIn Tina's LinkedIn Christian LinkedInGary's LinkedIn***RECOMMENDED EPISODES→ #173: Doug Ducey, 23rd Governor of Arizona→ #174: Jack McCain & Tim Roemer - National Security from State to Local Governments→ #175: Ralph Johnson, Allen Ohanian, Martha Goodwin, and Dr. Muriel Reid – The Human Firewall: Cybersecurity's Next Frontier***WHEN YOU'RE READY

Kevin Werbach speaks with Diya Wynn, the responsible AI lead at Amazon Web Services (AWS). Diya shares how she pioneered a formal practice for ethical AI at AWS, and explains AWS's “Well-Architected” framework to assist customers in responsibly deploying AI. Kevin and Diya also discuss the significance of diversity and human bias in AI systems, revealing the necessity of incorporating diverse perspectives to create more equitable AI outcomes.  Diya Wynn leads a team at AWS that helps customers implement responsible AI practices. She has over 25 years of experience as a technologist scaling products for acquisition; driving inclusion, diversity & equity initiatives; and leading operational transformation. She serves on the AWS Health Equity Initiative Review Committee; mentors at Tulane University, Spelman College, and GMI; was a mayoral appointee in Environment Affairs for six years; and guest lectures regularly on responsible and inclusive technology. Responsible AI for the greater good: insights from AWS's Diya Wynn  Ethics In AI: A Conversation With Diya Wynn, AWS Responsible AI Lead   Want to learn more? ​​Engage live with Professor Werbach and other Wharton faculty experts in Wharton's new Strategies for Accountable AI online executive education program. It's perfect for managers, entrepreneurs, and advisors looking to harness AI's power while addressing its risks.

Recorded at the 2024 Phoenix Live Podcast Tour at GMI on April 1st to 3rd.Ralph Johnson, the State CISO, Washington State brings a wealth of experience to his role as having previously served as CISO for Los Angeles County and Chief Information Security and Privacy Officer for King County. His appointment was strongly endorsed by Bill Kehoe, who praised Johnson's skills, experience, and information security knowledge as assets that will enhance the state's enterprise security program and data protection efforts.Allen Ohanian, the Information Security Officer for the Department of Children and Family Services in Los Angeles County brings 20 years of experience in cybersecurity, risk management, and IT across both private and public sectors. His impressive career includes establishing innovative security programs, leading countywide cybersecurity initiatives, and earning multiple Information Security Officer of the Year awards, all while pursuing advanced degrees in cybersecurity, business administration, and psychology.Martha Goodwin, Senior Sales Engineer Director for SLED at SentinelOne is a seasoned SE Leader with over two decades of experience spanning pre-sales, customer training, post-sales support, and professional services in cybersecurity. Her expertise covers SIEM, deception, network, email, and endpoint security, coupled with a talent for translating complex technical concepts for audiences ranging from IT analysts to CISOs, making her a valuable asset in building strong customer relationships.Dr. Muriel Reid, CIO for the City of Jackson, Mississippi is an accomplished leader and educator with over 20 years of diverse experience spanning information technology, talent acquisition, education, and customer service. Her expertise in administration, coaching, and staff supervision is complemented by her strong communication skills, making her a versatile professional adept at bridging technology and business needs.In this episode you'll learn:The critical components of a human-centric cybersecurity strategy for government agenciesHow to design engaging cybersecurity training that resonates with public sector employeesStrategies for balancing AI and human judgment in threat detection and responseBest practices for building effective public-private partnerships in cybersecurityHow to cultivate a culture of security awareness across your organization and more!Full transcript and show notesRalph's LinkedIn Allen's LinkedIn Martha's LinkedInDr. Muriel's LinkedIn***RECOMMENDED NEXT EPISODES→ #173: Doug Ducey, 23rd Governor of Arizona→ Morgan Wright episodes (#120 & #168) ***WHEN YOU'RE READY

In this never-before-seen, closed-door only recording from the ​​2024 TechTables Phoenix Live Podcast​​, Navy veteran Jack McCain, CEO of Blue Sky Vantage, and former Director of Homeland Security and State CISO Tim Roemer & Chief Security Officer at GMI, reveal the alarming reality of how hostile nations are targeting our local communities with cyber attacks. You'll learn practical steps your agency must take now to avoid devastating consequences when an attack inevitably strikes.Don't miss this powerful episode on safeguarding your citizens from growing nation-state actors.------Before we get into this week's podcast, I wanted to give a special shout-out to TechTables podcast sponsors: SentinelOne, Verizon, and SAP.SentinelOne: Transforming state security with AI-powered solutions. Protecting 15,000+ endpoints across 25 agencies.Learn how SentinelOne empowers this state to stay secure or click here: https://assets.sentinelone.com/ghe/sentinelone-empowersVerizon Frontline: The advanced network that keeps first responders connected when it matters most.Check out the solutions built for first responders or click here: https://www.verizon.com/business/solutions/public-sector/public-safety/SAP: Driving digital transformation in cities like Copenhagen. See how they're making digital strides.Download the Case Study Now or click here: https://www.sap.com/documents/2021/02/10c410bc-cc7d-0010-87a3-c30de2ffd8ff.html

Tim Roemer's journey has taken him from Scottsdale to the White House and back home again as chief security officer at GMI. After he graduated from Horizon High, he joined the CIA where he served 10 years providing security updates to the President. He returned to Arizona as director of Homeland Security for Gov. Ducey. Now his work in the private sector is helping GMI, a Scottsdale company, safeguard organizations across the globe against cyber threats. Tune in for a look at Tim's remarkable career.

In this episode of the Micro Binfie Podcast, hosts Dr. Andrew Page and Dr. Lee Katz delve into the fascinating world of hash databases and their application in cgMLST (core genome Multilocus Sequence Typing) for microbial bioinformatics. The discussion begins with the challenges faced by bioinformaticians due to siloed MLST databases across the globe, which hinder synchronization and effective genomic surveillance. To address these issues, the concept of using hash databases for allele identification is introduced. Hashing allows for the creation of unique identifiers for genetic sequences, enabling easier database synchronization without the need for extensive system support or resources. Dr. Katz explains the principle of hashing and its application in genomics, where even a single nucleotide polymorphism (SNP) can result in a different hash, making it a perfect solution for distinguishing alleles. Various hashing algorithms, such as MD5 and SHA-256, are discussed, along with their advantages and potential risks of hash collisions. Despite these risks, the use of more complex hashes has been shown to significantly reduce the probability of such collisions. The episode also explores practical aspects of implementing hash databases in bioinformatics software, highlighting the need for exact matching algorithms due to the nature of hashing. Existing tools like eToKi and upcoming software are mentioned as examples of applications that can utilize hash databases. Furthermore, the conversation touches on the concept of sequence types in cgMLST and the challenges associated with naming and standardizing them in a decentralized database system. Alternatives like allele codes are mentioned, which could potentially simplify the representation of sequence types. Finally, the potential for adopting this hashing approach within larger bioinformatics organizations like Phage or GMI is discussed, with an emphasis on the need for a standardized and community-supported framework to ensure the longevity and effectiveness of hash databases in microbial genomics. This episode provides a comprehensive overview of how hash databases can revolutionize microbial genomics by solving long-standing issues of database synchronization and allele identification, paving the way for more efficient and collaborative genomic surveillance worldwide.

Mark your calendars for the 2024 Phoenix Live Podcast Tour April 1st-3rd, 2024 in Scottsdale, Arizona. Learn more here: https://www.techtables.com/2024-phoenix-live-podcast-tourBefore we get into this week's podcast, I wanted to give a special shout-out to TechTables podcast sponsors: SentinelOne, Verizon, and SAP.SentinelOne's AI-powered security platform to break down silos and protect this state's entire enterprise with real-time data and control. With seamless updates and overhead reduction securing 15,000+ endpoints across 25 agencies, SentinelOne partners to protect critical assets across states and agencies. Learn how SentinelOne empowers this state to stay secure.Verizon Frontline. The advanced network for first responders on the front lines. It's your mission. It's your Verizon.More than 35,000 agencies rely on Verizon Frontline and its mission-critical solutions. Check out the solutions built for first responders.Overwhelmed by Digital Transformation? Here's How One City Keeps Pace in the Digital Age.Provide residents and city employees with an even better, happier life through digital transformation.Download the Case Study Now--------

Lo que produce el gozo del Señor! graceradiolife@gmI.com --- Send in a voice message: https://podcasters.spotify.com/pod/show/grace537/message

As a Thanksgiving treat, we're bringing all members one of our favorite Real Vision Plus pieces of the year. Enjoy. In this episode, Julien Bittel, head of macro research at GMI, explains how the leading and lagging indicators for inflation, employment, liquidity, and more drive shifts in the business cycle and dictate market sentiment. Recorded on October 8, 2023. This is the biggest Black Friday discount we've offered. Right now, we're giving you a 50% discount across all Real Vision membership tiers, and members get to apply their existing membership credits as an additional discount. Learn how to upgrade here: realvision.com/earlyblackfriday Unlock the potential to showcase your brand to our global audience. Contact us at partnerships@realvision.com for advertising inquiries. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Jon Browning had a lightning bolt hit him after a quarter of century with Microsoft. He started Global Mentorship Initiative (GMI). GMI university partners nominate motivated, high potential students as they prepare to graduate and enter the workforce. We match these students with mentors from our diverse corporate partners. The mentorship focuses on building confidence and developing soft skills transferable across any industry. Our program leverages digital resources to create real, human connections that transcend distance and difference.I personally had the opportunity to mentor a Senior forensic student in Baltimore. It was an eye opening, mentoring process. that led to a successful placement with the Baltimore Police Department. Visit globalmentorship.org

Join us for an insightful episode of BioTalk as host Rich Bendis welcomes back Rachel King, the visionary CEO of the Biotechnology Innovation Organization (BIO), bringing with her a remarkable journey spanning roles as an entrepreneur, board member, and advocate for the biotech industry. In this engaging installment, Rachel offers a glimpse into her evolution from consulting to industry, including her role as co-founder and former CEO of GlycoMimetics, Inc. (GMI). Discover the essence of BIO, its mission, and the global perspective it brings to the biotech landscape. Delve into Rachel's advocacy role and the critical issues facing the industry today, including pricing, congressional relations, and investment challenges. Gain insights into the state of the biotech industry and Rachel's projections for both the near and long term. Listen as we unravel the dynamic journey of Rachel King, a trailblazing leader in biotechnology, and gain a comprehensive perspective on the industry's present and future. Available now on all major podcasting platforms.

In this episode, Ben sits down with Jon Moore and Gaby Goldberg of The Chernin Group Crypto. At TCG, Gaby and Jon invest in web3 consumer applications and related infrastructure. Deploying out of a $120mm Fund I, TCG has and continues to invest in payments, collectives, gaming, entertainment, and consumer related utility products. The fund has a unified thesis around digital ownership unlocking net-new and business accretive opportunities. Tune in for unique insights into the application layer, mobile, wallets, and more. Let's get into it. Timestamps 2:00 What consumer use-case has the opportunity to onboard durable new users to crypto? 4:25 What is your background? 10:27 How has the team developed TCG's thesis and how has it evolved? 16:21 Are there examples of "passion" not driven by speculation/financialization in crypto? 19:08 Can you elaborate on how Arkive represents a new consumer experience? 24:21 Web2 brands integrating web3 tooling vs. web3 native brands? 31:03 How do you think about the integration of consumer with crypto infrastructure when investing? 36:18 How can crypto transition into mobile? 42:47 What is GMI vs. NGMI in crypto gaming? 43:41 What is GMI vs. NGMI in crypto music? 44:50 What is GMI vs. NGMI in crypto payments? 45:51 What is GMI vs. NGMI in DAOs? 46:50 What are some projects that you're excited about? 55:29 What is your spiciest take within crypto? 58:22 What is your spiciest take outside of crypto? 1:01:16 Where can people learn more about you? Resources Mentioned Bright Moments Hume Arkive Flamingo Medallion Pudgy Penguins Y00ts to Ethereum Rainbow Wallet Sphere Curio Jia Finance Fairer by TCG Ledger Recover Once Upon - Block explorer Connect with the guest TCG Crypto Gaby Goldberg Jon Moore Disclaimer  Ben Jacobs is a partner at Scenius Capital Management. All views expressed by Ben and the guests of this podcast are solely their opinions and do not reflect the opinions of Scenius Capital Management. Guests and the host may maintain positions in the assets or funds discussed in this podcast. You should not treat any opinion expressed by anyone on this podcast as a specific inducement to make a particular investment or follow a particular strategy but only as an expression of their personal opinion. This podcast is for informational purposes only.

In this podcast endocrine expert David C. Lieb, MD, FACP, FACE, interviews our AACE 2023 award-winning plenary speakers, Elizabeth N. Pearce, MD, MSc, FACE, Irl Hirsch, MD, and Richard J. Auchus, MD, PhD, FACE, about the latest advancements in thyroid disease, diabetes, and adrenal disease. Key topics include iodine deficiency, adjusting thyroid medication doses, the future of insulin therapy, GMI compared to A1C levels, screening for primary aldosteronism, Cushing's syndrome, adrenal steroid profiling, and more. 

In this presentation, Dr. Eric wish discusses his GMi signal/indicat and green line breakout setup. He walks through how to use both of these tools effectively and provides many different examples. Video: https://youtu.be/fYU-cz9j61g Enjoy! - The TL Team ----------------------------------------------------------------------------------------------------------------------- Learn Swing Trading from the US Investing Champion Oliver Kell (941% Return): https://www.traderlion.com/swing-trading-masterclass-oliver-kell/ Trade like a Pro—Fast! https://www.traderlion.com/tl-private-access/ https://bit.ly/34ktd3Y ----------------------------------------------------------------------------------------------------------------------- Stay in touch: Follow us on Twitter: https://www.twitter.com/TraderLion_ Like us on Facebook: https://www.facebook.com/TraderLion Our Blog: https://www.traderlion.com/blog/

Diya Wynn, Senior Practice Manager in Responsible AI for AWS Machine Learning Solutions Lab, joins Corey on Screaming in the Cloud to discuss her team's efforts to study and implement responsible practices when developing AI technology. Corey and Diya explore the ethical challenges of AI, and why it's so important to be looking ahead for potential issues before they arise. Diya explains why socially responsible AI is still a journey, and describes how her and her team at AWS are seeking to forge that path to help their customers implement the technology in a safe and ethical way. Diya also describes her approach to reducing human-caused bias in AI models. About DiyaDiya Wynn is the Senior Practice Manager in Responsible AI for AWS Machine Learning Solutions Lab. She leads the team that engages with customers globally to go from theory to practice - operationalizing standards for responsible Artificial Intelligence/Machine Learning and data. Diya leads discussions on taking intentional action to uncover potential unintended impacts, and mitigate risks related to the development, deployment and use of AI/ML systems. She leverages her more than 25 years of experience as a technologist scaling products for acquisition; driving inclusion, diversity & equity initiatives; leading operational transformation across industries and understanding of historical and systemic contexts to guide customers in establishing an AI/ML operating model that enables inclusive and responsible products. Additionally, she serves on non-profit boards including the AWS Health Equity Initiative Review Committee; mentors at Tulane University, Spelman College and GMI; was a mayoral appointee in Environment Affairs for 6 consecutive years and guest lectures regularly on responsible and inclusive technology. Diya studied Computer Science at Spelman College, the Management of Technology at New York University, and AI & Ethics at Harvard University Professional School and MIT Sloan School of Management.Links Referenced:Machine Learning is a Marvelously Executed Scam: https://www.lastweekinaws.com/blog/machine-learning-is-a-marvelously-executed-scam/ TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Tailscale SSH is a new, and arguably better way to SSH. Once you've enabled Tailscale SSH on your server and user devices, Tailscale takes care of the rest. So you don't need to manage, rotate, or distribute new SSH keys every time someone on your team leaves. Pretty cool, right? Tailscale gives each device in your network a node key to connect to your VPN, and uses that same key for SSH authorization and encryption. So basically you're SSHing the same way that you're already managing your network.So what's the benefit? Well, built-in key rotation, the ability to manage permissions as code, connectivity between any two devices, and reduced latency. You can even ask users to re-authenticate SSH connections for that extra bit of security to keep the compliance folks happy. Try Tailscale now - it's free forever for personal use.Corey: Kentik provides Cloud and NetOps teams with complete visibility into hybrid and multi-cloud networks. Ensure an amazing customer experience, reduce cloud and network costs, and optimize performance at scale — from internet to data center to container to cloud. Learn how you can get control of complex cloud networks at www.kentik.com, and see why companies like Zoom, Twitch, New Relic, Box, Ebay, Viasat, GoDaddy, booking.com, and many, many more choose Kentik as their network observability platform. Corey: Welcome to Screaming in the Cloud, I'm Corey Quinn. In a refreshing change of pace, I have decided to emerge from my home office cave studio thing and go to re:Invent and interview people in person. This is something of a challenge for me because it is way easier in person to punch me in the face, so we'll see how it winds up playing out. My guest today is Diya Wynn, Senior Practice Manager at AWS. Diya, what is a practice manager at AWS? What do you do?Diya: So, a practice manager, I guess you can think of it just like a manager of a team. I have a practice that's specifically focused on Responsible AI. And I mean, practices are just like you could have won in financial services or anything. It's a department, essentially. But more important than the practice in the title is actually what I get a chance to do, and that's working with our customers directly that are using and leveraging our AI/ML services to build products.And we have an opportunity to help them think about how are they using that technology in ways to have improvements or benefit individuals in society, but minimize the risk and the unintended impact or harm. And that's something that we get to do with customers over any industry as well as globally. And my team and I have been enjoying the opportunity to be able to help them along their Responsible AI journey.Corey: So, the idea of Responsible AI is… I'm going to sound old and date myself when I say this, but it feels like it's such a strange concept for me, someone who came up doing systems administration work in physical data centers. The responsible use of a server back when I was hands-on hardware was, “Well, you don't want to hit your coworker with a server no matter how obnoxious they are.” And it was fairly straightforward. It was clear: yes or no. And now it seems that whenever we talk about AI in society, in popular culture, from a technologist's point of view, the answer is always a deeply nuanced shade of gray. Help.Diya: Nuanced shade of gray. That's interesting. It is a little bit more challenging. I think that it is, you know, in one sense because of the notion of all of the data that we get to leverage, and our machine-learning models are reliant on data that has variations coming from, you know, historical sort of elements, things that are here baked with bias, all of that has to be considered. And I think when we think about some of the challenges and even the ways in which AI is being used, it means that we have to be much more mindful of its context, right?And these systems are being used in ways that we probably didn't think about servers being used in the past, but also are in the midst of some high-stakes decisions, right? Whether or not I might be identified or misidentified and inappropriately arrested or if I get the appropriate service that I was thinking about or whether or not there are associations related to my gender or my sexual preference. All of that matters, and so it does become much more of a nuanced conversation. Also because depending on the jurisdiction you're in, the region, what makes sense and what matters might differ slightly. So, it's a multidisciplinary problem or challenge that we need to think about what is the legality of this?And we have to think about social science sometimes and there's an element of ethics. And all of that plays into what becomes responsible, what is the right way in which we use the technology, what are the implications of technology? And so yes, it is a little bit more gray, but there are things that I think we have at our disposal to help us be able to respond to and put in place so that we really are doing the right things with technology.Corey: I've known Amazon across the board to be customer-obsessed, and they tell us that constantly—and I do believe it; I talk to an awful lot of Amazonians—and so much of what the company does comes directly from customer requests. I have to ask, what were customers asking that led to the creation of your group? Because it seems odd to me that you would have someone coming to you and saying, “Well, we built a ‘Hot Dog/Not A Hot Dog' image recognition app,” and, “Oopsie. It turns out our app is incredibly biased against Canadians. How do we fix this?” Like, that does not seem like a realistic conversation. What were the customer concerns? How are they articulated?Diya: No, that's really good. And you're right. They weren't asking the question in that way, but over the last five years or so, I would say, there has been an increase in interest and as well as concern about how AI is being used and the potential risks or the areas of unintended impact. And with this sort of heightened sensitivity or concern, both with our executives as well as members of common society, right—they're starting to talk about that more—they started to ask questions. They're using surfaces we want to be responsible in building.Now, some customers were saying that. And so, they would ask, “What are other customers doing? What should we be aware of? How do we or are there tools that we can use to make sure that we're minimizing bias in our systems? Are there things that we can think about in the way of privacy?”And oftentimes privacy and security are one of those areas that might come up first. And those were the kinds of questions. We actually did a survey asking a number of our customer-facing resources to find out what were customers asking so that we could begin to respond with a product or service that would actually meet that need. And I think we've done a great job in being able to respond to that in providing them assistance. And I think the other thing that we paid attention to was not just the customer requests but also what we're seeing in the marketplace. Part of our job is not only to respond to the customer need but also sometimes to see the need that they're going to have ahead of them because of the way in which the industry is moving. And I think we did a pretty good job of being able to see that and then start to provide service and respond to assist them.Corey: Yeah, it's almost like a rule that I believe it was Scott Hanselman that I stole it from where the third time that you're asked the same question, write a blog post, then that way you can do a full deep—Diya: Did he really say write a post? [laugh].Corey: Treatment of it. Yes, he did. And the idea is, write a blog post—because his blog is phenomenal—and that way, you have a really in-depth authoritative answer to that question and you don't have to ad-lib it off the cuff every time someone asks you in the future. And it feels like that's sort of an expression of what you did. You started off as a customer-facing team where they were asking you the same questions again and again and at some point it's, okay, we can either spend the rest of our lives scaling this team ad infinitum and winding up just answering the phone all day, or we can build a service that directly addresses and answers the question.Diya: Absolutely, absolutely. I think that's the way in which we scale, right, and then we have some consistency and structure in order to be able to respond and meet a need. What we were able to do was—and I think this is sort of the beauty of being at AWS and Amazon; we have this opportunity to create narratives and to see a need, and be able to identify and respond to that. And that's something that everybody can do, not just resigned to a VP or someone that's an executive, we all can do that. And that was an opportunity that I had: seeing the need, getting information and data, and being able to respond and say, “We need to come up with something.”And so, one of our first pieces of work was to actually define a framework. How would we engage? What would be that repeatable process or structure for us, framework that we can leverage with our customers every time to help them think through, look around corners, understand where there's risk, be better informed, and make better-informed decisions about how they were using the technology or what ways they could minimize bias? And so, that framework for us was important. And then we have now tools and services as well that were underway, you know, on our product side, if you will, that are complementing—or that, you know, complement the work.So, not only here's a process, here's a framework and structure, but also here are tools that in technology you can bring to bear to help you automate, to help you understand performance, or even you know, help you minimize the bias and risk.Corey: What's interesting to me, in a very different part of the world than AI, I live in AWS costing because I decided, I don't know, I should just go and try and be miserable for the rest of my life and look at bills all day. But whenever I talk to clients, they asked the same question: what are other customers doing, as you alluded to a few minutes ago? And that feels like it's a universal question. I feel like every customer, no matter in what discipline or what area they're in, is firmly convinced that somewhere out there is this utopian, platonic ideal of the perfect company that has figured all of this stuff out and we're all constantly searching for them. Like, there's got to be someone who has solved this problem the right way.And in several cases, I've had to tell clients that you are actually one of the best in the world and furthest advanced at this particular thing. That customer, the closest we've got to them is you, so we should be asking you these questions. And for whatever it's worth, no one ever likes hearing that because, “Like, oh, we're doing something wild.” It's like—Diya: [crosstalk 00:10:15] pioneers.Corey: —“Well, we got to solve this ourselves? That's terrible.”Diya: Well, it's interesting you say that because it is a common question. I think customers have an expectation that because we are AWS, we've seen a lot. And I think that's true. There are tens of thousands of customers that are using our services, we have conversations with companies all across the world, so we do have some perspective of what other customers are doing and that's certainly something that we can bring to the table. But the other part of this is that this is really a new area. This is a sort of new space, that we're focused on trustworthy and Responsible AI, and there aren't a ton of customers that are doing this—or companies at all—that have it entirely answered, that have—you know, we're all on a journey.So, these are, I would say, early stages. And we do have the benefit of being large, having a lot of customers, having some experience in building services as well as helping our customers build products, having a team that's focused on looking at standards and working with standards bodies globally, having teams that are working on our understanding what we're doing in regulation and public policy. And so, all of that we bring to bear when we start talking about, you know, this with our customers. But we don't have all the answers; we're on a journey like them. And I think that's something that we have to be comfortable with, to some degree, that this is an evolving area and we're learning. And we're investing even in research to help us continue to move forward. But there's a lot that we know, that there's a lot that we can bring to the table, and we can help our customers in that regard.Corey: Now, this might very well be old news and well understood and my understanding is laughably naive when this gets released, but as of this recording, a few hours beforehand, you released something called Service Cards. And I have to say, my initial glance at this was honestly one of disappointment when I saw what it was because what I was hoping for, with—when you ever see ‘service' and ‘cards' together, is these are going to be printable cardboard, little cards that I can slip into the Monopoly board game I have at home and game night at home is going to be so traumatic for my kids afterwards. Like, “What's a Fargate?” Says the five-year-old, and there we go. “It means that daddy is not going to passing go, going directly to jail with you. Have fun,” it's great. But I don't think that's what it is.Diya: No, not at all. Not at all. So, it is very similar to the context that people might be familiar with around model cards, being able to give definition and understanding of a model that's being used. For us, we sort of took that concept at one step beyond that in that, you know, just providing a model card isn't sufficient necessarily, especially when there are multiple services or multiple models being used for any one of our services. But what our Service Cards allow us to do is to provide a better understanding of the intended use of the service, you know, and the model that's underpinning that, give context for the performance of that service, give guidelines for our customers to be able to understand how was it best used and how does it best perform.And that's a degree of transparency that we're providing under the hood, for our customers to really help them as well be much more responsible and how they're building on top of those. And it gives them clarity because there is a growing interest in the marketplace for our customers to hold their vendors—or companies to hold their vendors responsible, right, making sure that they're doing the right things and covering off, are we building well? Do we have, like, the customer or enough of demographic covered? What the performance looks like. And this is a really big opportunity for us to be transparent with our customers about how our services are being built and give them a little bit more of that guardrail that we were talking about—guidelines—how to best use it as they look to build upon those.Corey: Not in any way, shape, or form to besmirch the importance of a lot of the areas that you're covering on this, but on some level, I'm just envious in that it would be so nice to have that for every AWS service, of this is how it is—Diya: Uh-oh [laugh].Corey: —actually intended to be used. Because to me, I look at it and all I see is database, database, really expensive database, probably a database, and, like, none of those are designed to be databases. Like, “You lack imagination,” is my approach. And no, it just turns out I'm terrible at computers, but I'm also enthusiastic and those are terrible combinations. But I would love to see breakdowns around things like that as far as intended use, potential pitfalls, and increasingly as we start seeing more and more services get machine learning mixed in, for lack of a better term, increasingly we're going to start to see areas where the ethical implications absolutely are going to be creeping in. Which is a wild thing to say about, I don't know, a service that recommends how to right-size instances having ethical concerns. But it's not that unreasonable.Diya: Well, I can't make any promises about us having those kinds of instructions or guidelines for some of our other services, but we are certainly committed to being able to provide this transparency across our AI/ML services. And again, that's something I will say that's a journey. We've released a few today; there are others that are going to come. We're going to continue to iterate and evolve so that we can get through our services. And there's a lot of work behind that, right?It's not just that we wrote up this document, but it is providing transparency. But it also means that our teams are doing a great bit in terms of the diligence to be able to provide that feedback, to be able to test their models, understand their datasets, you know, provide information about the datasets in public—you know, for the public datasets that are being tested against, and also have the structure for them to train their models appropriately. So, there's a lot going into the development of those that may not be immediately transparent, but really is core to our commitment to how we're building our services now.Corey: It's a new area in many respects because, like, to be very direct. If I wind up misusing or being surprised by a bad implementation of something in most cases in AWS context, the disaster area looks a lot closer to I get a big bill. Which—and this [unintelligible 00:16:35] is going to sound bizarre, but here we are, it's only money. Money can be fixed. I can cry and sob to support and get that fixed.With things like machine learning and AI, the stakes are significantly higher because given some of the use cases and given some of the rapid emerging technology areas in which these things are being tested and deployed, it hurts people if it gets wrong. And an AWS bill is painful, but not in a damaging to populations level. Yet. I'm sure at some point, it becomes so large it becomes its own micro-economy, I guess the way those credits are now, but it's a very different way.Diya: Right. Absolutely. So, I think that's why our work from a responsibility perspective is important. But I think it's also valuable for customers to understand, we're taking a step forward and being able to help them. Very much like what we do with well-architected, right? We have a framework, we have best practices and guidance that is being provided so that our customers who are using our cloud services really know what's the best.This is very much like those Service Cards, right? Here's the best conditions in order to be able to use and get the greatest value out of your cloud investment. The same thing is what we're doing with this approach in helping our customers in the Responsible AI way. Here's the best, sort of, best practices, guidance, guardrails, tools that are going to help you make the most out of your investment in AI and minimize where there's this unintended or potential areas of potential harm that you were describing. And you're right, there are high stakes use cases, right, that we want to make sure or want to be able to help and equip our customers to think more about intentionally and be prepared to be able to hopefully have a governance structure, people aligned, processes, technology to really be able to minimize that, right? We want to reduce the blast radius.[midroll 00:18:37]Corey: One thing I want to call out as well is that as much as we love in tech to pretend that we have invented all of these things ourselves—like, we see it all the time; like, “No one really knows how to hire, there's no real scientific study on this.” “Yes, there are. There are multi-decade longitudinal studies at places like GM and whatnot.” And, “No, no, no tech is different. There's no way to know this. La la la.”And that's great. We have to invent these things ourselves. But bias has been a thing in business decisions, even ones that are not directly caused by humans, for a long time. An easy example is in many cases, credit ratings and decisions whether to grant credit or not. Like, they were not using machine learning in the 90s to do this, but strangely, depending upon a wide variety of factors that are not actually things that are under your control as a person, you are deemed to be a good credit risk versus a bad credit risk.And as a result, I think one of the best terms I heard in the early days when machine learning started getting big, was just referring to it as bias laundering. Well, we've had versions of that for a long time. Now, at least it seems like this shines a light on it if nothing else, and gives us an opportunity to address it.Diya: Absolutely. Oh, I'd love that, right? The opportunity to address it. So, one of the things that I often share with folks is we all have bias, right? And so, like you said we've had bias in a number of cases. Now, you know, in some cases, bias is understandable. We all have it. It is the thing that often—we talk about the sort of like mental shortcuts, things that we do that help us to respond rapidly in the world in the vast array of information that we're taking in all the time. So—Corey: You're an Amazonian. You yourself bias for action.Diya: Exactly. Right? So, we have bias. Now, the intent is that we want to be able to disrupt that so that we don't make decisions, oftentimes, that could be harmful, right? So, we have proclivities, desires, interest, right, that kind of folds into our bias, but there are other things, our background, where we went to school, you know, experiences that we had, information that we've been taking that also helped to drive towards some of those biases.So, that's one element, right, understanding that. A human bias gets infiltrated into our systems. And there was a study in AI now—I think it was 2019—that talked about that, right, that our systems are often biased by—or the bias is introduced, you know, sometimes by individuals. And part of the necessity for us to be able to eliminate that is understanding that we have bias, do things to interrupt it, and then also bringing in diversity, right? Because some of our biases are just that we don't have enough of the right perspectives in the room; we don't have enough of the right people involved, right?And so, being able to sort of widen the net, making sure that we're involving the outliers, I think are important to us being able to eliminate bias as well. And then there are tools that we can use. But then you also bring up something interesting here in terms of the data, right? And there's a part that education plays a good role in helping us understand the things like what you described our institutional biases baked into our data that also can come out in decisions that are now being made. And the more that we use AI in these ways, the more there is risk for that, right?So, that's why this effort in Responsible AI, understanding how we mitigate bias, understanding how we invite the right people in, the inclusion of the right perspectives, thinking about the outliers, thinking about whether or not this is the right problem for us to solve with AI is important, right, so that we can minimize those areas where bias is just another thing that we continue to propagate.Corey: So, a year or two ago, I wrote a blog post titled Machine Learning is a Marvelously Executed Scam. And it was talking about selling digital pickaxes into a data gold rush.Diya: I [crosstalk 00:22:30] remember this one [laugh].Corey: And it was a lot of fun. In fact, the Head of Analyst Relations at AWS for Machine Learning responded by sending me a Minecraft pickaxe made out of foam, which is now in my home office hung behind my office and I get a comment at least three times a week on that. It was absolutely genius as far as rebuttal go. And I've got to find some way to wind up responding to her in kind one of these days.But it felt like it was a solution in search of a problem. And I no longer hold so closely to that particular opinion, in no small part due to the fact that, as you're discussing, this area is fraught, it's under an awful lot of scrutiny, large companies who use these things and then those tools get it wrong are going to basically wind up being castigated for it. And yet, they are clearly realizing enough value from machine learning that it is worth the risk. And these are companies whose entire business, start to finish, is managing and mitigating risk. There is something there or suddenly everyone has taken leave of their senses. I don't quite buy that second option, so I'm guessing it's the first.Diya: So, the question is, is it worth the risk? And I would say, I think some people might or some companies might have started to step into that area thinking that it is, but it's not. And that's what we're saying and that's what we're hearing in the industry [unintelligible 00:23:51], that it's not worth the risk. And you're hearing from customers, outcries from others, government officials, right, all of them are saying, like, “It's not worth the risk and we have to pay attention to that.”But I think that there's certainly value and we're seeing that, right? We're solving previously unattainable problems with AI. We want to be able to continue to do that, but give people the means to be able to sort of minimize where there is risk and recognize that this is not a risk that's worth us taking. So, the potential for reputational harm and the damage that will do is real, right? When a company is called out for the fact that they've discriminated and they're unfairly evaluating homes, for instance, for people of color in certain communities, right, that's not something that's going to be tolerated or accepted.And so, you have people really calling those things out so that we start to—organizations do the right things and not think that risk is worth the [unintelligible 00:24:52]. It is very well worth the risk to use AI, but we've got to do it responsibly. There's so much value in what we are able to accomplish. So, we're seeing, you know, even with Covid, being able to advance, like, the technology around vaccinations and how that was done and accelerated with machine learning, or being able to respond to some of the needs that small businesses and others had, you know, during Covid, being able to continuate their service because we didn't have people in businesses or in offices, a lot of that was advanced during that time as a result of AI. We want to be able to see advances like that and companies be able to continue to innovate, and so we want to be able to do that without the risk, without the sort of impact that we're talking about, the negative impact. And I think that's why the work is so important.Corey: Do you believe that societally we're closer to striking the right balance?Diya: We're on our way. I think this is certainly a journey. There is a lot of attention on this in the right ways. And my hope—and certainly, that's why I'm in a role like this—that we can actually invite the right voices into the room. One of the things—and one of my colleagues said this earlier today, and I think it was a really, really great point, right—as we are seeing—first of all, we never thought that we would have, like, ethicists roles and sort of Responsible AI folks, and chief ethics officers. That was not something that existed in the context of, sort of, machine learning, and that's something that it's evolved in the last, you know, few years.But the other thing that we're seeing is that the folks that are sitting in those roles are increasingly diverse and are helping to drive the focus on the inclusion that we need and the value of making sure that those voices are there so that we can build in inclusive and responsible ways. And that's one of the things that I think is helping us get there, right? We're not entirely there, but I think that we're on a path. And the more that we can have conversations like this, the more that companies are starting to pay attention and take intentional action, right, to build ethically and to have the trust in the technology and the products that they build, and to do that in responsible ways, we'll get there.Corey: I really want to thank you for taking so much time to talk through what you're up to with me.Diya: I am super excited and glad that you were able to have me on. I love talking about this, so it's great. And I think it's one of the ways that we get more people aware, and hopefully, it sparks the interest in companies to take their own Responsible AI journey.Corey: Thank you so much for your time.Diya: Thanks for having me.Corey: I appreciate it. Diya Wynn, Senior Practice Manager at AWS. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice along with an angry insulting comment, presumably because you're Canadian.Diya: [laugh].Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.

As we dig back into the ever-evolving world of retail in the Season 2 opener of The Longer Game, we're talking with Carter Jensen, the Sr. Manager of Global Commerce at GMI, aka General Mills, about how they're taking brands we all grew up with and loved into the future with us. How we engage with these brands has to change because, well, how we engage with each other is different. Listen in to find out what Carter eats for lunch these days and what's going on in CPG in 2022. The Longer Game is a podcast focused on leaning into the trends and advancements in retail so brands see a clearer path to success across ALL channels. We're looking at retail in a whole new way, looking to better understand the future of retail. It's Retail Reimagined. Sharing hope about the future. No one channel can a business sustain. Go omnichannel. Like what you're hearing? Subscribe to our channel and make sure to click or tap the bell so you get notified whenever new episodes drop. Want to learn more about The Longer Game? Head over to https://thelongergame.com to read show notes, watch more episodes, or contact us. Follow us on LinkedIn: https://linkedin.com/company/thelongergame Follow us on Instagram: https://instagram.com/thelongergame Follow us on Facebook: https://facebook.com/thelongergame Follow us on Twitter: https://twitter.com/the_longer_game Our guest Guest Name is Carter Jensen. Carter is the Sr. Manager of Global Commerce at GMI, aka General Mills. Carter joined General Mills in 2020 amid ecommerce disruption driven by the Covid-19 pandemic. With a mission to help accelerate ecommerce capabilities, he supported the vision of “Connected Commerce” across the organization, transforming the go-to-market strategy for the company's collection of leading, global brands. You can find them at... Website: CarterJensen.com Find them on LinkedIn: linkedin.com/in/CarterJensen Michael Maher, the host, would love to connect with you. Reach out to him at… Email: michael@thinkcartology.com LinkedIn: https://linkedin.com/in/immichaelmaher This podcast is sponsored by Cartology and Podcastify Me. Cartology is a customized done-for-you service agency that helps brands accelerate growth and get profitable on the Amazon marketplace. They work directly with brands to create a strategy and then go right out and execute it. Want to find out more? Website: https://thinkcartology.com Find Cartology on LinkedIn: https://linkedin.com/company/cartology Find Cartology on Instagram: https://instagram.com/thinkcartology Find Cartology on Facebook: https://facebook.com/thinkcartology Podcastify Me is designed to help coaches of all kinds enter the podcasting space with minimal lift for them. And, inviting past, current, and future clients to your show as part of your marketing and sales process sets you apart from your competition, at a time when podcasting is really gaining popularity. Website: https://podcastify.me Find Podcastify Me on LinkedIn: https://www.linkedin.com/company/podcastify-me/ Find Podcastify Me on Instagram: https://www.instagram.com/podcastify.me/ Find Podcastify Me on YouTube: https://www.youtube.com/channel/UCf2biqOTN2UbZ5aaM4Sx6NQ --- Send in a voice message: https://podcasters.spotify.com/pod/show/thelongergame/message Support this podcast: https://podcasters.spotify.com/pod/show/thelongergame/support

Nick is a software engineer and a true crypto OG doing his first iterations since 2010. But was after having his first contact with a subjective consensus protocol that a new path unfold. That was the birth of Upshot, a protocol that today gives deep insight and appraisals into NFT markets for DeFi, a new intersection of possibilities. Time Stamps00:00 - 5:39: Who's Nick5:40 - 10:14: Upshot10:15 - 13:05: Subjective Consensus into NFTs13:06 - 19:19: Appraising more than FP19:20 - 25:00: Walkthrough from User25:01 - 28:40: Future of DeFi w/ NFTs28:41 - 35:44: GMI 35:45 -  41:47: Current State41:48 - 47:05: New Metrics Features47:06 - 47:22: Team 47:23 - 51:07: Manifold and OE 51:08 - : Tracking and GMI56:06 - 1:01:37: Gaming Space1:01:38 - 1:03:17: Rounding OffGuest SocialsTwitter: https://twitter.com/UpshotHQWebsite https://upshot.xyz Discord https://discord.com/invite/upshotUseful Links Based On ConversationBenDAO: https://www.benddao.xyz/Phunks: https://opensea.io/collection/crypto-phunksnftX: https://nftx.io/Uniswap: https://uniswap.org/Manifold: https://manifold.xyz/GlossaryImpermanent Loss - Impermanent loss is a risk that liquidity providers in automated market maker (AMM) systems face due to changes in the price ratio of the tokens they provide. If the ratio of tokens changes too much, the liquidity provider may end up with fewer assets than they originally provided, resulting in losses. Longs - In finance, a "long" position refers to the act of buying an asset in the hopes that it will increase in value over time, enabling the investor to sell it at a profit.Shorts - In finance, shorting refers to borrowing and selling an asset in the hopes of repurchasing it at a lower price to make a profit. It's essentially betting against the asset's price, with the goal of making money if the asset's price falls.Liquidity Pool - A liquidity pool is a collection of funds in a decentralized finance (DeFi) protocol that is locked in a smart contract. These funds are used to facilitate trading, and users can earn rewards for providing liquidity to the pool. Liquidity pools help to ensure that there is enough liquidity available to facilitate trades in DeFi protocols.Open Editions - Open Editions are NFTs that have an unlimited number of copies available for purchase. Unlike limited editions that have a fixed number of copies, open editions can be minted continuously. This means that anyone can purchase an open edition NFT at any time, and the number of copies in circulation can continue to increase indefinitely.Podcast Host: BunzyTwitter: https://twitter.com/0xBunzyBlockTalk || Pineapple WorkshopWebsite: https://pineappleworkshop.com/Twitter: https://twitter.com/poweredby_pwDiscord: https://discord.gg/geNCbMYsZY

VIDEOS: “We are on a path of escalation to nuclear war, nothing less” – Jeffrey D. Sachs – 8:46 The WEF plan to REMAKE America just got a HUGE boost from Putin | Redacted with Clayton Morris – 7:15 She admitted it. – 0:58 Google Is A Drug Company and Censors Health News  Neil Oliver – ‘…they're herding us towards mass conformity….'   Can Eating Peppers Help You Live Longer Tulane University September 28, 2022 People who have a taste for chili peppers and other hot spicy foods may live longer, research suggests. A new study of more than 500,000 Chinese adults over seven years finds that participants who ate foods flavored with chili peppers every day reduced their risk of premature dying by 14 percent, as compared to people who ate chili peppers less than once a week. “Even among those who consumed spicy foods less frequently [one to two days a week], the beneficial effects could be observed,” says Lu Qi, professor of epidemiology at Tulane University. “Indeed, moderate increase of spicy foods would benefit.” While his study, published in the BMJ, doesn't address other foods, earlier research has indicated that horseradish, black pepper, garlic, and ginger may offer similar benefits.”There also is preliminary data from other studies showing such potential,” Qi says. Capsaicin in chili peppers may be what protects health, Qi says. It reduces risk of obesity, offers antibacterial properties, and helps protect against diabetes, cardiovascular disease, cancer, and other conditions. Chili peppers also improve inflammation and reduce blood pressure and oxidative stress. Omega-3 may be helpful for attention, impulse control in adolescents University Pompeu Fabra (Spain), October 3 2022. Research findings reported in European Child & Adolescent Psychiatry revealed that higher blood levels of the omega-3 fatty acids docosahexaenoic acid (DHA) and alpha-linolenic acid (ALA) were respectively associated with better test scores for attention and impulsiveness among adolescent boys and girls compared with adolescents who had lower levels. “Polyunsaturated fatty acids are critical for brain development and function, and their deficiency may have long-term functional consequences” authors Ariadna Pinar-Marti and colleagues explained. “Despite the established importance of DHA in brain development, few studies have evaluated whether it plays a role in the attention performance of healthy adolescents,” commented study coordinator Jordi Júlvez, PhD. “In addition, the possible role of alpha-linolenic acid (ALA), another omega-3 but of plant origin, has not been as extensively studied.” In comparison with adolescents whose DHA levels were among the lowest one-third of participants, those whose levels were among the top third showed better attention performance as indicated by scores for reaction time and conflict response. Higher ALA levels were associated with less impulsivity. “The role of ALA in attention control is still unclear, but this finding may be clinically relevant, as impulsivity is a feature of several psychiatric conditions, such as ADHD,” Dr Pinar-Martí noted. Weighted blankets found to increase melatonin Uppsala University (Sweden), October 3, 2022 A new study from Uppsala University shows that using a weighted blanket at bedtime increases melatonin in young adults. This hormone increases in response to darkness, and some evidence suggests that it promotes sleep. The findings are published in the Journal of Sleep Research. Previous research has shown that weighted blankets may ease insomnia in humans. Researchers from Uppsala University in Sweden experimented with 26 young men and women to examine if the bedtime use of a weighted blanket increases the production of sleep-promoting and anti-stress hormones like melatonin and oxytocin. In addition, they investigated whether the bedtime use of a weighted blanket (12% of participants' body weight) reduced the activity of stress systems in the body. To this end, saliva was collected repeatedly from participants while they were covered with either a weighted or a light blanket to measure melatonin, oxytocin, cortisol, and the activity of the fight and flight sympathetic nervous system. “Using a weighted blanket increased melatonin concentrations in saliva by about 30%. However, no differences in oxytocin, cortisol, and the activity of the sympathetic nervous system were observed between the weighted and light blanket conditions,” says Elisa Meth, first author and Ph.D. student at the Department of Pharmaceutical Biosciences at Uppsala University. “Our study may offer a mechanism explaining why weighted blankets may exert some therapeutic benefits, such as improved sleep. However, our findings rely on a small sample and investigated only the acute effects of a weighted blanket. Thus, larger trials are needed, including an investigation of whether the observed effects of a weighted blanket on melatonin are sustained over longer periods,” says senior author Christian Benedict, Associate Professor of Pharmacology at the Department of Pharmaceutical Biosciences at Uppsala University. Clinical Trial: Vegetable Extract may Treat Autism Better than Drugs Harvard Medical School, September 30, 2022 A recent clinical trial has shown that one vegetable extract may have astounding positive effects on those with autism – broccoli extract. Along with an extensive Autism Spectrum Disease research database at GMI, there is evidence suggesting that broccoli extract (along with avoiding heavy metals, minimizing glyphosate exposure, and eradicating the diet of gluten) shows promise in improving ASD. The active ingredient in broccoli that seems to help is called sulforaphane, a molecule found in cruciferous vegetables such as broccoli, cabbage, and Brussels sprouts. A groundbreaking study published in The Proceedings of the National Academy of Sciences USA titled, “Sulforaphane treatment of autism spectrum disorder (ASD),” found that a broccoli sprout extract significantly improved the behavior of boys and men (those who most often suffer from autism). Sulforaphane was selected, in part, because its physiological effects are well characterized and ideal for those with ASD: “Dietary sulforaphane, of recognized low toxicity, was selected for its capacity to reverse abnormalities that have been associated with ASD, including oxidative stress and lower antioxidant capacity, depressed glutathione synthesis, reduced mitochondrial function and oxidative phosphorylation, increased lipid peroxidation, and neuroinflammmation.” The placebo-controlled, randomized pilot study of 44 males, ages 13-27, showed that after 18 weeks of treatment with a sulforaphane-rich broccoli sprout extract, 46% had noticeable improvements in social interactions and 42% has improvements in verbal communication. More than half of all participants also showed a decrease in irritability, hyperactivity, and repetitive movements. Of note, once treatment with broccoli extract stopped, most of the behaviors associated with autism returned. The dosing schedule was determined by body weight: · 100 lbs or less: one capsule containing 50 µmol (232 mg) of sulforaphane-rich broccoli extract was given daily · 101–199 lbs, 100 µmol (two capsules of 232 mg each) of sulforaphane-rich broccoli extract was given daily · More than 200 lbs: 150 µmol (three capsules of 232 mg each) of sulforaphane-rich broccoli extract was given daily Social media use linked to developing depression regardless of personality University of Arkansas, October 3, 2022 Researchers in public policy and education recently found that young adults who use more social media are significantly more likely to develop depression within six months, regardless of personality type. Published in the Journal of Affective Disorders Reports, the study, “Associations between social media use, personality structure, and development of depression,” was co-authored by Renae Merrill, a doctoral student in the Public Policy Program at the University of Arkansas. This new study found strong and linear associations of depression across all personality traits.” Among the study's findings was that people with high agreeableness were 49% less likely to become depressed than people with low agreeableness. Additionally, those with high neuroticism were twice as likely to develop depression than those with low neuroticism when using more than 300 minutes of social media per day. More importantly, for each personality trait, social media use was strongly associated with the development of depression. The sample of more than 1,000 U.S. adults between the ages of 18 to 30 was from data collected by Primack and his colleagues at the University of Pittsburgh. The authors suggest that problematic social comparison can enhance negative feelings of oneself and others, which could explain how risk of depression increases with increased social media use. Engaging primarily in negative content can also enhance these feelings. And lastly, engaging in more social media reduces opportunities for in-person interactions and activities outside of the home. “Findings from this study are important during a time of technology expansion and integration,” Merrill said. “Connecting to people virtually may increase the risk of miscommunication or misperception that leads to relationship difficulties and potential risk for developing mental health problems.” “People have innate emotional needs for social connection and understanding,” Merrill added. “For example, social media experiences can be improved by becoming more aware of our emotions and our connection with others in various life circumstances. This awareness helps improve relationship quality by simply reaching shared meaning and understanding through more effective communication and concern for others and ourselves. Despite our differences, we have the ability to create a culture of empathy and kindness.” Which grains you eat can impact your risk of getting heart disease earlier Isfahan University of Medical Sciences (Iran), October 3, 2022 In one of the first studies to examine the relationship between different types of grain intake and premature coronary artery disease in the Middle East, researchers found a higher intake of refined grain was associated with an increased risk of premature coronary artery disease in an Iranian population, while eating whole grains was associated with reduced risk. According to the researchers, previous epidemiological studies have reported an association between different types of grain intake with the risk of coronary artery disease. The current study evaluated the association between refined and whole grains consumption and risk of PCAD in an Iranian population. Premature coronary artery disease (PCAD) refers to atherosclerotic narrowing of coronary arteries in males under 55 years old or in females under 65 years old. It is often asymptomatic early in the course of the disease but may lead to chest pain (angina) and/or heart attack with progressive development of narrowing (stenosis) or plaque rupture of the arterial wall. Risk factors for PCAD include smoking, high cholesterol, high blood pressure and diabetes. Whole grains are defined as containing the entire grain, while refined grains have been milled—ground into flour or meal—to improve shelf life but they lose important nutrients in the process. The ACC/American Heart Association Guideline on the Primary Prevention of Cardiovascular Disease recommends a diet that emphasizes the intake of vegetables, fruits, legumes, whole grains and fish to decrease heart disease risk factors. The study recruited 2099 individuals with PCAD from hospitals with catheterization labs in different cities and ethnicities throughout Iran who underwent coronary angiography (women aged ≤ 70 and men ≤ 60). In total, 1,168 patients with normal coronary arteries were included in the control group, while 1,369 patients with CAD with obstruction equal or above 75% in at least a single coronary artery or ≥ 50% in the left main coronary artery made up the case group. After adjusting for confounders, a higher intake of refined grains was associated with an increased risk of PCAD, while whole grain intake was inversely related to reduced risk of PCAD.

How is "big CPG" different than emerging brands? Your initial response might be something like they are slower, less tech savy, and less innovative than the emerging brands. After this chat with Carter Jensen, Global Lead of DTC, for General Mills I can say that those commonly held assumptions just aren't true. Carter mentions a few GMI brands in the episode here are just a few of them:Ratio Foods Doolies Epic ProvisionsHere's what we cover in today's episode: Carter shares the three tier framework that he builds the DTC strategy around at General Mills We go in depth around what DTC to Learn means to Carter Carter shares how their incubator brands learn and validate ideas Carter explains that the DTC teams at General Mills are using most of the same tools as today's emerging brands We explain why Shopify has been huge for their growth We go in depth around DTC to Brand Build Carter shares an example of how they use DTC to grow the Wheaties brand and community We go in depth around DTC for Sales  Carter explains that they need to use DTC to boost their already established distribution... not take away from it

My guest today is Daniel Fitzgerald, CEO and President of Apollo Intelligence. Founded in 2020, Apollo provides access to 2M healthcare stakeholders worldwide — including physicians, patients, caregivers, and allied healthcare professionals — serving the life science insights industry on its mission to accelerate health innovation to improve life.  They support 80 of the top global-100 life science firms, as well as global market research agencies and consultancies, across 14 different countries in the Americas, Europe, and Asia. Prior to joining Apollo, Dan has been part of the bedrock of the market research industry serving as CEO of InCrowd, Managing Partner of Reimagine, Chief Client and Marketing Officer of Lightspeed, and GM of Global Market Insite. Find Daniel Online: LinkedIn: https://www.linkedin.com/in/daniel-s-fitzgerald-024a2926/ Apollo Intelligence: https://apollointelligence.net/  Find Jamin Online: Email: jamin@happymr.com LinkedIn: www.linkedin.com/in/jaminbrazilTwitter: www.twitter.com/jaminbrazil  Find Us Online:  Twitter: www.twitter.com/happymrxp LinkedIn: www.linkedin.com/company/happymarketresearch Facebook: www.facebook.com/happymrxp Website: www.happymr.com  Music:  “Clap Along” by Auditionauti: https://audionautix.com  This Episode is Sponsored by: The Michigan State University's Master of Science in Marketing Research Program delivers the #1 ranked insights and analytics graduate degree in three formats:  Full-time on campus Full-time online Part-time online NEW FOR 2022:  If you can't commit to their full degree program, simply begin with one of their 3-course certificates: Insights Design or Insights Analysis.  In addition to the certification, all the courses you complete will build toward your graduation. If you are looking to achieve your full potential, check out MSMU's programs at: broad.msu.edu/marketing. HubUX is a research operation platform for private panel management, and qualitative automation including video audition questions, and surveys.  For a limited time, user seats are free. If you'd like to learn more or create your own account, visit hubux.com.  [00:00:00] Jamin Brazil: Hey everybody, you're listening to the Happy Market Research Podcast. This is take two. I'm with Dan Fitzgerald, CEO, and president of Apollo Intelligence. Founded in 2020, Apollo provides access to 2,000,000 healthcare stakeholders worldwide, including physicians, patients, caregivers, and allied healthcare professionals, serving the life science insights industry on its mission to accelerate health innovation to improve all of our lives. They support 80 of the top global 100 life science firms as well as global market research agencies and consultancies across 14 different countries in the Americas, Europe, and Asia. Prior to joining Apollo, Dan has been part of the bedrock of the market research industry, serving as CEO of InCrowd, managing partner at Reimagine, chief client and marketing officer at LIGHTSPEED, and general manager of GMI, Global Market Insight. One of my biggest customers at Decipher. Dan, it is an absolute privilege to have you on the show. Thank you for joining me. [00:01:10] Dan Fitzgerald: Hey, Jamin. And it's great to be with you as well. [00:01:15] Jamin Brazil: The Michigan State University's Master of Science in marketing and research program delivers the number one ranked insights and analytics degree in three formats. Full time on campus, full time online, and part-time online. New for 2022 if you can't commit to their full degree program, simply begin with one of their three course certifications. Insights design or insights analysis. In addition to the certification, all the courses you complete will build towards your graduation. If you're looking to achieve your full potential, check out MSM U's program at B-R-O-A-D dot MSU dot edu slash marketing. Again,

On today's episode Anthony Casablanca shares the importance of understanding the emotional side of change and how to lead your team through times of uncertainty. Listen in as Deborah and Anthony discuss the impact of change on a team, creating a communication strategy to navigate change, understanding the role of grief at work, and how to address emotions before they impact performance. Anthony Casablanca is an accomplished senior executive, having spent thirty-one years in various leadership roles. Anthony earned his undergraduate degree from Wright State University in Dayton, Ohio, and a master's degree from Kettering University (formerly GMI). Anthony spent most of his leadership career working for Batesville Casket Company, a recognized leader in the funeral service industry. There he served as the vice president of human resources and then the vice president of manufacturing and logistics. Anthony is a former Human Resource Executive of the Year in the State of Indiana. Anthony would go on to become the president of another subsidiary of the Hillenbrand family of companies. Anthony spent his entire career studying the art of leadership with an emphasis on purpose-driven leadership principles. Anthony has spoken at various industry conventions, was a featured speaker at an international sales, service, and operations meeting for Batesville Casket Company, and has led cultural transformations and conducted leadership training classes helping leaders prepare for and navigate change. Over his career, Anthony successfully led cultural and transformational change initiatives. Through these experiences, Anthony learned that when the foundational leadership and cultural elements he now teaches were in place, change efforts were successful. These experiences also taught him the very real and emotional impact change has on people. After retiring from Hillenbrand Inc., Anthony began consulting to help organizations and leaders discover their true purpose. In 2019, Anthony and his brother Guy, a dually licensed funeral director, co-authored “The Dying Art of Leadership” and launched their leadership training company, GriefLeadersTM to help organizations Change How Change is Implemented. You can connect with Anthony on Linkedin: https://www.linkedin.com/in/anthonycasablanca/ Create a personal career strategy that develops the leadership and communication skills you need to assess challenges, showcase your skills, and demonstrate your ability to be a C-Suite Leader. Learn more about the C-Suite Academy here: https://bit.ly/csawaitlist22 See omnystudio.com/listener for privacy information.

Randi was involved with GO Ministries International for over a decade. Her uncle, Steve Munds, founded GMI, she started touring with them at 9-years-old as a summer intern. She then officially joined as soon as she graduated high school and stayed for 6 years. Her immediately family was all involved in GMI and have since left. Don't forget to come back next week for Part 2. See more of Our Journey at www.ourjourneypodcast.com https://www.tiktok.com/@ourjourneypod https://www.instagram.com/ourjourneypod/ https://www.facebook.com/ourjourneypodcast

Welcome to another exciting episode of the Data Gurus Podcast! Today, Sima is happy to have Tiama Hanson-Drury, the Chief Product Officer of Minna Technologies, joining her! For Tiama, a product is all about understanding customer pain and solving it in a way that works well for her business. In this episode, she talks about her journey and explains how she discovered her sweet spot and reached the point in her career where she is right now. She also dives into product development, monetizing a product, reducing uncertainty around a new product, and best practices. Tiama's journey After graduating from college, Tiama planned to spend two years in a small business and two years in a medium business, followed by two years in a large business. Then she planned to go back to college to get her Ph.D. After that, she wanted to become an executive coach and work with CEOs because she loves business. She spent two years working for a wine business and then moved on to a fast-growing tech company called Global Market Insight (GMI). She stayed there a lot longer than two years because she kept asking for promotion opportunities and getting them. That led her to her current career in product development. Making a move After working at GMI for six years, the company got acquired by WPP Kantar, and Tiama began to consider making a move. She wanted to make informed decisions based not only on what someone had said but also on what they had done, and she wanted to have all that data in one place. Building a product Then Tiama started working with the company's chief scientist, a data science team, and a marketing team, and they built a product. Monetizing a product More and more clients started asking for the product. Tiama realized that if something was built for multiple people with multiple perspectives, and it was built to be repeatable and scalable, it could be monetized. Because after the first sale, the gross margin on all the other sales is much better. Moving into the product space Tiama told the CCO and the company's new CMO that she was interested in the new discipline and asked if there was anything she could do in that line or else she might consider leaving to do it. So they gave her a chance to move into the product space. She started in product marketing because she had a degree in communications. Since then, she has constantly been moving from one product to another. Product For Tiama, a software product is about understanding customer pain and knowing that they, as a business, can solve it in a way that also works for the business. Invest now for a return later People in service businesses considering experimenting with a product line need to understand the reality of launching their new product. 95% of new products fail, so they have to be willing to invest now for a return in a year to eighteen months. Reducing uncertainty around the success of a new product Gaining insights, doing market and customer research, reading white papers that support trends around what you want to do, and knowing that you have realistic expectations and the DNA to do it, will help reduce uncertainty around launching a new product. Best practices As an industry, we need to look into adjacent areas to see what we can learn about building a product. Many product leaders within the industry know that. They also understand the practice of building a product for one persona at a time. Think about using best practices, and learn from those who have built products well. Three fundamental parts  For Tiama, a product has three fundamental parts: Discovery Delivery Optimization Don't sell vaporware You have only one chance to make a first impression. That's why you should never over-promise or under-deliver. Bringing a client into the co-creation It takes a skilled navigator to bring a client into the co-creation of a product. Do it in the discovery stage.

A Conversation With Blockchain Chamber Of Commerce VP Linda Goetze Episode 26 is the Season 1 finale for the GamiFi Everything podcast, featuring Linda Goetze, Vice President of the Board of Directors for the Blockchain Chamber of Commerce and advisor to the Coinintelligence Fund. Linda shares some breaking news about a new crypto protocol that creates NFTs that can upgrade and evolve. She also dives into the insights she's gained as an OG of the crypto ecosystem, now in her 10th year. Marcus and Linda then discuss some deep topics: why does fear of the known and unknown influence crypto adoption, the importance of raising awareness of underrepresented communities to the sustainability of the metaverse, and how gaming is redefining education. Marcus expresses his gratitude to the GamiFi team and wishes farewell to the GamiFi community on his final episode as podcast host. Stay tuned for more details about Season 2. As a reminder, the GamiFi $GMI token is only available on: PancakeSwap: (https://pancakeswap.finance/swap?outputCurrency=0x93D8d25E3C9A847a5Da79F79ecaC89461FEcA846) MEXC (https://www.mexc.com/exchange/GMI_USDT). Be sure to use $GMI token contract address 0x93D8d25E3C9A847a5Da79F79ecaC89461FEcA846. Also, staking is now live on the GamiFi site, and you can earn up to 225% APY by staking your $GMI tokens. Visit https://app.gamifi.gg/ to learn more. In case you missed it, GamiFi's second IDO (Time Raiders) launched on March 17th and is currently being distributed. Follow GamiFi on Social Media Official Site: https://gamifi.gg/Twitter: https://twitter.com/gamifiggTelegram: https://t.me/GamiFiOfficialDiscord: https://discord.com/invite/T4Q3ytzE3ALinkedIn: https://www.linkedin.com/company/gamifi-gg/ The views expressed on air during GamiFi Everything: Beyond the Metaverse do not represent the views of the RAGE Works staff, partners, or affiliates. 

A Conversation With Digital Storytellers Founder Xander Simms Episode 25 of the podcast features Xander Simms, Creative Technologist and Founder of Digital Storytellers Inc. Xander shares a touching story of how he unofficially started his metaverse journey years ago, leveraging technology to create accessibility for a non-verbal child. He then explains how the metaverse has continued to help increase accessibility, sharing a project he recently supported that launched the first NFT wedding dress created by a blind designer. Xander later describes his hope for a singularity of value creation in the metaverse over the next 12 months, similar to the "Big Bang". As a reminder, the GamiFi $GMI token is only available on: PancakeSwap: (https://pancakeswap.finance/swap?outputCurrency=0x93D8d25E3C9A847a5Da79F79ecaC89461FEcA846) MEXC (https://www.mexc.com/exchange/GMI_USDT). Be sure to use $GMI token contract address 0x93D8d25E3C9A847a5Da79F79ecaC89461FEcA846. Also, staking is now live on the GamiFi site, and you can earn up to 225% APY by staking your $GMI tokens. Visit https://app.gamifi.gg/ to learn more. In case you missed it, GamiFi's second IDO (Time Raiders) launched on March 17th and is currently being distributed. Follow GamiFi on Social Media Official Site: https://gamifi.gg/Twitter: https://twitter.com/gamifiggTelegram: https://t.me/GamiFiOfficialDiscord: https://discord.com/invite/T4Q3ytzE3ALinkedIn: https://www.linkedin.com/company/gamifi-gg/ The views expressed on air during GamiFi Everything: Beyond the Metaverse do not represent the views of the RAGE Works staff, partners, or affiliates. 

A Conversation With Shibafriend Co-CEO Alan Yeap Episode 24 of the podcast features Alan Yeap, Co-CEO of Shibafriend. Alan tells a compelling story about how Shibafriend was created by a female CEO, which he was brought on to replace, and why he made the wise decision to share the CEO role with her instead. In addition, Alan does a deep dive into the exciting features that set Shibafriend apart from the typical games in the play2earn ecosystem and explains how that will help Shibafriend reach mainstream brands and gamers. As a reminder, the GamiFi $GMI token is only available on: PancakeSwap: (https://pancakeswap.finance/swap?outputCurrency=0x93D8d25E3C9A847a5Da79F79ecaC89461FEcA846) MEXC (https://www.mexc.com/exchange/GMI_USDT). Be sure to use $GMI token contract address 0x93D8d25E3C9A847a5Da79F79ecaC89461FEcA846. Also, staking is now live on the GamiFi site, and you can earn up to 225% APY by staking your $GMI tokens. Visit https://app.gamifi.gg/ to learn more. In case you missed it, GamiFi's second IDO (Time Raiders) launched on March 17th and is currently being distributed. Follow GamiFi on Social Media Official Site: https://gamifi.gg/Twitter: https://twitter.com/gamifiggTelegram: https://t.me/GamiFiOfficialDiscord: https://discord.com/invite/T4Q3ytzE3ALinkedIn: https://www.linkedin.com/company/gamifi-gg/ The views expressed on air during GamiFi Everything: Beyond the Metaverse do not represent the views of the RAGE Works staff, partners, or affiliates. Check Out Some of the Other Shows on the RAGE Works Network Black is the New Black: https://www.rageworksnetwork.com/show/bitnb/Turnbuckle Tabloid: https://www.rageworksnetwork.com/show/tbt/Toys & Tech of the Trade: https://www.rageworksnetwork.com/show/ttott/Trek Untold: https://www.rageworksnetwork.com/show/trek-untold/The Eat 4 Life Podcast: https://www.rageworksnetwork.com/show/eat4life/

Ep #110 - This week on the podcast, I'm joined by Lowell Ricklefs, the founder and managing partner of Traction Advising, a premier boutique M&A firm focusing on B2B SAAS companies between $5M and $20M ARR. Lowell has orchestrated acquisitions of over 30 technology companies, and specializes in helping founders find the right acquirer while negotiating the best deal. Lowell twice built the leading company in the insights data collection industry: at GMI (sold to WPP for $100M) and Toluna (sold to a private equity firm for $260M). He later co-founded FlexMinder (Healthcare IT sold to JellyVision). He was involved in over 12 acquisitions from the buy side. He knows how and why buyers buy, is an expert on selling small technology companies, and is able to negotiate better deals with a higher likelihood of closing. Lowell is hands on with every transaction from start to finish. Learn More About Lowell Ricklefs and Traction Advising: Visit the Traction Advising website: https://www.tractionadvising.com/ Follow Lowell Ricklefs on LinkedIn: https://www.linkedin.com/in/lowellricklefs/ Also, please remember to subscribe, rate, and leave a written review for the show if you find value in it. Your reviews help this show to reach a wider audience and I appreciate everyone that has been leaving them. FOLLOW CHARLES GAUDET ON SOCIAL MEDIA: Follow Charles Gaudet on LinkedIn: https://linkedin.com/in/charlesgaudet Follow Charles Gaudet on Facebook: https://facebook.com/charlesgaudet Follow Charles Gaudet on Twitter: https://twitter.com/charlesgaudet   VISIT THE PREDICTABLE PROFITS WEBSITE: https://PredictableProfits.com