从4000小时到数百万小时：给人类装传感器，喂饱机器人基础模型

There's an idea in science fiction that is when robotics and AI has ultimately fully generalized and solved all tasks that humans can do, the final frontier is going to be organic. Final frontier is going to be biological. And you see this in like Asimov, you see this in Ender's Game, even in Hitchhikers at the very end. The planet ultimately was this giant organic computer trying to process and understand what the meaning of life was or what the question was. Today, I'm delighted to have Josh from Mecca on the show. Josh is founder and CEO of Mecca AI, which is building a data and deployment company for robotics. I and I think the whole ecosystem think that robotics is about to enter a golden age. And unlike LLMs, where we had so much free human data from the internet, robotics is fundamentally data constrained. And I think your position in the ecosystem as the leading data factory to bring robotic data and physical data in distribution for models is just a really, really interesting perch. So I'm excited to use this conversation today to chat about how does egocentric data and data collection work in robotics? What are the actual operations and economics of operating a data factory? And your overall thoughts on robotics research and deployment. Thanks for joining the show. Amazing. Thanks for having me. Awesome. Maybe to start off, for those who aren't familiar, what is Mecca and how are you looking to impact the world of robotics? So Mecca is, like you said, the data and deployment company for Physically AI. What that means is today we create, we process, and ultimately we license a lot of the data that goes into training these robotics foundation models. The big problem across robotics is that we don't have the luxury of the cold start that LLMs had with the internet. Fundamentally, a lot of this robotics data is three things. It's pixels, it's actions, and it's logic. And so we don't have access to a large amount of this curated and processed and clean data to be able to then train these models to understand what it is to be in the physical world. So a lot of our job is pretty much to, one, create the different types of hardware, such as sensorizing and wearables for humans. The different operations to go and collect and partner with individual contributors, businesses, tasks across different environments. And then ultimately post-process and own the quality gates to make these data sets useful and teach robots about the world. Without having been given the benefits of a giant, free, many trillions of tokens, maybe lots of trillions, trillions of trillions of tokens of internet corpus, do you think that robotics, you know, having to pay for and operationally collect all the data that's going into the mix, is it even a feasible challenge? Like, is it economically feasible without having had the gift of the internet? I think we still have the luxury of a lot of things that was given from the internet. So we aren't starting from zero, ultimately. I think you can see it across LLMs and data across LLMs. Pre-training data was always driven by the labs, largely, across web crawlers, things like that. It didn't become a large market. Today in robotics, we do see pre-training as quite a large market. And that could be taken in several forms. So that could be that we are teaching video models how to generate more physics-grounded realities of what the world looks like. And we get to leverage all the work that has gone into the BLMs and different types of world models before to make that possible. Is it possible and scalable to get to that volume of data? I think that ultimately comes down to what commercial use case can you unlock at which levels of scales of data? And our strong belief is along that journey that we will be able to unlock a lot of the simple pick-and-place tasks along the beginning of what we're creating. And towards that long tail, that is going to be many orders of magnitudes larger than it is today. I did a little homework for this. Your website is very whimsical. It says that you are teaching robots about carbon life. What does that mean? I actually made the website. I love it. Like the night before we did our first tweet announcement or something like that. There's an idea in science fiction that is when robotics and AI has ultimately fully generalized and solved all tasks that humans can do. The final frontier is going to be organic. Final frontier is going to be biological. And you see this in Asimov. You see this in Ender's Game. You could even in Hitchhikers at the very end. The planet ultimately was this giant organic computer trying to process and understand what the meaning of life was or what the question was. And so I think in the same vein for us, that is the human brain runs on just 20 watts. We are self-healing. And so along the journey of teaching and getting robots to fully generalize, we'll slowly show them what carbon life is made of. Love that. Okay. You come from an untraditional background for robotics. I think you started in the fintech world and the sneaker world, if I'm not mistaken. Tell me about how you and your co-founder found your way to robotics. It was quite an interesting journey. Yeah. So before this, I was building essentially a toast for cloud kitchens. This meant we did everything from back of the house order management. At one point in my fourth year of university, I was skipping class to go deploy and install kiosk into rural Rochester, New York, where we had to stay all nights replacing a lot of these different front of the houses for restaurants. My co-founder built a stable coin. They ultimately sold to Coinbase, but he's been this very obsessive personality. He was a top thousand in four competitive games, including League of Legends. And I think a big part of what brought us together, one, we're both Canadian. And two is we have this big science fiction grounding. And so the belief ultimately was that payment was a hard and difficult problem. But whatever we worked on next, we wanted to be something that seemed exciting that the future was going to be incredible. And robotics seemed like a great space to get into. I remember walking into your office and then immediately seeing those like mechatronic robots and then the Kawasaki Corleos on the walleye. The inner child in you must be very happy about what you're doing these days. Yeah, absolutely. Coming from a non-robotics background, what do you think your advantages are in building this business? And where has it presented challenges? I think the advantages is when we entered the space, we had to go and do this full discovery process. We reached out to hundreds of folks that were in the space that we appreciated. We read every paper that we get our hands on. We went to visit the different robot labs. And what we saw in late 24, roughly, GPT was taking off. Robotics was still evolving from this traditional robotics era. And it seems obvious to us at the very least that data and compute would become very important across the industry. And so moving towards that, we got to read and see some of the papers like Ego4D from a fresh lens and say, hey, there's about 4,000 hours of this human demonstration data. And when you actually went in to look at the data, you would realize some of the data sets would be daily life of folks like bowling, for example. There was very broad use cases and potential applications at the 4,000 hour scale. And so coming into that with a fresh lens, we were able to, from the first principles approach, I'd say, have a general intuition of what would be important and then iterate very closely with people that were much smarter than us that had different views of what the future would be and be able to stay close to iterate on what the product would become. Yeah. Interesting. It seems like robotics has just changed so quickly as a field that almost coming from the field can be an advantage and a disadvantage because people come from different eras of, you know, what's important in the training mix and things like that. Yeah. Maybe for our audience, just say a word on like, what is egocentric data? And I think you really started your company kind of making a bet behind it. Obviously, it's much more distributed now, but what is egocentric data? Why did you decide to bet the company on it at the beginning? Egocentric data is pretty much video data from humans demonstrating and doing tasks from a first-person POV. The high-level idea was that from the gap of human data to robotics data, you could close a lot of the vision gap. Where robotics experience the world from POV, can we also show it what it is like to interact with the world from first-person as well? Why we bet on it was pretty much when we explored the space. A lot of what we saw was robotics data at the time was teleoperation, which was humans controlling robots to do demonstrations. It was very hard to scale at the time. And from what we saw across GPT and a lot of the principles of scaling up, we had to find ways to scale up data much more significantly. And I think some of our early intuitions were that humans and robots share a lot of similarities. Humanoids were just starting to take off. Humans and humanoids look the same. Why couldn't we turn humans into this fleet-style learning that Tesla has to be able to all contribute into understanding and ground the physics of the world? And so what does that look like? You're like mounting an iPhone on somebody's forehead and just letting them go about their day? Or what does that look like in practice? Yeah, when we first started, our very earliest pilot was we created a mobile app. We got all the Chinese aunties and uncles to record themselves cooking, cleaning, doing tasks. That obviously evolved a lot. We tried a number of things. We did UMI data. We did all the sorts at the beginning. And that's evolved quite a bit into all the custom hardware that we have to produce now and all the different partnerships that we have to have across different industries. And ultimately, we're sensorizing humans. And that's the broader category. How important is it to get force data from the sensors? Because one of the things I've always thought about is in self-driving, you don't really need force data as much, right? Whereas with robotics, especially with manipulation, you really care about how hard am I squeezing that door handle or picking up that egg? And it seems to me that video doesn't quite capture that. You might be able to approximate it, but it doesn't really capture it. How important is capturing force data? And how do you think about getting that? I think some of the substitutes for force data that we've seen would be approaches like wrist cameras currently across both humans and robots, and being able to see the contact time between the hand and the different objects that you interact with. That obviously is kind of a hacky way, ultimately, to get to understanding how much force in newtons it takes to pull open a chair or twist a bottle cap. I think force data will be incredibly important. It's actually one of the products that we've been building out in that when you iterate with many different robotics foundation labs, you realize to close this gap, one, we have to create new hardware. So we've been developing things like gloves for humans to wear. And in those gloves, being able to collect the information and how much force it takes to do your daily life and different tasks. I'd love to just spend a bit of time on the actual operations of a data factory, because I think there's so much money that's gone into the LLM data factories. There's so much money that is starting to be poured into and I think will be poured into the robot data factories. And the way they work is just mysterious and intriguing to me. So I'd love to ask you a bit about the operations of it, if that's okay. Yeah, let's do it. Okay. So how does it work? Like if I am, you know, if I want to make an extra buck, do I just sign up on your website to start, you know, to put a camera on my, on my forehead and start sending you data or how does it work? So we actually have a lot of different approaches on the operator side. One would be, we have a direct consumer approach. And so through direct mobile apps, can you sign up from anywhere in the world? Can you start contributing? Can you, can you register which profession you're in? And can you get streamed a sequence of tasks to do such as cleaning your bedroom, cooking your breakfast, cleaning up afterwards? This is a very lightweight approach to get that diversity ultimately of this pre-training data. The second approach that you have to get into is ultimately there is so much more beyond the home to collect. And so we had to build out a lot of this B2B engine and this muscle of being able to recruit businesses across this incredible taxonomy of all physical human work, which is across bakeries. It could be a PCB manufacturing line, everything to... Podcasting. Podcasting. There's not too much handwork in podcasting just yet. Trying to make a quick buck here. We should have equipped ourselves. And that's ultimately how contributors come into the mix. But the actual data engine for the data factory is so much more sophisticated and much more painful than folks can expect. And that is that a lot of the things that we have to distribute into the world didn't exist until we designed and produced this hardware. Even across tactile gloves is a fantastic example. There is nothing off the shelf that you can purchase and that you can wear and that reliably tells you how much force you are applying day to day and turn that into ready data sets for these different robotics foundation models. So we have to have that hardware iteration loop to have these three, four different product cycles, that engineering muscle to actually think and iterate what is going to be useful and then scale that production into the world. And that becomes this logistics of distributing into these thousands of homes, thousands of businesses to collect that, to upload that. We've had to set up these hundred gigabyte upload lines in a lot of the places that we operate in just to sustain the hundreds of petabytes of data that we have to deal with at this point. And then you have to process, you have to make sure your quality gates are incredible, and then all of the computer vision to make it useful ultimately. So let's say you decide, okay, in my training mix, there's not enough podcasters. There's not enough, you know, I don't know, donut bakers. Do you go out and literally go knock on doors of donut bakers then to sign them up to the platform or how does that work? Well, we'll generally track kind of like a heat map across different jobs, industries, and ways we think about diversity. And across that heat map, if ultimately we think, oh, not enough Sonia's podcasting and we have to go get that, that's actually why we're here today. This is our secret message. That's why there's cameras everywhere. No, you're never automating me out of this. No, I'm kidding. Please do. Okay. Interesting. Like I've heard that, you know, there's a lot of quality issues in robot data collection generally, and that a lot of the hours of robot data sample that get sent in end up not being accepted. Why is that? And what are you doing about that problem? This lesson came from even originally the different types of robot data. There have been these very large scale robot factories that have spun up from just large scale warehouses, repetitively doing tasks, trying to scale up teleoperation data. Some of the learnings there came from the research in that there is some threshold of diversity where you oversaturate how much robot data you need or how much data in general you need. Across human data, quality, you can imagine, is actually quite a bit of a moving target as we grow. The amount of edge cases that you have in the real world that you have to deal with are very extreme. And you will always see something new that you haven't seen. So for us, it's been about, can you think about the heuristics to be able to catch a lot of the different things as it's processed? Can you have the different engines to escalate very quickly? And ultimately, we will learn new things about quality and diversity that weren't true a week ago, even. And so the nimbleness to move very quickly to create and adapt your engine to catch that. What's an example of something that you would catch? Well, I think fraud is an underestimated one across data industries in general. We're ultimately creating data that doesn't exist yet versus when you're filtering through the internet. And so when you are, you're coming across very creative and interesting. Like people are sending generated videos or what? Yeah, very creative people where I think at one point we have folks recording YouTube videos like POV working at McDonald's and trying to submit that as contribution pretty much. Wow. Never underestimate how entrepreneurial people can be. That's amazing. Yeah, very much. And so these have been like part of the painful lessons where, yes, you have to create a lot of the gates, but ultimately you have to also be there where collection is happening. You have to go see how it's done. You have to figure out an incentive model that actually incentivizes people to create good contribution. How do you think about your right to earn a margin long term? And, you know, I can imagine a Mecca competitor comes to Sonya and says, Sonya, I really want your podcast data. I could imagine that, you know, the margin that you're able to own as an intermediary ends up getting squeezed. I think the contributor amount in terms of the margin cost is actually not the largest factor at play here in terms of the end-to-end making sure this data is useful. So even though that baseline is always going to be there and maybe it becomes more and more competitive, there will be ways to differentiate and make sure that your data is verifiably high quality at very large scale with the right hardware, the operations, and the software. I think when we think about these products, ultimately it is that we do want to scale up and mature these products. And just like any product that begins to scale up, you want to optimize it. But over time, you may have different products that evolve. And so when you think about this egocentric data of podcasting as simply one of your SKUs, and you then realize that there is going to be different types of data that are going to be important, such as maybe we move from the Chinese restaurant from the side of the road. Can we start moving into the Michelin star restaurants over time? Can you get more and more closer into what it means to be Federer than it means to be an average tennis player? So these products will change and how these products are created will also change. Really interesting. What scale of data has already been collected, do you think? And how much data do you think is going to be collected over the next few years? I think we finally entered a world where we are in millions of hours of data. And coming from the world where Ego4D of 4,000 hours two years ago, where we were then, is a huge step up. But then comparatively, and you think about autonomous driving as an example, Tesla spent over a decade solving for two degrees of freedom, ultimately, of acceleration and direction, millions of cars on the road. They have a lot more than a few million hours of video data at this point. But so relatively, we're still many orders of magnitudes away from where we have to be, ultimately. So on one hand, that sounds like opportunity for you. That sounds like a lot of revenue for the robot data factories. On the other hand, as somebody that invests in the robot foundation models, I look at that and I say like, oh, shoot, are we ever going to get to the promised land? Because I've done the same math as you on like a car has two or three degrees of freedom. A robot has- Even dozens in the hand. Yeah, exactly. And so you combine that with like, you probably need forced data. And there's a way more, you know, it's just a way harder environment to operate in. And I get to like, you might need billions, trillions, quadrillions of hours. Is there even enough GDP in the world to support the cost to bring that in? Like, what do you think? I think it's about unlocking commercial use cases as we go. So it doesn't have to be so much binary in that, oh, one day we've unlocked robotics or not. It is going to be a slow creep up into the world. A great ways to get there is that with some of this base pyramid built up where we are seeing the generalizations, we are seeing the scaling laws at play. That gives people the courage to scale up. And with that courage, can we one, have some generalizability in some of these environments? Can we then also collect a lot of the in-domain, in-task data that closes that gap in post-training? So maybe I can collect data exactly from this room, exactly in this podcast demonstration to actually close the gap of what is it in a lot of cases for nines of reliability in these in these real world environments. That's a helpful framework. So it's not about a binary of getting from zero to one. It's about we're going to increasingly get, you know, robots that can do an increasing set of capabilities. And we will unlock a lot of tasks along the way. Yeah. One curiosity, though, is that, you know, the data that is most plentiful right now is like it seems like home data, right? Just because like everyone, everyone can film that. It's pretty easy to get. But a robot in the home almost seems like one of the most difficult environments to deploy in. And so I'm curious what applications you think will hit first in robotics, because like where the data is most readily available seems almost the opposite of where robotics might be easiest to deploy. The home is going to be some of the environments that we have to be most diligent on by far. And a lot of these benchmarking and evals that have just started to mature across robotics, making sure that the robot doesn't fall on the baby in the homes, for example. I think the initial tasks will be much more constrained as we go along that spectrum in that very goal oriented where you can hill climb some of these tasks, where you have existing SOPs for what it means to do that job and that work. Like a laundry folding robot. It could be laundry folding. Laundry folding. We spent about a year and a half plus trying to solve. And I think it will absolutely get solved in the near term. A lot of these pick and place, these packaging tasks that you see on assembling lines that had to be manipulation bots that couldn't be solved by traditional robotics. We will get there within the next year or two. It is a good transition to talk about deployment. I'd love to talk to you about, you know, robot deployment overall as a concept. You've said that useful robots can already be deployed today. Where do you think that's the case? I think people underestimate how diverse robotics can be. And if we constrain ourselves to, one, humanoids are incredibly popular, it will be a very popular terminal state of robotics as well. But beyond that, there are so many different form factors that exist in robotics today. You could look at different markets, even across hotels in China or Southeast Asia, for example, where there are robots that are doing services, that are doing cleaning, that are delivering from after your Uber Eats gets to the door, from the front desk to your hotel room door that exists. Many of these bots are widely deployed across the world today. They are just starting to get popularized across the U.S. and North America. I think some of the first bots that have to get deployed will be even just the industrial cleaning bots, for example. A lot of like thematic robotics that are out there. And then slowly stepping up and up from there, where you start to solve early manipulation tasks along that spectrum, all the way up until we get to that humanoid solving out of the box, any task in the future. You mentioned Tesla and FSD earlier in this show. When do you think we're going to get to the point where there is a little bit of an FSD-like flywheel? And when we get to that point, how do you see your role as a company evolving? When we will get there, I think we're a while away. But it'll be a slow transition in that when you are slowly solving some of the commercial tasks, you will also be able to finally get some of these flywheels. Then you can get some of the in-deployment data where you get the failure cases, you get the correction data. That will be absolutely important in starting up this robot flywheel. How far that is depends on that spectrum and across saturating different types of tasks and domains that aren't completed or solved for yet. So it's quite a while away. When we do get there, the thing is, I believe that Mecca will look very different along that process. In that one, we're not fully subscribed to only egocentric data. We believe generally that a lot of different SKUs, the entire data soup is important. And so will we be producing the Michelin star data at some point in the future? Will we be teaching some sort of edge case about the real world that the robots haven't gotten to yet? If we believe that the world is incredibly vast and rich in detail, there's going to be no shortage of things to teach. And then even in that world, we do have this angle where we believe that Mecca will very much contribute to the deployment of robots. And so in some of these environments, in closing that gap of being able to do that task in that domain, in that environment, can we actually be the platform where we collect that domain data, we post train, we help support the enterprises and white glove hold them into what it means to be in a world filled with robotics? We see Mecca maturing into that. I'd love to take your predictions on how you think deployment, diffusion is going to work out in the physical world. And maybe if I can analogize what's happened in the LLM world, there's like a few different ways that deploy codes have happened, right? There's vertical specific application companies. There's the foundation model companies themselves that are scaling up GoToMarket. And then there's, you know, all these FDE, deploy code, service code, whatever you want to call them, you know, just armies of people helping enterprises adopt AI at scale. Those seem like the broad vectors that are disseminating tokens across the economy. What do you think the analog will be in the physical world? We believe very much in both the vertical and the horizontal across robotics. So the full verticals that are very task specific all the way from hardware to models to data, the tackling. We also believe in an ecosystem where there's going to be very vast in different shapes and sizes of hardware of robotics. And there will be different types of open models that exist in the world. I think it will look something like a modern robotics version of a systems integrator. That in a traditional version, you are installing the product. It's incredibly competitive. It's tight margins. There is now a world where you can actually install and help make that product become more data-driven and productive over time. So can you actually have the bot you installed last week do something that you didn't think was possible a few days ago even? Can it help you start expanding the scope of what your business is capable of doing? So that's ultimately the milestone that we're hoping to aim for. Okay. But you would tell me as an investor. Now give me some investing advice. You'd invest in both the general and the vertical robots then. Yeah. What are your favorite vertical robot categories? I think the best ones will be the industrial bots to start. Those are going to be ones that already have a lot of traditional robotics in the mix. They're task constrained like we talked about. Like data center maintenance robots? Yeah. You just don't have to expect a baby to appear and have to think about that. And not fall on it, for example. So the world is much more constrained than in the wild. Other categories we're excited about are just spaces that are even feasible and possible early on. And so some of the approaches in the hotels, for example, where you could actually start delivering from the front desk to the hotel rooms, be able to actually have some manipulation to hand off, are exciting to us. And then places that humans were not capable of. A joke that we make in some of our all hands in the company would be if one day Mecca was able to help deploy a robot that mines an asteroid, we've probably done a pretty good job. It's just, it's a job and a task that completely doesn't exist, that humans can't do, that we have no idea how to do just yet. But it seems inevitable. Yeah. I love that. Reminds me of, you know, even, I'm not thinking interplanetary yet, but, you know, like, have you heard of Gecko Robotics? What they do to help maintain chips and things like that. I think it's really cool. Absolutely. Can you say a word on, and whatever you're willing to share publicly, the slate of hardware that you've developed and where the labs currently are most interested in collecting bespoke data? Some of the iteration came from when we started with the mobile app. We quickly were able to scale that. There were a lot of virtues in that product. The downsides, ultimately, were you don't get as much rich information about the world as you could have. And so some of the things that we had to develop very early on were things like Waymo-fying people, ultimately. And in this sensorization, can you build things like these baseball caps that are equipped with different cameras and sensors? We build a central hub that then connects to all these different accessories, such as the tactile gloves, external cameras, and more. All of which is trying to capture how do you interact with the world on a day-to-day and hopefully be able to clone that behavior. Interesting. What is Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? 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Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? The other introductions that I hear most frequently to the types of data being collected are, you know, you could watch millions of hours of tennis data and you still wouldn't know how to beat Roger Federer, right? And I'm curious what you think of that and what it means for where we might be headed in terms of the way robots learn. Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? 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Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse? Egoverse?