开源胜出、AGI 已至，斯科塞斯的 AI 工具箱：Cerebras 与 Black Forest Labs CEO 对话

(00:00:00) We are in the race for super intelligence and Andrew Feldman is back and obviously CEO and founder of Cerebris, doing inference chips, pioneered the space, had a successful IPO. We've talked about this a couple of times. We got to see each other in January at Davos, IPO happens. The boys and I got to sit with you recently. That was fun. At Liquidity. That was really fun. I had a great discussion with the boys, but I wanted to deep dive with you about a couple of topics. The first one is the build out of AI. We've never seen a build out like this since, you know, the Great Wall of China. Right. Who knows since? The pyramids. Right. I mean, it feels like the amount of capital, time and intelligent people on the planet (00:00:49) dedicating themselves to the build out of something. I can't think of anything in our lifetimes with perhaps, you know, before our lifetimes, the war effort. Right. This is a mobilization and a scale that we read about, we hear about, but you're actually doing it. You have customers who are building data centers and you're a key piece of that. I'm going all in. AppLovin started with an $8 domain and no VC funding and became one of the largest ad platforms in the world. Now that same engine powers AppLovin ads for e-commerce. Your ads run inside mobile games, reaching over a billion people with full screen, distraction-free attention. The platform finds buyers and optimizes for profit. You set the target, it does the rest. (00:01:36) One cookware brand went from $4 million to $16 million, turned profitable and is on pace for $80 million this year. Visit applovin.com slash all in to launch your first campaign today. Maybe you could just enlighten us in 2026. What is Cerebras doing and what is happening with this build out out in Texas? These are some gigantic, gigantic efforts. The size and scope of what is being built, the physical size and scope. Usually when we talk about software or we talk about hardware, we're talking about chips or boxes and they don't have the same sort of physical enormity. Right. Right. And what we're talking about now are data centers that are in the next several years going to (00:02:22) use more power than the previous 50 years on earth took. Wow. Right. We're talking about individual buildings, the size of football fields that have more power coming into them than midsize cities. And they're being built, they're being built across the US. They're being built in Canada. They're being built throughout the Nordics. They're being built here in Paris and throughout France and Europe, in the Middle East, in nations that sort of weren't front and center in anybody's mind previously. You know, Kazakhstan, Tajikistan are building out Georgia, building out data centers of size, Armenia. Everybody's sort of focused. Every country and every state, obviously in America feels they need to participate in this. And the people who are buying the capacity, the OpenAI, Anthropics, SpaceX AI, (00:03:14) the Googles, they are insatiable right now. Yeah. And they're building how many years out? When you talk to them, they were ordering chips from Cerebrus before you were finished with the chips. They're putting orders in ahead of time. The irony is, unlike many sort of exciting times in technology, they're trying to capture yesterday's demand, right? The demand is way outstripping our ability to build data centers and to fill them with hardware. All right. And so, you know, we have a $25 billion backlog. $25 billion backlog. And we are not alone in that. The OpenAI, Anthropic, you go through this list of (00:04:03) Google wants more data centers, Microsoft wants more data centers, AWS wants more data centers, right? All of these players are not chasing sort of, if you build it, they will come. They're chasing, the demand is booked. Right. How do we keep them from leaving? Right. And that's extremely unusual. It's very unusual. And now we have people who are, you know, we have a term for a token maxing. Yeah. And there's a great debate. Is this actually creating value? I'm curious where you stand. You know, is it even possible that this much demand could be created if value did not exist? There is clearly massive value happening. Yeah. But there's also massive experimentation. (00:04:49) Oh, for sure. You know, I liken this to when we first started with AWS and it was so good to get around your own IT organization. Right. That you told every engineer, yeah, go ahead, put on your credit card, sign up. Yeah. Right. And a lot of it was really useful. And some of it was like, God, I wish we didn't do that. Yeah. And so for sure there's experimentation, but it doesn't mean that the net value isn't enormous. It means some of it is going to go nowhere. And, you know, it was the same. I remember when Costco opened up in the Palo Alto area in 1988 and people used to shop Costco like they shop Safeway. They go down every aisle. Yes. That's a horrible way to shop Costco because you end up with four things you didn't need and each was (00:05:39) $22. Right. And as people got more sort of accustomed to it, you'd go to the back, you'd get the chicken. Yeah. 18 cupcakes for the kid's birthday party. Bang. You were out. Strategic. Strategic. And it's exactly the same. I think at first people opened up and said, everybody as much tokens as you want. And in enterprises, there's no open loop. We don't give sort of any resource unconstrained to people. And now we're jumping on and saying, whoa, all right, these guys should have as much as they need. They're enormously productive. Over here, we can use maybe an open source model, maybe a cheaper model over here. And now we're sort of running like a business. And we're really seeing a certain type of person emerge who knows how to deploy this (00:06:25) technology. Systems thinking. Yes. Which developers kind of have innately. CEOs tend to be great strategists and understand systems. But the intelligence is getting so much better every step along the way that I'm watching individuals, typically startup founders, but also venture capitalists and associates who work at my venture firm. They start playing with the tool and then the tool starts playing with them. They start to go, oh, I haven't clearly defined what my goal is. I don't understand what a system is. I've never heard about making a requirements document. And the software's like, do you have a requirements document? What's your goal? The AI starts telling people (00:07:11) you're token maxing and you need to get a little more focused here. One of my colleagues 20 years ago, a really smart, smart computer scientist said, computer's really dumb. They do exactly what you tell them. And at first, prompting was like that. Right? You modified your prompt a little bit and it changed the answer. Dramatically. Dramatically. And increasingly, it's understanding what your intent was. Right. Right. And if you have a chance to play with Fable or 5.6 from OpenAI, increasingly, you don't have to get the prompt just right. You don't have to be a prompt whisperer. Instead, you ask it and it says, well, here are some things. And by the way, maybe you wanted the chart to go (00:07:58) two ways. You wanted a line in a bar. And it's like, well, that's exactly what I wanted. I didn't ask for it, but that is better. And so it's understanding intent. And that's a huge leap. Which, if we were sitting here two years ago, the idea, we would never have been able to predict in a short 24 months that it would go from being a great summarizer researcher of web results to actually understanding your intent and then providing a solution and abstracting it all from you. That's right. Which is a very weird thing. I don't know if you've played with the Ermes agent yet. Have you played with it yet? I mean, I asked it just this morning and I was given a secret BitTensor (00:08:43) project that has the new ZAI model 5.2. And they gave me- GLM 5.2. GLM 5.2. So somebody in that BitTensor, I think you understand BitTensor, you've heard of it, the distributed crypto project. And so they have all this extra capacity. I was, a whisperer told me, probably some capacity in China that has free energy. Okay, fine. So they gave me unlimited capacity. So I started having to do some really crazy jobs where I was saying like, every hour, I want you to tell me what the trends in the world are that nobody else has identified yet. And you can do whatever you want to do that. But my goal is to be the smartest trend hunter in the world. And I watched what it was doing in the background and it started debating itself on where it should find the things. I said, well, we should probably go to (00:09:31) Hacker News and Reddit. And then it was like, yeah, but there's also social media and trends tend to manifest on Instagram. That's a reasoning model. You were watching a reasoning model work out. Yeah. Isn't that interesting? I mean, that's amazing. And it was collapsed. So as a civilian who doesn't hit the uncollapsed moment, and if you were using ChatGPT 3.5 or you were using 4.8, whatever it was, and you haven't used this new level of reasoning and inference and unlimited compute, essentially, it opened my eyes just this morning of what a world of unlimited tokens might look like. Right. Because unlimited tokens, I believe, means unlimited reasoning. It does. What does that mean? (00:10:19) Yeah. I mean, if you run these for 25 or 48 hours, you get amazing things now. And what if by using Srebres we were 15 times faster and then you ran it for 24 hours? Right. And you got weeks or months worth of thinking. Yeah. And I mean, it is extraordinary. And I think one of the things is people like Ilya and Sam in the early days were saying this was coming. Right. Right. And I think when you look back, you say to yourself, holy crap, those guys saw it. Yeah. They could see around the corner. That's right. And the rest of us were like, what? I'm not sure. Sure. It's when we had Sam on all in at one point. He and he said, oh, you know, I'd love (00:11:08) to come on at some point. I said, sure. Come on. And he was talking about it. He said, you know, I said, what's next? He said, reasoning. I said, unpack that. What does it mean? He's like, well, understanding what your intent was, just as you're saying, and then figuring out a strategy and then maybe talking to other agents and other threads about like, is this the right thing to do and vetting each other's work? And I'm like, wow, we have come a long way from guess the next word. Right. Right. Fill the sentence in, you know, summarize this PDF. Now, Cerebris is at the center of this because this reasoning is inference. This reasoning is inference and it's computationally intensive. Right. Right. And so fast compute makes this sort of work fast and sort of tractable. It doesn't cripple it by taking a huge amount of time to get a good answer. And so it's exactly the fact (00:11:58) that this reasoning consumes a huge amount of tokens internally that allows a blisteringly fast machine like ours. And I brought one because I'm never far without, you know, when one costs half a billion to make, you bring it everywhere with you. We were tossing this back and forth at Davos. What's the model number of this one? This was in the first eight or 10. Got it. So this has a special place. This has a special place. I mean, my wife says it's like I'm a kid with a dirt bike for his eighth birthday. He's in his bedroom at night. I carry him with me. I mean, when you have, you know, your next party at the house, I highly recommend just a little hors d'oeuvres. I think it'd be like a great fit. It would be a great fit if you had some. That's right. But what we're looking at here (00:12:47) is the ability to do that reasoning at scale. And what is Moore's law for inference and for cerebris? Do you have something internally you discuss as we're going to double this every X time period? So all chips prior to us in the processor world followed Moore's law. Got it. And we broke it. Doubling every 18 months. Doubling about every 18 months. Got it. And we crushed it with this chip and we've carved out a whole new trajectory. And my view is in the next 18 months, we'll be way over 2X. Interesting. And so I think that early in an architecture, (00:13:35) you have room to do much better than what was traditionally Moore's law. Now, if you've got a 20-year-old architecture, like the GPU, it's much harder. Right. You have to rely on things like smaller geometry, right? Going to the next fab node. But in a newer architecture, you have a huge amount of room still to learn about the work that is being presented and make optimizations that give you huge gains. How do you run the company? Just being the CEO now in the age of AI, you have $25 billion in demand. You have to deploy at just an incredible blistering pace. You have to hire people. You have to create a roadmap. I don't mean to give you a panic attack (00:14:25) here. You have to keep up with somebody like OpenAI who's moving so unbelievably quickly. Yes. Right. And they're competitive. You got to keep up. Right. Right. Your hardware, your software, your deployments have to keep up with some of the fastest moving organizations in history. They're demanding customers. They're not pushovers for sure. Yeah. And also potentially competitors down the road. Look, I think there is so much demand right now that there is no silicon that will go unused. Yeah. Right. But why is an OpenAI releasing jalapeno? Why is Amazon making their own chips? You see this reoccurring trend. Is it a way to let you know, to let Jensen and NVIDIA (00:15:12) know, hey, we can do this too. So we need good pricing. Is it a little bit of a flex that way? Or is that the future that they're going to be in your business? No, I think nobody likes being dependent. And I think some of the lessons learned by the hyperscalers of the x86 world is they were dependent on Intel. And some of the lessons learned by the GPU makers was they were dependent on a small number of hyperscalers. Yeah. And they wanted more customers. And so they set about to help fund these neoclouds. And so I think mostly it's about an opportunity to control at least an important part (00:15:58) of your destiny. Got it. And I think that's a very reasonable thing. I think you don't have to sort of make the fastest chip. You just can't be entirely dependent on other people's chips. And that dependency has become a hot topic. I'm not sure if you caught the episodes over the last two weeks, but we've been talking over the last year about open source. I've been championing that a lot just because I was early into open claw and quickly started using Kimmy and was like, wait a second, I'm blowing out my claw tokens. But this Kimmy, I can't tell the difference. And then we started smart routing it. And suddenly this open source started to figure out reasoning and the gap has suddenly closed this year. You don't want to take your Ferrari to the grocery store. (00:16:49) Right. There are times you want to drive your fun car. Yeah. Right. And there are times you want to throw the kids in and don't worry if their Cheerios on the floor. Minivan time. Right. There's minivan time. And I think that as the sort of sophistication of the user grows, right, you're going to have hard problems and those are going to be frontier model problems. They're going to be open AI problems. They're going to be anthropic problems. They're going to be Gemini problems. And behind that, they're going to be a lot of ordinary problems. Right. I mean, if you think about a company, you know how much time is spent cutting things out of work day and getting it in a different cell for. Yeah. Right. Think about the cutting and pasting economy is real. That's right. And this doesn't need, right, gold medal math. No. What this needs is sort of rock solid open source (00:17:38) capabilities. Yeah. And if you think about what, I mean, we've been thinking a lot about it in GNA, but a huge amount of GNA, all right, is not invention. Right. And you may not need sort of the most sophisticated agents for this. And another card that's turned over recently is some folks maybe have concerns with the ambition of the frontier models and maybe sharing their data, data leakage and sovereignty of intelligence. And they're saying, hey, our company is going to choose, maybe we're in a regulated industry, finance, healthcare, HIPAA, you know, FINRA, all kinds of different regulations. We need to have this on prem. And we want to have domestically. And we'd like (00:18:31) an open source version where we have a little bit more control. Yeah. And I think- Are you seeing that now? We are seeing that for sure. And I think OpenAI made a good call releasing OSS 120B some months back. That was a good open source model. But I think in the US, we need more domestic open source models. We need to give the world a choice, right? If they want to run open source right now, it's OSS 120B or Chinese models. NVIDIA has some. NVIDIA has seen the same opportunity to push open source models. I think giving them more power might be sort of- Well, I was about to- That was, you cut me off at the past. Like, my understanding was Jensen was like, hey, we don't even want to talk about these open source models we have because our customers- (00:19:18) Right. We're now going to be competing with Sam, Dario, Elon, Sergey. Like, do we want to be in that position? Right. So, but we do need some more champions here. And it's open source so people can fork it. But that puts you in a more neutral position. That's right. We run today, we run GLM, we run Kimi, we run the Quen set of models, and we run OpenAI's models, the closed source ones. We run models for, say, GlaxoSmithKline, which they wrote and developed. We run models for our partner in the UAE, G42 and MBZ UAI. Yeah. That are their models that they designed. So, we have a wide variety. (00:20:05) So, sovereignty is a trend. Sovereignty is a trend. And I think the government's actions with regard to Fable and 5-6, where they said, oh, whoa, let's think. And then we can act. I think sort of, particularly here in Europe, was a bit of a wake-up call. And when you saw this going down, there's a layer of partisanship in our country right now. It's pretty fervent. Dario is pretty explicitly, you know, not part of this administration. They've been very adversarial. Both sides have admitted that. They're starting to work it out now. So, it's hard, I think, for us, not being in the room with these parties, to understand what's partisanship, what's gamesmanship here. But do you believe that what they released was truly dangerous (00:20:57) for cyber warfare, for cyber attacks, and that if you were to rate Dario's, not communication, because he's a very effervescent communicator, I think is a diplomatic way to say it, but to have a scheduled, rolled-out release. Right. We'll put aside the government's control of it. But do you think that is a wise thing for us to do at this point? And do you think there was actually a major threat there? So, what's interesting is I hadn't seen it before. Right. And I think, you know, if we just step back and say, is it reasonable? I don't know whether this was the right time, but at a time, that a model is sufficiently (00:21:45) creative in its thinking, that it poses a meaningful threat. For the government to say, we'd like you to roll it out in steps. Yeah. Now, this doesn't seem unreasonable to me. Not at all. Right. I mean, we do this with powerful pharmaceuticals. Right. We'd like, I mean, we're certainly not encouraging seven years of trial and the amount of paperwork and all the garbage that has accrued to the FDA. But with a powerful new technology, it certainly doesn't seem unreasonable to say, hey guys, let's at least do some red teaming at the government. Yeah. So, we know our defenses can block this. Yeah. Have we checked? Have we checked? The infrastructure of the country? Of the NSA. Have we checked the infrastructure of, right? And can you give us two or three weeks to patch any obvious holes that are found? (00:22:34) This doesn't seem to be an unreasonable thing for the government to ask. Right. But we, in this very polarized time, put on top of it, well, oh my God, it's President Trump doing it. And then you have to think, well, what if it was President AOC or President anybody in between the two extremes? Right. I think the polarization hurts a great deal. It hurts clear thinking. Right. It hurts clear thinking. And both sides are going to do some dumb things and some really smart things. Right. Right. And in fact, what I found is that the people in the government are trying really hard. The rank and file. The rank and file are trying really hard. And this is moving fast. And I think that an ability to set aside some of the polarization and say, (00:23:23) how do we do this in a reasonable manner? I mean, we want Dario and Sam competing like crazy. We want the bet. It's been awesome to watch. It's awesome. Yeah. Right. It's good for the technology. It's good for entrepreneurs to see, even with thousands of people, this is what you can continue to achieve. Right. Right. This is a drive. Kick Google in the ass. That's right. Make them get sharper. That's right. Everybody got better. Everybody got better because of that. We want that. And we certainly don't want to become sort of a region where the first thing we want is regulated. Right. Right. But as it gets more powerful. And the industry really should do a better job of regulating itself, perhaps. And it did seem like they were starting that process, but then the communication was lacking. (00:24:10) Maybe. Yeah. It's, you know, I think not only are they racing hard, but they're inventing this as they go to. Yeah. Right. There's not a playbook. No. Right. They're inventing the, we say, oh, just put on guardrails. Well, they have to design the guardrails. Sure. Right. The guardrails have an impact. You know, one of the things that fast does is it makes the guardrails less painful. And so that, that is, we, we, you know, we discovered that in the last six weeks is that the very guardrails can add time and make it feel slower. And so fast ships like ours can really help that. But so they're racing against competition. They're racing against their own sense of greatness. Yeah. Right. Which is maybe even the biggest driver here. And I think that they're earnest trying to (00:24:59) think about how to do the right thing. And all of those are, are, are mixed in this bucket. And, and sometimes you're on one side rather than the other. Yeah. And as you're saying, this is a first time, right? That's right. When we, when 3.5 came out, it wasn't like when we're using chat GPT 2.5, 3.5, it was taking down networks. Right. But in talking to Nikesh from Palo Alto networks, I asked him like, Hey, well, how would you grade this? And he said, we put it against our software and we found bugs we were not aware of. Yes. And it killed them. Yeah. He said, we had to stop everything we're doing and do patches for six weeks. Right. And that's when, you know, right. I mean, Nikesh leads, you know, maybe the leading (00:25:45) security software firm. Right. And when it finds in an hour, right. Tens of critical opens, you're like, Whoa, this is a powerful tool. Yeah. We need to think. And maybe you, you show it to a group first. Right. Maybe you, I don't know what the right thing is, but I mean, red teaming. And, uh, we've always had just when you were releasing the new version of, um, an operating system, you know, when you have your iPhone, you can say, I want to be part of the beta. That's right. You know? Right. And there's like two other betas that you don't even get the chance to opt into as consumers. And those ones are for security. Those ones are for, you know, making sure you don't lose your data or data. That's right. Disappear or leak or corruption. Yeah. Any, any number of these things. I think we can also, (00:26:34) we know that there will be a massive data leak. Of course. We, we, we know this. Yeah. Right. And it's like, uh, Warren Buffett talked about the reinsurance industry that, you know, something bad's going to happen. You don't know when. Yeah. But you got to save up for it. Right. You put money away for it. Reinsurance. Yeah. But there will be a tornado. There will be a massive earthquake. I mean, we, we know this and we can do our best to plan, but there'll be a massive breach and there'll, there'll be, and we, we have to steel ourselves in advance and we have to think about it and think about the right response at the time and sort of prepare ourselves for a future that is in specific unknown, but in general, we're pretty sure it's going, something's going to happen. Something will happen. Right. Yeah. And yeah, it's typically a black swan, right? That's right. (00:27:23) By definition, it's going to be something we didn't consider or a question we didn't know to ask. Right. But, but even knowing that there's some unknown unknowns is a useful place to start. Yeah. What are we not asking ourselves? That's right. With reasoning, the AI is going to be able to tell us, Hey, schmuck humans. That's right. By the way, here's what you're not thinking about. This is now my closing sentence. When I do my prompting is I need you to make me a prompt that will help me do this trend scouting for an example. And then I always say at the end, please check your work. Right. And then tell me what I haven't considered in terms of my goals and give me, ask me some questions every time you run the job. And that has changed everything because it's like, I checked my work, by the way, this was incorrect. Right. And I'm wondering, (00:28:13) Hey, would you like me to also do this? And some of the tools like perplexity do that automatically. They give you your next three prompts. Right. But if you give it explicit instructions, my Lord, is it good at that. So, you know, over the course of the last 10 years, as I was raising money, I thought one of the smarter questions I got at the end of a conversation where someone asked, what was the smartest question you heard that wasn't covered by what I asked? It's incredible. Right. Now that's somebody who's curious and thinking and humble and trying to sort of use this to get a picture of the space. And to the extent that you can ask the AI that, and that it can sort of broaden your view, you know, maybe what questions should I have asked to be an expert in this? What would a PhD level question or ask of this or a gold medal math? I mean, (00:29:07) I think those are sort of questions that you know, you don't even know how to ask. Which, you know, if we start thinking about AGI and super intelligence, you know, they're just definitions, but they're important definitions, I think, to kind of keep in mind because they're waypoints. That's right. And AGI, I think, I suspect you'll agree with me that we've hit it. We just haven't exactly deployed it fully. We have artificial general intelligence. Now, it feels like when we're talking about these reasoning moments and, you know, the ability for it to be as smart as any human, but let's talk about- Certainly by any definition we had 20 years ago, we've hit it. Yes. (00:29:52) Right. I mean, if you think about it, oh, there was a Turing test, blew it away. Yes. I mean, you think about that any period of time, sort of 10, 15, 20, 30, 40, 50 years ago, any definition we would have previously put forward, we've blown past it. And so- Which goes back to our previous point of like, do we know the questions asked? That's right. 20 years ago, science fiction authors, you know, had their say and we answered all their questions. Right. If they were to look at this today, they'd be like, well, I'm out of- I'm out of questions. I'm out of questions. Sorry. And that's where sort of listening to people who sound sometimes like they're on the fringe. Right. Yeah. When Ilya was talking eight or 10 years ago about the need for safety and you're like, (00:30:40) what? Dead right. Yeah. Right. When Elon was talking about building rockets and driving the cost to near zero of a launch vehicle, you're like, what? There it is. And now you can see. And that's, I think that's why it's really fun to be a technologist now. Right. Well, and with these tools specifically, you know, we're talking about building all these tools and then the tools are starting to build themselves in this recursive loop. That's right. We're kind of just starting to see people apply loops. In fact, loop maxing became when I was doing my trends, when I did my trend thing, it kept picking up looping and it kept picking up the maxing stuff. And it created a buzzword for me, loop maxing. (00:31:27) Right. And then it magically, people started talking about loop maxing. And I was like, wow, this is really weird. It anticipated that this would, other humans would come up with this word, but talk a little bit about recursive and then the road to super intelligence. And do you have a way, Andrew, that you think about super intelligence and what it will mean for humanity and how we will define it and how we'll experience it? Yeah. I think let's begin on loop maxing or sort of recursive learning. I think what Sam and Ilya and then later Dario and Dennis saw six years ago or five years ago was that powerful recursive (00:32:17) gains are exponential, right? You get better, you do it again. And if you continue to get gain, the slope of that curve is so steep. Yeah. And that we're just beginning to see that now. You ask it a question, you learn from the results, you ask it to do it again. The results get better and more information is added. Your answer gets better. You ask it to do again, it covers more material. And these sort of loops are producing sort of not a little bit better answers, but vastly better answers. Yeah. And that is enormously powerful because we don't quite know where it ends. Right. You keep throwing compute at it. I mean, how much better does the answer get? You know, (00:33:05) we run out of tokens or our budget or, but holy cow. I mean, when does the exponential stop or does the answer keep going up and up and up to the right? Yeah. And that's sort of an enormously interesting intellectual question right now. Yeah. Like when do we run out of problems to solve? Well, that's right. And when are the problems no longer sort of intellectual problems and they're now people problems? Yeah. Right. How to organize people to get done what the AI asked for. Right. I mean, as you know, running your company, a lot of your problems aren't hard intellectual problems. They're people working together problems. Yeah. Right. And motivation. Motivation. You spend a lot of time as a leader spraying WD-40 on your team. (00:33:53) Right. Right. So friction is reduced. And how do we learn about those from AI? Incredible. How do we get behavioral insight from AI? And I think that's some of the things the world models are going to bring us as they begin to watch human behavior. Yeah. We didn't even get to that. This is going to be for another interview. But when these things jump off the screens. Right. And they're in the real world and the recursiveness starts, not trying to solve math problems. Right. You know, humanity's most difficult ones. But hey, you know, there's an incredible world out here and here's the Palace of Versailles. Right. You're just like now we're like, make me a new version of Salesforce. And we're like, (00:34:39) hey, you know what? I'd like a Palace of Versailles. I've got a hundred acres somewhere out in Texas or Nevada. I'll just send a thousand optimists out there. Make me the Palace of Versailles. Right. Sounds fantastical, but the Palace of Versailles would seem fantastical to people who lived a thousand years before it. And it was fantastic, I think, to the people who built it. Yeah. Right. Even to the builders. I think they were awed at it as they built it. Yeah. They're compounding recursive learning. That's right. And generations we talked about. You had a really such a great insight of in building this place, you had generations of Masons. Yeah. I think in all these large projects, often there were families who were specialists and you apprenticed on your father, your uncle. And when you had a project that took 50 or 70 or 100 (00:35:31) years, you might have three or four generations of the same family, right? The same stonemason family working on the same structure. And passing on the learnings. And passing on the learnings. New innovations. Right. Which is what we've modeled with this new. That's right. Models and what you're building in the infrastructure. It's pretty incredible. It's pretty cool. When you think about it. Especially when we're sitting here. But the pace. And that's what, I mean, I think the problem with human learning is it often moves at the pace of a generation. And like elephants and other large mammals, we don't have generations, but every 15 or 20 years. And if you want to move really quickly across generations, you want them happening more like (00:36:22) drosophilae, like fruit fly. You want two a day. Yeah. Right. Then you see that in genetics. That's why we study them in genetics. Because learning encoded in the DNA, you can study over thousands of generations. And I think that what we're getting is that equivalent in AI. We're getting sort of learning so quickly over the equivalent of thousands of generations. Yeah. Darwin would be in awe of this pace of evolution. That's exactly right. You think about it as there was, I remember when I was getting my psychology. And they were teaching us about paradigms. And I was like, trying to understand how the paradigms shifted. And the professor said to me, Jason, which you have to understand is paradigms don't die. (00:37:09) They don't. People do. That's right. That's how Freud. He and Thomas Kuhn. That's right. Freud and Skinner and Young. It took them dying. That's right. For the next generation. The new generation. To question it. And that was 20 years. Yeah. Sometimes 40 years. Right. As their students maintained positions of leadership until someone said, maybe we could do it differently. And I think what you're seeing is this iteration is a shortening of the intergeneration gap. And the learning is so fast. It's always so great to talk to you because, one, it's just intellectually, so your approach to it is so intellectually rigorous. (00:37:55) But also, with so much P-Doom in the world, I feel so good that you're such an optimist about this technology. And you're building it with such thoughtfulness. And I think for people who are hearing these horror stories about AI and job loss and everything, they need to understand there are people like yourself who are building this in an incredibly thoughtful way. And this is going to be a net benefit for humanity that just is unimaginable. We have a shot with this technology. So not our children nor anyone they know dies of cancer. All right. I mean, say it like that. There will be some dislocation in the economy. Sure. There will be. There was dislocation when cars came and it was a bad deal to be a guy who shooed horses. (00:38:41) Right. Or built carriages. Yeah. But you got to also against that, you know, make your tea of the cons and the pros. Yeah. Right. There's a shot that our children, none of them nor their people they love will die of cancer. And that's one thing that we can work on with this technology and we will have great purchase on. And I think you begin listing those and then it's a more thoughtful discussion. Yeah. Unlimited energy. That's right. Unlimited calories. Unlimited knowledge. Unlimited education. Unlimited housing. And how we do it. We imagine, imagine sort of, we know how to teach children and we don't do it. Right. Aristotle was a tutor to Alexander the Great. Socrates was his tutor. We know that if you give a child a tutor and the tutor modifies the teaching for the child, (00:39:28) they learn better. That's not how we do teaching classes. Factory farming. That's right. We teach to some sort of mid-level. Imagine if we built agents that taught children for their way of learning. Right. Right. And here's a way, we've been doing it the same way for a thousand years. And during that entire time, we knew how to do it better and we chose not to. Yeah. And here's a way we can do it. Put that on the pro side. And so as long as we're sort of thoughtfully and fairly writing the good and the bad, I think it'll come out really well. You got to get out there, Andrew. Keep communicating your version of the world because some people see around the corner and they get a little nervous and, okay, fair enough. But I think the ledger, as you describe it, is heavily weighted towards abundance. (00:40:15) I think it will create abundance. Yeah. Massive abundance. Andrew. Pleasure. Always a pleasure to talk to you. I'll see you in six months for our checkup. That'll be great. Industries, capital, and intelligence are converging into a single interconnected system and the infrastructure behind it needs to evolve just as quickly. NASDAQ was built for this moment, powering more than 135 marketplaces and regulators globally and connecting capital to companies shaping the future. As the innovation economy accelerates, connectivity becomes the critical asset. NASDAQ is the leading technology platform that makes it possible and scalable. Learn more at nasdaq.com. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. (00:41:00) NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. ! NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs. NASDAQ is the co-founder and CEO of Black Forest Labs Labs. NASDAQ is the co-founder and CEO of Black Forest Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:42:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:43:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:44:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:45:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:46:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:47:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:48:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:49:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:50:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:51:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:52:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:53:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:54:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:55:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:56:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:57:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:58:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (00:59:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (01:00:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (01:01:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (01:02:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs (01:03:02) Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs Labs