Speed bumps or no speed bumps, it's always a nice bumpy right whenever Abella's involved. 😊 #AbellaDanger #Brunette #PAWG #Pornstar #Interracial #Porn #BlackDick #BigDick #Hairy #ReverseCowgirl #NSFW
29,396 次观看 • 5 个月前 •via X (Twitter)
0 条评论
暂无评论
原始帖子的评论将显示在这里
![Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:](https://image.24vids.com/tw-1994972575768727743/amplify_video_thumb/1994972220930621440/img/_5CCZJyz89oDfqQJ.jpg)
!["[Stargate] is Operation Warp Speed 2.0...Trump funded Operation Warp Speed one...He put $18 billion behind it...if you [have] been supporting Trump on the theory that he's gonna help Americans end the great poisoning, it's sure to say you [are] sadly disappointed." Investment banker, former HUD official, and founder of the Solari Report (The Solari Report | Catherine Austin Fitts) Catherine Austin Fitts describes on a recent episode of the Children’s Health Defense series Financial Rebellion how the newly proposed "Stargate" AI project proposed by the Trump administration is "Operation Warp Speed 2.0" and will ultimately be funded by American taxpayers despite claims that it'll be privately funded. Furthermore, Fitts notes that mRNA-injection "mega-factories" are being built around the world according to feedback she's received from the investment community, signaling no drawback on the wildly damaging and dangerous bioweapons technology. (Bioweapons technology is *my term* for the mRNA injections.) "Here we go with vaccines again. This is Operation Warp Speed 2.0," Fitts says, referring to Stargate. "Remember, Trump funded Operation Warp Speed one. He authorized it. He put $18 billion behind it. Now we're talking about $500 billion, [and] it was presented as private investment, [but] I'm calling hooey on that; it's not private investment because a lot of the people involved are government contractors. And now that we're running the government finances on a secret basis, you're not going to come up with this $500 billion privately. One way or another, it's coming through the government. "So this is a Manhattan kind of project to win at AI," Fitts adds. "Now, if the cover story is vaccines and they're really serious, what you need to know is that mRNA vaccines, first of all, it's not a vaccine. I'm assuming they're calling it a vaccine because they want indemnification. MRNA technology is poison." Fitts goes on to say: "We're talking about technology that we know of as of today—based on Denis Rancourt's work, Mark Skidmore's work—has killed and disabled millions of people. And...you need indemnity if you're a pharmaceutical company and you're injecting mRNA technology into people; you need indemnification because financially, you can't afford to kill that many people without being indemnified, right? So this is very dangerous stuff. And there are two things that are dangerous to the average person: One is they're going to take all your data, including private healthcare data, consolidate it into an operation where it can be consolidated with all other data in a way that builds a digital control grid. This is the infrastructure for the digital concentration camp. But second, if they really do give people mRNA technologies or inject us with mRNA, a lot of people are going to die. And what's frightening is we've seen that the adverse event coming off of mRNA injections is cancer. "Let me take you back. I just wanna take you back to before the COVID injections rolled out. I received a business newsletter, which I find to be very accurate and very good. And...before the injections rolled out...I got a copy, and they had just a little paragraph or so that said investors are very excited about what they're going to get with mRNA technology because...after COVID injections are made, they're going to make injections. And then they proceeded to describe a whole series of diseases that were in fact the adverse events from vaccines. "So if you look at the business model, it's fantastic because you give people poison, they get symptoms of toxicity, then you declare those disease and then you make another poison to give them. Do you know what I mean? And around and around you go. It's an incredibly profitable financial treadmill. And we have seen investors all around the world being proposed to build these huge mega-factories of just-in-time vaccines. So this is happening not just in the U. S, but if my feedback from the investment community is correct, these mega-factories are being built all around the world... "Now finally, here's the question: The people who've been making this stuff, when the injections come out and get injected, what we're finding in the materials is all sorts of materials that nobody knows what they are. So we have secret ingredients again. So the question is, for the healthcare component of this, what's the real goal? And your guess is good as mine; but if you look at how extraordinarily dangerous mRNA technology is, what you need to know is, one: you've got a real privacy issue here because you have many of the top contractors, government contractors, private corporations in the world who [are] going to have unbelievable access to all your data. They're going to have more data on you than you have on you because it's going to be 24/7 coming in on the sort of the digital technology. "So you've got a real privacy issue, number one. And number two: if there's any effort to force you to take this stuff, you're really talking about a mass atrocity. So this is Warp Speed 2, combined with an incredible competition between us and China over AI. And it's extraordinary. And one of the things that was interesting is if you look at the record of Ellison and Altman, you know, who we could go into, it was remarkable that the president would stand up and propose putting a half a trillion dollars into their pocket to do these things. "So I would say if you if you were the people who've been supporting Trump on the theory that he's gonna help Americans end the great poisoning, it's sure to say you were sadly disappointed. And the one only thing I could do to make you feel better is I think the real goal here is to build the data center infrastructure for a control grid and a digital ID and an all digital financial system. Healthcare is just the excuse."](https://image.24vids.com/tw-1882968559334125641/ext_tw_video_thumb/1882961943985520640/pu/img/omuhrmMnR3cK2UnN.jpg)
![prompt：[STYLE / CINEMATIC SETUP] 30 seconds, 16:9 landscape, high definition and fine detail. A photorealistic AAA third-person urban action game. Bright weekday morning, realistic city surroundings, escalating absurd comedy, serious and natural character reactions. One continuous shot from 0–27 seconds. Exactly one hard cut at 27 seconds, followed by a locked two-person medium shot. Real-time motion, no slow motion. A compact, physically connected route: Stopped train carriage at a ground-level metro station → open exit passage at the end of the platform → bicycle parking outside → small wedding setup outside an office building → open company entrance → reception attendance terminal. The station exit is directly beside the office building. No underground platforms, long staircases or travel across multiple city blocks. The bicycle is approximately 25 meters from the office entrance. The wedding setup includes a low platform with broad, shallow ramps on both ends and a central arch made from thin paper flowers and balloons. At reception, a broad, thick, securely attached loading ramp connects the floor to a low, wide countertop. An attendance terminal is mounted on the wall at its far end. Establish these structures before they are used. Begin beside and slightly in front of the woman, then continuously move behind her right shoulder as she exits the train. Follow at approximately 1.5 meters, normally with a 47-degree field of view, widening smoothly to 63 degrees for obstacles. During cycling, primarily frame her upper body, handlebars and forward route. The rear passenger stays near the frame edge or behind her, with his face clearly revealed only after the final cut. No sexualized body framing. Vehicle: one dark-green vintage city bicycle with a metal front basket, black saddle, extended rear passenger seat, conventional pedals and chain, and no motor. Owner: adult man wearing a white shirt, black trousers, crooked tie and black-framed glasses, holding a steamed bun. At his first appearance, he is already sitting sideways on the rear seat, both feet supporting the bicycle, looking at his phone. The front saddle is empty. The woman does not know he is her boss. Chinese dialogue, no dialogue subtitles. Quiet, urgent electronic game music. Clear train chimes, running footsteps, bicycle chain and bell, tires hitting ramps, bursting balloons and attendance confirmation. Classic GTA-style flat HUD, thick black outlines, bold Simplified Chinese, translucent black backing and three-percent margins. Upper left: 【L 移动】【R 视角】【□ 互动】【○ 冲刺】, changing the final line to 【○ 猛蹬】 during cycling. Upper right: white fist icon, green health bar, thin stamina bar, currency 【$000000028】. Lower left: square grayscale neighborhood map, white player arrow and yellow office route. Top center: gold-brown notification banner, yellow main line and white secondary line, one notification at a time. Opening bottom mission: 【赶在九点前打卡】. An additional upper-right countdown starts at 【00:24】 and stops at successful clock-in at 24 seconds. [IMAGE REFERENCES] Use the newly uploaded subway photograph as the sole identity and wardrobe reference, replacing all previous character references. Preserve the same adult woman's facial structure, features, skin tone, dark eyes, dark brown-black hair, wispy bangs, hair tied behind her head, loose facial strands and body proportions. Preserve her ivory fine-ribbed fitted sleeveless tank top; dark-blue denim miniskirt with its waistband, button and pocket construction; white sneakers; delicate silver necklace and small pendant; black shoulder bag hanging from her left shoulder at her left side. The bag swings naturally. She may secure it against her side with her left upper arm. It never disappears or changes sides. Clothing retains normal coverage throughout movement. Extract identity and appearance only. Do not reproduce the raised-arm pose, high camera angle, image text or watermark. [TIMELINE SECOND BY SECOND] 0–4s: [Front-side medium shot continuously moving to rear-right tracking, real-time speed] She stands beside the doors of the stopped train when her phone announces: “还有二十四秒，你就迟到了。” The doors open; her eyes widen, she stuffs the phone into her bag and bolts onto the platform as the camera exits with her and moves behind her right shoulder, revealing the nearby exit passage. HUD: 【主线任务：保住全勤】 4–8s: [Full-body third-person tracking with light running movement, real-time speed] She rushes through the open exit passage, passing a station attendant who has just raised a hand to ask her to slow down; she shouts: “上班！救命！” Outside, she spots the dark-green bicycle a few meters away: its front saddle is empty, while its owner sits sideways on the rear seat, eating a bun and looking at his phone with both feet on the ground. HUD: 【发现载具】 / 【道德暂时下线】 8–13s: [Rear-side medium-full tracking shot, real-time speed] She grabs the handlebars, mounts the empty front saddle and immediately pedals away, shouting: “借一下！要迟到了！” The owner rocks backward as he is carried away, quickly lifts his feet and secures himself to the rear seat; he holds the bun between his teeth, hurriedly pockets his phone and grips the seat firmly, while she pedals hard without looking back. HUD: 【载具获取成功】 / 【附赠原车主一名】 13–18s: [Rear-right tracking, widening to establish the platform and bystanders, real-time speed] The wedding setup blocks the direct route, so she pedals up its broad ramp and breaks through the arch's thin central paper flowers; paper tears and two balloons burst. A bride arranging her bouquet beside the route releases it in surprise, dropping it into the front basket; the bicycle descends the opposite ramp, making the rear passenger bounce and settle as he grips tightly, his bun still secure. HUD: 【穿越婚礼副本】 / 【获得道具：捧花】 18–23s: [Continuous medium-full tracking, rising along the loading ramp, real-time speed] She rides through the open office entrance and takes the already established loading ramp onto the low, wide reception counter; the front and rear wheels strike the countertop in succession with two dull thumps. The receptionist steps aside clutching documents as the woman applies the brakes; the bouquet sways in the basket, a torn paper flower hangs from the handlebars, and the woman's body still obscures the passenger's face. HUD: 【已进入公司】 / 【请勿在前台骑行】 23–24s: [Front-right medium close-up, small continuous lateral movement, real-time speed] The bicycle stops; she plants her left foot on the broad countertop and holds the handlebars with her left hand, pressing her right index finger against the wall-mounted attendance reader. The countdown reaches zero as the terminal announces: “打卡成功。” HUD: 【极限打卡】 / 【全勤奖保住了】 24–27s: [Front-side medium close-up, camera settles, real-time speed] She folds over the handlebars, panting, then raises her head with a relieved smile; the receptionist looks behind her toward the offscreen passenger, suddenly stands straight and respectfully says: “老板早。” Her smile freezes, her eyes slowly glance backward and her shoulders tense; hold the framing without revealing the owner's face. HUD: 【触发隐藏事件】 / 【后座身份正在确认】 27–30s: [The only hard cut, locked side-on two-person medium shot, real-time speed] Show her still astride the front saddle with her left foot braced on the counter, and the boss still sitting sideways on the same rear seat, tie crooked, glasses slipping down his nose, holding half a steamed bun. He looks at her and calmly says: “下班也按这个速度。” She gives one stiff nod, and he casually takes another bite; retain the bouquet, torn paper flower and shoulder bag. HUD: 【老板已送达】 / 【解锁岗位：专属司机】 [STYLE & QUALITY BOOSTERS] Maintain facial identity, hair, clothing and bag placement. Realistic skin, hair, ribbed fabric, denim, metal frame and rubber tires. The central joke is taking the bicycle together with its owner, followed by increasingly absurd commuting shortcuts. Her pedaling drives the bicycle; wheels contact surfaces, chain movement matches the pedals, and steering and braking convey weight and inertia. The owner occupies the rear seat from his first appearance, lifts his feet at departure and remains on the same bicycle. His body responds naturally to ramps and bumps. The final cut changes only the viewing angle, never their positions. Retain the bouquet in the basket after the wedding sequence and the torn paper flower after the collision. Wedding participants and the receptionist remain outside the travel path and are unharmed. Primary negative constraints: 1. No face changes, costume changes, duplicate protagonist or return to the previous blonde hospital-gown character. 2. No cuts, teleportation, time jumps or concealed edits during 0–27 seconds. One hard cut at 27 seconds; lock the final three seconds. 3. No self-driving bicycle, motorcycle transformation, levitation or body/frame clipping. The owner never suddenly appears or disappears. 4. No upskirt framing, wardrobe exposure, injuries or additional chase subplots. 5. No dialogue subtitles, English HUD, photographic watermark, playback controls or slow motion. Maintain HUD placement and scale across the cut.](https://image.24vids.com/tw-2099800722225393727/amplify_video_thumb/2099800142052499456/img/DFMJCygyTvnoPj1m.jpg)
![I'm starting to get a little excited about this... "Everyone in the room gasped. Even the staffers just gasped when they saw that video. There's not a single aerial thing that can do something like that." ~Burlison 🛸 Disclosure Soon? Plus: Burlison Complained to the White House About Not Getting Recognition. Does He Have a Point? 🛸 (I appreciate the honesty from Rep. Eric Burlison but I still have some questions and comments, which are in ( ) ). Burlison: "I know that there's more to come because the White House called me and said, 'Hey, can you talk nice about this release of videos?' And I said, 'I don't even know what's in it. You guys have not engaged with us, at all.'" (Whomever is in charge of communicating with Congress on this issue needs to step up their game. I mean, Burlison has been one of the most vocal Reps. and they're not giving him a heads up on such an important, initial release? That's political, and common-sense, malpractice.) Burlison: "And I'm probably spilling the tea here a little bit, but... And they said, 'You're right, and we're now at a point where we need you, because we don't really...you've kind of done the recon on this, we don't really know where to go...take the investigation from here. Like, we kind of know where to ask and we have videos that we're still declassifying, we have content that we're still declassifying. But as far as like, where to take the investigation, who do you think we should talk to?' "And I'm like, 'You're really asking me? Of all... You're asking me who to go talk to? So I gave them a (audio cut out so I'll try my best) list of people that I've always wanted (to talk to) but they never really talked to me because they're all protected under their non-disclosure agreements and, you know, all of that kind of stuff." (If the White House wants any of those folks to talk to them, involve Trump, and/or Vance and Hegseth. That may convince them to risk it and start talking. And members of Congress need to do whatever they can to get protection, amnesty and anything else whistleblowers need to feel comfortable enough to come forward. Make it happen!) Burlison: "And so, I said, 'But I wanna...I wanna be, you gotta bring me in because I've been doing all of this work with no recognition, and so, if now, things are going to start getting real and real disclosure is gonna happen, I deserve to be a part of this.' And so, that's kind of where things are." (People may get on Burlison's case for being worried about recognition but can you blame him? He should be getting lots of praise, directly from Trump, IN PUBLIC (which will help his re-election efforts), and be given as much information as Rubio has in the National-Security-Advisor position. Maybe make Burlison and Grusch the co-UAP Czars?) Burlison: "And last night, we had about an hour and a half briefing with AARO, where we saw about 30 UAP videos. Some of them are incredible." (This is really encouraging but, as always, I wanna see some of these videos with my own eyes and see if any of them get the masses and media talking about this 24/7. That's when we'll know we're getting closer to more and more disclosure. But what about Disclosure, capital D? Meaning, will we learn about any alleged crashes, bodies, craft or analysis about intent and origin? I don't know.) Burlison: "And I'll describe the two of [the videos I saw]. One of them is of an object that is flying at a very aggressive speed, I don't know how fast. And then suddenly just instantaneously goes from whatever speed is flying... And my guess is it's flying with the wind, probably 80 miles an hour in the atmosphere, and then suddenly just goes like, Mach speed. I mean, it just instantly takes off." (Mach 1 = 767 mph. Again, we need to see this video to figure out if it's actually going as fast as Burlison claims. And if it's released, it would be nice to have data from more than one sensor.) Burlison: "It was one of those moments where everyone in the room gasped. Everyone. Even the staffers who are like, the most skeptical people in the room just gasped when they saw that video. Because there's not a single aerial thing that can do something like that, that can pull that off. (This gives me hope that future videos will contain better footage. Just how much better? Stay tuned, I guess. I've heard things hyped privately in the past and they usually fall flat and don't get the attention of the masses when made public.) Burlison: "The other video was one of a submarine, I think it's a Russian sub that we're filming. And there are about three objects that are buzzing around this Russian sub that the camera operator - who's supposed to be watching the Russian sub - suddenly starts realizing, I've gotta be watching these objects." Kim St. Onge: "I love how passionate you are about UFOs, by the way, which is why we trust you with all our UFO updates." (They then proceeded to cut off his UFO talk and changed the subject. SMDFH!!! Hope we get to see lots more videos!)](https://image.24vids.com/tw-2056135523002142818/amplify_video_thumb/2055747267202867200/img/uzrt0rWN-miyvaem.jpg)
