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SSniperwolf taking that long white dick inside that thicc jiggly bubble butt like the good eslut she is 🥵😍🍆🤤 #tribute #trib #jerktrib #nutty #gooner #egirl #horny #gooning
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Violet Myers revealed that a 5 to 6-inch d1ck is her perfect boyfriend size not too big, not too small. She said bigger d1cks work better on camera because the penetration is easier to see, but in her personal life she prefers an average sized d1ck. She also admitted she has a small cervix, making very big d1cks uncomfortable and sometimes forcing her to pause filming to give her vag1na a break. “I feel like four as long as it has some girth is good too because I don't know about other women but my vag1na is pretty I have a very small cervix so l can't really take d1ck like that when I'm taking it on camera I feel like I'm struggling sometimes I'll be like pause the camera like I need give my vag1na a break”
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I wish there were more White people that think exactly like I do 😤😮💨 I feel like I'm the only one who takes anything seriously and means what I say. I also feel like I'm the only Racially loyal one, who would put the White Race first. There is no one who takes anything seriously online or in person... What is the point of me even making an effort, when there is no one who will defend their White Nations anyway? I have been doing videos like this for far too long now and still no one cares but me. I just want my White daughter to have a better future than I did and to only know that Good White people exist, so she can have a safe and happy environment growing up.... But it seems that life is fading now because White people are constantly being too nice to non-whites and saying they are not racists. Either you are a full on "racist" or you are not. There is no in-between. I only prefer White people who are full on "racists" who are Racially Loyal like myself, but clearly they don't seem to exist.
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Lex P talked about the whole Megan Thee Stallion and Klay Thompson situation and she started to go off on Klay, saying mega was cheated on and betrayed by somebody who she thought was about to be her husband and she is the victim in this whole situation🥀 “Mega ain’t had no build been with klay anyway, his ankles barely work, y’all are praising Klay, why are we praising someone for being disloyal, y’all don’t just like meg, y’all are praising a man for cheating on somebody, since when are we on white people side bro, klay is not a good person, he is a bad person with that long ahh nose, i don’t trust people with white mama’s, we all will know for the rest of your white ahh life that you a bitch ahh ngga”
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![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)
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Bitch, I do not play about eighth grade star aka opinions based girl….. yeah everybody’s laughing now, but it literally does not take away the fact that you are so fucking ugly like I had his ass blocked on my old account before I even saw this because every time he pops up on my for you page I just have to scroll because he’s so fucking ugly that it pisses me off he looks like he could be casted on Netflix and not in a good way. He seriously needs to find a new haircut because what the fuck is that middle part? and of course he’s a Nepo baby, and the best part is I just found out that he used to be fat, I bet he’s an anorexic asf lowkey all shade and that’s the only way his pathetic ass could lose weight and I know he didn’t use laxatives cause he’s definitely a bottom, the type that one bitch from White Lotus would say as a fem and NOT a butch. You can tell he wants to be Matthew from big mouth but he’s more of a Charlie from heartstopper, you can also tell that he’s never got his ass beat, well maybe once in middle school and then his white ass republican parents freaked the fuck out and got the kid suspended or some shit. Of course he’s obsessed with Diet Coke, he’s so obviously wants to be a white woman that it is embarrassing. Middle schoolers definitely go up to him and be like “oh my God you’re famous on TikTok” and he thinks he’s the shit but the rest of the population don’t give a fuck and the way his mom is a teacher…… I know that she probably yells at the fucking class for everything but really she’s just taking it out on these kids because she knows deep down that she wishes her son was like them instead of a fucking loser who makes strawberry bagels on TikTok not to mention everybody praised him for doing angel tree, bitch that’s nothing he probably just used some of his allowance money to look like a good person so y’all bitches would suck his dick. Omg in all of his fucking videos he tries to sound cute with that feminine ass voice and it gives me a secondhand embarrassment down. he thinks he’s Troye Sivan, but he will never admit that he probably listens to fucking country music and Taylor Swift so he can fit in with all of his Christian girl friends and that’s it. You can tell he peaked in high school. He was definitely the “token gay” and the homophobic straight boys loved him because he was “funny“ but it was all surface level, TikTok humor that seems funny to them. of course he reposted something about drinking on antidepressants. Just because you had a white claw and some lexapro(which explains why he used to be fat as fuck) doesn’t make you whatever a fucking vibe you’re trying to go for. He looks like he jerks off to the Lululemon maternity catalog while pretending he’s Harry Styles. I saw on TikTok that he has a girlfriend and I can’t tell if it’s just for attention or if he’s actually still dl so that his parents don’t freak the fuck out on him and he has to get a real job. This one girl posted him on TikTok, shaking his hips and drinking wine…… like who do you think you fucking are bro? like genuinely who the fuck do you think you are?🔑 he also reposted a TikTok about Lorde “looking a mess” I really don’t give a fuck if it was a joke. You have no place to be talking about somebody’s looks when he looks like that……. and y’all know what I mean, the type of guy who fucks all the dl in the room because he has zero confidence. The type of guy who has all the latest Apple products for no reason. I mean seriously he looks like he eats fucking rocks. Apparently he caused a lot of drama on TikTok (of course) and blamed his OCD like what kind of dumb ass excuse is that but then again he probably asked his mom what to say and then she sent him a script and he went with it. Somebody keeps on copying his videos and it’s pissing him off. I think it’s so hilarious…… you realize you’re just another copy and paste of every other wanna be “ influencer”. Don’t ever try coming for eighth grade start again.🔑🔑
Travis🦋
90,814 views • 3 months ago
![You may be wondering why one of the MSP Troopers who investigated Officer #JohnOKeefe’s murder is glaringly absent from the prosecutions witness list in the #KarenReadTrial. Is that because her testimony might be devastating for the claim that Jen McCabe didn’t make that google search at 2:27am? Yes, it’d appear so! (Part 1 of 2). Attorney Yannetti gave us some insight yesterday into why Massachusetts State Police Trooper Kathleen Prince is notably missing from the state’s witness list, but is on the defense’s witness list. Why would the state want to avoid calling the Trooper that interviewed star “witness” Jen McCabe on February 1, 2022? After all, unlike Lead Investigator Michael Proctor’s unrecorded & unsigned interview of Jen, Trooper Prince’s interview is far more detailed and thorough, and Prince also didn’t wait several months to write down & document her interview like Proctor did. Well, perhaps it’s because Trooper Prince’s is the only interview of Jen McCabe’s that ever even mentions anything about the Google search & from Jen McCabe herself. However, this is not the only lie seemingly told by Jen McCabe that Trooper Prince’s interview will expose. McCabe claims that: “Karen was told to sit in a car. Jen went over to Karen when she was in the car…Jen also stated that while her and Karen were in the back of the car, Karen was yelling and screaming one moment and then completely calm the next. Jen said that they prayed the "Our Father" together. Karen then immediately yelled at Jen two times to Google, "How long do you have to be left outside to die from hypothermia?" That’s a big problem for McCabe, which the state probably later realized, hence why they took the creative liberty to completely distort & change Jen’s statements in their later court filings, particularly about how the Google search came about. Why? Jen probably didn’t realize when making this statement to Trooper Prince, who she’s not close friends with unlike Michael Proctor, that the Police cars have dash-cams in them. Thus, if this conversation ever actually happened, like Jen claims, then it’d all be recorded on dash-cam footage. It’s convenient how Jen’s story about when #KarenRead purportedly asked her to Google such a thing also happened inside a car where nobody else would be able to hear it to corroborate Jen’s story. But again, in this early stage of the investigation, Jen likely was unaware that her entire claim would be debunked by the dash-cams that would’ve captured it as proof. So to her, being inside a car was the perfect explanation for why no one else at the scene heard Karen ask that. Because Karen never actually asked it! THIS is Jen McCabe framing Karen Read. But beyond this, we also now have confirmation from Officer Mullaney that Karen & Jen never got into a car together while he was there. So this entire story seems to have been fabricated altogether. In a desperate attempt to remedy this critical fact issue, the state just completely fabricated new testimony in its documents, stating that immediately upon finding John’s body, ‘The defendant then yelled at Ms. McCabe twice to Google, "How long do you have to be left outside to die from hypothermia?’, or something to that effect.” What else was fabricated? The claim that Jen McCabe was taking any measures to try to help save Officer O’Keefe’s life, unlike Read who was. In her interview with Prince, Jen claims: “[She] observed Karen to have blood on her hands and face. Karen was holding her hands up and was saying, ‘I have my period.’ Jen said that she told Karen that was not her blood that it was John's blood from them doing CPR.” Jen McCabe’s trying to win favor for trying to help save John’s life—something she did not do—when she says that the blood on Karen’s face “was John’s blood from them doing CPR”. Except “they” didn’t do CPR. Only Karen Read did CPR as confirmed by yesterday’s 3 state’s first responder witnesses who confirmed as such. #FreeKarenRead](https://image.24vids.com/tw-1785892041332789759/amplify_video_thumb/1785891962073026560/img/eHUMp_K_9vqSWTtf.jpg)