Hello, everyone. Very much welcome to Cube DDR, a dance of predictive scoring with MLOP step by step. This is a very big, fancy room for one of the final sessions of Fridays. I'm very excited to have you all here for such a fun topic, hopefully, to finalize an amazing Cube call for everyone. Hopefully, it's a fun topic. Let's see how it goes. Even though our session itself, it's about dance, dance revolution, DDR, all of these kind of maybe fun things, actually, the space of AI, ML, and sports is a big one. So as you can see here from some of the newspaper clippings that we show here, there's a lot of different players working in this field with big budget, big teams. We obviously were two people doing a really simple demo here on CubeCon and everything, but as you can see, there's, you know, articles from everywhere, from game-changing AI applications in the sports industry to hobbies predicting rugby matches, to proper journal academic studies looking into predictive models around this, and it's not just limited to physical sports, there's also eSports, so you can see the article there as well. The eSports one is quite interesting because there's a really big difference between trying to get data and like make sense of stuff from the real world using sensors, right? But in eSports, everything is very objective and you can often instrument the game. So there's like a huge difference there. Exactly. So there's a lot of different people, a lot of different varieties of approaches there. And it is not just CubeCon that's been taken over by AI, it is the world of sports as well. We can, I think, make a broad assumption here as you can see. But it's definitely coming for sports as well. So we are here talking about dance dance revolution, but who here has heard about dance dance revolution before they saw this session? I figured, so I thought we would have a lot of people here who are actually enthusiastic about the game, so that's really great to hear or to see from all of your hands. So if someone is completely unfamiliar, it is a game that was released in 1998, and it has really kind of kicked off all of these gaming fitness hardware kind of space as well. It's very popular. It's been featured in South Park, for example, and we have here the pads, actually soft pad versions, as you can see, and it is simply played by, you know, putting your foot to the right arrows and so forth. As you can maybe see from the fact that we have the pads here, we might have a demonstration coming up in this presentation as well, which is, I think, hopefully the final part as well. So who are we and why are we bubbling on here? So hi, I'm Annie. I'm CMO at Vision. I'm a CNCS ambassador at Azure MVV as well. I've been doing a lot of things around communities and cloud-native for years now, so very excited to be speaking about MLOps and dance dance revolution here today. Yeah, co-presenting. My name is Lee Kapili. It's been really fun working on this with Annie and I'm a developer advocate and R&D engineer working on Tanzu with VMware by Broadcom. Also a Kubernetes contributor, and you might know me from the Flux project. You'll see that I have a little boba next to my picture there and that's because me and my friend Kunal also created a initiative called SIG Boba, a very unserious group of people trying to have fun in parties that maybe aren't so focused around beer at happy hour, so. Definitely a fun event. So then dance dance revolution, MLOps, Kubernetes, why are we talking about all of these three things here? Well, I think it's actually a very good example of sports AIML space, so you saw those fancy examples in the beginning and the big teams and big organizations spending money on this thing, but actually when you have the eSports space and then you have the actual physical, like football, basketball space, dance dance revolution, as you can see in this video as well, combines them both. So you have the eSports element because you can actually get data from the video screen, you have all of that eSports kind of combated to there, but then you have the actual physical playing as well. So I think it's actually a fun, but at the same time the perfect example for this space as well. So we have broken it down essentially to a few different parts for a very simplistic view into how predictive scoring for dance dance revolution could look like, will look like, and looks like on this presentation as well. So you obviously kick off with computer vision, so we need to have a visibility into what is happening and obviously in some cases it could be the physical playing of movement of the legs and so forth, or in some cases it can be computer vision into looking into the screen off the eSports portion of the game, AKA the scores and all of these that are running in the screen. Predictive scoring would be the other one where you're actually then taking that data in and figuring out insights from there and making the predictions. And then ML Ops would be obviously the cloud page of the communities part where you actually put stuff into production if you were to run this as a massive experiment or actually running in production for teams on a larger scale than just on one machine on your own essentially. Yeah, but let's continue. So the computer vision aspect, of course it could be in a simplistic way. So we have one demo coming up, but we don't have a demo for the simple computer vision part because I thought we wanted the challenge, we wanted to go for the second part already, not just for the first part, but in the most simplest view, it would be detecting when a dance pad is occupied. So you would have obviously a bounding box there that shows you that, okay, this is the area that is the arrow that we need to hit. And then when you have a foot hitting it, you would see that, okay, that is actually affecting this and we are seeing the actual data, that is when the score is scored and it's all good. There's a lot of opportunity actually in this technique because you would think that just like a foot touching a panel is kind of a subtle thing to try and pick up in a camera. And the camera angle and things can like really change how a computer model can perform. I mean, if something is just a couple of degrees off then like you might not be getting the same kind of detection rights, but something that we can't really demonstrate with just like some soft pads that we bought off of the internet and put into a backpack here is that when you find a dance dance revolution machine at an arcade, it's actually quite a spectacle. And every time you touch your foot on the pad, they light up and that's pretty clear signal to a computer that you could get from a camera. This strategy is pretty viable, I would say. Yeah, it is, it is. And obviously if anyone wants to try it at home, you can use, for example, yellow and open VC and train it with your own dataset of, you know, taking, for example, images of your foot on the pad and then other objects or something that you wanna train that it recognizes what are the objects and why are they there and so forth. And obviously in a even grander scheme of things, as Liz said, go for different other, you know, indicators for what's happening in the game and why is it succeeding, what is going well. And even then, for example, in sports like football or some of these, then you would obviously relay on different, you know, indicators such as, you know, I don't know, hardness of breathing or something like that or speed of running or whatever it might be that's an indicator that player is performing in a certain way. Then predictive scoring, which is the kind of part where we focused a bit more on this. So we have a bit of an OCR and algorithm demo here coming up. So OCR in itself, if you're not familiar, it's a technology that has changed the way that we can manage all of these things. So OCR optical character recognition is for exactly that recognizing the characters. So you have YOLO and these all kind of other computer vision models that are full then identifying, for example, objects, for example, a foot on a pad, but then OCR is for text, digits, numbers and so forth. So it's been around for a bit since properly conceived in the mid 20th century. So it's been really helpful in helping out with this. Now our demo here today will be using OCR with OpenCV, Pythasarac and non-vegetarian term in winning the year-to-year player real-time. We won't go to the demo immediately after this because we're gonna show you a bit about how did we arrive to this actual iteration that we will actually show on the stage then. But in general, so we are using OpenCV and Pythasarac. We're using OpenCV for the image processing and Pythasarac for the simple interface with OCR. And then we capture a video feed, which is actually because this is a bit of a fun demo by using an actual camera, which we determined was honestly the simplest way to do this because we tried a lot of streaming with a lot of different options, but it all was a bit not as good as this option for sure. Then we do a bit of pre-processing on it. So we do grayscale by including Gaussian blur and reduce noise and thresholding to create a binary image as well because the numbers we're not showing up properly. And then we do the number detection. So we detect the numbers and then we determine who is the winning player at any given time. We can definitely show the differences in the layers for the pre-processing on all of the image frames to show why those techniques were necessary and we'll pull that up in a moment. So you can see here, this was one of the first iterations. We used YouTube video footage for this because it was actually really easy to control the amount of points or the amount of points that the player has because if you're playing, we would have to constantly be playing a game to get more points. That's pretty floored when you came up with that. Yeah, yeah. You see, it's just like, this is the cool thing about collaborating, like Ani went and was like, hey, I kind of need some input data. Like, do you think we could find a video of this thing? And then the problem is, oh, well, we only've got like a single player. The nice thing is that Dance Sense Revolution is pretty symmetrical. And so like, maybe it's not like being fully clear here, like Ani was able to point the webcam and then use her window manager to just clone the video, put them side by side, and then slide them at different points in the video to make somebody lose or win. And this was like a really great way to synthetically get training data to iterate on the computational technique using OpenCV and all of that without actually having to play the game over and over and over again because we tried a bunch of things and it did not work. Exactly. For sure. This is the iteration where you see where I had applied the grayscale because without it, it was not showing up at all. But as you can see here, we have actually a lot of data coming in here as well as in addition to the numbers, so the scores, the points, we see also all the text that is on the screen as well, which is obviously not useful for us for the predictive scoring point of things. Then moving on to the next iteration, we still have the grayscale and everything so that we can actually clearly see the numbers. And this is exactly the point where I had two videos running that Lee described before where I could switch then between the two videos to show which one has better numbers or who is winning and I could really in real time see that, okay, player one is currently winning, then I switch the point in the video so that the other player was winning because I could control it exactly who is winning and then it switched to the player two winning, for example, so I did it a few times here and it is not perfect so we have no numbers detected happening occasionally as well, even on this version, but you know, this is not such serious demo so that we would need to have perfect accuracy at all times, but we have there, you can see it was player one, player two, then player one, player two, so those were the points when I switched between who is winning because I could control that very clearly. Okay, so now we're moving to the actual live demo part where we're using the OCR to do this and for this we obviously have the pads here, I will be our camera operator with me, as graciously being well-entold maybe. Yes, yeah, let's clap it up for with me. Whitney Lee, everybody. Join us our second dancer here so that we can see. I do wanna give the heads up that we had some challenges with the background coloring at the moment so it was not as accurate as in some of our tests, but we'll see how it goes. You should be seeing the data pull up at least, but let's see, does it detect the winners as good as it could be? Yeah, we have a very noisy background that literally says no background image and it messes with the contrast of the numbers, yeah, cool. Yeah, to the AV team, maybe we could pull up the feed of the video game now. That would be possible. Sweet, and then is there a way to kind of show both of them at the same time or do we have to flip between them? Maybe there's not a way to show both at the moment. Cool, yeah, if we can't show both, then we just want just the laptop screen, which would be, yeah, awesome, thanks. So now you can see the data starting picked up there and we will start the game soon. Yeah, and then you'll need to pull that open screen to the output window up to, yeah. We're using extended mode because we are brave presenters, so two separate screens. Yeah, there we go, nice. Great, yeah, so we've got a webcam feed here and then we've got the game loaded up and let's go ahead and step up here. I feel like we've done this before. No, yeah, Whitney wants everyone to know on the stream right now that she had no idea that she was gonna be on stage playing Dance Dance Revolution, so yeah, okay, let's pull up. This is, I think we're on like a five or a six right now. It's pretty tame, but it's the default scroll speed so it's a little bit quick. Oh yeah, and then, sorry, one second. Let's make sure that we get our audio too. That's a good point. And cool, we should have audio now.  Yeah, we're kicking them around a bit. What do you think, Ani? What? Do we have some data coming in? There's definitely data coming in, but I think the numbers are not as good as you can, but I think the audience can see them being picked up though. And then, Dewey, do you want me to disable that printer for all of the stream of text that's coming in? Because that's a little bit of a visual noise, isn't it? This is a lot, yeah. All right, cool. I love that the audience is dancing along as well. Perfectly done. Thank you, Whitney, amazing dancing. Particularly surprised. That was really amazing. Well done. OK, then we can get back to the deck as well. There we go. I would be able to take maybe some time to show some of the moving pieces there. Of course, yeah, sure, sure. Yeah, I just know you're not... We're using my laptop. Ani wrote a lot of this project, and then I was able to pop over to her Git repo. I like the extra challenge of dancing and then showing the code for Lee as well. Isn't it extra fun? Be like out of breath. Cool, yeah, all right. So this is the code that's currently running up here. We have a Python file that has a pre-process function and OCR function, a determined winner function, and then popping into the main logic loop here. We have to open up a capture. So you can see that there's a couple of different kinds of ways to capture things. One of the most interesting, especially like when you're in development, is this particular sort of strategy. I'll make sure that this is a little bit larger so we can probably see. And so you can just pass in a file of a video, and that video could be from the real world or it could be from your window manager. But then there are a couple of different ways that you can get streams of videos. SRT is secure, reliable transport. I don't know what REST stands for, but it's kind of a competing alternative. Both of these protocols are actually not fully adopted yet because they're newer than what's like, say, if you stream to Twitch, it's RTMP. And even before that, a lot of webcams, like IP cams and stuff, use RTSP. It's like real-time streaming protocol, real-time media protocol, message protocol, sorry. So these work another way as well is to serve your file on HTTP. And FFMPEG is all under the hood here. OpenCV is leveraging FFMPEG to do all of the codec and encoding and decoding transformations so that we can actually get the frames. So once you set up the video capture, we are able to basically go into a loop where we can read frames. Behind this is actually something that's messing with the buffer. We were using a subclass, and we call the preprocess function, which Ani, you implemented this over here. What were these pieces again? So these are the ones that make it grayscale, grassy, and blur. Because before we added these, it did not work at all. Well, to be honest, you could get a lot of the text feed by that point, but the numbers were not standing up enough so that the machine could actually properly detect them. And to be frank, that is also some of the challenges that we have right now with the background, because it really, this is one of the parts. Like if your data is bad that's coming in, you will have a lot of challenges, no matter what, like the basic principle of ML and AI. So with these, we were able to at least attract some amount of numbers, and depending on the background color, we would get better data or worse data. So with these three, we're settings that work really well with some of the step-mania images and themes. But if you were to try this at home, because actually we will give you a link to the code after this, you would obviously have to optimize for the theme that you're using. You would obviously have to optimize for the setup that you're using, on which exact parameters for the pre-processing work best for your setup. Kind of going beyond that, we want to then get the text out of the OCR function. Here, we're just doing some quick hacky Python string manipulation. There's a list comprehension over here that's grabbing any sort of percentage. That actually worked on a different theme that we were playing with. And then this bigger numbers list is what was being used in this example. Oh, that's because it would be actually quite simple if we just have one number that we could track. But the great part about Dance Dance Revolution is that you have all those numbers popping up, like perfect and different scores. Yeah, your combo counter, and that's not always there. And so you have to sort of make sense of the screen space based off of what's coming out of OCR. Some bounding box stuff would definitely eliminate that. Yeah, we also explored using ROI, so regions of interest, to highlight the area. But on this timeframe, getting that working was a huge issue. And I spent way, way, way too much time debugging that. But that would definitely be a good way of implementing so that the OCR could focus on only a single region. And actually, you know, get that. So going cloud native. Because what was that, Annie? Some 78% of models? Yeah, so most models never make it to production, which means that for MLOps in particular, which has been a theme across KubeCon right now in general, is most models never make it to production, which means that the bottleneck is in the going to production phase.  So then when you wanted to go, you know, for example, if you would have a bit bigger, maybe use case scenarios, so then dance dance revolution dancing, then you would want to actually go to production. You would want to have multiple different people collaborating, maybe data from different sources, globally and so forth. So that is going to production with all of those elements is the part where things fall true quite often. And it's over 50% or even over 70% that never make it to production. Let's look a little bit at where the pain starts to come from, right? So I'm a data scientist, I'm not actually a data scientist, but I'm just pretending to be for this example, right?  And then my DevOps person says, oh, gosh, can we try to figure out how to run your app? And I'm like, what is a Docker file?  I'm like, okay, I mean, that seems like reasonable. And so, you know, if you're lucky, you know, you can just try and do the things that work on like your MacBook, you know, or the things that work on your Linux workstation, and maybe you can get them to map into, you know, whatever operating system is under here. But then you find yourself in like a world of pain, and you read some stack overflow and suddenly your Docker file maybe looks a little bit more like this. And it's not fun at all.  Like why am I trying to learn how to build FFMPEG and OpenCV, and what is a codec? And at the end of the day, I'm really just trying to get my app in there so that I could go and run my fun computations. But there's all of this infrastructure work involved. Let's not even talk about Kubernetes.  And you know, why do I need Carvel now? And what is a image shaw? And how come I have to pin it? Is that, I guess that must be important for security. Hopefully we don't need signatures and all of that, right? But the reality is when we try to do real stuff, there's all of these people that are involved in the productionalization of these models and of the execution of the thing that actually produces the valuable questions and answers. So we do have the ability, you know, to build this into a Docker container. Right now, this was just running on the host. But then, you know, you can come in here and you're like, okay, you know, Docker builds, you know, and then let's give it an image name and we're just gonna pretend that we don't need to tag that in version it, whatever you can see. I've already done this build and so all of the painful things that normally happen have already now happened, right? Then we can run a Docker container, right? And then now we can try and run our application. This was currently trying to read from a webcam and that's not gonna work, right? Let's exit here and then we won't enter bash.  But what we do have is the ability to load test data. Now I can't just load from a local file system because that's a container. It came off of a container registry. It's running on a distributed computer that I don't control, right? So one of the things that we can do is we can like put our test data or stream our test data from some network accessible server. Here we're using tail scale just to have a way to get back to the host from the Linux virtual machine that's running on Mac. And if I come into here now and then I run, let's just do a build really quick. Make sure our code is up to date. Yeah, I enabled that. And we've got a little bit, I think that's a web server log. We'll just double check. Yeah, so I'm running a little Python web server on my machine, nothing complicated there. And then hopefully if I were to try to actually run my code now, then we are able to actually, yeah, so we can actually read from the frames of that. And if I change to a different video capture strategy, I learned all of this from hacking on Anni's code. So just do another build just do another build just so that we're not faking, mounting volumes or anything. Then we should be able to get a few more, sorry. We've got to also turn off the fact that we are displaying some builds. It was complaining about the lack of a display server there. There we are, cool. All right, so we're now doing OCR on all of the frames in the video as our capture mode, cool. Yeah, perfect. We covered a bit of this already. So as we talked about it there before most models never make it into production and as Lee really good highlighted here some of the practical challenges and thoughts that go into these things. And as a kind of side note as well, there's so much bust around LLMs and how to make MLops, Cloud Native and all of these things work for them. Well, as a reminder, Generative AI and Predictive AI are obviously have a bit of a different need so that obviously impacts the ways that you would do MLops for these two as well as on what actual demos that you can run, for example, in these kind of scenarios. So this is a picture from the CNCF AI White Paper that was just published what two days ago, a really, really good read, highly recommend. And I think it highlights really well what are the different needs for Generative AI and Predictive AI and what we are doing here is Predictive AI AKA it's taking in input and predicting or describing that in a certain way, but Generative AI is the LLM, the Chachi PTs of the world, which then actually generates something new based on data that it has ingested before essentially. So those are the two different things. And as you can see, the scale needs are very, very different across the board there. And then we... Generative AI is also not usually used for analysis per se, right? But then we produce artifacts through Generative AI. And so like while in the sports use case, like we might be really trying our best with computer vision and integrating with APIs of structured output like in say League of Legends or Dota, then we're trying to find information about what's either happening right now or also very likely like what happened throughout all of history. But then with Generative AI, maybe a DDR, Dance Dance Revolution example might be like, what if we looked at every step chart in the world and then tried to produce some new ones? Exactly, yeah. And actually a good example of one application could be also, using historical player data from one of these Belayer repositories and using that to determine before the battle has even started who would win the Dance Dance Revolution battle as well. So you could get into sports bidding with that front as well. But then just a bit of recap there. We talked a lot about this already. So Q-readings and Cloud Native are a really great fit for ML Ops, but it does really, you need to kind of figure out how to make sure that your environments and everything works for the data centric, model complex and scale needs of your applications across the board before we have very specific industry wide ranging like ML Ops best practices using DevOps best practices are really, really helpful way to get started. And then Lee spoke a bit about Q-dots as well and I do want to add something there or? You know, there's just, there's a lot of pain for somebody who's just trying to slice data to actually operationalize their code. 70% of models not making it to prod. That's a huge issue there. And so you can see that even just in trying to containerize some computer vision code, we run into issues like, oh, the developers laptop had, you know, a display device. So the developers laptop had webcams, you know, and it was, they were able to debug. And so it's like, now you're thinking about the distributed computer. Kubernetes, like, how is our device for it? Is it easy to use? Definitely not. The complexity starts to get to a point where, you know, like running these on the distributed computer becomes an interesting operational problem. Exactly. Then just highlighting a few parts. We are running out of time a bit, but I highly recommend checking out QPLO, Open Elementary, for example, for MLOC projects and KSGPD for actually, you know, Generative AI for Kubernetes. And these are all also highlighted in the new AI white paper by CNCF. So you can read a bit more about them there. And then we have some resources. So you can see the link here where you can find the code. We will probably add some more did-fits today, some of the other variations that we tried, some of the things that we tried to fix for this one and so forth. So there might be a few different versions that you can try out at home as well, but there's already one version. And then if you're looking for a practical guide to MLOPS, I really like the book by Noah and Alfredo. So I highly recommend checking that out. CNCF AI white paper is linked there as well. And then if you're looking for specific materials for computer vision, learning, open CV has both free as well as paid materials that you can check out. And then we were meant to have a... Yes, Lee? I think we're getting kicked off. Yes, I think we are getting kicked off. We had an idea of having even more dance dance revolution playing here, but I think we have run out of time. So we probably don't have the time to do that. But thank you so much for everyone for tuning in.


So, uh, gunsmith cats. All right. So way back at the RIT anime club, right? And like one of the first one, I don't know if this is the first, but one of the first showings we went to was this anime. This was before we ran the last time I saw this before yesterday. Yep. Uh, and so that was like, what 20 years ago. Yeah. This O.A.V. came out in like 96. So I was a freshman in high school, so I didn't see it until the year 2000 when we went to RIT. Right. And I remember watching it was there was the girl with guns, the car, the girl with a bunch of grenades.  And watching it in a big group, it was a lot of fun. And people got real excited. Yep. The crowd went crazy. It was only three people were laughing hysterically at certain parts. Yeah. It was only three episodes. It's pretty well animated. Like it had a pretty high production value for like the era, like it looks good. Like a lot of that much more recently, dark horse released all of the manga, gunsmith cats and gunsmith cats burst and I bought it all and read it all. I don't know if I kept any of it and may or may not if I do have it. It's on one of my back shelves hiding behind other manga. Yep. I never actually read the manga, but you know what? Even though I read absolutely all of it, I remember almost none of it. Basically. Yeah. It was not memorable. But anyway, there's the OAV more like more of it than I expected.  And she has a gun knife, which is a real thing. And the third episode is a little spooky. And it gets a little dire toward the end and people die. And then I also remember she had the Shelby Cobra. Right. So there's this related thing called like being writing, being writing, being it, which is a character being bandit who is in the manga and is not in this anime, but he has his own spinoff anime, which is just a movie called Writing Bean. I have not seen writing beans. I was in high school. I watched the writing bean anime VHS from the RIT anime club. I took it out of the library and I watched it once and I returned it. I want to say I rented it from Blockbuster. I don't remember much of it. Pretty sure I got it from Blockbuster video. All of these things were made by a man named Kenichi Sonoda. So we have to know about this guy. And this is a Japanese dude, right? Who I don't know how significant he is in Japan, right? Because it seems like his Wikipedia article is not long. It seems like he had his day in Japan where he was big and like his manga was big enough to be animated, right? Into movies and O.A.V.s. But it seems like he was bigger in America, at least compared to other manga artists, which I think partly because Writing Bean and Gunsmith Cats came out at two like big inflection points for American anime fandom and got for the time pretty wide releases. Well, I think it's also I know he definitely came to conventions here and made appearances and stuff. I don't know how I remember seeing people cosplaying as Raleigh Vincent at early Otacons, but also this dude, part of maybe he's part of the reason he was coming to America and doing, you know, and stuff like this is because this guy is an American fanboy, but he loves cars, he loves guns. He loves American cars. Yes, he loves boobs and butts and naked ladies, right? And that's the kind of guy he is. And weird. Technically, she's 30 years old, nonsense. He's a problematic figure. So if you look at the manga and the anime, like the, you know, the guns are so perfectly illustrated down to the tiniest detail of the model after real guns. The Shelby Cobra is drawn in like ridiculously higher resolution and fidelity, but I'm like initial D. The characters are also drawn well. Right. The characters are drawn well, but still much less than the car. Yeah. And it seems like most of the work he's had in the anime industry other than the things he's worked on that were his original works were basically character designed for other things. He hasn't, you know, done a lot of original stuff. Also, it seems like he hasn't done anything at all in a large number of years. So I know what he's up to. And according to Wikipedia, he's working at his family's candy store. Yep.  Right.  What a Wikipedia article.  Yeah. But that is a sentence for a Wikipedia article. I think he was also done for and bubblegum. I don't know if this is true, but I get the because it was also another guy who did this. I forget his name. But I think he's also done like American comic stuff a little bit, maybe anyway. Yeah. But yeah, there are other people who know a lot more about this guy and can probably talk for hours about, you know, what the deal with him is. But you have to know about this dude to understand this work. Now, one thing I would say is we're going to focus on the O.A.V. because that's what I watched yesterday. You do not need to know anything about the Gunsmithcats universe to watch that O.A.V. If you read the Gunsmithcats manga, you will get the full story of Gunsmithcats. It was originally just the Gunsmithcat. Who are these people? How did they get together? How did they get in this business? What their deal is? That is less interesting than the O.A.V. Right. You'll also in the manga get some really bad stuff, right? So if you don't know the girl, Minnie May, right? Yeah. She is in the manga. You will learn that she is actually like a teenager, basically. And in the anime, in the first episode, there's a very short scene where like she looks at a photo of a dude longingly and they don't mention that dude again. In the manga, that dude is her boyfriend. He's there. He's way old and she's a teenager.  Doesn't she like take drugs to keep herself young? I don't know about that. I don't remember.  It is interesting that the boyfriend's name is Ken and the guy we just talked about, who's the creator, is Ken Ichi Sonoda. Yes, May is tiny and young, appearing for her age. Having taken growth stunting drugs in an effort to stay attractive to Ken, the aforementioned guy, she can pass for a child of 10 or 12. Don't read the manga. I read it. Do not read the manga. I don't know if I still have it, though. Yeah. But yeah, other than like the creepy facts like that, though the manga is basically just a lot like these OAVs, only just more of it. But the beauty of the OAV is that a lot of 90s anime have this dynamic.  And watching Gunsmith Cats again really reminded me, it's not the best anime, it's got a lot of flaws, but it reminded me of all the things I really liked about these 90s OAVs.   But it is 90 minutes of animation with one contiguous story that just happens to be chopped up into three acts with an opener and a closer between separating the acts. If you get to the end of it. The opener is basically the cowboy bebop opener. Or the cowboy bebop opener is the Gunsmith Cats opener. Didi-dee-dee-dee. It's jazz music with colors and panning and you know. Three chromatic guns. If you cut out the openers and closers and pasted all three of these together, it would be a movie. Yup. Like a made for TV movie quality level, right? Not like a, you know, movie cinema movie quality movie. So what is this movie? It is basically we're in the city of Chicago and there are these two women who run a gun shop. Yup. They're also bounty hunters. Yes. They are bounty hunters. They run a gun shop. The gun shop is perfectly legal and like regulated gun stuff. Like their customers are cops because that's just how it is. But they also have a large collection of extremely illegal firearms to support their bounty hunting. Right.  The main character is a hyper competent gun nut.  Also car nut. Yup. Private investigator drives a Shelby Cobra. If you don't know what a Shelby Cobra is, it's a very rare and special car. It's a very American muscle car. Yup. There aren't many of them. That is enough. It's not ultra rare. Yeah. But it's pretty rare. There's a lot of money you can get one. There are a lot of adjacent cars, but the Cobra was a rare one. Anyway, her partner is basically an explosives expert in the same vein as movies like Predator. You have the team and there's always the one on the team. Well, it's like Team Fortress. He got Gun Person and Demoman. Yup. You see him gun and Demoman.   Not clumsy, careless. Yes, but the explosives are usually reserved for traps when they're far away from them, right? She's throwing usually flashbangs and smoke.  Yup. That's when the anime club got excited. Yup. And then you get the long-distance shot. You see the house like way in the background. You just hear a pop and then smoke comes out the window. Yup. That kind of stuff. So the movie... These three episodes are... They're not telling you any of the plot of the characters. There's no real character development or anything that happens here. They're assuming that you already read the manga or you don't have to have, right? But they don't bother introducing the characters very much although you'll get it right away. Yup. It's just a side story. I don't believe this story was in the manga at all. It's like a complete side story. Sort of like a cowboy bebop movie, right? It's just like a side story. Doesn't interfere, right? You could insert it pretty much anywhere into the main plot. It's like he's a bad guy. He has the story of them dealing with the bad guy, the ATF agent, the cops, the Russian, the whole situation, right? And then resolving it over the course of 90 minutes then the end back to status quo. And it has a pretty good progression. Like the three acts work pretty well. Like act one is basically the somewhat silly plot where they get roped into some hijinks and the cops sort of extort them into helping the cops with this dangerous investigation. Yup. And it's mostly funny.  Right. They thought they took care of it in episode one, but no, episode two. Actually, this is a much bigger deal and you don't want to be involved, but now you sort of are being forced to and a big shootout thing happens. Yup.   Right. That crazy Russian person who you shot at and is upset and is now coming after you and also the big boss has been revealed the end episode three. And it holds up pretty well. Like if you're an anime fan, it's worth watching. It's just on YouTube. Like you could just go watch it right now. Well, it's on YouTube illegally. But yeah, the things that are positive about it, if you really like illustrations of guns and cars, but there's also parts of the gun fighting. There are also lots of scenes where there are just excuses for characters to have a lot less clothes on than otherwise. But what's funny, and like Dave Reilly said this a bunch of times in the past, but it's almost wholesome compared to what that kind of fan service is in the modern era. That's true. Also some of the scenes, right? For example, it's like these two girls living together in a house, right? So for them to be like sitting in the kitchen table, wearing only their underwear talking is like, I feel like normal. That's like, that's how I live my life. Right, it's like that seems normal and they're just illustrating it.  But like every scene that's most egregious is in episode two, where the bad guy attacks by using the warehouse crane hook. Yup, yup, I was going to bring that up.  And it just rips your bra. And it just so happens to tear her shirt open right down the middle and tear her bra open right down the middle. 90s anime, ladies and gentlemen, try the field. And it's a giant hook. It's not like something, you know, it's like, really? It's not like a sword slash cutting the shirt open. But the other, like the good positive aspect and the neat thing about it is that it's set in Chicago and they did their research. It is like... Well, cause he's, and you know, this guy is a fanboy of guns, cars, America, boobs. Well, crime, he's like, yeah, Chicago. So the anime was adapted by Takeshi Mori, who according to this article, didn't know anything about guns at all. Nope. But knew a lot about cars. So they actually took some of the production crew to go to Chicago to scout locations, to get backgrounds, to do research. They actually visited gun shops and they went to a police academy and got firearms training. And then they used that to make the anime. Trying hard. Yup. And they actually went so far as to get a Shelby Cobra GT 500 and record the sound effects for the anime. No, they didn't buy the car. They just found someone who let them listen to it. Yeah. And they got the sound effects and made it legit. I was gonna say. And it's funny cause half the, like the gun play is very noir. Like goons just fall over instantly dead. You don't see a lot of blood or any blood. But then a few moments are much more realistic about the guns. Like it's a weird thing to point out, but ricochets are hyper realistic for some reason. But the noir stuff is just John Wick style.  The gunfights are really fun though. Like they're more interesting than most of the noir gunfights I would say after episode four of noir. But yeah, if you want a nostalgia trip back to nineties anime, if you don't mind some of the negative parts of nineties anime. If you want to see like in a really tight example of what popular nineties anime was like, this is like, this is the shortest path to experiencing that. And if you- You can also learn about this Kenichi Isenoda guy who was important slash infamous. Yup. And a time and a place in this really narrow.   Every now and then some nerds like us will do a panel at a random anime convention talking about, so you ever heard of this guy? Let's talk about this guy none of you have heard of. Yup. But I now kind of have this itch I need to scratch. I kind of want to watch like bubblegum crisis again. But you did character design for that, right? Yeah. But like that nineties anime, like I'm starting to feel it again. Bubblegum crisis I think is much better than this. Yeah, but bubblegum crisis is also not great. No, I just is better than this. Yup. That doesn't mean it's great. The only really important part of bubblegum crisis is that the 80 police suck. Yeah. This has been Geek Nights with Rym and Scott. Special thanks to DJ Pretzel for the opening music. Kat Lee for WebZone and Brando K for the logo.


Stop. No doubt these five salary negotiation tips now so you don't get underpaid. Number three, back up your desired salary with evidence. A case study of your best project, an impressive portfolio, achievements in previous work with real data, a profile backed by solid evidence will prove what you're worth, give you leverage for further negotiation. Number four, prepare for the worst. It's not wrong to expect getting what you've asked for, but keep it real and always prep yourself. Have a backup plan, be willing to walk away, separate your ego from the outcome. Number five, money isn't the decisive factor. Companies can always offer you more money. Consider their non-salary forms of compensation too, sabbatical leaves, performance bonus, extra vacation days, equity, etc. This is Chris, always here for a sharing Sunday. Follow me for more hacks and tips.


Anyhow, Q&A. Let's get to it. Let's see.  Yeah Name calling is not cool. It is not cool trust no one in 2023 to verify. Yeah, that's it Like rocks like right here inside black rocks a bell weather for where things are going I think it's gonna be a pretty good day when they come out and say this is it As far as crypto goes But yeah Kramer did go to college amazingly I Don't think anybody went to college to get stupid. No, but I think they went there to spend a lot of money That's for sure. Yes. Thank you, Bikki for all the links. I appreciate it Yeah, be real. Nobody has savings. I always got a good point. I saw Some data that came out that took a look at the amount of debts that's on The average Americans credit card it is skyrocketed skyrocketed and also Data that looks at the amount of savings that people actually have and during the pandemic When we were air dropping money like crazy We had a pretty good amount of savings and now that we're out of that time frame and people had a chance to spend things The actual savings accounts have been dwindling rapidly and then once that runs out people put things on credit cards And then when that runs out then we go through Credit crunch foreclosures, and you know the rest. So that's where I think we're going in 2023 I don't have much faith in 2023.  Not really I mean trust me. There's other things to be down about. It's not the market The market is the market and we just had to be realist about it This is the time to invest into things and you know, one's gonna want to hear this well some some may not come Want to hear this but you're you're you're a long ways off from from from really hitting all-time highs And so this is the time to think to yourself. What's my options? I? Can sell everything and take massive losses.  It's not like I mean if you're here right now, you're not a tourist But at some point you're gonna lose a little faith.  I think if you're here right now you're probably one of the one of the rare few that sticks around for the whole thing and That's it and then of course the next bull run that happens, you know Then of course the big question will be can you sell in time? And that's that's harder than I think that's one of the harder things to do than buying dips Yeah, it's a good question. So isn't that what black black rock jay Morgan I've been doing developing their own private blockchain on top of certain products. Yeah, I don't really know so like You'll have Ethereum working with different different companies to make their own private blockchain. So I don't know not a developer exactly how that works, but As time goes on, you know, you have you have black you can have private blockchain within your organization and bring people on to it, but What's the point of that in a in a in a private ledger? I don't get it. So like that's the whole beauty of it. You have these ledgers, you know scattered about on You know hundreds or thousands or tens of thousands of different nodes and everybody can can take a look at it And they can verify it and they can be validated and they can go through that's that's where the power is the power is not just I we've got two ledgers one here and one at Pete's place and Me and Pete verify it. So you're good. That's not that if I think as time goes on people have to understand what blockchain technology is Then they'll kind of understand it Okay Rob, which does better 25 Tesla stock of Bitcoin probably Bitcoin. I feel more comfortable with Bitcoin, honestly I have some Tesla stock I'm not big into stock but stocks, but I own some a more bigger answer. Excuse me crypto and And real estate No, I didn't who's Ben McKenzie, I don't know that person Uh, your view on polka dot thinks I'll probably do pretty well depends on if they can keep developing and move forward It all depends. It's just like it's just like regular businesses, you know, you can have a great idea and have a good, you know foundation But if you don't Grow expand get a new customer base have real utility and actually make it out Then if you're not growing you're dying and that's just how business is So if polka dot can't you know bring in, you know, other huge businesses that Want to build on top of it or want to actually use it and have real use case and real utility and distinguish themselves They will die just like all the rest of them I'm rooting for them. I I own some Let's see Ah, it's dorked What's the exchange you trust the most during these times or what do you look at when evaluating risk in an exchange?  It's a good question. I forgot. Let's see There's this website Uh, I don't know if you've heard of it but it's band teaches crypto and it's free and There's this part That first of all, I'm just letting you know it's free to a point Because everything's on there is an affiliate link. So like when you click on Where are we? When you click on like the coin base thing, uh, that's an affiliate link But they haven't really paid out any affiliates anyhow. It's kind of sucks Uh, and then kraken That it is an affiliate link and also coup coins affiliate link, but these are the ones I use but back to the question rob You know coinbase was established in july 2012 and kraken was july 2011. Can you believe that? Yeah, long time Long time kraken kraken. Oh, I gotta change that jessie pal is not the ceo anymore. There's giant lube 7 2017 so Yeah, it just depends on how long things have been around and I gotta tell you longevity kind of gets into me So like for me, I just buy everything on coinbase and I transfer it to my ledger Very simple and the ones that can't go to my ledger like near I just put it into my my near wallet That's it Dan you are not doing dca with james and many more. Uh, no Rock can you grow a big point senator? I've already had one Uh, but I just I trimmed it down Ran ran not dan now. Yeah, ran's on the show now. Good. That's good I just don't have It's just too too toxic Uh I was I was tired of the I was tired of the Of the negativity on my show So I just had to step away Yeah, sin city. What's up gents? Where'd you guys go? God dang it. Where'd it go?  Check out their channel sin city crypto. I used to live there for two years Well, henderson or hendertucky is people in las vegas called Yeah, I think rob hides all his gold bars in his mom. You know where I own most of my gold um in uh Can I trust In my crypto ira because they allow me to do crypto and golden silver. Actually, I have golden silver in there so yes Exactly bah humbug Oh That's a good one. Let's see Partner There is um Let me see here. What? Come on. There we go So bicky says do you think crypto crypto Suffers from the gardener hype cycle. I think I'm a big believer in cycles Predominantly here and then you can see here gardener hike So this is this peak of inflated expectations This is every every all-time high and every all-time high Is the same thing happening over and over again every four years so like in 2013 You know bitcoin people just thought it was gonna be the that was it people are gonna You know fomo in and and they did and that was it but of course, you know it dies down because people like wait wait Who how many people use this? Not that many. Okay, cool. And then of course in 2017 we thought it was gonna be the biggest thing ever And uh, then we started to realize wait who who's gonna use this and then of course this you know The cme had their and their futures contract Which allowed them to short the hell out of bitcoin and then just everything just demolished And then of course here we are again uh in 2021 and we just got way ahead of ourselves and I personally believe that one of the problems with with 2021 because it was it was just different than here and here There's no blow off top per se But there was a good one. Caitlyn long was on mark moss's channel and she was talking about How the bull run was stolen this time? basically because of of uh over leverage and rehypothecation and Just the demand for not real Crypto not real bitcoin but paper bitcoin and it screwed everything up and that's And that was part of that was all these rehypothecation centralized players and that's I think what screwed everything up Now we know our we know the lessons here And I think it's going to be a better It's a better session. Hopefully in this next upcoming cycle, but we'll see So yes to to answer bicky's question in a long drawn-off manner It's always the same thing and you can see like doesn't this match every four-year cycle Like look at this It goes up a little blop and down right up Down and a little blop up Yeah, yeah Jesus crimely Christmas that looks exactly like it Look at that So yeah, and then the next one was a little bit screwy, but and you can see how Where we're going So good question, bicky Ah, let's see Do you think defi will do them do well the next cycle? I don't know I always thought that gaming would do better Gaming and nfts not the goofy nfts that we buy now. They're stupid but like the ones that like actually have like utility and Like nft for like your id or nft for like real estate or or tokenization of like assets like like Blackrock co is talking about so That's where I I see more things going, you know real utility and actually does something Yeah, jane chow I doubt binance's boy is buying voyager Huh, how much that billion is bnb that we know is insolvent Financially I play old voyager asset very special. I was cuckoing. We've been next to the earnstake I don't know about cuckoing, but as I understood it And jane and everybody can can correct me on this one. I understand this was a cash deal It wasn't they weren't putting up their bnb coin For voyager I don't think so Because that would make no sense because if you think about it, that's what That's what ftx was doing.  That was the deal Yeah, Aristotle said yeah past performance is never indication of future performance. Exactly. So I tell my wife all the time So let's see Any good ai projects No, but I mean There's some great websites. I should I should share these websites with everybody. They're pretty great like you can do like automations of Like script gpt and you can do things with like a victory App.pick3.ai you can roll your your videos through and spit out some really good texts. It's awesome A rational says a second half of 2023 everything rises. I hope you're right That'd be great What are your top 10 coins you pull from drip drops and go down a rob I haven't pulled anything from there and I don't know how long are any of them worth anything That that I guess that'd be the big question. I like what drip drops is doing. It's pretty cool You know when you when you stake with you know With like D news or something for your cardano. It actually gives you some other tokens, which great Fetch it. Yeah, fetch AI Fan man. Hey, Rob. Did you ever buy in the cryptiverse? Yes. I bought four parcels. I've not much happened with them Don't expect them to anything happen for like three or four years Let's see CZ is obviously more trustworthy than spf if you think so Whichever comes the bag Yeah, me and Tasman the same boat. I want to get all my money back to That's it also I'm going to let you guys on a little secret I am going to do a giveaway and All let's be honest. How many of us are in peak performance shape? Raise your hand exactly nobody so New year's resolution is coming up, right? We want to get into shape in some way shape or form, right? so I you can do anything you want to but the easy way I found to get in shape is just to walk Just just start walking more and for that we're going to use sweat coin because I am heavily biased because I own a bunch of it And the sweat coin app is free to download. Did you know that you can download for free? And what we're going to do is the top 30 people Right now there's a leaderboard. There's only like 120 people in there So what I want you to do is we're going to download the sweat coin app. We're going to start january 1st We're going to stop january Is it 30 or 31 days in january? I forget And what we're going to do is the top 30 people you guys are going to win things and we've I've already got some some Some good deals lined up. First of all, when is going to get Uh an end of the cryptiverse Premium membership. You're also going to get a coin ledger Membership which is to for taxes.  That's my big big spiel. That's all i got I gotta go So everybody thanks for stopping by appreciate you if you like today's video give it a thumbs up consider subscribing Maybe youtube will notify you maybe not it's a crapshoot. That's it for today So thanks so much for stopping by appreciate you i'll see you guys in the next one. Have a great day. Adios Thank you


I don't like planned. I don't like, I like some structure, but very like movable structure. And the only reason why is because I find that my greatest experiences have come kind of at spur of the moment. Something happens and I just go, yes, let's go do it. And so I love the unknown. We're about to take off and go to Roman Four Days and we have a place where we're living for a month and then after that, there's just no idea. We're gonna go somewhere in Europe. We might stay there, we might go to other places, but we have six months to fill. The first month is planned. We have an Airbnb. After that, something's gonna happen and we'll just kind of move around and go with it. But I love the unknown. But I think that most people, it scares them because once again, your brain is trying to have, trying to know what's going to happen in the future, to know that you're gonna stay alive. For me, once again, I just try to, I try to turn that part of my brain off and go, let's just go with it because some of my best experiences in my life were never planned in the first place.


I think that's the biggest thing and big part of this game is, you know, being consistent, not only being consistent, but showing up every day. So, uh, just trying to, you know, try to hit those two things and see how everything goes.