Artificial Intelligence is a pretty big deal. In the recent years, we have begun to see its early manifestation in the form of things like smart assistants on our phones or business analytics. However, despite its growing popularity, few understand the basics of artificial intelligence and are instead more concerned with the risks that come with it, which we will look at in another video. Anyways, in this video, we'll be taking a look at the very basics of artificial intelligence, including machine learning, neural networks, and artificial neurons, and explore some types of artificial intelligence used today, without any of the complicated math or coding. It starts with one question. Can machines think? This is the question English computer scientist Alan Turing asked in his 1950 published paper, Computer Machinery Intelligence and Learning. In order to answer this question, Turing proposed the idea of a Turing test. The setup of the Turing test is as follows. A human interrogator asks a question to an unknown subject, which can be a human or computer, and within a given amount of time, the interrogator must guess whether or not the subject is a human or computer based on the response. Therefore, if the interrogator wrongly mistakes a computer's response for a human's response, then the computer shows a success at thinking, thereby passing the Turing test. Turing then goes on to propose the ideas of computers simulating human beings. Over 50 years later, we are witnessing the dawn of a new era in computing with things like Google Assistant or Siri to something more advanced such as Google Duplex. So what exactly is the science, or in this case, the computer science, behind this popular yet mysterious wonder we call artificial intelligence? You see, the thing is that there is no straightforward answer, unfortunately. So where do we start? Well, in order to understand artificial intelligence, we need to understand something called neural networks. Neural networking, simply put, is a form of computing modeled after the human brain. Just like how the human brain is composed of neurons, neural networks are composed of what we call artificial or virtual neurons. The first type of artificial neuron we'll be looking at today is also one of the most basic ones, the perceptron neuron. Perceptrons work by taking in several binary inputs and then giving one binary output. The output of the perceptron is dictated by whether or not the sum of the input values meet a parameter or what we call a threshold value set by the neuron. If the sum of the input value is less than or equal to the threshold value, then neuron outputs a value of zero. And if it's greater than a threshold value, then neuron produces a value of one. To demonstrate how a perceptron algorithm works, let's take a look at an example. And as always, you're free to skip past the example if you already got the concept. Anyways, let's say that the new Avengers movie is coming out to your theater on Saturday.  You then contemplate whether or not you should go by taking into account three factors, whether or not you're available, whether or not you'll be able to make it on time, and whether or not you'll have room in your stomach after attending the birthday party that takes place before the movie. Now, let's assign each of the factors a corresponding variable, x1, x2 and x3. And for each variable, a binary value of zero or one is assigned. With the value of one being assigned if the condition for that variable is met, and a value of zero if the condition for that variable is not met. For example, for x1, if you're able to make it on time, x1 is given a value of one, and if not, it's given a value of zero. For the second variable, if you have room in your stomach after the party, then x2 is given a value of one and zero if not. And I'm pretty sure you get the idea of this by now, so we'll just skip x3. Anyways, after you do this, you assign a threshold value, which in this scenario, let's say is two. And to sum of x1, x2, and x3, it's greater than the threshold value, which in this case is two. Then the neuron gives an output of one. And if it's equal or less than two, then the neuron admits an output of zero. Additionally, if some factors are more important than others, such as whether or not you be able to make it on time, then you assign each factor a weight instead of a binary value, which in this case would be W1, W2, and W3 respectively. And the weight can be an integer other than one or zero, such as six. Depending on how important a factor is with the higher number or weight corresponding with that factor having more importance. Perceptron output can be modeled with the equation Wx plus B, where W is a vector or a table of data containing the weight input values. And x is a vector carrying the binary input values. And B is a threshold of the neuron times negative one. If Wx plus B is less than zero, then an output of zero is given. And if it's greater than zero, an output of one is given. Again, if you don't know how to multiply the two vectors, that's totally fine, is we aren't going to be really focusing that much on the math in this video. Perceptron neurons aren't all that bad. But the thing is that in modern computing, other forms of artificial neurons are usually preferred. The reason for this is that even a slight change in the weight or value of the factor of a perceptron neuron can cause the neuron to give a completely different output, which can significantly impact the whole computing network if it's composed of perceptron neurons. And so to combat this issue, we tend to use a more popular form of artificial neurons known as sigmoid neurons. Sigmoid neurons are similar to perceptron neurons in the sense that they take in multiple factors and even assign weights to them depending on the importance of the factor. However, they differ in the sense that instead of being limited to receiving the binary input value of zero or one, they can receive any value between zero and one, so it's just 0.783. The sigmoid neuron's output is modeled by the sigmoid function sigma times wx plus b. And while we aren't going to go much into the math, I thought it would be worthwhile for you to look at the graph of the sigmoid function. As shown by the graph, the output of a sigmoid function can range anywhere within zero to one. That's all you need to know as far as the basics of sigmoid neurons are concerned. Sigmoid and perceptron neurons aren't the only types of artificial neurons. There are other types of more complex artificial neurons which we will talk about in future videos for sure. Anyways, as stated above, these many types of artificial neurons come together in what we call neural networks, which are computing forms modeled after the brain. There are many types of neural networks, but in this video, we'll be looking at a brief overview of the most popular ones used for machine learning today and in technologies you may use. The first and perhaps the most important type of neural network we'll be looking at today are feedforward neural networks. FFNs are considered to be one of the most basic types of neural networks and are the basis for other types of neural networks such as convolutional neural networks. FFNs are created by multiple layers of perceptron neurons. An FFN can be divided into three parts, an input layer filled with input nodes, which take in the input values, the hidden layer in the middle, which are filled with what we call hidden nodes, which do all the calculations and processing with the inputs, and an output layer filled with output nodes, which give the output for the neural network. Oh, and just to put this in because it's important to know, this architecture is what all neural networks to some extent are composed of. Thus is the reason FFNs are considered to be the most basic framework of all neural networks. That being said, it doesn't matter to me if you don't fully get every other type of neural network in this video. As long as you understand the basic concept of FFNs, we should be good. The next type of neural network we'll be looking at is a recurrent neural network. Recurrent neural networks take in a list of sequential data and try to make predictions about it. However, the neural network must have access to a large data set, which it is trained to use in order to draw knowledge to make predictions about the input data it's given. For example, let's say we have a recurrent neural network and use the giant history textbook as our database, and then we input it into the neural network. George Washington was the first president of the recurrent neural network in this case understands that we're looking for a specific noun, and we'll then use the information from the data set, which in this case is the textbook to predict the missing noun that would complete the sentence, which in this case is the United States. Recurrent neural networks are used in many text-to-speech applications. So if you use anything like Google Translate or something similar, chances are a recurrent neural network was used. The third type of neural network we will be looking at is a Kohanen neural network. Kohanen neural networks are a type of self-organizing neural network. Self-organizing neural networks, such as the Kohanen model, can take inputs of data with multiple dimensions and then create a low-quality two-dimensional visual representation of it, which we call a map. Then using this map, the neural network can look for patterns in the data and cluster the data into its appropriate groups. Now, you're probably wondering, what exactly is data with multiple dimensions? Well, dimensions are attributes associated with the data. For example, a data set on temperature has multiple dimensions slash attributes to it, such as time, altitude, latitude, and longitude. Kohanen neural networks have applications in the medical field, where it can be used to cluster medical data into different categories based on multiple attributes of the medical data, such as blood type, patient history, patient height, which can be helpful for determining what type of disease a patient carries. The fourth and final neural network we'll be looking at today is a convolutional neural network. Convolutional neural networks are used when it comes to making sense of images. CNNs classify group and recognize objects within images. If you've used any sort of facial recognition technology, chances are a CNN was used. The process of how a CNN works is quite complicated. But to simply put it, a CNN takes in things such as the information about every pixel of an image, such as the pixel color, and the image width and height, and makes sense of it by trying to look for patterns in the image, such as shapes, textures, and borders. Keep in mind that for a convolutional neural network, the hidden layers in this case are the convolutional layers. The convolutional layers process the patterns in the image. A convolutional network can have layers besides a convolutional layer. But for this video, we're just going to show you the most basic structure and go more in-depth than later videos. The bottom line is that CNNs are good for interpreting and processing any sort of visual data. That's all we're going to be talking about in this video, as far as neural networks are concerned. Keep in mind there are way more than the four types of neural networks that I showed you. But for the sake of keeping this video as short and simple as possible, I limited it to what I thought was four of the most important neural networks. With that being said, you're probably wondering, how exactly do we get these neural networks to act in a certain way? Well, that's what we call deep learning. Deep learning falls into a broader category of what we call machine learning, which is getting computers to perform a certain desirable action without explicit programming. There are two types of machine learning, supervised learning and unsupervised learning. First, let's take a look at supervised learning. Supervised learning is the more used of the two forms. Supervised learning is where you have a bunch of input values and a set of output values for each given input values. The computer uses an algorithm to predict the correct output values for the given input values. For example, let's say we have a set of input values called x, each of which have a corresponding output value, which we call y. We can use an algorithm, which in this case is the function y equals 2x, to predict the output, which in this case would be the y output value for every x input value. That wasn't bad. Anyways, now let's take a look at unsupervised learning. Unsupervised learning is where you have a bunch of input values, but no corresponding output values. That's because the goal of unsupervised learning is not to predict the output values, but to analyze and find patterns in the input data. It does this by clustering and association. Clustering is where the data is grouped based on similar trends the data might share with each other, such as grouping theater customers by their movie preference. Association is used to find trends and correlations in data. For example, when analyzing search data, we tend to find that people who search up the science first on YouTube also tend to subscribe to science and educational channels in addition to having a really, really good taste in YouTube channels, wink, wink. Anyways, that's all for today. Keep in mind this video was scratching the very surface of AI to help those of you who don't have a strong technical background get a better understanding of the subject, which is why we didn't get into any complicated math or coding. We will return to this topic in later videos where we'll begin to take a little in-depth look at some of the math and coding. I'm so happy you made it this far in the video and I really look forward to educating you more on science and technology in future videos. If there is any more topics in artificial intelligence you want me to cover, be sure to let me know in the comments below. And also, don't forget to subscribe and stay tuned for more science videos.


The study shows that lifestyle changes such as changes in mobility, dietary, housing or consumption behavior can contribute more than 20% of the overall greenhouse gas emission reductions required for net zero by 2050. Behavior and technology-oriented scenarios are tested individually and in combination for the EU, plus the UK and Switzerland. The impacts of behavioral change vary across sectors, with significant GHG emission reduction potential and broader benefits. Changes in travel behavior limit the rising demand for electricity, natural resources and infrastructure costs from the electrification of passenger transport. Adopting a healthy diet reduces emissions substantially compared to intensifying agricultural practices while at the same time making cropland available for conservation or bioenergy crops. The trade-offs between energy and food may be substantially alleviated when deploying technological and behavioral changes simultaneously. Without behavioral change, the dependency of Europe on carbon removal technologies for its net zero ambitions increases. This article was authored by Luis Costa, Vincent Morro, Boris Thern and others. We are article.tv, links in the description below.


Hi, I'm Denshi, and in today's video, I'm gonna talk once again about a bit of a meme program, and that's DWM, or Succes's Dynamic Window Manager. Now I'm using it right now, and I have been using it for the last few weeks to just test things out. And the way Dynamic Window Manager works is that instead of having all the windows, you know, just floating like you would have in a desktop environment like KDE, instead of doing that with DWM, as soon as you spawn a window, they begin tiling, and they have a specific tiling lance that redirects you to the newest window as soon as you spawn it. So what does that actually mean? Let's take a look. I'm gonna open up a terminal window, which I can do by opening up my D menu over here, and have it launch a program. So I'm gonna do st, that's my terminal, and I'm gonna press enter. And as you can see, it opens up over here. And the way I have it configured is that on the left, new window shows up. The other one goes to half of the size of the screen. And now I'm working over here. I can type whatever I want in here. I'm gonna just put the music player on. You can work in here, you know, just scroll through my music, I want to play a song or something. And then as soon as you're done, oh, well, I can just move my mouse over here or use Vim keys to go back to the other window. That's an interesting thing about DWM is that focusing between windows is as easy as moving your mouse. So if I move my mouse over here, well, now I'm working in here. But if I move my mouse over here, while I'm scrolling and doing everything here, it's the same as if you were clicking in a floating window manager is normally in KDE or something. If there was a window in front of me, like I open new floating one like this, I'd have to go over there and click on it. And then it would let me do stuff in it. With DWM, you just hover over something and boom, it works. Now, there are a few other things you can do with DWM. Like for example, while in the tiling layout, which is this little symbol over here at the top, you click that and then it's just to the floating layout. So now, oh, you can spawn new windows. And as you can see, they're floating. If you hold down your modifier key, which I've set to the super key in the left button, you can move them out, right click and resize them, whatever. And that applies to every single window. Then click this again and go, they go back. You can open a new window, then just drag it out and make it floating even though you're in the tiling layout. So you can have maybe one floating window here, and then you're still working with the tiling ones. So they sort of live on two separate planes, like I said. But the most interesting thing about DWM is how it's configured. So let's take a look at that. I'm going to go over here in the terminal and go to the directory where I have the local source code for DWM. This one over here, as you can see, it's very minimal software. I think it's less than 2000 single lines of code, which is very, very, very small. In fact, this has a very small memory footprint and runs very efficiently. And I definitely recommend it if you're into window managers, you need something that's very, very minimal. But anyways, how do I customize it? Well, in something like Openbox or i3 or whatever, you normally have configuration files, not with DWM. You do have a configuration file, it's config.h, this one over here, but it's located in the source code. So essentially, you have to make your own build of DWM to use it.  However, if you back up your config.h and specifically the patches you applied to it, which we'll talk about later, then you can essentially have your own custom version of DWM without really having to do anything. Just keeping the config backed up and the patches. So I'm going to show how to do those separate things. The first thing I want to show off is the patches. On the Succes website, you click on patches over here, there's a bunch of them for different things like, for example, you're got a system tray, and I got the patch that adds functionality like auto starts. So I got certain programs to start automatically, that kind of thing. There's a lot of these, as you can see, you just download these are just single files like this, for example, actual full screen, that kind of thing. This is a single file. It download this, I put them all in a folder called patches or directory.  So patches just a Unix utility and you send over the patch, the patch itself. So let's say I want to activate, yeah, there you go, the auto start one over here. If I were to run this program, then it would essentially apply all of the conditions stated in here on all of these files over here, at least the ones specified in the patch. So anything that this patches modify will be applied when I run this command. I'm not going to run it because it's already applied. Now the important thing to note is that this does not at all metal with that previously aforementioned config.h. Regardless of what you do, this always stays the same, config.h. What changes is config.def.h. That's the default config. Now patches change this to add any variables they expect to the change. So for example, if I had this alpha patch, then it's going to add the new alpha functionality that lets you have transparent D menu at the top, which I currently don't have. And if I were to apply that, then it would not only modify the code itself, so modify a few C files and stuff, but it would add some options regarding to, hey, how transparent I want it to be, what sort of colors I want for the transparency, and that kind of, it adds different rules that you then have to copy over to the aforementioned config.h, the regular config. So that's just something to note. Okay, one last thing about DWM. The text over here at the top, that can be customized. So if you go to the regular DWM, as you can see, there's custom text up here. If you launch DWM by default, however, up here in the top, it only ever shows DWM and the version of it. So like 6.2 or something. Now the cool thing is you can get a program called DWM blocks. And I got over here, DWM blocks right here. And this program over here runs and modifies the contents of this. So it essentially modifies the variable that changes this text, every, however intervals you want. So in my case, I got a few things that I got a little price indicator over there balance or something. I got the date in seconds. And I have that set to update every one second, as you can see, while the balance updates every 240 seconds. And I have the battery capacity, which is just this class power supply about zero capacity, whatever. And I have that update every 10 seconds. So I can see all these three separate pieces of data with a divider, which in my case is a little UNIX pipe icon, right here at the top, just in case, you know, if I want to see it. So I just wanted to note that because this is quite a cool feature of DWM having that little customizable text at the top. Pretty sure you've seen that with Luke Smith and his DWM blocks build. And he does all the cool stuff where you can click stuff. I don't have that, but I do have just this generic text. I don't really need to be able to click anything. But anyways, I hope you enjoyed this video on DWM. If you really want to try it out, then the website is linked in the description. I've been Denshi. Goodbye.


Your knees are achy and they're creaky and they make lots of noises, especially on the warm-up set. All right, so today I'm gonna talk about knee sleeves and really we're gonna bring out three of the more popular types of knee sleeves. Knee sleeves, I've got a Harbinger pair here. I've got the ones I typically wear when I train, which is the Ray-Ban pair, and then I have a pair of SBDs. We'll talk about the difference between all three of these. I'll actually put them on my knees so that you can see. And so let's talk about the main reason we use knee sleeves. The main reason we use these is just to keep our knees warm, especially as we get into the cold months for those of you that train in your garage, in the wintertime and it's cold. Your knees are achy and they're creaky and they make lots of noises, especially on the warm-up sets. And so we wanna put these on before we ever start our warm-up sets. I put these on, I put my squat shoes on, and I start squatting even with the empty bar, body weight, start going up, because I want to keep my knees warm. It provides some basic compression and warmth to the knees. In general, it should not help the lift. It shouldn't give you poundages on your lift. And we're gonna talk about that here in a minute. So, most good knee sleeves are gonna be made out of some version of neoprene, right? So neoprene works. And all three of these are, but they come in different thicknesses, right? And so one of the ones you'll see, so this is Harbinger, bought at just a basic sporting goods store here in town. Mueller is the other brand. And I don't have relationships with any of these brands. I want to just give you a really clean review. So this Harbinger is a five millimeter thick. You will sometimes find kind of a cloth version that's three millimeters thick. They're really worthless or they don't do much at all. They don't even keep your knees very warm. And so this neoprene knee sleeve, this is Harbinger's five millimeters thick. You can see the Ray-Ban knee sleeve is seven millimeters thick. You can see it's a little thicker. And the SBDs are also seven millimeters, but look at how much bigger it is to the Ray-Ban, right? This is a big, thick, this is a competitive lifting knee sleeve. And I'll talk about why this isn't my favorite, at least for most general audiences. Okay, so let's go to the Harbinger. So Harbinger, Mueller, et cetera. So what I do, you just put them on. A lot of times I'll put some lintiment on. And so you're just gonna start to slide it up.  And I can already tell with this knee sleeve, you can see how easy it went on. It's pretty thin. And this is the problem that I have with these cheaper Sporting Good Store knee sleeves. They don't last very long. You've heard the term, buy once, cry once. When you see the price tag of the other two, you're gonna understand what I mean. A knee sleeve like this is gonna cost about 30 bucks, but here's the problem. It's not just about how long it lasts. It's about even in the middle of the workout, this knee sleeve is gonna work its way down a little bit and I'm gonna have to keep grabbing it and pulling it up. And it's really frustrating. I want a knee sleeve that's gonna stick in place. I don't put chalk on this. I don't put anything sticky. I need a good knee sleeve that's gonna provide the warmth that I need, make my knees feel good and isn't going to move. And my experience with these cheaper brands is that they don't do that very well. And then second, they just wear out faster. I think just wear out. And so, not a huge fan of these cheaper ones. Look, if you're on a budget, just know you're gonna buy a new set of these once a year, once every six to eight months. That's how often you're gonna buy it. And there's about 30, 35 bucks for a pair. Next, my favorite knee sleeve. These are the Ray Vans. Let me tell you why they're my favorite knee sleeve. First off, they're seven millimeters thick. I got the blue ones. These are the originals. I think they come in other colors now. Some of the other colors are smaller. They're like the five millimeter. The seven is what you want. You'll notice they're not super long, right? This is my second pair of Ray Band knee sleeves. The first pair I bought in 2001, and they lasted until 2018. They lasted me 17 years. 17 years later, they still work just as well as they did on day one. They were just brown and smelled like a homeless guy's socks. And that's the problem with knee sleeves. They don't smell very good after all. These aren't that bad. Got a little tiger balm on it. A little whiff of that. So it's not bad for a little aperitivo. But they do all of the stuff that I want, which is it can pull up relatively smooth. You can see how easy that comes onto my knee. Covers my knee well. I can already feel significantly more compression on these knee sleeves than I did with the Harbingers or with the cheaper Mueller brand. And the best thing about them is once they go in place, they don't move. They don't cut off my blood circulation. I don't feel like I need to get them off as quickly as possible. They feel great through the whole workout, right? So I wear these every time I squat, and I typically wear them if I'm gonna press heavy. The reason I wear them when I press heavy is not because I'm gonna have a lot of knee bend, it's the opposite. I sort of want something for appropriate set of feedback that tells me not to bend my knees on a press. Now, I don't like them on the deadlift. I tend to not like them on the deadlift because if you deadlift correctly, and you've watched our videos in the past, the bar should drag up your shin. And if it does drag up your shin, it's actually gonna catch a lot of times on this little, the bottom of the knee sleeve. So I don't like that. So a lot of times what I'll do is if I have my squat shoes on, I've completed my squat workout. I still have my squat shoes on, I'm going to deadlift. I'll just take these, flip them inside out and slide them down. And I'll just put them right here and I'll wear them on top of my shoes. Of course I don't have my shoes on right now. I'll have them on top of my shoes. And then I can set the bar here and drag up and still have my shins and my knees and my thighs to drag the bar up. So it works perfectly fine on the deadlift. I don't have to take my shoes off, take the knee sleeves off and whatnot. So I really do love the Ray-Bans. Again, they last forever. They'll smell terrible after a while. Certainly there are detergents out there now that you can wash things with. Over time they're gonna smell bad because they're neoprene.  As a matter of fact, the old pair I had, I give to an old training partner. He still wears them. So they're 25 years old and going strong. Those are the Ray-Bans. All right, much more popular over the last few years are the SBDs. And these are extremely rigid, extremely rigid. This is, there's actually a right and a left. This is the right one. I had never put these on until a few weeks ago because I love the Ray-Bans so much. Now here's the deal. I'm gonna show you what it looks like to put these on. It's gonna be comical. These for sure provide some amount of support. I mean, already just look at how long this is. So we know that the quads wrap around my knee and attach mostly at the tibial tuberosity right there. There's no reason for this to be lower than that, not much lower. So if they're up in here, they're actually grabbing all of that quad muscle. And you'll be surprised if you put all three of these on, go into the bottom of the squat. There's way more compression with this. It's not as much compression as a knee wrap that power lifters use, but it's close. The first time I put this on, I would say I legitimately got 30 or 40 pounds out of these. Now, my guess is, is that they're gonna stretch out over time. If I were gonna lift in a federation that allows knee sleeves, and it still counts as raw lifting, I'd probably buy a brand new pair of these for every single competitive meet that I had. So what I might actually do is wear these for training, and then when I get ready to do a meet, I might put these on because it's a lot more help. But it's not very fun to put on. So here we go. I'm gonna do the best I can, I'm not gonna fake it. Here we go, same thing. So I'll put my thumbs underneath, I'm gonna kind of turn it over, grab and pull, and you can see it just kind of hits a point where it just won't go anymore. And again, I can barely bend my knee. I mean, it's not even close to the same amount of compression. It's five or six X more compression than the Ray-Ban is gonna give or certainly the Harbinger cheaper Mueller set. So as you can see, the SPDs are really thick, really compressive as I pull my foot back. The amount of compression on this knee sleeve compared to the other two is four X, five X, I mean, significantly more. So the other thing is it's not very comfortable. I mean, again, it's very, very tight at the thigh. It's very tight at the calf. I can't hardly get it up anymore. So this is actually probably really good for competitive lifting. This is not gonna be my everyday knee sleeve. I just don't like it that much. Some people do, some people learn how to wear them. I'm sure they stretch out a little bit over time. To me, again, I wanna wear knee sleeves for the reason they were invented, which is just to keep the knee warm and loose and feeling good. When I squatted with this, I mean, I can really feel that it bunches up behind your knee, almost like a pair of knee wraps will for powerlifting knee wraps. And you can tell there's a significant amount of rebound that's gonna be in this. So again, if you wanna set PRs, wearing the heaviest knee sleeves you can, this is probably the way to go. If you wanna just keep your knees warm because it's winter time and you wanna feel better, this is probably the way to go. So again, great for competition, probably not an everyday knee sleeve. This one is my pick for everyday knee sleeve. Again, it's gonna last forever. Again, mine lasted almost two decades. These will probably last about the same, but these are gonna be a lot more comfortable. And I'd stay away with the cheaper Harbinger or Mueller type knee sleeves. Now again, on the price, these Harbingers, Mueller's, they're gonna be somewhere in that $30 range. When you start to look up the price for SBDs and Ray-Bans, you're gonna see a price $40, $45, $50. It's often for one. And so sometimes people accidentally just buy one, thinking, yeah, $50, $55 for a pair of knee sleeves seems about right, it's not. And so again, remember what you're buying. You've got one pair for 30 bucks, but you're gonna have to replace every six to 12 months. You got another pair that you can buy. It's gonna last a decade or more. And the same thing for this. And so it just depends on what your goals are.  Competition, this is probably the way to go. So now you know everything you need to know about knee sleeves. For other great videos about accessory equipment, like wrist straps, wrist wraps, how to use them, everybody puts those on wrong, check the playlist right up here. And so that's it for today's video. Thank you so much for watching. I'll see you in the next video. Bye bye.


just because you're doing the menial and the mediocre now doesn't mean that one day you won't step into a great calling and this doesn't necessarily mean that everyone's going to be a preacher that everyone's going to be a social media sensation or that everyone's going to be ultra wealthier that everyone's going to speak to world leaders that's not what I'm saying at all I'm saying that you will fulfill the divine purpose that God has placed over your life even if you feel overlooked even if you feel underappreciated even if you feel like you're not accomplishing anything God sees where you are in the season of hiddenness


Thanks so much for having us or, you know, being with us today. We really appreciate it. Do you want to tell us a little bit about who you are and what your background is? Yeah, sure. Well, first of all, thanks for having me. And so, yeah, I'm Catherine Paganini. I'm the head of marketing at Boyan. That's the creator of LinkerD. And yeah, so very involved in the LinkerD community, but I also do a lot of things with the CNCF, so I'm the co-chair of the TAG Contributor Strategy and the Business Value Subcommittee. Oh, gotcha. Why don't you tell us a little bit about LinkerD first, because I think, you know, there may not be a ton of people who know what it's for. Yeah, so basically, LinkerD is a service mesh. It's actually the first service mesh, and it's the, and our team actually is the team who coined that term, which is pretty crazy. And it's the only graduated service mesh, and kind of known for its simplicity, right? So that is, yeah, kind of what we're trying to do, make it easier for people to adopt a service mesh, which provides observability, reliability, and security features at a platform level, right, to make it easy, because that's really, really critical features that you need. Right, yeah. And what people don't realize a lot of the time when they're kind of architecting an application kind of in their head, is like the complexity of like kind of running all the time, right?  So you just, was it the first LinkerD day that was yesterday? Yeah, yeah, yeah. So LinkerD day was yesterday, very first time. And I'm very proud that we managed to have like 100% end user content. And for me, it's always really, like my favorite part of the job is really working with the community, working with end users and empowering them to, or help them, you know, tell the stories. Because I feel, I don't know, like, even at like a cube comma, I think like there are far too many vendors speaking, and I know it's always difficult to kind of get, it's not as easy, right, to get end users to speak, but like I think end users can't, right? Yeah, yeah. And because end user stories are so much more powerful, right? Because it's like, I know vendors are always biased, right? Like even if you believe that you are telling the truth, which we all are, right? But we are biased, right? Because that's why we work for those companies, right? And then having end users who are actually using it in production and sharing their experience with peers, I think that's the most powerful, those are the most powerful stories. So, yeah, so we had Adidas share their story and lots of other companies, and it was like really, really great, I think, like, so really proud about that. That's cool. Was there any particular stories that really, you know, kind of caught your ear as it were? Well, I think like all, well, one of them is, I think there was like a very catchy story as well. What was the name of the talk? Like going to production with a team of one, right? And she basically got it, the whole thing, like she was responsible for the platform and at the end, it was just her, and she got Lingardee up and running, and it was new to the service mesh, and like, yeah, just hearing that it doesn't have to be complex, right? Right, right. So, it is so, like, I already adopted Kubernetes, it's complex enough, I don't want to add an additional complexity later, right? And it's true, sometimes that's the case, but it doesn't have to be, right? Like, there are simple ways of doing that, and then like hearing her story and how she kind of managed to do it with one person is kind of like, speaks for itself that it doesn't have to be complex. So, I think that was kind of like a good story that. Yeah, yeah, no, that is a cool story. I mean, it's always nice, especially like someone who's new to the thing, right? And is able to, you know, kind of understand enough that they need it, right? And then figure out how to actually implement it and then deploy it. You know, that's not a trivial exercise, you know? So, that's really cool. And so, what do you think is kind of next for Lingardee? Do you want to kind of come back at, you know, in North America and do the same kind of thing, or sorry, for Lingardee Day? Yeah, or, you know, or do you maybe do in smaller events? Yeah, we're thinking about it. I think like it was a really great experience. If we do it again as well, we would love to have like also like a very big end user focus, but yeah, so we'll have to see. We'll have to see, but like for now, I think like if the CNCF is happy with it and so like probably, but it is also like a lot of, of course, it's a CNCF event, but it's a lot of work on our end as well, especially if you want to encourage end users to speak, because you have to find them, right? It's like vendors are always having to speak about what they do, right? But identifying the end users and encouraging them and say, because they're busy is a lot of work.  Yeah, I run a smallish conference. It's about 400 people called DevConf US and the US and it is there's a lot of work that goes into it that I don't think people realize how, you know, how much effort it really is to, you know, kind of put a show on. And, you know, my running joke is for the first two years of it, both years, I blew out a pair of shoes with the amount of walking around I did for the three days of trying to keep the fires put out, you know. So what do you hope to see at the rest of the coupon? Like what is, you know, the hope for the rest of the conference? Well, so it's it's fun because it's like after Lincredi Day, it feels like I was like, oh, actually, conference just started. So, yeah, so, I mean, I'm going to be at the Lincredi kiosk mostly. We also have like a buoyant booth. Of course, we're going to be there and hopefully get like really good conversations and tell, you know, more people about Lincredi. And but also like I'll be on two panels. So looking forward to that one for the tag contributed strategy. So yeah, yeah. Yeah. So I'm a co-chair of the tag contributed strategy. And one of the things that we always tried to do was like, well, once is like, or maybe I should just say like what the tag contributed strategy actually does. So basically, the tag contributed strategy is that tax of technical advisory group or the CNCF that tries to help projects be successful. So it's like more geared towards helping maintainers, project maintainers. And I love the mission because basically what we're trying to achieve is to create like a cross-project community. Because we always talk in open source about communities, but we mostly talk about our own communities. Right. Like, yeah. And I think we figured that out. People know it's important, right? Like, but I don't think we kind of have really done a good job yet to kind of have the cross-project community, right? Because it's like every project is has going, yeah. And it's going through the same problems, right? Like in every problem that a project is going through, someone else has already gone through, right? And a lot of projects try to reinvent the wheel each time, right? And there is no need for that, right? And our, like basically the CNCF is our common home, right? All our projects live there and it provides this platform for us to connect. And so, yeah, I think what we want to do is kind of build that cross-project community so that we talk with each other, exchange ideas, learn from one another, and then, yeah, just make it easier, yeah, like, yeah, because people have best practices identified or more mature projects have identified best practices. And let's tell your project those best practices, right? Don't let them do the same mistakes. And then there are lots of resources and things like that. And so I think like it's incredibly valuable, but not a lot of people know about it, but not a lot of maintainers. And that's the other challenge, because it's like, maintainers is like a subgroup within our community, right? And like reaching them is not that easy. And so this panel, because we always talk about the resources we create, this panel, we wanted to talk about what we get out of that, right? Because it's a lot of the, a lot of times we talk about contributing and people think about contributing as like giving and it is giving, right? But it, you get actually so much more out of it than you give. Yeah. And so we wanted to make a little twist and talk about like really like us and how it has benefited us in our careers and everything, right? To be a part of that community that you should be getting to. Yeah, yeah, because it is really, I think, like once you start, you get hooked and there is no going back. Yeah, I mean, one of the things when I kind of first joined the Thor community, you know, and got really kind of involved in it. One of the things that I found, which was really interesting was, you know, like how I could have like kind of an impact, you know, on every location that I went to. And, you know, and that there was a Thor community everywhere. And so I could kind of, you know, participate in it wherever I happen to be traveling, which is a really, you know, kind of nice feeling to have that community out there. And, you know, I think with Kubernetes, I think we're starting to see more of that, as you say, you know, kind of trying to get the overall community to also be, you know, more together rather than the individual kind of silos, which is kind of good for everyone. Yeah, yeah. But it is sometimes, yeah, sometimes it is like, I feel like people like containers are so busy that sometimes they feel it's like they don't have time, right, to do an initial, engage in an additional community. But I think what people maybe like, I don't know if they don't realize, but it's like, if I think you end up saving time too, right, like for instance, if you need, need to know about something, right, like or when I joined the tag, one thing that I need, like I just had just joined LinkedIn and I had no idea because I was new to open source, how how communities take and whatever. So it provides, it gives you the chance to actually talk to people who will be very gracious with their time. So I interviewed several maintainers who took, I think a whole hour to talk to me and share their best practices. And if I were, I couldn't just go to people and say, like, hey, I want to learn this just for LinkedIn. Can you tell us to me, right? Like, but I told them, hey, I want to learn this new to open source. And I want to contribute, like I want to summarize the best practices, you know, like in a doc that we can share with all the projects. And then suddenly people really gracious, like they're really kind of taking the time to do that. So, like, you don't have access to those types of resources. If you're doing it just for yourself. So yeah, not enough, right, to get that critical mass to, you know, because, you know, everyone's busy.  Yeah, that's a big deal. The, yeah, I think that's, we've also interviewed like Chris Short on the show before, you know, who's always been a big kind of contributor, you know, participant in trying to get the same kinds of things. So yeah, I think it's, I think in some ways it's a magic open source. But the other thing I was going to say was that one of the things that it takes a while to learn, I think, is as a manager, right, is how to delegate, right? And how, you know, and how to teach somebody to do something new, even though it's going to take them longer to do it the first time. I think there's a lot of that with kind of the contributor world as well is that, you know, the temptation is just do it yourself, right, rather than kind of opening it up for others, because it feels easier. But I think you don't realize, right, is that as you grow the community, your, you know, your whatever force multiplier goes way up, you know? Yeah. Well, it's always an invent, you have to invest a little bit, right, and then for it to kind of save you time. So it's like it's counterintuitive, because first you think, and it's not counterintuitive, but I think like it's just, it's hard to get over the hurdle. Yeah, you're so busy thinking about the now that sometimes it's like, yeah, you don't want to, yeah, you just focused on like what you need to do next, but like it is really an investment in the future, because it gets easier, right? Yeah, I think I want to turn left here, but I guess we'll go another, another block and turn left there. Cool, so what's the other panel you're doing? The other one is about, it's more around the work that I do for the Business Value Subcommittee. And so, again, let's maybe just start with what the Business Value Subcommittee is. So, yeah, I'm non-technical, right? And so when I, so 2017, I had a Kubernetes company and had to kind of wrap my head all around it, which was really, really difficult, because like all the content out there is written for a technical audience and it starts with contacts that I had, that you don't have if you're not technical. So it's really hard, right? Like, so I really struggled. I had actually just, that he really actually bought a introduction to computer science book and like, because I had to build kind of like that, that the baseline to actually even understand what people are talking about. And then suddenly, like a few years later, like people from my network, not people who don't necessarily work in cloud native started to ask me, so, hey, my company is doing Kubernetes now, can you explain that to me? And I was like, oh, you know, like, there are a lot of people who are non-technical, who need to know about this and who are basically the same, what is I, what is right? Like, and not everyone has a time or a dedication to kind of go through and like really kind of learn it, right? And so I, in between, like I started writing articles that I kind of, for the news stack, like really basic fundamentals, fundamental articles and got really, really good feedback which kind of showed that there is a need for that, right? And then at some point I said like, okay, actually a CNCF, I mean it's great, I'm really grateful for the new stack for publishing it and it was a really great experience, but at some point I was like, shouldn't a CNCF on this kind of content? You know, like it's like they have, they have content for technical people, but like it's really important, it's an important part of their mission to kind of help the business side as well, right? Because they need to approve all these projects, right? So if you had, let's, C-Suite needs to understand the basic concepts, they don't need to code and stuff, but they need to understand what cloud native is, how it works and why they should adopt it, right? Because otherwise how can they even have these conversations with engineering, right? And so we formed the Business Value Subcommittee, which basically focuses on trying to explain these concepts in terms that, that, yeah, a business audience understands, but also making it easy for engineers, right? Like if engineer is new to cloud native, like having content that is really easily accessible and simple language makes it easier to understand, right? Then something that's full of technical terms and, yeah, like even like simple is always easier and friendlier when you're getting started. Well, I think one of the things that people, especially non-technical people, don't realize about cloud native is that it's kind of fundamentally at odds with a lot of the way you're taught programming. So when you take a computer science degree or whatever, I kind of refer to it as like everything's very like serial, you know, everything kind of goes in a straight line. And when you are doing something that is cloud native or event driven or something like that, you know, you kind of have all these things like popping off all over the place and writing your applications such that you can respond to that kind of scenario is kind of weird. And it's a mindset shift for even for technical people. And I think that, you know, like you're kind of mostly focused on the non-technical people, but I think there is definitely a lot to be learned for your average technical person to try to wrap their hand around. How do you design systems that run in this kind of unusual, you know, model? Although it's getting increasingly unusual or increasingly usual. You know, because you have to be able to operate your systems this way. Otherwise, it just, you know, you can't run a modern application anymore, you know. So it's really interesting. And is that, so would you say that's related to the glossary that you work on? Well, yeah, so basically, like so we formed the business value subcommittee and then we realized, okay, if we're talking about these things, the first thing we need to do is explain the terms. Right? Like you cannot just start, like, so we need to first come to an agreement. What are, how do we talk about these terms? And so the glossary was basically, so it's a cloud-related glossary, it's open source, anyone can contribute and help improve it. So that's the idea, right, to have like it's community driven. So one thing that's important is that is community driven, it's vendor-neutral, right, and it's a living document. And so the glossary, I think, was launched a year now ago. And as I mentioned, tries to explain these concepts in simple words. We try to stay away from technical terms whenever possible using examples people can relate to when we're talking about applications. Let's talk about end-user applications like your Gmail account or things that people use to make it more friendly for people who are not technical. Yeah, I think that's the big challenge both in the definitions and the words themselves, right, is our industry, I say this kind of thing a lot, but our industry is so young, right? I kind of jokingly, it's like medicine's been around for thousands of years, so we have a pretty good idea of what all the words mean. And with the software world, we're kind of inventing new stuff all the time and approaches and models and styles and everything. And so as a result, we are not very good about solidifying around terminology. And so I think it's even more useful, I think, than people even realize, not just from an educational perspective, but also it's hugely useful to be able to pin down what a term means so that we can at least collectively agree that there was a pigeon that flew by because I would throw back to the geese, but the collectively agree on what the terms mean, which I think is hugely important and really difficult. I talk about it with my students. It's like, you know, if you walk into an organization and they say PM, that could be program manager, project manager, or product manager, and it could be any of those. And none of those jobs are the same, but they will all have used the acronym meaning one of them exclusively. And it'll be a different one at different places. So one of the things I, like I said, that I find super difficult in our industry is terminology and kind of pinning it down so that you and I, when we say PM, both mean the same thing. And I think it's even crazier, right? When you talk about, like I said before, like event-driven architectures which are even newer than traditional end-tier models and stuff. So yeah, super cool. Well, something that's like, just because it's like something that is also very cool is that we have several localization teams localizing it. And so I don't even remember how many languages are live right now. That's a good sign. That's a good sign. Well, it's not that many, but it's like, I don't know, have the exact numbers, but like, definitely German, Spanish, Korean, Mandarin, or probably forgetting something. But it's like an ordu just became live yesterday. So you have to have like at least 10 terms to go live. And that's another Italian as well as live. So, because that's another thing, like our, like in our world, everything is written in English, like most of it. And like we were just talking about how complex cloud native is, right? And so if you're trying to learn it, and then it's in a foreign language on top of that, makes it even more difficult, right? And then like really kind of having content in different languages makes it much more accessible because some people like it just depends on which some countries are really like, like people or engineers, most engineers do speak very well English. Others not, right? It's not really, it's not in some countries, it's not common, you know, to have like good, like be proficient in English, right? And you cannot, you know, like, and then you're basically excluding all these people because you're not giving them tools, they need to kind of learn these concepts, right? So I think like the localization efforts are really important too. Yeah. And especially those kind languages, when I get like these languages like order, I was like, this is so cool. Yeah, right, right. When they, yeah, I mean that's, personally, one of the things I like the most in the kind of open source world is when you get something completely unexpected, like unlooked for, as a contribution to whatever you're working on.  And you know, and people started using it, right? And I was so excited because I just kind of built it, you know, to solve a problem I was having. And you know, but then people started using it. And then I didn't really need it anymore. And so we had some people take it over because they used it and they continued to use it. And I was just, I was so excited, you know, completely unexpected. You know, I think that's one of the really cool things about open source.  But in, you know, in being a tech guy, right? Like the community I see the most is, is tech communities. So yeah, it's really interesting. Well, why don't we kind of end the interview there? And you know, thank you so much for your time. We really appreciate it. And we will continue our merry way to go over lots and lots of speed bumps. But I hope it wasn't too exciting. Yeah, well, thanks so much for having it. This was a lot of fun to talk about all the little things that we're doing out there. Right, right.


on YouTube family, no, from Kicking in the Razz TV. Here's another video and today I'm just with a little tip video and because I know you guys love these and you guys have been requesting tips so I got a tip or question as in what type of rod to use, what's the best rod for pretty much all around fishing. So pretty much this is my opinion and what I like. It all depends on what lake you're fishing to.  There's tons of different aspects that determine what's the best all around rod if you're just going to try to find a rod that fits pretty much everything. So but there's probably around two rods that I would recommend and this isn't a video saying St. Croix is the best rod. I want you to go purchase one of these. This isn't a type of deal. I'm just discussing on the action and the power of the rod and what I think is best for all around fishing. So if you guys are new to fishing and you're wanting a new rod and you're wondering what action and what power to get, this is the right video for you. So pretty much like a medium heavy power fast action or a medium power fast action, any one of those right there for the fact of you can pretty much use almost all baits like a medium heavy fast action.  You can throw pretty much almost everything within especially for you pond fishing. You're not wanting to carry a lot of rods around and so you're kind of wanting something that you can throw everything on. But say I was, it all depends like I said on lake.  Say I'm out fishing ledges on Pickwick. I'm on a heavy rod you know. Say I'm throwing a jig and I'm fishing in that 30 foot water. You're going to want more of a backbone. You're going to want that heavy powered rod. Just because when you hook site you're going to want to penetrate the hook into the fish and have as much power as you can. But for the most all around rod, really that medium heavy fast action, medium fast action rod. The medium fast action rods would be good with like rattle traps, would be good with like chatter baits as well, just a little worm, like a light shaky head, Texas rig, stuff like that work great as well. And especially in shallow water, like if you're fishing shallow water that's kind of moderately open and you're not like punching, but if you're just fishing shallow water don't worry about that. Just go with the regular like medium heavy fast action rod. You can throw and miss your stuff on it. Like this rod right here, this is actually the Legend Tournament with Synchro rods. This is one of the rods that I prefer to love using. This is a 7-1 medium heavy power fast action. And on this one it says spinner bait slash jigging worm. So I mean pretty much just covering most of the baits.  See what I mean? It's just the variety is so open on that style of rod. But and what is the difference between the medium and the medium heavy? Medium heavy is going to have a little bit more, you know, it's going to have a little little more up in the rod. So that's pretty much what I would prefer more for like a jigging everything. If I had a medium rod and more than likely I want to be throwing a jig on it, even if I was in super shallow water, I don't want a really, really flimsy rod. So pretty much if I had to pick one all-around rod, and if you guys were looking for one all-around rod for pretty much any style of fishing, you're just wanting to go out there and be able to throw multiple baits, be able to pick up a rod and just take one rod with you because you're going out on a friend's boat or you're going out and you're fishing this pond. So you only want one or two rods. Definitely go with like a medium heavy, fast action rod. You're just a regular medium heavy rod. I mean you can't go wrong with it. I haven't so many of these as ridiculous. This is just a regular legend tournament about sinker rods. I have I believe four or five of that exact same rod because I just love it and I can fish pretty much everything with it. So if you guys got some good tips out of this video, please give the video a thumbs up. If you have any more questions and any more topics that you want me to go over, please leave a comment below. I'd love to make the video taking tons of suggestions. So please leave a comment below and thank you guys so much for the support. As you guys know, hit 100K on Instagram. Giveaways are going to be coming soon. I can't thank you guys enough for that. It's amazing.    So I'll be sure to keep on uploading those as well as tip videos to teach. Let's do you guys some more about fishing that are new to fishing. So thank you guys for watching. Please give the video a thumbs up. See you guys next time.