I'm a dedicated crew chief. Dedicated crew chief maintains the aircraft day-to-day maintenance to make sure the pilot's ready to go. Seeing the jet, massive Gs, maneuvers that it does, it's an experience for sure. Being an F16 and being pulled through all the G maneuvers, it's a heavy strain on your body. It's a lot of, like, strange noises and grunts and whatnot to keep from passing out or look to be part of the team. It's going to be a good year.


did an interview. So as you know, Powell is the chairman of the Federal Reserve. He was appointed by Trump. He is not an economist by training. I'm not sure what he is by training kind of a business guy by training a finance guy maybe, but not an economist by training. He just did an important speech at Jackson Hole. Now they didn't do it literally at Jackson Hole because they couldn't be there, but once a year there is a conference at Jackson Hole that the Fed holds, and the Federal Reserve chairman always gives an important speech about monetary policy and about the Fed policy at this event. And this year, Jerome Powell basically gave the speech saying that they're changing the way they look at inflation. This is price inflation. So mostly I'm going to use inflation as price inflation. When I'm not, I will differentiate. I will tell you that I'm speaking about monetary inflation, which is the proper use of inflation. But since everybody, including the Fed, talks about inflation as price inflation, it's just easier to use their terminology for now. So he basically announced that instead of targeting 2% inflation, which is what they've been doing unsuccessfully, by the way, because according to their measures, we have not reached 2% inflation for really since the financial crisis and even before that inflation wasn't a 2%. And I think the last time inflation was at 3% was, I don't know, in the 1980s or something. It's been a long, long time since we've had price inflation, at least in the way that the Fed measures it. They have now shifted where they're measuring inflation to, well, not the way they measure inflation, but the way they're targeting inflation to some kind of average inflation. So that indeed what he's saying is that we're actually shooting for more than 2% inflation. And as long as over time the average is 2%, that's fine. And if we overshoot 2%, it's no big deal. We just want inflation. Please, please give us some price inflation. That's what we want, which is interesting because often with these kind of topics, when you want something, you actually get it. Psychology does play a difference. And if people are convinced that the Fed really wants inflation, they'll start pricing that in. And that could have pretty interesting consequences in the economy. Anyway, a few days ago, Joe and Paul gave an interview to NPR, which Daniel Weishard, thank you, Daniel, sent me and asked for me to comment on. And it is a actually really interesting interview because it shows the thinking kind of in a little bit. I found the speech that he gave to Jackson Hole just gibberish and difficult to follow and stuff. Here I think it's clear about what he means. It's still gibberish in a sense, but at least it's clearer in terms of what his intentions are, what how he thinks about the economy. And I think that's really, really interesting. This is the most important, at least politically, the most important economic voice in the country. More important, I think, than Trump, more important, I think, than minutiae in terms of setting the economic policy of the United States. I think that what the Fed does can tank the economy faster or can elevate the economy faster, well, cause damage in a variety of different ways and in more interesting ways than even the federal government can. And it's usually hidden. That is, the damage they do is usually hidden for most people. Whereas if taxes go up, you feel it. If regulations go up or down, you feel it. If taxes go down, you feel it. When the Fed does stuff, you don't know that what you're feeling is caused by what the Fed has done. So it is a stealth operator that has a profound impact on the economy. Probably bigger than anything a president does or the Treasury does.  But most presidents, what they do is at the margins, what Trump has done, but even for the bad, what Bush and what Obama did, was all at the margin, all negative at the margin. But nobody's done anything positive, big, maybe ever, ever. Closest we came was Jimmy Carter and Reagan, who did a lot of interesting stuff on regulation, and then Reagan did some stuff on taxes that was beneficial. And cutting taxes is not necessarily it. Cutting taxes is not necessarily beneficial. Spending is what matters. But anyway, I don't want to go there. I want to go to Jerome Powell. So I thought I'd go over this interview. Unfortunately, I didn't find it like a video because then we would have done a commentary section. So I'll just have to read what Jerome Powell actually says, and then comment on it that way. So I know it's a little less entertaining and less efficient, but we'll do that. Okay.  There is in the history of economics, a famous curve called the Phillips Curve. And the Phillips Curve basically suggests a relationship between unemployment and inflation. And the idea is that when unemployment is low, inflation is high. And when unemployment is high, inflation is low. And if you want to reduce unemployment, you need to increase inflation. That is, it assumes not just a empirical relationship, but also a causal relationship. That's called the Phillips Curve, right? It's a relationship between unemployment and inflation. And for many, many, many years, it was considered like the gospel. And it was always criticized by people like Milton Friedman by the by the Austrian School of Economics. Everybody said, no, no, no, no, this is wrong. The Phillips Curve is not true. Here's the economic theory to show it and it doesn't work. There is no tradeoff between inflation and unemployment. Those are not two things that you trade off. There's no economic world in which that really happens, that that is, that's the causal mechanism. You're misunderstanding unemployment and you're misunderstanding inflation. Anyway, the Keynesians, the traditional economists didn't pay attention and they followed the Phillips Curve. Now, in the late 1970s, we had what seemed like a empirical refutation of the Phillips Curve. Because in the late 1970s, we had both inflation, high inflation over 10% and high unemployment over 10%. That's not supposed to happen. If you have high inflation, unemployment should be low. And if you have high unemployment, inflation should be low. That's what I said, right? If you have high inflation, unemployment should be low. The other way around. So we had stagflation, stagnation, low economic growth, high unemployment and high inflation. By the way, the causal mechanism supposedly is this. If the economy does well, when the economy does well, wages go up. There's no discussion about how wages go up. But wages go up. When wages go up, people have more money to spend. People spend more money. People having more money to spend and spending that money drives prices up, which causes inflation. That's supposed to be the mechanism, right?   And that way, inflation goes down because the demand for the goods goes down. And that is a mechanistic view of economic growth. It is the view that economic growth is driven by interest rates and really nothing else. Though it drives economic growth, there's low interest rates. And when you have low interest rates, the economy grows dramatically. It's partially why Europe has had negative interest rates because the idea is that negative interest rates do what? Well, if low interest rates drive the economy higher dramatically, the negative interest rates should drive the economy dramatically higher. So they're trying to get economic growth by having low interest rates, negative interest rates. It's not succeeding. Japan, by the way, has tried that since the early 1990s. Low, low, low interest rates. Try to get economic growth. The economy doesn't grow. So we've had stagnflation. We had high inflation, low economic growth, high unemployment. Now we've had low interest rates, low economic growth, low unemployment. So we have had over the last 50 years massive amounts of empirical evidence that disprove the Phillips Cove. The Phillips Cove is wrong. Why is it wrong? Because it doesn't understand economic growth. And this is stunning to me. It's truly stunning that economists, mainstream economists have no conception of where economic growth comes from. They have no conception of what drives an economy. Because what really drives an economy, you cannot model mathematically. And I think, by the way, this is true of even some free market economists who think that everything's kind of mechanistic because everything is driven by levers and poles and levers. So if you follow this number, then that number has an impact on this number, you can understand everything just by doing that. But the economy is not mechanistic. What actually drives the economy? And I'll get to Jerome Paul's quote in a minute. What actually drives an economy is entrepreneurship and innovation. Entrepreneurship and called it ingenuity. Entrepreneurship and ideas. Entrepreneurship is, as I would define it, the application of that ingenuity, those ideas to the problem of production. Because Navarro says that what drives an economy is human action, no. And I think this is where prexology gets it all wrong. I don't think it's just, I don't think it's human action in the sense of prexology or in the sense of without explaining. It's not because consumption is also human action and consumption does not drive the economy. It contributes. But what really drives economic growth is a particular activity, a particular form of human action. And that particular form of human action is entrepreneurial, you know, Israel Kurtzner called it entrepreneurial discovery. I would call it entrepreneurial ingenuity, entrepreneurial innovation, creating, making, figuring out, discovering new better ways to do things, to make things, to advance things. It's not just about interest rates. That's a mechanistic view where you lower interest rate, capital becomes more available. We deploy more capital to a particular problem. That increases production. No, it doesn't increase production if there were no ideas, if there are no entrepreneurs, if there's no creation, if there's no ingenuity, if there's nothing new being made, if there's no thinking. What drives production is thinking, but a particular kind of thinking. A thinking as applied to production that expands the boundaries of human knowledge, that expands what is possible for us, expands what we can do. And it's individuals doing that thinking. This is what I think libertarian economists miss it. Even the Austrians don't quite get the entrepreneur.  Israel Kurtzner comes close, but they all miss it because they don't recognize the value at the end of the day of innovation. And of the innovator, even Matt Ridley, who comes really, really close because he recognized the importance of innovation because that's really what drives economic growth. Doesn't recognize fully the individual, the importance of individual in that equation. Individual thinker. So what drives an economy is not any particular thing that government does. The only thing that government can do to drive an economy is get out of the way, that is, allow people the freedom, the freedom of thought and the freedom of action to have the ideas and to test them in reality. That's it. It's not about interest rates. I mean, it is indirectly because interest rates distort and pervert the pricing mechanism, which is an important signal in the economy, but that's not the essential. It's not about taxes, although again, has a huge impact. It's primarily more than anything else about control, about regulations, about controlling business and entrepreneurs. If you want to get the economy going, you got to take the shackles off, the shackles off. Now some of the shackles are taxes and interest rates, but the primary shackles are controls and regulations at all levels of government and it's everywhere and it's insidious and it's, that's why the industries that have the most shackles grow the least. The industries that have the least shackles grow the most. You can tax the industries that have the least shackles more and they'll still grow the most because the shackles are what are really important. It's not even the incentives and you can see this in places like Scandinavia. People say, how does Scandinavia do so well in spite of the fact that they have very high taxes? They do so well because they're less regulated. They're less controlled. They're taxed heavily, but the taxation is not what drives economic growth, the lower taxation. What drives, and this is what Republicans will never understand, what drives it is the freedom of the mind to think new thoughts, to innovate and to apply those in action. And now you need, and you saw that in the late 19th century, early 20th century, when government was the least regulatory that it's ever been, you saw the biggest innovations, the fastest progress, the fastest economic growth in all of human history because the human mind was free to build, to create, to make. But what is truly astounding is that economists now, this idea of innovation, this idea that innovation is important to progress, this idea that there is a factor there that needs to be considered, has only been introduced into the profession of economics 20, 30 years ago by a guy named Roma, an economist by the name of Roma, who won a Nobel Prize for this. He introduced into the production function innovation of the economy because it's all mathematics, right? But you can model it mathematically. And the fact is it's still not truly in the models, not at the Fed. Never, I've not heard, you know, maybe a little bit on the green span, but certainly not from power. Any discussion of technology, innovation as responsible for growth, all you hear about is money. It's all they can understand is the flows of money. It's all they care about. It's all they can model. It's all they can project. So they think that by pulling this level, by pulling that level, by using interest rates this way, moving interest rates that way, they get economic growth and they don't understand why it doesn't happen. So for example, one of the things he doesn't understand is they had low interest rates since the financial crisis. And yet inflation was low and unemployment was low and economic growth was low. And again, that's something that can happen in their model. If unemployment is low, it must mean the economy is doing well. If the economy is doing well, there must be inflation because everything is driven by money. So here's the quote. And he's complaining about they don't understand why unemployment is so low. Unemployment can be even lower than we thought because we used to think unemployment couldn't go like below 5%. Then we thought and not result in troubling levels of inflation because they because they viewed the relationship between unemployment and inflation is crucial. They didn't understand why did unemployment under Obama and then under Trump go way down all the way down to under 3.5% and there was no inflation. How can that be? Why didn't employees demand an increase in wages and then use that increase in wages to go buy stuff which would drive prices up? He says we had 3.5% unemployment which was sort of the lowest period of sustained unemployment in 50 years and we didn't see inflation. If you go back 50 years, inflation would have reacted very strongly to low levels of unemployment. No, it's not how it works. It never has been how it works. What has happened? Oh, wow. I can't pronounce your name. Thank you for that contribution because it's all in Greek letters but the question is relevant. So I'll use it here. It's $100 question. So definitely. Would it be safe to say the comprehensive deregulation of the economy would cause deflation which would be counterproductive to the Fed's mandate? Yes. And I'm getting to that. And not only that, but I would argue that we have seen massive deflation, deflation, good deflation in a sense of prices going down. We've seen that over the last 30 years partially as a result of deregulation under Jimmy Carter and Ronald Reagan. I like to give Jimmy Carter credit for a lot of deregulation because he did do a lot of deregulation and nobody else other than myself gives him credit for that. Friend of Avastado, I like that name. Friend of Avastado. Thank you for the contribution and thank you for the question. So the reason we have seen low unemployment, in other words, people employed across the board is because actually the economy of the United States over the last since the 1980s has done well. I know that's incredibly counter to everything that everybody says because if you look at the wage numbers and you look at everything else, it doesn't look like we've done well, but we have done well. I'd say the last 10 years have been weaker under Obama and Trump, but even then the economy, the standard of living quality of life actually grew faster than the numbers suggest. And again, the way economists measure everything in economics is flawed dramatically. And the reason we have done well is because of this effect that friend of Avastado mentions. Deregulation in the 70s and 80s brought about dramatic increases in efficiency, in productivity, and dramatic decline in prices. Just think about air travel. Now, this is hard to comprehend that air travel today suddenly in inflation adjusted dollars, but even in just dollars is dramatically not a little bit dramatically cheaper than it was in the 1960s and 70s. The availability of air travel. And now I'm not talking about COVID, let's talk about pre COVID. The availability of travel, the ability to go anywhere in the world at unbelievably low prices with unbelievably few constraints other than TSA, was a massive increase to the quality of life of Americans that is not measured by any economic statistic. And the decline in prices of airlines because of improved technology, but primarily because of the deregulation of the airlines that had it under Jimmy Carter, the improvement and the cost reductions massively increased quality of life, standard of living, and reduced inflation as measured by like the Federal Reserve. Prices went down. And even over the last 30 years, and it's not just, let me just say this, it's not just the price. It used to be that there were only certain airports you could go to, but now there are thousands of airports you can go to, and there are dozens of airlines you can use. So the availability of getting to particular places has increased dramatically. To go to certain places, they might have been once upon a time one flight a week. Now there's a flight every day, maybe three flights every day. How do you measure that availability from an economics perspective? How do you measure the availability of hourly flights to the place you want to go, the convenience of that? How do you measure that as an economist? Well, there is no number that you can use to measure it. There is no number you can use to measure it. So deregulation and innovation, innovation, the hub and spoke method, I mean, not just innovation technologies, but innovation in management, in processes, in distribution. I mean, think about what Walmart did. So one is deregulation. But the second is lower trade barriers. The fact that tariffs since World War II, but certainly since the 1980s have been declined dramatically across the board, across the world, including in China, tariffs has gone down dramatically until Trump. That created an entire industry. First, there was the innovation of shipping, the container, which is a massive innovation. They reduced the cost of shipping dramatically, dramatically. The ability of ship things in containers is so cheaper than shipping things in crates and away. And then you build container ships, just built for containers. And these ships, you can build them standardized. And all the containers are standardized. And all the cranes are standardized. And everything is standardized. That reduced the cost of shipping dramatically, just at the same time as tariffs were coming down, just at the same time as production in China, in Thailand, in Taiwan, in South Korea, in Taiwan, in Hong Kong, in Singapore, was ramping up. We call that globalization, by the way, was a massive impact on quality of life and standard of living. A massive impact on cost reductions. We can focus on computers. And computers are essential even for that, the logistics, the networks. You need computers to manage all that. But even more impactful is the invention of the container, which had profound dramatic impacts on the world. And then the deregulation of trade, which allowed us to import cheap products from China, had a profound impact on the standard of living of Americans, a profound impact on inflation being lower, trucking deregulation within the United States and the ability to ship stuff from Mexico because of NAFTA. Again, lowered the cost of goods, trucking deregulation again, happened under Jimmy Carter. So you get lower and lower and lower costs because of innovation, because of deregulation. And that's not captured in wages. Because the wages can actually buy more. The wages can actually buy more. Even when it comes to food, we went from a period in which I grew up in the 60s and 70s, where we were assured we were heading towards famine, and where every few years there was famine somewhere around the world, to an era, relatively quickly, where the problem is obesity, where the problem is abundance, where the problem is too much food. Again, what does that mean? Technology, innovation, in this case, what's called the Green Revolution, made food abundant all over the world. And that has to do with corporate farming, the fact that the fact that the fact that the family farm died is a good thing because you brought in the ability to grow food on scale, which was done. So the value of deregulation, of innovation, of ingenuity, of increased productivity, of entrepreneurship, that is what resulted in an economy that grew actually faster than the numbers would suggest. That is the reason we had record unemployment, record low unemployment. It's not because of interest rates. Interest rates are a factor that influence, but mainly what the Fed does can cause damage. They're not what drives the economy, they're not what drives production, they're not what drives success, economic success. But all the Fed can think about is interest rates. All the Fed can think about is this relationship between inflation and unemployment which doesn't exist. Sometimes it does, but not because there's a relationship there, cause a relationship, because other factors are playing into it. So thank you, Noel. The invention of the container is massive. You can read about it, just look it up online. It is one of the great contributors to human prosperity in the late 20th and the 21st centuries. But the Fed, an economist generally, cannot comprehend this. They cannot, they don't know how to deal with it. They don't know what to do with it. They can't model it mathematically. They can't put it into a formula. It doesn't fit the mechanistic view of economics. So in my view we have had improvement in standard of living for the last 50 years for many in the population, not everybody, and it's uneven. The way this happens it's very uneven. It creates all kinds of distortions because the Fed is still manipulating interest rates. The federal government is still taxing in bizarre kind of ways. The federal government is still regulating and regulating heavily some industries and less heavily other industries and so on. So it's uneven in terms of how it happens, but it has happened and even though wages have been stagnant for some people, this standard of living quality of life has actually gone up even if they won't admit it. And the fact that unemployment has been so low is partially a feature of globalization. Americans didn't lose jobs because of China. America gained jobs because of China. There are many, many more jobs in America because of trade with China. There are many, many more jobs because of trade with Mexico, because of NAFTA, because of trade with Canada, because of trade with everybody. Thank you, yes sir. Trade is a job multiplier, not a job destroyer. So economists don't get this stuff now. What we need today, what I call the new intellectual would be any man or woman who is willing to think. Meaning any man or woman who knows that man's life must be guided by reason, by the intellect, not by feelings, wishes, wins, or mystic revelations. Any man or woman who values his life and who does not want to give in to today's cult of despair, cynicism and impotence and does not intend to give up the world to the dark ages and to the role of the collectivist roads. All right, before we go on, reminder, please like the show. We've got 163 live listeners right now, 30 likes. That should be at least 100. I figure at least 100 of you actually like the show.  There you go. Start liking it. I want to see that go to 100. All it takes is a click of a click of a thing, whether you're looking at this and you know, the likes matter. It's not an issue of my ego. It's an issue of the algorithm. The more you like something, the more the algorithm likes it. So, you know, if you don't like the show, give it a thumbs down. Let's see your actual views being reflected in the likes. But if you like it, don't just sit there, help get the show promoted. Of course, you should also share and you can support the show at your own book show.com slash support on Patreon or subscribe star or locals and show your support for all, for the work, for the value, hopefully you're receiving from this.  You'll know what shows are on when they're on. You'll get notified. So, yes, like, share, subscribe, support, like, share, subscribe, support. There you go. Easy. Do one or all of those please.


Thank you again for swapping in your lunch for my talk. We're going to talk about the developer sandbox. And so this ego slide can't really be missed in my presentations at least. It always gives me the minute to breathe and find my room on the stage and talk a little bit about me. So I'm one of the old guys, like literally, I'm in the industry since almost 18 years. I started out on the Java application server side, worked with Java versions that nobody can even find on an archived website anymore. And I wrote some books. I did work a lot with customers. I've sweat, blood, and tears implementing systems. And you're more than happy to follow me on Twitter, on Mastodon, wherever your social journey takes you. You will find me under my nickname, which is My Fear. And that is probably a story for a beer or a glass of wine or a glass of water, depending on, which I will absolutely invite all of you who are interested to, to grab me and talk to me about it. Who has not seen this slide, this picture ever before? Okay, congratulations, you are lucky. So this is the overview picture, the landscape of what supposedly we as developers are using when we're developing and working with Kubernetes. This is the famous CNCF, like Cloud Native Foundation Landscape. And it's basically categorized into various feature blocks and every feature block, if you go to that website that I did link deliberately down here in the slides in like almost unreadable text, then you can drill down into each of these blocks and you'll find company logos and products and open source projects. And for every need that you could eventually ever face on Kubernetes, you will find a solution or 20 or 100, various solutions for the same category. So this is, it turned out to be my nemesis because as an architect, I'm always responsible to like pick the right things for the right job. Hey, my life was so easy 10 years ago when I had like an application server and two or three vendors pretty much delivering the same standards. So it was comparably easy, I knew what I get. Today, if I have to build an application from scratch, I really literally all I know is it's potentially going to be Kubernetes underneath, but everything else is gonna be something that I need to solve on the way getting to my solution. So why did I put all these little texts down there? So obviously for your reference, so you can download the slides afterwards, hopefully. And it's so small because I used to call it homework. That is something that my girls hate for a living and they always push it off or write like teensy little notes during class, what they have to do at home. So, and because it's so small, I decided to call it your homework. So whatever you see down in the slides is like a comment, you can look it up afterwards, dive down into specific topics or learn more information about it. So what do we do with this, like with this landscape? Honestly, there's a ton of people who actually want to explain how this is supposed to work. And if you've written one or two applications or even more, if you're a seasoned developer, you know exactly how that guy here feels because he had to explain choices, technology choices to an audience, mostly to his team maybe or like try to defend the decision being made in one of these fancy DevOps teams today. So it's all been a congratulation and interpretation of non-functional requirements and how they could be put into software. That is a really lengthy process from time to time. And given the amount of choice that we have today as developers, as DevOps engineers, as platform engineers, as operations people, as even architects, if they still exist out there, it is a tough job, right? And this guy's probably smoking for a reason. So honestly, as a developer, this is what I really want. I just, I want to code. Like that's a famous joke that I found on Twitter and I used to have a screenshot in here that I removed for obvious reasons, but it basically tells the story of why developers want to earn so much money because all we want to do is we buy a cabin in the woods with fiber connection to like sit somewhere in a basement and write code and not be disturbed. Like this is the only reason, right? I want to be productive as a developer. I don't want to care about landscapes. I don't like necessarily want to have choice. I have my frameworks that I do understand, that I love, that I can work with. And all I want to do is put business requirements into this beautiful language of choice. And me being a Java champion, this obviously is Java with or without emojis. So you have to put up with the fact that I am deeply in love with Java and I'm taking every single fight I can with anybody talking about JavaScript. So if you're up for it, take me up for it. So there's plenty of approaches to tackle on complexity. One of the approaches is to deliver an architectural framework, something that you might have in your company or see in your customers. Some call it blueprints, some call it master architectures and it's always something very universe-like. So some people invested a lot of thoughts around how this could look like or not and the result may or may not be practical, ultimately. What I do think it really takes in terms of managing complexity is making choices. So at the root of our problems today coming down from this large landscape into something that is still manageable is managing complexity. And one way that you could look at, and this is what this session is about today, is managing complexity by embracing an opinionated view. And this is exactly what we're gonna talk about. So we're gonna talk about an opinionated developer experience with all the tools Red Hat has to offer. And if you've been sitting in one or two of the sessions earlier or if you will be sitting in the next sessions coming up in this room, you will experience one or the other tool. What I'm trying to do is give you a head start, something that you can take with you and just play with for a specific scenario. So the slide you're looking at basically pictures a developer experience view onto a Kubernetes infrastructure. So why is this developer oriented? Because it basically starts at the very top with what developer experience is about. So you see something like development in your IDE. You see Quarkus as like the JVM framework of choice. You also see Podman Desktop, that was a session before, which helps you containerize your applications and put them into pods and all the way into Kubernetes clusters. You also see something like version control because that's obviously the other thing that we also interact with on a regular basis. Developer Hub, we had a session about that which basically is like the developer portal. Red Hat has an offering in a Dev Preview which is called Red Hat Developer Hub. You can like look it up, take a look at the slides later on and give it a test drive yourself. That's all nothing I wanna talk about today. What I wanna talk about is this whole picture, like how do I as an individual developer manage to get into all of these technologies that obviously are already a smaller choice of the bigger landscape and still stay productive? Like how do I manage this complexity? This is the biggest challenge, isn't it? And to just give you an idea how this looks like, if I distribute all these various tools onto a software development lifecycle, we'll have the inner loop to the left, which basically is everything you do on your left. So that is crafting your beautiful source code in whatever language except JavaScript, containerizing everything, making sure it runs locally, trying to find some mini Kubernetes maybe to test at least a little bit of the configuration. If you're brave enough, you go all the way into some like XML configurations and do your own magic, but that's up to you. Then you'll start like local debugging cycles and do whatever is necessary to the point it works on your machine or on my machine, right? And that's the point where we all hit the big green button and push directly to, depending on how brave you are, either master branch or your feature branch, and off it goes, right? So then we'll sit there, hit refresh and see what the built magic is doing. And this is the outer loop, right? So outer loop is basically not my responsibility as a developer, it does magic and the result should be something introduction at some point or in test for the sake of or in like some kind of staging environment. But there's so much more, there's compliance, there's security and whatever. Still the right part somewhat hard to set up, like on a developer laptop, almost impossible and mine's already pretty large from like a physical footprint and size, but ultimately it's impossible, right? You need a playground, literally a sandbox and ideally that should be something that is at your disposal because honestly, you wanna know when I learn? I learn when the kids are in bed, like literally in the evening hours. So I do not learn while I'm working, which is a pity, but it just, there's no time to fit it in. At the end of the day, I wanna have a full blown ecosystem that I can play with whenever I want. So my personal sandbox and this is exactly what the developer sandbox is and the full name is fancy and not fancy. So it's the Red Hat OpenShift developer sandbox. It's like an always on environment that you can access and play around with. You have your private namespace, nobody else can access that. You can do whatever you want in that namespace. There's a couple of administrative functions that are not available from like an OpenShift feature perspective, but ultimately in that namespace, you're free to do whatever you want. It has a comfortable 14 gigabytes of RAM at your disposal and 40 gig of storage. And we just bumped that up a couple of weeks ago. So that should be more than enough to even try out a little bit more complex scenarios at that point. And the fun part is you do not have to configure it. Like literally that sandbox comes with all the buckets and all the toys. It's all pre-installed for you. So all you have to do is sign up and get your 30 days free trial. That's pretty much what you can do with it. And this is just an excerpt of what's available in the sandbox in terms of operators. We are not gonna talk a ton about these operators today. So let me at least talk about the web terminal operator, which is one of my most favorite operators in OpenShift at all. So it basically is an operator that spins up an instance where you can like use Qtl and everything you ever need in terms of Kubernetes tools without having to have it installed locally. So it like it's your terminal view into your namespace at that point. So what that basically allows you to do is take one of these fancy new flip phones, open your sandbox browser instance and just start playing and working with it while you're in the park walking the dog or something else, right? So it gives you full flexibility and all the tools that you might need are directly at your disposal. And there's even more because we also have the depth spaces operator that basically spins up an Eclipse J installation. So you can use Eclipse J as a browser based IDE directly for all your projects. You might have seen that earlier and you will see this over the turn of the day but that frees you to take with you in terms of like developing development devices, whatever small size you ever dreamt of. So no real need to carry these heavy bricks around and still be able to play. Yeah, and this is obviously demo time, right? So we'll see how that goes. And if my window breaks or not, I was actually brave enough to, let's see if we can do that. So developers, redhead.com is like the sandbox and this is literally all you need. You have a red big button over here where you can sign up, which I already did. So this process should be a little bit faster but it does not require you to put in any information at all. Like literally no credit card, no nothing. It's protected by Google capture. So as soon as everything is set up and the system believes that you're not a robot or an AI, you're more than happy to absolutely proceed. And this is what you'll be greeted with. This is your OpenShift namespace. And as you can see, I'm directly jumping into this project. It's a fancy combination of my first and my last name and I get a lot of comments about it. But yeah, let's do that offline later. And you can also see that there's no resources found. So this obviously is an empty cluster, which is perfect. The sandbox itself knows two user personas. We have the developer persona who we all are and we also have the administrator. Love them or hate them, we might need them sooner or later. So never talk bad about them. That's what I'm also not gonna do today. What I wanted to show you in the administrator view is that you have a ton of operators at your disposal and you can see the full list of already available and installed operators by just clicking here and scrolling through. Some familiar names, Camel, has ever heard about Camel and played around with it. It's more like on the Java integration side. And yeah, again, I'm a Java guy, so apologize for that. Another one is the GitOps Prima operator. It is part of the upstream Conveyor IO community. So imagine you playing around with a sandbox for 30 days setting up your beloved pet project and you're about to lose access because after 30 days, we'll just terminate your free engagement. I'm sorry for that in advance. But the GitOps Prima operator basically allows you to export everything you configured. So you can literally take your YAML with you in your teensy little bag and make sure it's being safe and secured and deployed to another cluster whenever you wanna have a chance. Okay, what else can we do? Obviously, if we have no resources, we need to start adding some. So there's a variety of options and let's talk to the UX guys at some point about what should be done first here. What I wanted to show you first is definitely the guided documentation that's available. This is always a good step for new users. So you can get a grasp about what's health checks, how can I add health charts from the repository? How can I deploy a Quarkus container image, which is my favorite JVM framework? And it's all guided.  But if that's all already too much and if you don't wanna navigate that, you can also start from pre-existing and reticurated container images. And I do actually, I did that before. Guess what? Because I was afraid it's gonna break like in Elon's demo. But we'll see how far I get today. I wanna spin up a MySQL database just for the fun of it. And obviously, this is not supposed to be a serverless deployment. And I also wanna make sure I have a couple of deployment information in here. And because I could not learn them by heart, I still need to copy and paste. So we need our MySQL user and password to prime the image. We also need a root user and root password. And we need to quickly define a database. Let's see. Password, can you already guess what kind of application we're gonna get started with? You can, right? So let's create, oh, MySQL database. Real quick, and the topology view instantly shows you that something is happening. And obviously you can dig into this. You've seen the little blue circle that indicated that this pot scaled from zero to one. And obviously it's now up and running. All the deployments are here, perfect. You can look into all the deployment details. But let's forget about this for a while because spinning up a database is interesting but not really a developer task. So we only need that for our application, right? So what we really wanna do is bring our application source code into this, right? So let's import from Git. I think that's what we all know. So we have our nice little application sitting somewhere in Git. And in this case, it's the Spring Pet Clinic demo. And yes, everybody's excited, it's red on screen. Yes, I was scared to death, man. If I've seen that the first time. So let's switch the build strategy that's employed here. Our source to image feature, which is basically what we're doing. We're taking source code and letting the sandbox turn this into a container image. So that's kind of what's happening behind the scenes. And there's various strategies to get to that final image. You can use a dev file, which is more or less known, a full description of your project. You could use the standard Docker file, or you could use one of the predefined builder images that come with the OpenShift developer sandbox. And in this case, did I mention that I'm a Java guy? Who else is programming in Java? Love you. Shoot out to all of you. So let's take UBI 17, which is like the universal base image, which is a rel version that you are free to use. And it has an OpenJDK 17 baked in. So let's just use that. I am pretty sure that we need to tweak a couple of things here. So that's also not a serverless deployment. And we also wanna make sure that we have a little bit of a build configuration in here because this thing needs, oh, that's not gonna work. This thing needs to know where our database sits and how that profile in the application in spring is actually gonna be activated. So we'll add the MySQL URL here. Anything else we need? I don't think so. So let's create. And the deployment was created successfully in this case. And as you can witness, this is basically starting a build in the developer sandbox. So you don't really need to build your images locally.  And this takes a while. We can actually take a look into this part if I'm not completely mistaken. This is the build. This is not where I want it to be. Anyway, let's not mess around. Downloading Maven dependencies, making sure everything's in place, the application should be up and running at some point. And when that point is reached, you can ultimately also access this here. Guess what? When I tried this earlier, this was not taking as long. But anyway, you did understand the concept behind all of what I wanted to show you. And for the sake of this presentation, this is the tutorial where you can play around and try for yourself, like the full Spring Pet Clinic demo. Just go there, take a brief, deep dive into the OpenShift developer sandbox. But if we'd only had one tutorial that would not really be helpful, right? So as a matter of fact, there's a ton more that you could do besides the guided documentation inside the sandbox. There's also a ton of tutorials available that you could just walk through. And you can also learn the basics of Kubernetes because that's not an artificial pass that you've just looked at. It is a full-blown Kubernetes system. So if you're new to that world, if you wanna get started, if you wanna learn the basics around like pots, labels, deployments, all that coming together, ultimately to form a full-blown application and its infrastructure, this is exactly where you can start. And if that isn't enough, you can also take it to the next level and think about some specific architectures on Kubernetes. Obviously, it's meant for and built for fully distributed microservice-like architectures. So taking maybe JDK 17 and Quarkus for a spin, developing a couple of microservices and having them all run, seeing how autoscaling works in Kubernetes with the topology view that I showed you earlier, all this is beautifully crafted in these tutorials and you could easily just walk through. And the best part is when the kids are in bed, you're also more than welcome to do this next to a glass of wine, which will make learning a lot easier, according to my information. OpenShift Pipelines, that is a fancy way of saying we also support Tecton as an open source build system. It's pretty Kubernetes native. So it gives you a feel how build steps are actually executed on top of Kubernetes pods. So it makes it a lot easier to scale your build infrastructure. And if you ever wondered what Tecton is and how that works, that is another great tutorial for you to actually take a look at. We did briefly talk about the application exporter, like the GitOps primer. And I have like the fancy four letters Rosa on here. OpenShift Developer Sandbox is just the entry level free offering. Whenever you're convinced and you're like, hey boss, I need this thing. And you wanna tell your boss what to buy, it's not the Developer Sandbox. Your boss needs to buy OpenShift on AWS. And the short form is Rosa. So keep that wonderful acronym in the back of your head. And whenever your boss asks you what next to buy, send them there, forget everything else.  Build and run your containers on a Kubernetes environment that is reasonably bigger than your local laptop and gives you a lot more features and tooling outside of the plain Kubernetes infrastructure.  Like it's all pre-baked in here. So this is what we have today is like on the left. You can get a sneak peek of the developer experience features of the whole Red Hat ecosystem with the Developer Sandbox. You also have access to the API management and obviously also there's a data science part in it. That is Jupyter Notebook. So not really a Java kind of guy thingy, but if you ever thought about, yeah, give me a notebook. I wanna play around with it and see how that AI data science thing works. That is also something that you can do as of today with the Developer Sandbox. Next, you'll be able to try out the Developer Hub. So if you ever thought about testing out backstage on top of like this curated stack, the Red Hat Developer Hub is gonna be your next big bet. And you will see more stuff coming. And this is like as usual, no guarantees, no promises, this is no roadmap. So it's just like a sneak peek into what might be coming. This is the slide you can screenshot if you want. It takes you directly to developers.redhat.com slash sandbox. And it is really, really easy as you've seen. It's just a matter of clicks and a little bit of waiting. And you'll be at the point of your dreams where you have your own Kubernetes cluster at your disposal. And if everybody's pulling the cameras down, I will also make sure to hint you to the developer program in general where we have like 100 interactive sessions. That is something that doesn't even require the Developer Sandbox, but comes like with learning experiences being spun off for you as you start your session. It's fully guided. So there's ton of topics from everything like Jakarta E development, Node.js development and all that kind of stuff. You just pick and choose where you wanna learn more and it will be available. And yeah, blunt pitch.  So we wrote a book which was published at O'Reilly and it's also translated in traditional Chinese which I'm personally very proud of. And you can get it. So either via this link as like a PDF downloadable edition, the GitHub repository has all the source code if you wanna play around with the examples in the book. It basically guides you from like traditional 10 year old architectures on a Java application server all the way into modern serverless deployments. So yeah, play it around whenever you like. And this is the big surprise. So we'll have print out copies like hot copies here. And if you come by tomorrow afternoon-ish 12 10 to be precise as a German, you'll be able to maybe snack a copy that gets signed by me and Natale. Natale's sitting there, Natale Wave. So yeah, we too did this. It's not gonna be this big. So it's more like this big, but you'll still enjoy it. And it has a ton of valuable information in it. What else, don't forget to register for the Reddit developers program. There's a ton more books about Potman, about security, about trusted application pipelines. Literally the basics, Unix cheat sheets, if you wanna learn more about Linux and how their commands work, and it's all free. Like free as in free beer, as in open source, as in community, we like sharing is caring. And that is exactly what you get if you register here. My standard reminder, after the tough times we've all been through, don't forget to close your internal browser tabs, please. All this stuff that's going on right now everywhere in politics, in personal lives, in wherever, is something that we need to cope with. And this is my general reminder, please take care for your health, make sure you're prioritizing yourself, and learning is part of this. So make sure you will learn a little bit and grow personally and stay healthy on that journey. Thank you all so much for being here, and I'm looking forward to talking to you.


In today's video, I'll be showing you how I use FinVis to find a really good trading setup. This setup is called a false breakout and I do trade it almost on a daily basis or every time that it happens. As usual, this video is not going to be financial advice, this is going to be for information purpose only. And I will link all the best tools to day trade in the description, so don't forget to check it out. So the first thing you're going to do is go over FinVis.com and this website is honestly really good. I'm always surprised how much there's good stuff on it, even if it's completely free. The only thing that's annoying, it's always the ads, but you can do anything about that if you don't pay. So when it comes to finding setup, it's interesting that people tell you which setup to look for, but it's really important that you understand how those setups are made and why they happen over and over again. So the first example we're going to be looking at is going to be on IONQ and I will be going over multiple examples. So we can see over here that the stock made a nice move up over here. And at this point, I'm going to be looking for this little red day over here. So the 0630. What's interesting about this day over here or how it is behaving is that it's very far from its moving average. We can see the blue line is the 10 SMA. The green line is the 20 SMA and orange is going to be the 50 SMA. So we're going to see that the stock is way above and it made a big move up and it doesn't create a base. The way it works normally is stocks that are breaking out out of a shitty base. So the stock didn't really have time to consolidate are going to fell more often than not. So this is kind of the idea behind this trade. So you definitely want to be just looking for stocks that are moving really higher from its moving average and that are falling just above. So the setup on the daily always looks like this over here. And it's very important that you remember how it really looked because it really looked bullish. A lot of people are going to be looking to buy this and I can understand because it looks like it's going to go much higher. But the way it works when we go to the intraday chart, you can see that, you know, it makes them move higher, it traps a lot of longs and then it just fells below VWAP. So this is a really good example to remember. And I'll go through other chart that are doing the same exact thing. By the way, at any point during this video, don't forget to check out the links in description. I did link all the best tools for day trading included broker scanner charts and all of that stuff. Also, if you want to participate to win one year of trading view, the only thing you have to do is just comment your favorite indicator in the comment section before the end of the month. So let's get back to the video. Well, actually, this is today and this is for PLTR. So the same thing, the stock was breaking above and it just made a significant move higher already. And then it just fell from the high. So you're looking for stock that are distant from their moving average and that are failing to hold above VWAP. So the stock made a move. And then we're looking to capture kind of the down move over here. So a pretty simple setup. And there's some other good example with this name. If we just go back to this day over here, which is the 0607, we have a pretty much exact same setup. Really strong move up is this big red day over here. We're really far from its moving average. And then we failed to hold VWAP. If we look at Mara over here, we can see that, you know, it made a significant move up at this point. We're breaking above its previous high. We're failing and we get below VWAP. And all of a sudden, you know, we have a nice trend day. So we can go back and see the same thing. You know, stock makes a big move up and it tries to break above a recent high and it just fails right from above. So it's always the same thing. Strong move up fell to hold VWAP intraday. And you can kind of get short risking, you know, a decent level. So we'll look at the last example, which is on a Joby. And it's the same exact thing, right? The stock makes a really big move up. It consolidates for one day, tries to break above this previous day high. It just fails to hold VWAP. And all of a sudden, we have a nice trend day once again. So the setup is quite simple. You're looking for a stock that already made a big move up. And the way you're going to find this is always going to be with volume. After that, you just need to set yourself some alerts above the previous day high or the significant high if the stock is in a base. When the stock breaks out of the base, you're looking for the stock to move up. And then you're looking for the stock to fail to hold above VWAP. So it's a very simple thing. So we're going to do all this in FinVis to really understand how to find it. So now we're back in FinVis. And what we're going to be looking for for the first thing, this is always important. You want to have volume. If you don't have volume, you're not going to have a good setup or it's going to be very, very hard to trade. So I normally look for stock that trades above at least 20 millions. You could sometimes get away with less. But I highly recommend that you look for the higher trading volume stock. The reason why is just going to be easier. The spread is going to be less and you can have less random price action. You're going to be able to have more confidence in your trading. So over 20 million is a good start. The next thing we talked about are we saw from every single chart that we looked at. It was that the price was above the 20 SMA. So I can look for price 10 percent above the 20 SMA. And I'm going to do the same thing with the 50 SMA, so 10 percent above the 50 SMA. So this is really going to show you just the stock that have the chart that we're looking for, because if we look at all the example, we're all distance from the SMA. If we look at the PLTR like we did have before, we're always extended from the SMA, always extended from the SMA. If we look at something like a Mara, same thing. If we look back at IONQ, we can also see that we were really above the SMA. So it's very important that we are actually extended from these moving average. So now that we do have a basic scan, so you have 10 percent above. You have 10 percent above the 50 and you have high volume. So the next thing you're going to do is just click on snapshot. This is really good because it's going to show you exactly what we're looking for. And something that you can do over here, depending on if you're seeing too many things, you're looking for a more large cap or small cap or whatsoever. You can always change the market cap. I personally don't. I will let everything there and I will filter it just after whenever I look at the chart and the news. So you can see that CVNA made a really, really big move and this is today. So the market is just closing. So you can go ahead in this chart and go in your platform of choice and just set an alert above this high. You're going to do the same thing, example, on this one. And you're going to start doing the same thing on every chart that is over here. So we have Fubo, it made a high. So I'm going to put an alert just above the high over here. G.O.E.V. already made the move. So I'm not going to be looking at it, but you have this stock over here key. So this one is a regional bank. So all the research is their news, is their news in the sector, all of that stuff. And I would just set an alert. Same thing over here. This is another regional bank ETF. I would probably not trade it because it's an ETF, but over here, I would look at Mara and set some alerts and KLA would put an alert above the three dollars and over here, PLTR already failed today. So this was good. And over here, I will do the same thing on plug. I will set some alert riot. I will set some alert Rivian. I will set some alert above this high over here. So at this point, you probably get the principle about this trade. And if we look at Rivian, you're going to see how many alerts do I have, right? So I do have on pretty much any stock that made a move recently. So I'll just link these two together. And over here, you're going to be able to see that I do have alerts pretty much on anything that made a move recently. So I have the NVDA because it made a significant move. And actually, it was a really good example just over here on the 14. So if I just look back at this day, pretty simple trade, right? Big day to day before. And then you just wait for the failure of VWAP. And then you have a nice trend down. And that's pretty much the only thing you have to do. So other than that, it's just going to be about reading the news, understanding market cycle and all of that stuff. But I personally really enjoyed the setup and I just scan every night. I just set some alerts. And after that, I just wait for them to trigger and I look at the chart. I just see if there's a setup, if there's news or anything like that. And I execute and hopefully there's a good turn of event. So thanks for watching. I hope you enjoyed this video. If you did, like and subscribe and I'll see you guys in the next one.


Hi, and how are you all today? The question says finally amount of an annuity of Rs. 2000 payable at the end of each three months for six years if money is worth 8% per annum compounded quarterly. We need to use the value of 1.02 raise to the part 24 as 1.608. So let's proceed with the solution. Here we are given the periodic payment towards this annuity that is represented by capital R as Rs. 2000. Rate of interest that is denoted by small r is given to us as 8% per annum. So compounded quarterly means dividing it by 4 that gives us 2% per quarter that is 0.02 per quarter. And further we are given the number of payments in six years sorry each year we are making four installments. So we have this is represented by N as 24 quarters. We need to find out the amount of an ordinary annuity. The formula for that is r multiplied by 1 plus r raised to the power n minus 1 upon r. What we need to do is we just need to fill up these three values and then solve it. It's 2000 multiplied by 1 plus 0.02 raised to the power 24 minus 1 upon 0.02. That is further equal to 2000 upon 0.02. 1.02 raised to the power 24 minus 1. Now on solving we have 2000 into 100 upon 2 into its value is given to us in the question as 1.608 right. So on using it and simplifying it we have 0.608 is getting multiplied by it and we have the answer as 608 double to 0. So this is the required answer to the question that is given to us it is 60800 right. So this completes the session hope you understood the whole concept well and enjoyed it to have a nice day ahead.


I buy tickets even before the program is announced. The wealth of knowledge and thought leadership that you're going to see here, it's the best in Asia and it's one of the best international conferences to attend. It's vendor-neutral and it's technology-agnostic. Get an insight into what kind of challenges different companies are trying to solve and how did they sort of go about solving those problems. I would say this is very good value for money. The quality of speakers and the Huisu of Vajalworld is always here. We recently had Dave Thomas, we've had Ward Cunningham, we've had all sorts of people talk at this conference, people who are very well known, very well respected. It's actually one of the most truly international conferences. If you look at the program schedule, you will see a variety of experiences and a variety of stories there. Whether you're talking about maybe the DevOps side of it, whether it is feature talkers, CICB, maybe test automation and product discovery, user experience, you have a lot of talented folks who are here as speakers. The idea of this is to not just share but also provide these recipes that engineers and managers and directors can take away and actually implement immediately. It gives you an opportunity to think from different perspectives, network with people, come out of your comfort zone and see what other people are doing. Professionally, I feel that my network has widened. Don't think about it, just book your tickets and go for it.