Sarms should you use them? Watch this our first caller is Wade from Michigan. Wait, what's happening? How can we help you? I? Was actually First of all, I want to appreciate I want to tell you that I appreciate all your insight and everything your podcast It's been really very informative for me, and I don't know can't say enough about you guys. You're fantastic Thank you. I was actually inquiring about myostatin inhibitors I'm curious about there. I know I've done some research on them And I know that there are or there's one in particular that has kind of drawn some interest with people it's called yk-11 and I Don't know a lot about myostatin. I know the antagonist is full of statin in your body and basically if you have lower Myostatin and you're able to produce larger muscle mass Can you guys share any insight on that?  Yeah, okay, so here's what's interesting about well first off This is a wonderful example of the the what the supplement industry likes to do when science comes out so years ago there was kind of this Discovery that if you if you inhibit myostatin and they did this pharmaceutical or they did this genetically with animals You get this kind of runaway muscle growth and there were these really dramatic pictures of mice, you know that were that had their myostatin inhibited or cows or dogs and it was like It was like a bodybuilder's wet dream I mean it made steroids it made steroids look like vitamin C in terms of just absolutely It's almost like a holy grail, you know, it kind of seems that it's crazy Now here's the problem the problem is the supplement industry saw this research and they quickly went into how can we market? Supplements that will promise to do the same thing so far Okay, now here's the truth creatine inhibits myostatin to a degree Loosene amino acid loosene can inhibit it to agree to a degree Resistance training will inhibit myostatin to a degree anything that helps promote muscle growth will inhibit myostatin to a degree Is there anything out there legally available? That will inhibit myostatin anywhere near the neighborhood of what the studies done on animals You know showed no, there's nothing that comes even close to that now you just referred to a Selective androgen receptor modulator like a research chemical And it really it's you know It's it's inhibition of myostatin is similar to what you'd find by taking Anything that activates the androgen receptor like testosterone or anabolic steroid. I my my firm belief around psalms is They're not researched very much.  We know what they don't do Psalms we're still kind of up in the air and I have yet to see any psalm that even comes close To what like testosterone would do but but besides that it really there's nothing natural on the market That's gonna do it like I said creatine Working out, but there's really nothing you're gonna take naturally. It's gonna do anything that's substantial Didn't we even speculate for a while like there's a lot of by builders going to Kuwait And then coming back just insanely jacked and We asked Ben Pekolsky about this right and he he just exclaimed that they had everything dialed in like To a degree where their food was all prepped for them their sleep was all regimented their you know Pharmaceutical grade testosterone was like one of the best, you know around so it was you know Literally they just like stacked everything perfectly In that environment. Yeah, I mean again, I would I would stay away from Any, you know, psalm or anything that's really not approved These are kind of experimental pharmaceutical drugs drugs some of them that people were using early on now We're finding oh probably not great for your liver or can cause issues with your vision or your or or cancer So I would stay away from them and again if you're gonna go that route where you're gonna start experimenting with Pharmaceuticals, I mean testosterone and anabolic steroids have been around for decades We know what they do what they don't do you're better and they're gonna give you better results But I don't even recommend obviously. I don't recommend that either as far as Concerned nothing's gonna nothing's gonna do anything wait wait. Are you are you in our mind pump hormones group by chance? In my what we have a we actually have a mind pump hormones group. Are you in that by chance? No? I'm not. No, okay. It's a free group. It's on Facebook. You should totally join it This is there's great. Okay, there's great conversation around psalms and testosterone and all that stuff Really smart people in there. Yeah. No, we have two brilliant doctors hormone doctors in there And so the grape and they they actually twice a month go on and do live Q&A's We could ask him anything you want and they're they're answering everybody. That's in the audience So super valuable totally free to you. I would recommend doing that And you can also always rest assured that if there's something that that's this groundbreaking that comes out and hit Some market you'll hear it on here probably first. I mean Sal's on every you know deep web black market, you know supplement forum that exists And he's always scouring the internet for what's the latest and greatest and coming out and he normally like a guinea pig We'll test himself So we'll definitely be on the front end of anything that comes out And I promise that we'll let our audience know if anything worth Trying or taking hits and Wade is 100% right if a true Effective myostatin inhibitor Comes out like one that really inhibits myostatin not one that inhibits it kind of like you know like on the like as a side Effect like creatine or whatever but one that really does an effective job We will all hear about it because it will be oh, absolutely. Yeah, it will it will it will make anabolic steroids obsolete literally you'll see 300-pound, you know point guards in the in the NBA It will be like nothing like we've ever seen before Based off of these you know initial animal studies by the way, there's there's side effect myostatin exists for a reason So it's not like it'd be this panacea where you take it.  So hope that helps you Right well, I do I do know that there is one When they when they actually tested the mice that there were some issues with their tendons and bones as well Yeah, you know your body kind of naturally regulates itself to the point to where your tendons and bones can actually hold that amount of Yeah, and even look at even with anabolic steroids You'll see people take high doses and the muscles will grow much faster than the support system So you'll see tears and issues In muscle tears or tendon tears or ligament tears Because those parts of the body take longer to build and to strengthen so but besides that there's other side effects Right, there's other things that we start to see down the road, but I mean again testosterone and anabolic I mean testosterone obviously it's what we already produce like some of the old-school Anabolics that have been around I've been around since the 50s 60s You know like they haven't really come out with any newer ones in a long time And we know what they do and what they don't do largely because they've been around for a long time these arms I mean the one you mentioned. I don't think there's a single human study like so No, there there is and I did a lot of research on that. There's a there's one other guy that Did a podcast on it. It was like 18 minutes long.  We don't we don't know yet. So until that until we know I'm not gonna recommend. I'm gonna tell people stay away Right, absolutely. All right. Thanks Wade. Thank you. Thank you.  We do look at all this weird crazy stuff And I get it like, you know, I'm this is coming for me But you know be careful because you don't want and I remember there's one side I remember the name of Sarm. I forgot what it was but I remember people were right we're writing in the forums this when I was reading about them I mean years ago when they first came out and people were like, yeah, it's really cool But after about six weeks you start to lose tail. No, like you start to lose your night vision at night It's harder to see.  I mean they've been popular since we've been talking about it Right like I remember when it first came on the scene But it ain't slowing down and there's start there's people that have podcasts dedicated to it now And that's all they talk about and people hustling. You know reminds me of you know this because you're in the cannabis market Do you remember how popular? Synthetic like cannabinoid remember like spice or whatever.  Yeah, that's why they're popular because they don't even do what testosterone does They don't even come close the only reason why people are using them is because they're afraid to go buy black market Anabolic and that's the part. That's so funny. I mean I get it though because I was a I was a young 20-year-old kid Who was thought the same thing to like I was willing to take every Experimental thing as long as it wasn't steroids. Yeah, then I could say I didn't I'd never touched steroids I never touched steroids, but it's like I'm trying all this other stuff I just did all the rejects. Yeah, all the all the stuff that's as dangerous make potentially more dangerous And they sell you on the idea that they're not and so it's like oh man Bro that mentality is why I use like super drawl method master like all these pro hormones turned out to be designer steroids So it's like waste of time right Hey, if you enjoyed that clip you can find the full episode here Or you can find other clips over here and be sure to subscribe


What's up everybody, Jason here for jasmeescasebase.com, 2022 Leaf Decadence Multisport One Box Break. RMB number one just sold out, guys. And again, guys, very fun product. Only three hits though, three autographs per box. You get three cards per box, three boxes per case. So this is a three box case. We'll have at least three breaks of these. And again, why is the way we distribute these hits are R&B styles. So again, everything should be numbered, numbered to 10 or less. And every box gets at least one multi-signed card. So we're gonna randomize customer names and the list of numbers, one or zero through nine. Whatever you get matched up with is what you get. And of course, your examples on how we determine who gets these hits, we go by the first number on the left side of the serial number dash. So there's your examples there. Now of course, if there is a redemption, guys, it's gonna go automatically to spot zero. And any non-numbered cards will be randomized to the group if there is any, but everything should be numbered. Now here's a dice roller, here's a customer names, BD, down to Steve Birch. Then we got zero, one, two, three, five, six, seven, eight, nine. Let's click dice roll and it is a six and a two eight times. Good luck. One, two, three, four, five, six, seven, eight. Six, two, eight, Michael down to Brad. Six, two, eight, eight times here. One, two, three, four, five, six, seven, eight. Eight times, eight times, six, down to one. Show you guys that that's eight. So Michael got a spot six, Steve with seven, Michael with zero, and you know, redemption Steve Birch, sorry, Steve Herrick with seven, Steve Birch with eight and five, Steve Herrick with three, BD with two, Philippa with four and nine, and then Brad Pauley, you have spot one. So there's your guys' list there. I don't think anybody wants to trade for numbers, but I'll give you like 30 seconds. And then if not guys, we'll start the break. Login number seven, number five, no thanks. All right, well, we tried. TWT then guys. All right, so again, this is a whole case. Leaf decadence, put a little YouTube sign on it. So again, we did post up the next one guys, and if you guys do want to run this back, let's do it. Top, middle, bottom, one, two for the top, three, four for the middle, five, six for the bottom. Let's see which one is generated here. Try to trade, oh yeah, that's a good one. Try to trade, couldn't trade mojo. Four, one, two, three, four. We're gonna go with the middle. Middle, I don't even know how to open this. I don't think I've ever opened this side of this new one. So I want to say they're all just, all just in there and laying flat. Oh, actually sorry, you slide out the box, that's all right. That's right TJ, it's gonna happen baby, fancy. Fancy schmancy. I don't want to show too much, but there's the first part. I should have guessed they're all gonna be covered anyways. All right, here we go guys, here we go. We got an armor leg. We got an armor leg, it's a leg. Looks like Kofax's leg, and yes it is. Sandy Kofax, and this looks beautiful, look at that. Two of six, two of six Sandy Kofax. Two of six, spot two is BD, very nice buddy. Next one, here's the multi-signed one. Three, the epic three, Ryan Sandberg, Mark Grace, and Sammy Sosa, so we're keeping it baseball. And it's three of three. Spot three going to Steve Herrick. I really like the dig of the card on it. I love that it's in that little frame as well. I like it. All right, let's see if we can switch up sports here for this last one. Last one, is that Ric Flair? Yes it is, Ric Flair, woo. Ric Flair autograph, that's number two out of five. BD with two hits, how about that? Two out of five. Not a bad case. I mean, I gotta assume, or instead of bad boxes, I gotta assume that there probably is some pretty massive hits in here throughout a whole three box case. So this was just a little taste of it. We did get the multi-auto, we got Ric Flair, and then we got Sandy Kofax. I got the next ones in the store guys. Again, I know it's a high risk, but it's a high reward when you do hit. Appreciate it folks. Jaspy'scasebricks.com.


So, as you guys know, we were entering positions at this 10,400 level, with our targets being 10,600 and 10,800 here. So, trades that we're looking at basically either back here at this Fibonacci level, the 61.8% level is about 10,400. Then there's main miner support here, 10,600. So, we also entered trades using this support. This is the main support you can definitely enter. And then the next trade that we're looking for is above 10,800. We're looking to enter trades above that on a breakout, so that'll be a breakout entry we have there. That's basic. So yeah, so 10,600 was where we were entering our trades. 10,800 was our first target, which as you can see we've blasted through. If you're looking to enter on a breakout, then the breakout in 2,200 or above is the 8.2% Fibonacci level as we have spoken about a lot. So, those are the two trades I'm looking at. If we break down the low, the support, then the next support is going to be down here at the 10,800 level. But the trades that we took this week, we took the breakout above 10,600. Took some profit at 10,800 that was here with the Fibonacci level. Took some profit at 11,000. And then now our next level is 11,200. What's up family and welcome back to Bitcoin Daily. Man, it was a crazy end of the week last week. A pretty wild weekend, as you guys will see. And as you guys just saw, last week we spoke about these levels in almost all our videos. Hopefully you guys took these trades, man, because we definitely took these trades. We signaled these trades in our trade group. And I think we all made some pretty good money on this move. So let's go ahead and check it out today's market analysis here. Let's go ahead and move this over. If you guys did not see those videos and did not get those trade alerts. Guys, please make sure to hit the subscribe button, the follow button, the like button. It really, really helps us out growing this channel. It costs $0 to follow us. And we give out trade ideas. We put out trade ideas all the time. Every single week, almost daily really. So please guys hit that follow. It helps us a ton. So let's just jump right in here to Bitcoin. As you guys can see here, we're now at $11,500. If we drop to the daily candles here. Let's take a look at the dailies. Look at this. Look at this beauty. Green, green, green, green, green. That's one, two, three, four, five, six days in a row of green, right? So we might see a pullback at some point here. Don't be surprised. Don't think it's the end of the world. Hold on. I want to go ahead and stretch this Fibonacci level. Fibonacci levels out a little bit. They're locked, of course. Let me see if I can stretch it out without messing it up. Think about here. Let me see where is this. But here should be fine. Close enough, right? Let me lock that back up. All right, so as you guys can see here, we have been very, very green, right? Guys, we spoke about all these levels and all these trade ideas last week. If you guys look back on our videos, we told you guys we're entering trades long positions at 10,400, which touched right here. This was the Fibonacci level, the 61.8% Fibonacci level, right? We told you guys we're entering breakout trades above 10,800. We also told you guys we're entering trades above 10,600. We're using that as a breakout point and we're using that as a support point. So this was another entry we gave you guys here. Told you guys we're entering at this Fibonacci level, which is a 50% level, plus the 10,800 level, right? So that was this point right here. We told you guys above this long term ascending support slash resistance line, we're using this as a breakout entry, which also coincided with the 11,000 resistance. So that was another point of entry, right? Then we told you guys above 11,200 was another Fibonacci level. This is Fibonacci level, the 38.2% Fibonacci level. So that was another point of entry, right? And now the last one, I don't know if we spoke about this one, this one we spoke about in our trade group, but 11,500 was our next one. And then this Fibonacci level right here, which is 11,700 range. And then after that is 12K really. So these are all levels we spoke about guys. We have been speaking these levels basically into existence, right? Over and over for the past few weeks, maybe even months while we've been watching all of this go on. So hopefully you guys have been following, hopefully you guys have been able to make some money. Just by following these trade ideas, you guys should be able to make some money pretty easily. If you guys want the exact signals when we're taking them, you guys could always join our trade group in the link below. But other than that, no, I'm not upgrading. Yeah guys, it's been pretty simple. Now here, what we're looking for here for new trade entries, we're using this 11,500 support here. So as long as that holds, you can enter as close to 11,500 as possible with your next target being 11,600, which it hit today. 11,700, which is the next Fibonacci level. And then 11,800, those are your next three targets. And then you want to basically buy back in on a break above 12,000. So that's basically what we're doing right now. If we see pullbacks below 11,500, then we're looking at 11,200 for an entry. The first pullback entry, 11,000, the second pullback entry. And then the third pullback entry would be this Fibonacci level, which is 10,800. So those are the entries for breakouts and supports that we're looking at here. As you guys know, we are bullish on this. We are currently in trades on Bitcoin. Hopefully you guys were able to get some of these trades that we put out over the last week or so. Let's go ahead and take a look at Ethereum. Ethereum is also taking off. We told you guys, 350 was the breakout for the entry. And here it is, here's 350. Then we told you the next breakout would be 360. This is also your first target. And there was a break right there, boom. Then the next one was 380. And then 390. So here it is. Guys, just as we called it, it played out. We spoke about all these levels, guys. Watch our previous videos. We've been speaking about these levels over and over. So now the next breakout entry would be above 390, but because 400 is so close, I'd rather just wait to 400. Or you could take this 390 breakout, take profit at 400. And then the next one would probably be like 410, this second level. But that's basically the plays right now on Ethereum. If you're looking for a pullback entry, then you're probably looking for it to pull back to 360. The second entry for that would be 350. So the breakout and pullback entries we're currently looking at. Bitcoin Cash, kind of the same, just kind of following here. We spoke about this level right here, which was 320. So this was the entry right here, 320. I mean 220, I'm sorry. 220 was the entry here that you wanted to enter. Now then the next level was around the 236th level I think we spoke about last time. So it looks like it's pulling back to that range. For breakout entries here, let's see, it looks like for some reason that 240 number is the one popping up a lot. So 240 could be a good entry, support entry here with 250 really being the main level you're looking for here as your first entry. So 240 is a support entry, and above 250 would be a breakout entry on Bitcoin Cash. If it breaks below this, then we're waiting on 220 again. 220 to 200 is the next support entry on Bitcoin Cash. Litecoin, same thing. We have six days, six green days in a row. We just finally hit that $50 mark. We just broke above it. We know above $50 is where I'm looking to enter. As close to $50 as possible is that support entry. And the breakout entry is probably above $52 here. That would be your next breakout entry and support entry. XRP, Ripple. Also a nice one, two, three, four, five, six days of green. We're currently at 25 cents. Finally, I spoke about that 25.50 mark here. So 25, as close to 25.50 is possible here for a support entry. Next support entry is at 24.50. And the breakout is above 26 cents. So those are the entries for Ripple. Chainlink, we also have another six days of green here, which is lovely. We broke above that $10 mark. I told you guys about over and over again, right? Here was a break. Look at the break. I just took off and now it's at 11.50. So entries here for support entry. You're looking to enter as close to $10 as possible for a breakout entry. We're probably looking at levels right now, probably above $12. So above $12 is the next breakout entry here for Chainlink. Those are the breakout and support entries I am looking at. Tezos, beautiful, also six days in a row of green, currently at 233. But we haven't really moved that much. We went from $2 to 234-ish. Breakout entries I'm looking for, probably above 235. But I would just do it above this previous high right here. So above 236. Support entries I'm looking at is 225 and 210. The two support entries I'm looking at here for Tezos. Next, Neo. Neo finally got some life in it. Started bouncing back up here. Bounced at that $16.50 area that we talked about last time. Currently at 1825. If I'm looking for a breakout entry, I'm looking above 1850. Above 1850 for an entry. Support entries, probably at 1750. So that's support and breakout entries I'm looking at. As you guys can see here in Bitcoin Futures, it did gap up. So we have to keep this in mind. We most likely will come back down to close this gap at some point. I think within the week. So don't be surprised if we go back down to the 11,200 level. That's a level we need to keep in mind and we need to watch for. Because we did gap up, we might come back to Tezos' 11,200 level before taking off to 12K. So that's what I think is a possibility in happening here. So we would have to wait and see if that's what actually happens. That's pretty much it. Let's look at Cardano. Cardano's also back up here. As you can see, we have pretty much a bull flag break here. But we're still kind of at a double top right now. So we have to be a little careful here because it is a double top. We are retesting the same exact level where we got rejected last time. So if I'm looking for an entry, I'm looking above 980 Satoshi's here.  That's pretty much it guys. That's pretty much the roundup of everything we've seen for this week. These are the levels I'm looking at right now for Bitcoin. These are the levels I'm looking at for Ethereum and all these other ones. I'm looking for the breakout entries and the support entries basically for my trades right now. That's pretty much it. Guys, make sure to follow. Please hit that follow button. Drop a comment. Tell us what you like about this, what you don't like. Give us some ideas what you want to see.  We're just going to keep on pushing. Tomorrow we have another video as you guys know. We try to drop 5 videos a week Monday through Friday. Good luck guys. Let's make some money. Peace and love.


Welcome, everyone. Let's get started here. Let me just connect this one. Yep. This is an infographic I got from the Multi-platform Trend Report from the Cloud Foundry website. What's really interesting here for me is if you look at the adoption of PASS amongst the enterprises, you can see 77% of PASS usage and 72% of containers, which is really interesting for me, because if you compare how many years ago PASS was there and where containers came from, 72% is a big leap. And 46% of serverless usage among enterprises. I mean, the people who have responded to this survey are IT decision makers who are saying that 39% of the respondents are using all these three types of abstractions. So what I want to talk to you today is about Cloud Foundry originally started out as a PASS and now has abstractions, which can run containers as well as serverless. So my question today to all of you is, is it possible to architect an app spanning across the abstractions in Cloud Foundry? So when I say abstractions here, I mean PASS, PASS, and PASS. So platform as a service, container as a service, and function as a service. So I'd like to start off with a little bit of my experience. When I started in the IT industry, there was only one way to develop an app. And it is a monolith. Every team member contributes to the same code base. And we never used to call it the monolith in those days. I mean, there were lots of them happening. And I just wanted to make an observation there that when we started building these applications, the focus amongst the developers, I mean, if you get into a development team, you can hear them talking about design patterns and code complexity, basically, because everyone is contributing to the same code base. So design patterns, we still have this book from the Gang of Four. I'm sure some of you would have read that as well. A 20-plus design pattern, some really cool object oriented techniques to use to have your code manageable to get a shared understanding of the code base across the team, right? I mean, that's where the focus was. And that's where the discussions were around. But soon there were some new architectural patterns that came out, like service-oriented architectures and then microservices, where we started basically taking that app and identifying the services, right, which make up that app and splitting that into different independently deployable artifacts. That's where the microservices world is heading. And then the focus changed from into domain-driven design, DevOps, platforms, distributed computing, right? I mean, what's really interesting for me is coming from developing monoliths during those years. And now nobody is talking about design patterns, having a shared understanding, because everyone is working on a really small code base, and all they're really interested about is what interfaces do these services expose. They don't care about what the internal implementation of those services are. These are just observations I'd like to make. Then another observation I had was innovations in cloud computing, basically driven by the cloud vendors like Amazon, Google Cloud, and Microsoft Azure, and also changes to the Linux kernel. I mean, innovations in the Linux kernel, the introduction of C groups, namespaces, and container technology in general has actually given rise to different abstractions called PAS, CAS, and FAS. So FAS is a platform as a service, as you all know. It's really built for running stateless apps. So when I say stateless apps, what I really mean is apps which have externalized their state. They don't do any kind of persisting to disk or any of those activities. And also, 12-factor apps, it's a set of guidelines how to make your app deployable on such a platform as a service. Then you have container as a service where it's ideal for running your commercial off-the-shelf products, stateful apps. For example, a stateful app would be if you're running some machine learning workloads. And if it is Apache Spark, for example, it does most of its compute in memory. But when there is no much memory left, it has to spill to disk and write to disk. Such kinds of applications can run, and it's well-suited for a container as a service. Even database, as you can see, many of the database vendors now containerizing their applications and giving it out for use. Then you have function as a service. These are basically you just push your function to the platform, and the platform takes care of running the function for you. I mean, basically give it a URL, maybe a Git URL, and it will take that URL, build the image for you. And when an event comes into the platform, it spins up the container or whatever technology it's using and to service that event. So why I wanted to set this up here is because I wanted to just show that we have got the choice of now moving these services into these different abstractions. So my question today is, I mean, how does Cloud Foundry really help you basically sprinkle a monolith into microservices? And then there are services within those boxes which are a good fit. Some of them are a good fit for a platform as a service. Some of them are a good fit for a container as a service and some for even-driven functions. So the next question I have is, so what does Cloud Foundry offer for a multi-platform world? I mean, how can you use Cloud Foundry to run such workloads with different abstractions? So this is what I was showing you earlier. So Cloud Foundry has something called as an application runtime, the CFAR, which is basically a mission statement. You can call it like the answer is Haiku, which says, here's my source code. Run it on the cloud for me. I don't care how. You just do a CF push and the application, the runtime, takes care of building the container for you, storing the container in a registry within the platform, doing your DNS, networking, security, and all that stuff. Even the load balancing is taken care for you. So you, as a developer, don't have to do anything about it. Then you have the Cloud Foundry container runtime, which was previously called Kubo, which means Kubernetes on Bosch. So it gives you an interface for you to create highly available Kubernetes clusters managed by Bosch. So on these two building blocks, you can actually have all your abstractions running. So that's the focus of my talk today of taking a demo app and showing how you can place the services which are well-suited for running on a pass, gas, and address. So that makes it multi-platform. And Bosch, if you're not aware, Bosch is a release engineering deployment management and lifecycle management tool, which has a component called CPI, which stands for Cloud Provider Interface. And that makes it cloud agnostic. So you could be running your path on the application runtime on Bosch. And Bosch might be deploying it onto AWS, for example. Or it could be VMware vSphere. Bosch abstracts out what you call the complexity of dealing with each cloud provider so that the operator doesn't have to deal with it himself. So it provides a good control plane for the operator to work with the platform. And that makes it multi-cloud. So you get a multi-cloud plus a multi-platform with Cloud Foundry. So this is the app we're going to have a look at today. What the app does is it has a few APIs and a UI, as you can see, which is deployed into a platform as a service. So given a website URL, the app is going to go fetch all the links from this website and then make a prediction as to what kind of website this is from a predefined list of categories. So I'll do a quick recorded demo of this. So since we are in Basel, I thought I'll do Basel.com, which is a tourism website. Let's see how it goes. So this is a recorded video for you. We'll get into the details of what exactly is happening behind the scenes and how this app has been deployed onto different abstractions. So the link has been submitted. What it's doing in the background, it's going and fetching all the links to the site Basel.com. So you can see the words. I mean, it does a little bit of stemming before it submits it to the machine learning module. So the fetching of links is happening on Function as a Service. We'll look into that soon. The machine learning prediction is done on Container as a Service, and the API and the UI is first done on the platform as a service. Let me just forward this a little bit. So it is predicted it as a travel website, which is pretty much right. So let's move on. I'd like to introduce you to this team, which is called the WebCAD team. So there are UX and API designers here. The UI designers are building their front ends. The API developers are using Spring Boot, Node.js. They need a Postgre as a Redis cache. They use a platform as a service so that they can go in and do a self-service kind of provisioning of their applications and their back ends. Then we have the data science team, right, with data scientists doing the machine learning work. They're doing their data science models for this prediction of the category of the website. Then you have the data engineering guys on this side who are responsible for ingesting data into their environment, right, and also building the data pipelines here. So this is how the stack looks up. The UI and APIs are using Spring Boot and Node.js. They're using their own Postgre and Redis. And that's running on the Cloud Foundry application runtime. And the machine learning team is using Spark, which is running on the Cloud Foundry container runtime. And we'll talk about the serverless and the event driven functions. It's using RIF, a plus K native. And all this is running on Bosch, as I mentioned earlier. Just so that I can set the context here, I'm using a vSphere environment with the Pivotal Cloud Foundry installed. As you can see, I have a Bosch director there and the Pivotal application service and the Pivotal container service. So the application service is running on top of, it's basically, Cloud Foundry application runtime. And the container service, Pivotal container service, is using the Cloud Foundry container runtime. So let's do the deploying of the front end API onto the application runtime. So I'll have to log into my VPN to do that. So just bear with me. Once that's connected, we should be able to push an app. Yeah, looks like it is connected now. OK. So the first step is I have this app here, which is just a Spring Boot app, which I'll be pushing it onto the platform, onto the Pivotal application service, which is running on the Cloud Foundry application runtime. So I'll just show you where it's running. CF target will show me that I'm targeting the API.System.PCF platform.com. That's where the platform is running. So I'm going to do a CF push. So this is the, here's my source code. Run it on the Cloud for me. I don't care how. Happening live. I've just pushed an app, which has a manifest, which is just describing that the name is WebCAD. That's the jar you need to deploy, and the name of the app as well. Once, as you can see, I've just pushed an app, and it's the build pack detection and creating a container or a droplet is taken care by the platform. And even storing that into a registry it's done by the platform itself. Now, this is the route for that app. I'm going to hit that. So this is the Apps Manager, which would be showing me the app here. So this is just deployed. I can click on it. If everything works, I should be able to see the UI. So that's the first part of deploying the application. Now, let's look at the second part of deploying the machine learning workloads. So this is done. Let's move on to the next one. Deploying the machine learning workloads. So the first step is to create a Kubernetes cluster using PKS, the command line tool. So I'll speak briefly about the PKS tool. So I'm logging into the environment. So PKS is a command line tool for you to spin up Kubernetes clusters, highly available Kubernetes clusters. So what I've done here is it takes 10 to 15 minutes to spin up a cluster. So I've done it in advance. What it's doing is as an operator you can go in and say PKS create a cluster. And the PKS API will talk to Bosch and then use the CF container runtime to create a highly available Kubernetes cluster for you. So once this command finishes, you have a highly available Kubernetes cluster. So that's what I've done already. The next step is for me to get the credentials for the cluster into my kube config. Then finally I deploy the machine learning workloads. I've done all this. Here you can kube CTL. So you can see there are a few namespaces here. You can see there's one called machine learning workloads where I have deployed my pod. That's where the Spark application is running right now. And if I look at my default namespace, there is nothing running at the moment. There is a link collector.  So deploying machine learning workloads with Spark 2.3, there has been native support for running Kubernetes. So you can now actually run Spark on Kubernetes. Final part is to run the function as a service. Before we get into that, I just wanted to talk a little bit about the landscape. You have AWS Lambda, Azure Functions, and Google Cloud Functions, which are function as a service provided by these vendors. You also have the open source function as a service. Now all these open source implementation have a build, scale, and eventing module. But the thing is they have slightly different implementation of this, which has created a lot of fragmentation and lock-in. So what a new initiative called Knative, which was started by Google with industry leaders, what it has done is it's a Kubernetes-based platform to build, deploy, and manage your modern serverless workloads. So it has the same serving, build, and eventing. Serving is basically scale to zero. Build is given your source code. It does source to image. Events is how do you manage subscriptions and delivery of events to these functions. So we'll be using RIF. It's an open source project from Pivotal. It stands for RIF as for functions. That's the URL if you're interested. Go have a look, please. It builds up on Knative, and is going to be the foundation for our future product called Pivotal Function Service. Now let's get into deploying these event-driven functions. So RIF, as I mentioned, uses Knative under the hood. So when you do a RIF system install manifest table, it goes and installs Knative for you under the hood. So the first step for me is to create a channel. I create a channel here with the link collector. So what it's basically doing is I'm creating two channels. These are Knative concepts. But since I'm using a Kafka bus here, it's going and creating an actual Kafka topic under the hood. So once I create these two channels, I'm going to have my function code deployed. So I'm saying RIF service create a link collector. And I'm saying that's the image you need to use when you start building the event. You need to start up this container. Now the last step is connecting the dots. I mean, you do a subscribe, RIF service subscribe, and say that, OK, link collector is my input. And whatever the cat's link collector service returns has to go into a different channel. Now RIF hides all the complexity of the yamls and all those things which you need to create and makes it really simple for you to build this pipeline. Now, so this is the RIF command line. So if I do a RIF channel list, you can see some of the channels which I have created. So what I'm going to do now is to show you the pipeline I've built. So I'll just explain what exactly this is doing. Initially, when I go and submit a link, a link goes into the link collector, which is picked up by the cat's link collector service. And it goes and says, OK, basil.com has 138 links, though the link fan out channel. And it puts those 138 individual links into the link fan out channel. The link fan out channel picks it up and puts it into the link crawler channel, which starts going and crawling these sites individually, the URLs individually. And finally, the cat's link crawler is putting that data into the link crawled channel. Basically, these are all going into a Kafka topic. The machine learning module, then there's another function which gets informed, which picks up all this data and sends it to the machine learning module and says, based on this data we have collected about this website, what do you think is the category which it belongs to? So this is the high level architecture of the app. As a user, you come in and submit a link to the web app, which puts it into a queue. Now the queue can be managed either as part of a platform as a service, because we have integrations with Service Broker and the Open Service Broker API. It could be managed there. Or it could be something running in your Kubernetes system as well. Whatever it is, those messages are picked up by the function as a service. So each of them starts executing it. And finally, the data goes into the machine learning app on this site. And finally, a prediction is made. And that response comes into queue, picked up by the web app and shown to the end user. So basically, you can see how you can place components, which makes sense for a particular abstraction in their own space. So normally, you shouldn't see anything running here. When you post a link, that's when something should appear here. So maybe I've kicked off something just before the presentation. That's why it's running. So with Knative, what happens is when there is no activity for a particular queue, the container is terminated. So if I just watch these parts now, normally you wouldn't see this cat's link collector here. But anyway, let's go and kick off a new web page. I mean, a new URL to. And then I'll switch to the console view so that you can see the event-driven functions starting up in response to the event. So you can see that the link collector is already there. So it has started up. And you can see the link fan out starting. And then the link crawler initializing and then starting up. So these are the event-driven functions which are happening in the background. They are going to crawl. And you can see those responses coming in in some time for all these links. So you can see that there are three containers in each part. So there is an Istio proxy. There is also a Q proxy. And you're actually user container. That's the business logic for your container there. So fortunately, this is not a recording. So I can't fast forward this. So it's going to take some time. So it's predicted it as a computer as an internet-related site based on the crawled content. OK. So this is the high-level architecture, as I mentioned. And finally, I'd like to just summarize what I was just talking about. So the initial question, is it possible to architect an app across these abstractions? Cloud Foundry, I think it is. I just showed you a demo of that. And the other thing I want to mention is choose the right abstractions for the job. Most of the time, when we go to customers, we see there are apps like APIs which need RDBMS or cache just used platform as a service. That's the right abstraction for running such workloads. Run workloads which need persistence and a lot of networking requirements on containers as a service. And even-driven apps which have these even-driven kind of patterns run it on a function as a service. With that, I'd like to end this talk. Thank you for your time.