Well, hello, everyone. Does anybody else notice the summer flying by? If you are here watching the show, we do the last Tuesday of every month. And so that means it's already the end of July. I'm not sure when that happened. So I'm Langdew White. I'm the host of the show. This is Cube Buy Example Insider. And what we do with the show is we try to interview people who are involved in the Kubernetes community somehow and talk to them about what they see coming next in Kubernetes. And why do we do this? Because if you've ever worked with open source before, you understand that product managers often do not know what is coming, because the challenge with open source is that even if somebody tells you you can't do something, you can spend your weekend and do it anyway. So as a result, it's often better to actually talk to the people who are actually involved. And so my co-host today is Daniel Oh, who you've all seen before, but Daniel would like to introduce yourself. Sure. Yeah, thanks for the rundown. Hey, everybody. Daniel Oh, I'm working for Red Hat as a developer at AboKid. And also, I'm really love to have some conversation with platform engineers and then SRE. I'm super happy to be back here. And then it'll be great time to have some conversation around the open data hub today. And Anish, do you want to introduce yourself? Yeah, sure. Hi, everyone. My name's Anish Astana. I'm a senior software engineer at Red Hat, who's been working on cloud-native things for five, six years now, street art college, actually. Last couple years for me have been focused mainly on machine learning on the cloud. So more from a platform perspective was this actual data science workloads, right? Like just enabling them, I guess is what I'd say. Most recently, I've been more involved with distributed computing. But again, that's pretty much so. And the machine learning we've asked. That's cool. And so one of the things we always like to ask people is, what got you into kind of the open source world in the first place? This is going to, well, this is not going to be as interesting as some of the answers you've had before. But I'd say three pizza. Red Hat had a firing event on campus. And I'd never heard of them before, that anyone machines used them to us and make the labs. I was like, OK, three pizza. I'll go check it out. I talked to some people that got an internship. And today, I can't actually imagine working in a company that doesn't do open source. I have friends, classmates, whatever, who worked at companies where they can't publish anything. Or you talk about this stuff, and that sounds insane. Yeah, it really corrupts you.  Like they didn't even want to let you retain the IP, much less kind of make it public. You always were doing it for a client. But after, I don't know how many years at Red Hat, I can't imagine working in the other way now. It's really quite interesting how much it changes your kind of perspective. So for everybody who's out there listening, if you haven't contributed to anything in the open source world, we recommend it. It really does make you a better programmer. It makes you better at playing well with others, those kinds of things. And you should definitely try it out. So the next follow-on question to that, of course, is what brought you towards Kubernetes? Yeah, so that's a great question, right? So boring answer, I interned on a team that was the OpenShift Business Unit, right? So we ended up just reading Kubernetes there. Over time, I've actually started seeing the value of Kubernetes initially. I was like, OK, what is this thing, right? Like we have virtual machines, so why am I bothering with it? But like, as I spent some time running an internal platform actually for Red Hat, the methods of containers reached out to me, right? Like being able to, Kubernetes is a state that has so much stuff for you. And it made at least my operations much easier than what it previously used to be. You know, I don't want to turn this into a talk of like, oh, yeah, you know, here's what you should use Kubernetes. Yeah, yeah, yeah. I don't want to be playing out for it, but I do. Yeah, well, I think that's a common experience, right? Especially if you start out in containers, you know, kind of working with containers, you quickly realize that not being able to orchestrate them is really a pain in the butt, even on your laptop, right? And so that basically explains the popularity of Docker Compose, right? Is that huge makes it so much easier to kind of orchestrate things, but then as soon as you want, it's something a little more complicated, right? That's when you kind of step into things like Kubernetes. But yeah, it's kind of cool. So I know you primarily are responsible for what's called the open data hub, or that team or whatever. Can you give a little bit of background on what that is? Yeah, sure. So I'll give you kind of the whole historical context behind it, right? Like when I joined Red Hat full-time, our team was working on running an internal platform for machine learning workloads. Previously, you had lots of different teams running their own sort of data platforms. And it just didn't make sense, because I mean, having on bespoke implementations. So we start building that out and already on Kubernetes. Over time, we realized that these problems are actually relevant to the community, right? And Red Hat does open source. So we said, OK, let's throw this out and reopen. And I realized I haven't actually talked about what it is. It's a machine learning platform, right? So kind of the primary idea behind it is that with any machine learning workload, like you don't have just data science or a model that's being served, right? You have a lot of different things that go into your overall workflow, right? You have to have your data ingestion. You have to have data storage. You have to have some way to automatically run workflows to clean up data, to train your models, right? For your actual data scientists to experiment with data. And you can get a scientist anyone, right? Like, you spoke around the data, come up with models, and you're on the train them, you're not served them. And in what you've served your models, that's not it, right? You have to wonder your models. You have to make sure that they're sort of behaving as you expect them to be, right? Like, if they're hitting their target metrics, whatever that may be. And that's really a pain in the butt to run, right? It's not easy to set up. And there's lots of great open source projects that do portions of it, but like sort of running it in one unified story is like, it's not for challenging. And that's really what the open data hub is doing is that we've seen like, here are some more user stories of workflows that we think should be supported well. We've looked at open source projects that are kind of very popular for those use cases, right? So like, you have to put in notebooks, right? How do you run that on the cloud? You have keep so pipelines for running repetitive workflows, right? You have that on all these kinds of things, right? How do you build them, bring them together into kind of a cohesive user experience, right? Because I look today, if I was to install each one individually, my users would hate me because the experience is not the same. I think UI is very different across the board. So how do you bring that together to something that's easy to deploy and then also easy to use for data scientists, right? You don't want to have people learning new UIS constantly and then going back and forth between the two pages, you're gonna make it, like, sort of easy. A lot of words. Any questions on that? Yeah, I got one question, maybe. Yeah. And then you're muted, I think. I was muted, yeah. But go ahead, Daniel. Yeah, so I got one question actually. Yeah, I can't read it as like a developer perspective. So if I understand currently, it's often data hub, like provide some of the AI platform and then data scientist stuff. And then as the developer standpoint, I'm just wondering, is there any kind of like a consumable API for developer to use like some AI, like a specification implementation? For example, like a generated AI, like a chat APT or co-pilot and then things like that. Is it some kind of platform to generate AI machine learning? However, like a developer is some user perspective. I wanna use the kind of API as a consumer rather than producer. So yeah, I'm just wondering, yeah, is there any kind of such a thing in the open data hub, like some feature or service, even if it's some kind of roadmap? Yeah, so the open data hub is like, there's an operator for it, which we're actually re-architecting right now and I can probably talk about that later. And sort of to your point, right, about like consuming or interacting with APIs, you can serve models using the open data hub, right? So as a user, could train a model or a producer, could train a model and serve it up for anyone to interact with and use. We've had a number of very interesting use cases come up where people have sort of models and they have lots of other APIs trying to sort of wrapping that and then interacting with the models and getting inside from them. You may or may not have seen some demos actually at Summit that involve the open data hub. Yeah, I saw that, so yeah, perfect. So about what you're, I mean, to further answer kind of Daniel's question, right? So the idea is that if like you have an organization and you wanna offer some of these models essentially to your customers and those customers might be like internal users or it could be external users, but the idea is that here's a platform that is going to, because one of the challenges I think is that you often have data scientists who are doing the work of kind of creating your AI or whatever, right, your models and they are not generally speaking trained as like total software engineers or especially not kind of operators, right? And so how do you make that model available that isn't, oh yeah, download this model and run this Python script on your command line, which I see a lot at BU from students is that, oh yeah, it's all done, it's all deployed and everything and you can just use it. All you gotta do is download this 50 gig model and install Python and these libraries and run it and it'll work just fine. So that's really the kind of problem that open data hub is trying to solve, right? Yeah, that's part of the problem, but yeah. Okay, so what I was gonna kind of ask a little bit more for background is like, so if I'm kind of an organization and I'm running some of these models or whatever, like what do you think the trigger point is where it becomes worthwhile to potentially deploy something like open data hub because it's like I was saying with containers and Kubernetes, right? There's a kind of a threshold you get to that says, oh, now you really need orchestration before it's gonna really burn you. Do you have a sense of where that is in like with open data hub? That's a great question, I'd say, right? Like so I'd say one thing that we've really tried to do is make it as easy as possible for you to set up or install, right? So if you have cluster admin or if you know some of the cluster admin on a cluster, you can go ahead and just install it today. Speaking personally, I think it's when I start, when you start working with more than a few people on your project, right? Like everyone has different ways of like, okay, you know, I wanna deploy work on my models, right? Yeah, you can work on, do data science work on your own machine. Probably shouldn't, but you could, right? Like when you start getting into a team that's more than a couple of people that has a shared environment where they need to be running the notebooks and then they need a consistent way to solve models, right? Like where you're out of like the student project, the OC stage, do like something like, oh, you know, like this is something people might actually care about or where you are working on a problem that people care about. I'd say that's right, my doors, right? Like, if today I was, I knew nothing about open data hub and any new data science, probably, okay. But I know a little bit about it and I am aware of all the different problems that can come up with a data science project. I think it makes 100% makes sense to use it. So what, but what I'm hearing is that it's kind of, it's almost people-based rather than problem-based, you know, in the sense of like, you know, like I was saying kind of with Kubernetes, like the trigger is that you can't keep track, right? Whereas it sounds more like with open data hub, it's more collaboration enablement, you know? So if you got two people sitting in the same room together, maybe you don't need it. But if you got, you know, five people spread around the world, it's probably a different story. So I think that's always a good thing to kind of know, right? Cause then you can have an eye out for the triggers when it might come in handy. So I wanted to ask next, what, you know, what is the user type you're really looking to support? You know, who's your primary customer as it were from a user persona perspective? So I'd say like there's really two main ones, right? One would be your platform administrators who don't wanna go crazy, right? Like running all these separate things is challenging, really challenging. So we wanna make it as easy for them as possible, right? To manage, you know, like what resources are available to their internal customers, right? What RBAC is set up, you know, those kinds of things, right? Or even what projects are available for people to use. And that's kind of like the boring backend side of it, right? But then from a user perspective today, I think I'm gonna focus is data scientists, right?  Like, we don't wanna have people forced to do that. We don't wanna force people to go into command lines, like mess around with things like that. And we wanna like really focus on data scientists using workforce that we're familiar with, in a way that makes sense for them. And we don't wanna force people to do things that don't really align with their skills that. Why are you only saying data scientists, right? Like a data scientist who doesn't care about something underlying, so. Yeah, there is just one case. I mean, so a lot of people heard about like a new role and responsibility around the AI platform, such as like a devil's engineer and then data scientists and then like a data engineers. So would you really explain what kind of responsibility and role for each like a data engineer and like a data scientist in terms of how they can use this open data hub with some like some features or like some specific role and responsibility. Yeah, and I can probably even talk through some projects that I've seen happen recently doing that, right? So the assigned is just the easiest one. So I'll start with that.  You look at the data, right? Like you go into your notebooks, you wanna access the data that's stored somewhere, you experiment with it and then you try running some models on top of or training some models using that data.  Data engineers would be more focused on keeping the data stored efficiently, right? Data ingestion pipelines, you know, you may have workflows that you're running regularly to compact and clean data, right? So you may have data coming as like JSON daily or whatever, right? And then you have a job running every day, every hour, every week, which compacts all the data into one single RK file, right? Or something similar. So that's where data engineers will focus. I have a really nice slide talking on somewhere else which I should have brought with me today, but like really clearly demarcates like where most guys would fit in. That means that like, you know, that's where they primarily play. That doesn't mean that's where it ends, right? Like you may have data scientists, right? Pipelines that will, pipeline workflows are used interchangeably. You may have them write pipelines that will regularly learn to retrain the model, right? Like they say, okay, the model is like this. We've had X new data come in. Let's make sure that the model is being kept up to date and trained. And you mentioned like the DevOps I think don't know is also ML Ops engineer, right? And they're more worried about like, sort of that bringing more software engineering and that practices to the full cycle. Personally, I feel like it's kind of just like, you know, just another term people have paid off, right? Like it's latest DevOps engineer, but you like you're just working ML Ops machine learning. That's because most people have DevOps engineer title, DevOps engineer title don't actually do DevOps. That's kind of part of the problem. Yeah, that's a big project if you haven't already. Yeah, yeah, sure. Yeah, that's a really good to know because I've been asked a lot of specifically, I mean, a lot of people actually ask me around the AI platform and then AI technology. And then they really looking forward to some, like how to change processes and then organization when they adopt AI platforms, like they want to create a new role and responsibility rather than just like a fancy DevOps engineer title. Yeah, that's really good to know. Thanks. And then your website reliability engineers, right? Like where did the lines and titles end? So that's why they really want to like bring up new AI workflow as a part of the organization. And then they need to also need a new role or like some responsibility to handle that. That's really good. So I guess you're kind of wondering is like, is this creating new specialties that are needed inside of an organization or do your existing skills kind of apply? Like, so like I did a master's and like with a focus on machine learning, right? So I think having some beta science background has helped me and got involved with all the work I'm doing, right? Like Ina's has run into a platform because I was able to at least at a high level, right? Like speak the language, there's a means for speaking, right? Like they'd say, oh, you know, like, we're trying to do this thing with this model, right? And like here's where it's breaking or you know, it's like high level terminology. Like being able to understand that helped me develop solutions the right way for them. Where that makes people feel like, hey, you know, like I'm trying to do something by torch or blah, blah, blah, I could like train this model and like it isn't working. And I wouldn't start competing from scratch. Like I'd be at a jump start versus someone who had no idea what's going on the data science thing.  Like you need to be able to pick up new technologies and like data science, but machine learning is just another evolution of that. So kind of related to that. So what do you consider your job? Do you consider yourself kind of a, I mean, not what you were trained for or what you could do, but just like kind of, you know, on your day to day, do you consider yourself more a data scientist? Do you consider yourself more a software engineer? Do you consider yourself more like a, you know, site reliability engineer or an operator, I often say for short? So today I'd say more of a software engineer. If you'd asked me this, like what are two years ago, I would have said operator or site reliability engineer. And recently I was being focused more on like building out the upstream and open source community around the open data level. And so with that, like I've transitioned more over to like just developing the project. One of the nice, really nice things that I'm gonna work on open source and then like in this space is that there's a lot of projects, right? There's a lot of communities with a lot of smart people in them that I get to work with and contribute to and then like learn from on which and like doing the open source horn, right? Like... Yeah, what they used to call like a force multiplier, right? Yeah, because of all the terminology that we use in tech comes from the military. So you said you were in the midst of re-architecting open data hub, like why? And what are you hoping it's gonna bring to the table? Oh yeah. So the open data originally started as a couple of fan scripts we ran to like spin up some pods on Kubernetes. This is like five years ago, right? Like, oh, like, here's this thing called operators. We should do that. And they had something called Ansible Operators. So he said, okay, this is gonna be the least amount of work for us to do. So let's just make an Ansible Operator. Well, I agree with you to get familiar with operator concepts. Ansible doesn't give us a lot of flexibility with what we want to do, right? Like, was this just having something like a goal board? So after I said, okay, like, that's me, this is a go-getter operator. We did that and we may have taken a lot of like that Ansible mindset over and do like, oh, you know, we'll just do all the things and like make something called a meta operator where we try to apply lots of different things and like keep our platform as generic as possible. One thing that really hurt us there was that we could do a lot of things okay, but like we weren't being able to really do anything well and like really focus in on like doing something very intelligent with like Jupyter Notebooks or with anything, you know, any sort of special logic. The most recent like iteration of this re-architecture now, which is ongoing is we are really bearing down the components that are deployed by the open data hub as core components and we're saying, okay, here are like really the core technologies that we think you should care about. Here's all this other stuff you could deploy and like that would be kind of nice to do, but like we're not gonna worry about that as a core. And by doing so, we're being able to invest more time and doing things intelligently, right? Where if you need advanced logic for dependencies or anything of that sort, we can do that. Yeah, sorry, I'm very excited about this re-architecture actually because historically we've really struggled with how many different things we were trying to do and now we're trying to do a few things better. Gotcha. And so they, and so how do you kind of see those things going forward? Like is it, you know, for lack of a better term, are you considering something more like a plug-in architecture, like a way to, for other people to participate in a formal way in a sense? Yeah, I'd say so. So, again, in the past, right? We said, if someone said, oh, we should go to the open data hub, it is a yes, and then we'd be like, okay, yeah, sure, we deploy it. Now we are sort of, we've built out a more formal community and sort of acceptance process, right? For like these plugins where people can compose them and like the relevant sticks for like, you know, ABC feature and then why we should care about this technology and then we will work on either tightly integrating it with the operator or like I said, plug-in architecture where they can plug into the UI and like have some sort of likely supported, supported, you know, we used to use the technology. So a mix of both, right? Like you'd probably start off as like a high level plug-in and then really nail that on for time. I gotcha. So, you know, I'm gonna ask this question kind of twice in a sense, but so both now and post-rearchitecture and I think the answer may be different depending, but basically it's like, can I run this on my laptop? Is this something I'd want to run on my laptop in the sense that, you know, if, especially, you know, I come from a software engineering side, so I sometimes have a hard time with doing the data science side because I'm like, I can't, I can't, you know, like I can do something in a Jupyter notebook, but ultimately it'll be in straight Python at some point. So I guess what I'm kind of wondering is like, can I, would it be worthwhile, especially as a training exercise to kind of set it up on my laptop so that when I'm going and working on this stuff, when I want to deploy it to production, right, or I want to throw it over the wall or, you know, start moving it into something that's not a toy, is it something that's got the, you know, is it from a resource requirements perspective, something I can run on kind or something on my laptop? That's a loaded question. And if you have a really, really kick-ass laptop you'd run anything on it. Yeah. 60-30. Exactly. You could run it on your laptop. As long as you're not going to be on your laptop, right, like it's just an image that you deploy on. Now, depending on how many differences, like portions of the open data have you're running, you know, you'll quickly run into resource limitations, right, like if you say, okay, I just want notebooks and the open data, we can do that easily. If you're just UI and notebooks, okay, fine. I said, okay, now I want to do pipelines and you're gonna do model serving. Then you're gonna start struggling on your laptop. My personal opinion is I probably wouldn't run it on my laptop, unless again, like I'm actively developing and like I don't feel like going through like a Kubernetes cluster or kind of whatever. Yeah, and this goes back to the earlier question, right? It's like it really makes sense when you have four or five people, one person is even less than that, right? Yeah, yeah, exactly. I got you. Do you think the same will be true post the rearchitecture? Or like with the, and if it kind of comes down to, it's people-based, right? Like, you know, maybe it's unrelated to the kind of the, you know, software overhead as much as it's just related to, it's not bringing a lot of value for an individual. Yeah, exactly, right? Like that's that's it. Like it's not bringing a lot of value for an individual. Okay. Which is why I'd say running on your laptop, probably overkill.  But I don't know everything that I need to deploy and run for it. If you're to just install the open data hub, you get all the core components that you need for a machine learning use case, right? Like all of our projects. So kind of from a learning perspective, it could still be useful for you because, you know, coming in, you don't know what you don't know. And open data hub can sort of help you with at least some of those problems or questions. Right, kind of knowing what the questions to ask are. Yeah, yeah. Exactly. Yeah, that's interesting. Yeah, I mean, that's one of the nice things about kind of pipelines in general, right? In a sense is that they tell you a lot about how stuff is supposed to work, assuming somebody who knew what they were doing set up the pipeline. Exactly. Which is, you know, always a question mark.  And so that's why I was wondering if it was like, if it made a lot of sense or if it was more of a independent thing. And it sounds like, I mean, you know, what we should really do, right, at some place like Boston University, right, is like have kind of central IT set up an open data hub. And then, you know, students or whoever could go and use the feature set, but it would be managed at a kind of institutional level. Yeah, exactly. And even if we're not working together, right? Even if we're working on individual projects or whatever. Yeah. I mean, I think that's one of the things I, I went to BU, like Boston University as well, right? Like, and that's one of the things I found most lacking was sort of like this sort of centralized infrastructure that I could really easily interact with for my data science classes, right? Like we had a lot of like that old school soft engineering stuff. But I was like, oh, I jumped into notebooks and like you're working in teams now and good luck. And I was like, ah, I barely know Python. You're asking me to like do these kinds of things. I've actually heard of some like people using the open data hub for classes where someone set up a cluster and they even set up race through resource requirements, right? They found that the students don't need to have like digs and gates of memory. So like I said, very small things and aggressive frameworks that would abuse the platform. And they found it worked well for more aggressive teaching. One of the things that I learned recently is GitHub code spaces. You can actually have GitHub code spaces use Jupyter Notebook instead of VS Code. That's neat. And you can set it as your default preference as well. So I might be using GitHub Classroom combined with GitHub Jupyter Notebook code spaces. I don't know what you call it for one of my classes in the fall to kind of solve some of that same problem. It's GitHub, so it's not open source, but it is a really nice way to get a Jupyter Notebook. And for me, it's also a good way to do like assignment distribution, which kind of leads me to my next question, which is what does open data hub or is it just not a problem that it's trying to solve? What does it do about the data, right? Like one of the huge challenges you have, particularly like I said, coming from the software engineering side where everything is in source control, except for the most important thing, which is my data. So does open data hub try to start to solve that? Or do you have any recommendations to kind of data scientists out there or software engineers out there who are working with a lot of data about how to do source control and not check in multi-gig files and to get? I don't. So this is something we are actively thinking about.  I think one of the projects I'm doing is Backadorm kind of a project or company, right? But like there are open source communities and projects around that stuff. But it's really hard, right? Like I have seen anywhere from like someone says, okay, you know, we have different folders with different versions on them. Do like something, you know, like APIs that built out around the storage, right? So like you have your S3 or whatever compatible storage and you build out an API on top of that, which has a versioning for you. I would say this is something like the open data hub is actually actively thinking about it, sort of versioning both from a data perspective and then also from a model perspective, right? You may have different versions of your model over time that are trained on different data sets or just with different parameters and how do they, like how do you keep track? How do you version them? This is somewhere on the roadmap for the next year or two. So if you're interested, state you and nor. Anthony? Yes? Well, one we're looking at if you haven't heard of it is called a DVC or data version control. And I think the idea of it is it kind of more integrates directly with your source control tools. My problem so far as well is I haven't found a particularly good solution that doesn't require both a server and client and getting something onto a server. A lot of the time is the challenge. So that's not as useful, but it is definitely worth checking out. I'll throw that one in the chat too because it's been around for quite a while and it does purport to solve some of these problems. And that's not something I've heard of. Admittedly, I have not spent a ton of time thinking about data versioning recently, but I can almost imagine whether my team members are screaming at me from behind this screen. What do you think? Exactly. Yeah, it's just, it's a problem for me a lot. And so, it's one of those things I've been, the only, even just for like a student class, right? Like the data is not privileged or anything, it's just, it's big. And I wanna stick it somewhere that I can make it accessible to the students without having to mail around a zip file, you know? And so that's, you know, there's various ways to do that. And, but I haven't found any particularly nice ones. And that's why I was kind of asking the question. So, Daniel, did you have another question you wanted to ask or? Yeah, for sure. Yes, as always, in case a lot of people are willing to see OpenData Hub after this, like a KB Insider, in a way to best like a practice or a process is for like a developer and the data scientists, even if like a platform engineer in terms of contributing back to this OpenData Hub, for example, creating a new component or something other stuff. Yeah, so lots of different ways, right? Like the first place at point, anyone that is Slack, like we have a community Slack for the OpenData Hub where, you know, the entire team lives, like the entire community lives, right? We've had people sort of join in from there and find things that they're interested in, right? Like it doesn't make sense for you to just throw stuff over linearly. Can they add a hashtag with the Anish in there when you're asking some questions to the Slack channel? Yeah, I don't know. Yeah, definitely. I can always directly add someone who can actually answer your questions. But no, yeah, like I understand, like I think the Slack is quite active. We also have in like a GitHub pages and like the community sort of like, yeah, with the OVH community repo, which I will draw in here as well. But I think the main place to interact with Slack, over there we have... Maybe there's the Slack invite link to the... Yes, that's, I have to monitor something desperately putting that up right now. Is it on the OpenData Hub, like website? Yeah, it should be. I can't figure out how to type it into the chat. I can do it if you, yeah. This is our place, right? So one of the things we've recently started doing is like we've established SIGs for all the different spaces that the OpenData Hub is staying in. Signature stands for Special Interest Group. So we have something that's focused on developer experiences, kind of the front-end sites. We have something for platform, which is more generically applicable to the entire OpenData Hub. Then we have stuff around the different working groups for like different marketing components. For more and so, we will provide links. That would be awesome. There you are. You guys just want all the links. Yeah, I just find out some... There are the community meetings, which is really awesome. So everybody interested in helping in the meeting, in the community meeting, they can ask any kind of question and then they will find out some of the opportunity how to contribute back to the community. In the end. Yeah. Yeah, I think some of the great things are open source, right? Like we do everything out in the open and all of our meetings are out in the open. So, anything you understand, you can do. Yeah, that would be awesome. You know, also, I can't resist to ask these kind of questions because I've got to ask some of the Reddit customers, they actually watch this KBE insider online but also like watching, we call the video as well. And then, so some of the people are actually haven't heard about like a push to AI. It's something like AI, like an enterprise product environment. And then, so can you really explain what is the differences between open data health and then like a push to AI, which is similar thing, like AI standpoints? Yeah, so I can also, so then like cut me off at any point, I didn't do too much detail. So, we have the open data, which is the community project, right? Like anyone can participate. And then, it's actually like we built out a downstream like Reddit product around it called Roads, which is Red Hat OpenShift Data Science, right? I think that's actually what they then went at some it was using Roads for a lot of these use cases that I just talked about. So, that's gonna take me an often today. As we've gathered more, as more interest has come up, we've sort of, I say we, right? Like it does mean like a sort of rebranding of sorts to something called OpenShift AI, which is broader than Roads, right? Like Roads is like, okay, I want to open data up. I want the whole kitchen sink, but not every customer wants the kitchen sink, right? Like, there may be someone who wants just pipelines, right? Or they want just models, so then that's fine, right? So, what do we call products under that umbrella? And those will be, you know, whatever they maybe call, right? But like that umbrella itself is OpenShift AI, right? And so- That's really good to know. Is, sorry, is the, is it often, offering models or is it helping you build models or? Oh, helping you build models, right? Like we, I don't want to say anything too prescriptive, right? Like we don't, or as far as I'm aware, we don't offer models or like train models for you or anything of that sort, right? Like our focus is more so on the platform level where we allow you to build your models to build and serve your models and all that. Okay.  Like because there's open data hub, right? And roads, is there something equivalent with OpenShift AI or is it kind of like the same thing in a sense? I'd say, speaking as just an engineer, right? I'd say OpenShift AI at compare more to open data hub or like the open data hub organization or community, right? And then within that, you may have specific components and then one installation part of all of these components is roads, which is install all the components. So I'd map OpenShift AI to open data hub and again, like all of this stuff is upstream all the way, right? Like we, everything happens out in the open. So there are no other organizations I'm aware of. All projects. Yeah, yeah, yeah. That's why I was just that, you know, because it has OpenShift in the name. That's why I wasn't sure if it was like the downstream product that there would also be, you know, some sort of upstream. Yeah, it's all open data hub. I'd say it's the upstream for everything. And then just at Hawaii, the OpenShift AI is a brand new name of OpenShift Data Scientist, aka Laws. So OpenShift AI is a brand new name of OpenShift Data Science.  So that's why we changed the name from Laws to OpenShift AI. So, but if I wanted, you know, if my company went and bought OpenShift AI, right, and then deployed it or whatever, and I wanted a new feature to show up, I should go contribute it at OpenData Hub.  But that's how that's the path that I would get my feature or my bug fix or whatever to land. Yeah, I'm understanding. Everything goes through OpenData Hub for us. Right, gotcha. Okay, that's why I was, that's what I was kind of really asking for is like, you know, if I, if I want something to show up in OpenShift AI, how do I, how do I make that happen? Right? Well, first you create an issue for it on GitHub and then you, and then the maintainers have to, but yeah, and that's, you know, yeah. Right, right. But it's on OpenData Hub. Not, there's no, that's kind of always looking for. Okay, that's cool. And so what, what do you think is gonna be, you know, the next big change or new feature or whatever, you know, the next three to six months that you think is most compelling, like that's the most interesting for you, not necessarily for your users or whatever, but for you, what do you think is the most interesting thing that's coming? Yeah, so one thing I already talked about was the re-architectural of the operator from a developer standpoint, I'm very happy, like very excited about this because it just makes our lives, all of our lives a lot easier. And then on something I'm personally working on is sort of distributed workloads functionality for the OpenData Hub. So, you know, this is in all the projects using like gray and something about old-flare. And really this is offering capabilities for like your data scientists or your data engineers to spin up jobs and like, you know, jobs scheduling and those kinds of things, but like he's actually great. For those who aren't aware, Ray is comparable to Spark, where it's kind of like just distributed about the Spark, not the Spark. That is comparable to, you know, this is a distributed engine, right? So you can submit a job, it's going to have like six different walkers and it'll like schedule stuff onto them. And Ray is kind of a more Kubernetes-needive Spark. At least that's how I personally think of it. That's cool. Yeah. I'm also looking for the link to Ray to drop in the chat. And yeah. So there is the Ray project. Yep, that's exactly, yeah. So something else to check out. I swear to God, Spark is like the most common name for anything. You know, there's even a Boston, there's an organization within the city of Boston that's also called Spark. You know, so. Really? Yeah. So it's like, it's everywhere and we always have to clarify. But yeah, I totally hear you. I mean, right now there's a comparison with BU Spark because I was talking to some of the other day and I said, oh yeah, like I'm working on this thing called Ray which is cloud native Spark. And they're like, what the heck does BU have to do with what you're working on? Yeah. Yeah. I normally have to clarify the other way. You know, so most of the time. The particularly entertaining thing is I'm actually going to offer a course in the spring that is going to cover Spark. So that should be entertaining. Well, I should talk about Spark. You should do it with Ray instead. Yeah, yeah. Well, maybe I should. Then I wouldn't have the name collision. Yeah. So, Daniels, did you have any more questions you wanted to ask? Not really. The Anish touch a lot of stuff. I'm really interesting. And then curious about the AI platform stuff. And then, you know what? So I'm more like a developer side. And then I'm really wondering, everybody's saying like an LLM and then like a JNAI stuff. And I'm really looking forward to like an open data hub, like some core key role, make it a more JNAI platform, like as a part of the Kubernetes native stuff and to probably like some cloud native platforms eventually in the end. Yeah. So I would say like the open data app today would probably support those kinds of workflows, right? Where we can help you build out your LLM, right? Like if you're not trained like a large language model and so we're through open data hub components, you could do that. I guess Daniel, you're more afraid to like just haven't generated like those capabilities built into the open data hub where it does something machine learning E for you. Like. Well, I think for me, at least it's a little bit of both. I mean, one kind of related question is like, you know, have you considered how you might integrate LLMA, you know, because they're quasi open license, you know, into open data hub such that when I, you know, if I wanted to check that box when I install open data hub, I could now offer LLMA as a service to my, you know, to my users, whatever that means, you know, or any of the other tools like that. That's definitely a very interesting idea. Not thought about it at all. Like all I've done with LLMA is like, okay, like, there's a website I can go to now and like, type in my questions and then have a fixed time for me. So that's. Right. Yeah. Yeah. And I totally hear you.  One of the things I go back to is like, there's a project in Apache land that's been around for a long time called Mahoot, right? And it's a bunch of basically crowdsourcing AI, like tools that you can kind of just use, right? And they're not the model side so much as like, you know, to kind of, it's the whole kit and caboodle to build one, right? They don't have the pain level of like an LLM from a, you know, how much, you know, data they need and all that jazz, but it's kind of a nice packaging, right? It's kind of a nice way to just kind of say, oh, you know, I just go here and I kind of look for the one I want, right? You're the kind of thing I want to use, right? And then I can kind of go off to the races. That's something interesting that like, again, we've talked about with some customers has been around like foundation models, right? So what happens is like, what a foundation model is, you have a company like IBM or Google or something who has all the money in the world, right? Like they just go and train like these massive, large language models, right? So like, for example, and it's great, I'm like, okay, I have a model that I've taught English, right? So it understands English, if I ask you something, you can answer that, right? That's all it does, right? Like it's in most languages. Now, as a customer, right? Like see if I am in the business of, if I sell a product, right? Like I can take this foundation model, which knows English, I'll give it all of my data so it understands my specific use case and then I can create a chat board around it so that it can answer questions related to support for my product, right? So if I have, I don't know, open shift, right? Like I can give it all the open shift customer support articles and then it can start answering questions about open shift, my comments, open shift issues. So, and the process of getting it from like, that base model into something that works for you, it's called fine tuning. And that's actually something we are tractable infrastructure for our support for the open data hub is like, okay, we'll be made those when they will show us more and easy. Gotcha.  And then, and kind of that would be the beginning of the pipeline and open data hub. And then I would write my code, whatever that means exactly, and put it in in the fine tuning layer and then out the other side, I could offer an API that was for my chat bot or whatever. Yeah, something like that, right? Yeah, exactly. Yeah. Yeah, that'd be very cool. You know, like I said, kind of the, you know, one of the things I actually really miss from kind of the really old days of open shift, but it's kind of sort of similarly there now, it's kind of like the offering of other services through like, you know, kind of marketplace, you know, an open data hub marketplace of those kinds of things might be also interesting. You know, especially if they, if you could, you know, you knew what the status of their, of their licensing was, for example. Yeah. But on that note, and before chat GPT just takes over the rest of the show. Thanks so much for coming and maybe we should wrap it up here. Yeah, because we are already in a ball scenario. So. Yeah, exactly, exactly. Yeah. So Anish, thank you again seriously for coming. We appreciate kind of getting more, you know, kind of understanding, exploring the space around Kubernetes and how it's growing. And, you know, for all you data people out there or anybody who needs to support data people out there, you should check out open data hub. And yeah, and we'll see you next month. Hey, thanks for having me. Thank you.


Okay, traders, welcome to today's live analysis session with me, Patrick Munley. Before we get going, a couple of things. One, if you can hear me and you can see the Tickmill welcome screen, if you could type a Y in the chat box just so I know we're ready to roll. Thanks for that. Second, with respect to the day session, I'm going to cover, I've got 26 charts, if we've got enough time to get through today, and if you have any questions or you want me to take a look at a chart that isn't on my watch list, then please wait till the end. I'll open up a brief Q&A session, which will give you an opportunity to ask any questions regarding today's content or any additional instruments you'd like me to take a look at. So before we get going into today's discussion, important to adhere to the risk disclaimer, the most important thing for you guys today is that the views expressed here by me today or any opinions regarding the charts or the setups are solely mine. They are not indicative of or representative of those held by Tickmill UK Limited or Tickmill Europe Limited. Okay, for those who are here for the first time today, I'm just going to give you a brief introduction to myself. After I graduated from King's College London, I joined a city PLC consulting firm after a few years of learning the ropes. I left and went on to successfully co-found and then exit a consulting startup in late 2004. I then moved on to explore my passion for markets.  And so I started day trading or more appropriately probably day gambling the S&P 500. And after some early beginners' luck, I started racking up some solid and then some quite significant gains. However, as is often the case, the beginners' luck ran out. As the market phase changed, I basically began to average down into what we're going to prove to be significant losing positions. I ultimately gave back all my gains and took a six-figure hit on my personal capital. Say this was a gut wrenching and severing experiences and understatements. I really had to stand back and figure out if it was feasible for me to make a living from the markets. So I decided to look around and see if I could identify traders who were doing that. And I sought out a mentor with an excellent trading track record. I worked with my mentor for a period of 18 months to two years. And during this time, I upped not just my technical game in terms of researching and developing a strategy that importantly suited my personality. I extensively back-tested and forward-tested that strategy and developed a rigorous risk management approach to underpin it. But most importantly, during that period of mentorship, I significantly developed my mental game. And probably the most important watershed shift I made was from being a highly goal-orientated individual focused on financial gains to becoming a purely process-orientated individual. So what does that actually mean? Well, it means I had to stop focusing on what I could make from the markets and had to start focusing solely on managing my mindset to allow me to consistently execute my trading strategy, oftentimes in the face of negative feedback from the markets in the form of losing trades. Once you become process-orientated and have a professional trading mindset, and you understand the true nature of trading as being a numbers game in which you're simply playing the probabilities, you lose the emotional investment and hellish emotional rollercoaster of living and dying by the outcomes of individual trades. I'm no longer concerned with the outcome of individual trades or even a string of trades. My focus is on the next 100 trades because I know if I focus on excellence in execution, my edge will demonstrate itself over an extended series of outcomes. My multi-strategy approach has delivered profitable annual returns since 2008, since 2013. I've also been managing investor capital through my managed account service, delivering annual positive returns. You can actually see the performance data on the screen there. Since 2010, I've personally mentored over 100 private traders of all experience levels from complete novices to former CME floor traders in developing the technical and mental skills to reap consistent returns from the markets. I've also consulted for numerous brokers and trading education brands, contributing written content, webinars and live presentation content, a range of topics from market analysis to trading strategy development and execution. In addition to my fund management and private mentoring, I'm also a resident market expert at Tick Mill, providing not just these webinars but also a daily technical market outlook and a chart of the day or a set of the day. I also provide some interactive videos in terms of setups I'm tracking in the markets. You can go on to the Tick Mill blog and you actually sign up to get these updates directly to your email via their website. My other passion project is that of head of trading and trader education for a leading trader education brand called FXcareerswap.com. We offer development and more importantly funding to retail trading towns. FXcareerswap, we don't just develop retail traders market and trading strategy knowledge, we work on mindset development and through a structured program that culminates in managing the firm's capital at zero personal financial risk on a profit share basis. For those that are interested in learning more about FXcareerswap, you can contact them directly by phone with number here or you can drop them an email and the guys in London will be sure to give you the relevant information. That gives you a flavor of where it is I'm coming from. Let's jump into the charts now. Like I say 26 charts to run through and we will look at some potential action areas. So by looking at the market structure and identifying areas of interest, we can identify potential trading opportunities. So we're going to start here with the dollar index. Dollar has been in obviously a sharp decline but we're in a corrective phase at the moment. Now that correction could have completed or potentially completed at this 1995 high. Balance of probability suggests to me that we should see another high to ostensibly test an equality objective up towards 91 to 91 50. Now if we do get up into this area I would certainly be looking at various reversal patterns to set short positions to target another leg to the downside to at least complete this interim wave structure in the 3-4 here. I'm looking for a wave 5. Wave 5 could actually extend down towards the 87 50 which if I just go out to the weekly charts just refresh your memory why that level is of significance because that basically is an equality objective versus this structure here. So the weekly charts we reviewed at the start of the year and we know that 87 50 is an ABC corrective target versus this swing high in terms of the dollar index. Back to the interim chart so what I'd be looking for here is a potential pullback now to test or retest this 89 80 area support. We've got projected daily range support down here at 90 and we've got the projected weekly range support just above 90. So what's for any bullish reversal patterns in this area you can set long positions targeting this 91 50 but certainly pay attention to how we respond in this in this area because I think this is going to be the next leg down in terms of the dollar index. An instrument I've had a request to take a look at this is the 10 year notes in in terms of price not yield. I do cover the yields with the guys in the trading on the trading floor for FX cruise swap but what I'm looking at here is the price so the bond pricing so as prices go up yields go down as prices go down yields come up and they operate in the inverse fashion and this pop that we've had in terms of the bond price the 10 sorry the notes the 10 year price has been basically supporting this dollar balance but I think we're going to start to run out of steam certainly as we get back into this 130 707 area I expect we're going to see another leg lower in terms of price for these for these 10 year notes so watch as we trade into this area for potential resistance and take another leg to the downside in terms of these these 10 year notes euro dollar. So we've held we've held the support here at the 2050 level I did have an equality objective that so we can still test versus this swing high at 122 26 the quality of target is actually 120 but for now we'll start this move off the lows is starting to to gather some steam I think we'll we should run into resistance certainly as we trade back into this 122 area now any pullback from there that finds support at 121 40s 121 50s would suggest that the correction here is complete and we're actually going to trade to retest highs at 123 40 and on to the upside objective the interim upside objective at 124 50 from there I'd anticipate we do see another corrective phase but pay attention to these two areas if we can get through this 121 60 watch how we trade at 122 pullback hold the 121 40 area for long positions to basically take out the price on route to this 124 50 zone sterling has uh has been experiencing some upside pressure the the the pop we've seen in sterling has really been driven more by the the cross the euro sterling as opposed to I think intrinsic sterling strength but certainly at the moment whilst we was broken out here and we hold these prior highs then I think we can test this ascending trend line just above the 138 which was a target I talked about back end of last year I think once we get into this zone then I think we can see sterling run out of a bit of steam in the interim and we could see a pullback back into test support back towards the 134 and this is sending trend line but ultimately I'm looking for sterling to trade higher over the course of the year to get a test of this 140 area like I say pay attention we're holding projected range a daily range resistance at 137 44 at the moment but if we do get through there then I think we get up and test this ascending trend line resistance but certainly watch for bearish reversal patterns in that zone to set set short positions the dollar yen continues to correct I'm looking for the dollar yen to get back down into this 103 20 area and from there I think we do then have the potential to set up a move to test up towards 104 50 so I'll be watching very carefully we've got daily projected range support weekly projected range support we've also got this projected descending trend line so watch how we trade as we come into this 103 area bullish reversal patterns there you get nice risk reward to get in on the long side certainly look for a test of descending trend line resistance and then if we get through there bull flag pattern would set us up into this 104 50 Aussie this like many of these these pairs that we're going to look at is in a consolidation cycle at the moment but ultimately I'm looking now for us to break higher here in terms of this Aussie I think we can potentially get a test up in towards the target which I have about 80 as an interim upside objective expect a bit more consolidation certainly today as we get back into this 78 area we've got projected weekly range resistance daily projected range resistance I think that could be the catalyst for another pullback to frustrate bulls in the interim but as we then hold 77 20s of support then there is the potential I think for us to break higher take out this triple top as those who I work with will know I think the third test of any ascending or sorry the third test of any structural resistance or support or trend line support or resistance has a tendency to hold once we start talking about the fourth or the fifth test I'm more in favor of a break and and then you can see a breakout occur so watch as we test 78 anticipate some resistance there on this third test pullbacks supported towards 77 20 should see us take out the highs on route to that 80 level key week similar type story here we're looking at potential inverse head and shoulders at highs here which like I say similar to the Aussie we get up today we're checking projected weekly range resistance above the 72 handle seeing a little bit of profit taking potentially develop here certainly I think as we get back into this 72 36 again another frustration movies these consolidation phases especially with these in these wave force scenarios are set basically to frustrate traders so you've really got to pick your spot so if we if we fade here towards the 72 30 area I expect a pullback whipsaw here back into the 71 40s but again we're watching for bullish reversal patterns here set long positions ultimately targeting a breach of the price 73 13 on route to a test of 74 so there are two way opportunities here if you if you look if you intend to fade this zone with a bearish for our reversal going to keep your wrist tight but because what we could be looking at is another is another leg here which could set up this move so we'd have ABC to the downside at the 70 level which is what I was looking for initially but this this move is looking a little impulsive at the moment but if you get in on the short side here and you once we get back down and test support here you certainly want to have a risk-free position because it could be that you've got that that move occurring but more likely than not at the moment looking at price action I think we should hold support and ultimately see a run higher loony obviously trades the inverse of the Aussie and the Kiwi continue to trade within this potential ending diagonal but my sense at this stage looking at the broader market we probably have more work to do on the downside with respect to the loony but again thinking in terms of two-way trading opportunities there is a countertrend opportunity here if we get a leg lower to test 125 70s which is this descending trend line on the wedge watch for bullish reversal patterns there and get nice risk reward certainly to trade for a test of descending trend line resistance up towards 127 30 swissie pulling back I've been watching this one I'm watching the potential inverse head and shoulders scenario in the swissie which I've talked about for for quite a while now let's just draw this one in I'm sure it's I'm sure it's apparent to all of you but we have the left shoulder here and we have the right sorry the head here and then we have the potential for a right shoulder to develop I'm watching support at 88 27 is the area of interest and we'll see if we can get in there on on the short side in terms of the swissie sterling yen looking for a pullback here back into the pivot support at 141 50 then I think we get another part pop higher consolidate again consolidation phase frustrating trades I think first and foremost but any move then back into this 140 130 area is is setting up then a opportunity on the long side to basically target 43 and the top side of this ascending trend line resistance so key areas in terms of the sterling yen are going to be this 141 20 belongs and counter train shorts above 143 euro yen euro yen again looks like if we want to get a retest of the underside of this trend line which we were holding so I'm looking for as we hold 125 28 let's see if I'm going to move back up into 126 70 projected weekly range resistance we really got until tomorrow but 126 80 area and if we if we fade there if we get into this area and we fade I think we've got another leg of downside to ultimately get a test of this 124 50 in terms of the euro yen so 124 70 so what's the various reversal patterns on this retest here if we get it as an opportunity to do something on the short side in the euro yen Aussie yen was looking for a break here don't know if we're going to get it at the moment looking for us to take out well we'd we'd retested this trend line support we found some bias on the initial test and and the pop higher ultimately faded at at this 80 or look like it's fading at the 84 60 we have some nice reversal patterns yesterday but haven't seen any follow-through to the downside so whilst we hold here just throwing this up here so what if we hold this trend line again from current levels then I look for a test of the 82 level but certainly that 82 level will be an opportunity to do something on the short side as we make that third test of the projected ascending trend line resistance watch for bearish reversal patterns in that zone to do set short positions and what we're always looking for on these new highs to give us to to give us the confirmation or the confidence to get in on account of trend position is we want to see and be paying attention to momentum divergence which which is important to suggest that the move as is a short term exhaustion move so we want to always be paying attention to that momentum divergence on these new highs euro Aussie this one has potential to put in a double bottom here I'm going to watch this for our close I think there's an opportunity potentially here to get longer euro Aussie to play for a move up into into this weekly range resistance 158 30 so potential double bottom here and like I've just said what we know since we've got some nice divergence so I'm going to see where we close our 2pm GMT because I think we could have an opportunity on the on the long side in terms of this euro Aussie versus this double bottom euro sterling looking to hold this trend line support but for me at the moment any any strength in euro sterling is ultimately a phase as I'm looking for us to get down into the 86 level on the weekly chart let me just pull that up for you guys so you can see what I'm talking about this is the area I really want to see tested if we're going to move into 86 then I'd certainly be looking for reversal patterns in the euro sterling to do something on the long side but between between now and there I think we see continued pressure in euro sterling so whilst we hold resistance here at the 89 level I've been looking for for lower prices in terms of euro sterling and ultimately targeting that 86 level on a weekly chart which would give us opportunities to get in on the long side sterling Kiwi we are looking for sterling Kiwi to now test into the support area at the 89 level compete complete a correction versus this move off the lows now this this sterling Kiwi could be interesting because what we've got here is is we've basically got a potentially got a double bottom of sorts technically not it doesn't meet the strict sorry strict criteria but to the naked eye you can see that we've held this area this 86-20 as support and what we could do here is we could get this move into this zone which would give us a long set up because I'm looking for a test of this 92 50 which could prove terminal for the next leg to the downside or what we might get here is this this test of this support area with bullish reversal patterns obviously to confirm the long trade we could be looking at a quite significant then inverse head and shoulder scenario so we hold here and we hold here you can see there that this double bottom would basically be the head and we would have we could be looking at a more significant low in terms of the sterling Kiwi so that's one to keep an eye on but certainly if you if you if you get into this trade you have the opportunity to get in here pay attention to how we trade at this 92 50 90 260 because this could be the next leg lower in terms of the the sterling Kiwi dollar yuan looking to hold support here at the weekly projected range supports 6.45 looking for bullish reversals here to get back up and retest the prior highs monthly projected range resistance held to the tick so pull back to weekly range support if we can get bullish reversal patterns here I think we can get another run another test of the 6.5 level in terms of the dollar yuan S&P 500 posted chart of the day today very interesting technical pattern developing here I will be paying attention to to any test here into the weekly r3 daily projected range resistance projected shred line resistance all coming in at around this 30 38 90 39 level bearish reversal patterns are an opportunity to get in on the short side certainly get a retest I would anticipate at this 38 30 area and potentially down to test projected range support projected ascending trend lines support at this 37 20 zone Dow looking if we can take out these highs then I think we get the Dow up into this 31,470 31,540 bearish reversal patterns there I think we can look safely to get a test of the 30,700 level maybe we get one more high there to frustrate the shorts but ultimately I'm looking then for us to test down towards this 30,000 level and the third test of this projected trend line here in terms of the Dow the DAX looking for pullbacks here to basically find support at this ascending trend line projected daily range support 13,800 watch for bullish reversal patterns there then I think that sets up the next leg higher in the DAX to test this ascending projected ascending trend line resistance 14,400 monthly projected range resistance coming in just above there at 14,470 so bearish reversal patterns here with momentum sorry momentum divergence confirmation or an opportunity on the short side there in the DAX for a more meaningful correction and Nikkei still looking for this trend line test here at the 28,000 level I think that's an opportunity on the long side to get a test up into the 29,500 level and then from there I think we can see a more sustained corrected move in terms of the Nikkei put see holding attempting to hold this ascending trend line support I think we might have to breach that and get a test and set up a wedge here let's see let's draw this in so something like this wouldn't surprise me in terms of the footsie and and then we hold these prior highs here at 6,650 and I think that sets up the blow off move up above 7,000 and then from there probably a more meaningful correction in terms of the footsie coming to the commodities gold looking for a test of 1880s to fail and set up the move that I'm looking for to test this 1760 area support from there I think we could put in a more meaningful bottom in terms of gold and trade higher but for now I'm bearish gold looking for this corrective phase certainly any move up into projected range daily range resistance 1890 bearish reversal patterns are an opportunity on the short side to my mind silver similar story can you move up into 2640 2650 bearish reversal patterns and I think we get this test of this major trend line support back down to 22 and then from there I think we could have a meaningful low in place and and I'm looking for a move up to test 29 in terms of silver so some great two-way opportunities I think coming in in the metals crude looking for us to test 55 and then from there I'm looking for a more meaningful pullback in terms of crude but ultimately then I look for us to hold the 51 level sending trend line support on route to to higher prices I'm looking for proof to trade up towards 60 dollars a barrel so watching for this support zone to get tested I think is is going to be pivotable crude in coming weeks copper looking for copper to test this resistance area we've got range resistance multiple range resistance daily weekly and the monthly which we've held once so potential here for a double top and again a frustrating move pull back to test the support zone down to 353 and then from there I think we can extend higher once this complex correction completes and ultimately get a test of this major ascending trend line resistance obviously we want to see a bunch of divergence as we get up into this area and then I think we can see a more meaningful correction in copper and that that correction in copper will fuel the correction in the commodity currencies from higher prices I think we get back down to this sending trend line support at 338 and then maybe a more meaningful base develops last but not least bitcoin we were looking for a correction last week and we saw saw the pullback we're now testing this trend line support here at the 31,000 area versus this swing high at the 40 so versus this structure let me just draw it in so it's clear so whilst we hold these highs then we do have an equality objective at 28,700 now if we get down to this area and we get bullish reversal patterns we've got projected weekly range support 28 100 monthly range support 2800 if we get down we get bullish reversal patterns here for those who aren't already in the crypto space this I think could be an opportunity with bullish reversal patterns to work to get long and ultimately then look for a retest and take out these highs on route to this interim 44,000 objective and really I remain constructive on on bitcoin we this junction for me to to reconsider things we need to take out this trend line support 22 22,000 to to really start thinking that we've seen a more meaningful high in terms of in terms of bitcoin for now this is to my mind is just a shake out and we should we should see higher prices so those are the charts and areas of interest for me as we head into the back end this week and early next week if you have any questions or any charts you'd like me to take a look at that I haven't covered in today's discussion now is the time to chime in you can either post into the chat box or I can unmute your mic and you can speak to me over audio equally if you don't have a question for everything's clear and in the chat box is is just as useful so I know that we're all on the on the same page and we can we can wrap this one up here okay so I didn't see any questions that means I must have done a great job of explaining everything I'm going to wrap this up here then guys and we will reconvene at the same time next week thanks very much for your time and I hope this helps


Hello, thanks for joining me. I'm at the Coat Water Park near Swindon. This is the reservoir and down here the scientists miniature railway are going to go and have a trip, which is just down here. You can just see a little cloud of steam. So we're going to have a ride on this railway. It's run by the North Wilkshire Model Engineering Society. It's quite an extensive railway. It goes off out into the woods. You can just see a little train pulling out. We're going to go and have a ride and see where this track takes us. It looks really quite exciting. I had a look on their website. There was a map and it looked like a very extensive railway. So what I'm going to do, I'm going to go buy my ticket and we'll go for a ride on the Coat Water Railway. Effectively, I ended much chance of us doing any line-siding today. My plan was to go for a possibly come here again in the future and perhaps do a bit more action from the line-side. Last but you know, the weather's pretty bad today, so we can't play around. You can see one of the integers there at the turn table. We might just catch a glimpse of steam. We've got one chance of seeing any more steam here today. I think it possibly would be down here. So if we come here again, we'll come here again. We'll get more line-side shots. It's a very interesting and complex network. I'm confusing myself now. We would have gone across there and then we finished by coming back across here. I can't remember. It's a very complex network of track. It's quite simple when you look at it on a map because you can see they had a smaller loop. Then they had the figure of eight-light loop going over the bridge and then they built the loop that went right out to the back. It was the last thing they built. Oh, can I see it? I think I can just see some movement. Yeah, I think we're going to see Linda steam pass. So this will probably be our one and only line-side shots. I'm going to let you enjoy that. The driver of Linda stopped and propelled her train into the engine shed. Linda propelled her train back into the engine shed. The rain isn't showing a great deal of sign of stopping. It's been called the Coat Water Railway. It certainly has lived up to its name with water, even though it's actually named after the water. That's in the big reservoir. So I've had a good hour and a half here. I've really enjoyed it. I would have stayed longer had the weather been better. But perhaps we'll come here again next year and we'll go and visit other miniature railways in the area. So this was a bit of a drive for me, this one. So I hope you enjoyed this video or when it's getting windy. If you're out this way, do come and visit the Coat Water Railway. It is probably one of the nicest miniature railways I've been on. Some of the most exciting scenery. It's got some real good gradient. So it really is a lovely railway to do come and visit. Perhaps just pick a better weather day than what I did. So thank you very much for watching. Please do feel free to like, subscribe, and comment. And from the Water of Coat Water.


There was like a percentage that 85% of the editors on Wikipedia are males and only 15% are females. So that was like a spark. My name is Maihesham. I'm from Egypt. I'm a Wikipedia education program leader in three universities. And how I started my journey with Wikipedia. Wikipedia education program is a global initiative around the world which aims at encouraging students in universities and schools to edit Wikipedia. I was student, a participating student, to translate articles from English Wikipedia into Arabic to enrich the Arabic content on the internet. After that the Wikipedia education program in Egypt was expanding very fast and they needed someone to help others and teach others how to edit Wikipedia. So I became a campus ambassador in my university. At that time, Wiki Women project was very successful. So I thought that I would start that project in Egypt and it would be the first of its kind in the whole Middle East. Actually in Egypt we have a very good environment for Wiki Women because most of the college or the university that is concerned with language translation and these kind of humanities studies, most of them are girls. And this is our target. I remember that in 2013, 88% of the contributions for Wiki education program in Egypt was females. They were translating articles about women. It was the end of 2014. They achieved the highest contributions ever in the history of the Wikipedia education program in Egypt. I love what I do and I love Wikipedia, I love the community and I'm so honored and it's my pleasure to be part of something like that.


Hello, and welcome to Escorto, episode 232! Season 5, probably. Today, we are saying how to ruin a brand-new kitchen. Starting greasy. I'm checking on it. Ah, a cup of tea! Why does this grape juice taste good? Even cheers, that's fine. It's gonna taste better once you cheers. Why do people drink wine? That doesn't taste good at all, when I can just drink. I like how it tastes good. Does it taste better than grape juice? No, but just grape juice.  Maybe you got the right kind of grape juice. What kind of grape juice? That's not why you get tipsy tipsy. Purple drink? Well, there you go. Alright, I'm drinking the wrong thing. Here's the plan. Breezy has chosen, or been chosen, to choose the ingredients for today's meal. And she chose to make chicken fettuccine alfredo. Absolutely not. Okay. Try it. Lizani, I was listening. Chicken parm! So this is all of our ingredients? Yeah, we have a lot of ingredients. We have a lot of ingredients. I think it's really cool. Yeah, it was from Algarve. Yeah, it's really heavy. First of all, do you know how to open cans? No. Oh, your thumb is broken. I'm disabled and that was offensive. Okay. Wait, we have to try this thing. Okay, yeah. So we also found Melissa White's melons. What was it called? Yeah, it was Melissa, like, Melissa's bitter melon or something. Yeah. Stroke's history. This looks gross. So we bought them. And we don't know if we're supposed to cook them or eat them. Do you want to try it? Wash it. Why don't you wash it?  That's better. No, but it's really gross. Really? Try it. Oh, God. It's that bad. I was going to try and eat it. It gets worse the more you chew it. Yeah, I chew it again and it got, like, twice as bad. That's so bad. Do you know how to cut onions? Yeah. Do you know that they make you cry? Do you have any goggles? I've never cried. You've never cried in your life? So it's very simple. When you're cutting onions, she doesn't even know how to use a knife. Do you want to use it sliced or diced? Well, it's half the onion. We paid for, we paid by the pound. Easily one or two. Chad, you never wash your hands when cooking. Actually, that day, it just adds to the flavor. It builds your immune system. Well, it also adds to the flavor, but he doesn't know that because he's just a chef in training right now. And a former doctor. As you can see, she says she would have done it the wrong way. That's okay because she's learning. That's one of the things that happens here on this channel, Chad, education. Onions are like ogres. They have layers. And the thing is, you want to separate the layers and then you want to chop.  And that is how big these are made. It's quite simple, really. If you cut it the other way first, this way it's not quite as good. Yeah? Wait, isn't this? This is a drizzle. It's not a drizzle. It is, it is, it is. No. It's because you don't know how to spray it properly. Well, okay. Is this really like... Yeah, drizzle. Drizzle. Just a little bit. How much? Wait, hold on. Should I heat it up?  Okay. We have sauce. We have paste. Can I smell it? What year do you think that was? You want to waft. So you don't burn the nostrils. Um, I would say this is a 2002. That's, I mean, that's a good year. It is a good year. It's a good year for tomato sauce. So do I just pour it in? No, no, no, no, no. No, sorry, sorry. I just handed it to you to inspect. What do I do with it? Right now we're just softening the onions in here, which by the way, they're not cut very well, but that's okay. Well, you'll do better next time. It's so bad. Why does anybody do this? There's a 0% chance anybody enjoys this. Because I'm, look, what I want. Stream, but then there are tons of people here. So I don't know. You would be right if you said that about my stream. Well, that's just a little bit, Tony's. So I just dump it. Yeah, but don't put the whole thing. We'll, we'll like, we're gonna, it's good enough. Oh, frick. So we have sauce and paste. Yeah. So paste, this is like thicker so it can add, it can be added as like thickening agent. Okay. Ready? And then this, yeah. So half? Yeah. And put one of these in there. Okay. You have to probably get it out with a little. Can I just? Here, no. Just get a spoon. Samore. Wow, look, look at this sauce. This is a good sauce. But you know what? It doesn't have any, what? It's gonna be a good sauce. Yeah, it's gonna be a good sauce. Yeah, 100%. What? That's some great drink. Can I try it? What does that taste like? So you have to just be aggressive with it and attack it. Yeah. Because if you're scared of it, then it's gonna just keep going. But if you stop it and you're like, hey, I'm moving it so that with the air release. I need extra lactose. I'm lactose free. I'm lactose insensitive.  That's why I got to take it out. Risk gamer? Yeah. FPS games? It's because I'm an MMO gamer. I know. This is just like every stream I've ever on, okay? I'm always doing all the tech. I'm doing all the everything. Okay. So I put this inside of this, right? Yes. You put the mommy inside the baby. Okay. Open up, lady. He's not done yet. Okay? You just want to get it nice. Yeah, yeah. I see what you're saying. Nice and coated. It's just a streaming backpack. It's just bread. Oh, Fred. I got bread. Like we're mixing chicken and egg together. That's why I said put the, dip the mommy and the baby. Oh. That's why I said that. Heat one inch of olive oil over medium high heat. One inch? Yeah, one inch. It's like. Yeah, it needs to cover. It's because it needs to cover this. What? One inch of olive oil? Yeah. Do we have a ruler? I trust you to know what one inch looks like. You look, never mind. What? I'm not gonna say no more. That's gotta be one inch. No, that's. At least. That's like a quarter of an inch. That's at least an inch. That's a quarter of an inch. Do we flip it? For three minutes. Do we bow them at the same time? Okay.  I don't know why I'm still eating it. Yeah, why are you still eating it? I don't know. It's not good. Yeah. That's been, it's not even been closed. It's been open for six months. Really? Yeah. It's been open though. They're all mixes. Yeah, they're all mixes. So do I flip it? Are you sure? Yeah. It's boiling. I'm hungry. I haven't eaten all day. There's snacks galore. Oh, there's snacks. There's snacks on this. Oh, God, you did. Oh, God, you did. It's so gross. See chat? This is where we're at so far. Yeah, yeah, that looks. Mm-hmm. Okay. So, but now we have to put the cheese on it. This is so much cheese. This feels like an excessive amount of cheese. How much cheese is too much cheese? Ooh. If you use like a little bit more, but I'm going to put more mozzarella on top anyway. Shh. So you might want to boil that. Yeah, like over here. Okay, now that begs. All right. Yeah. 30 minutes. So now what? Oh, pasta. Yeah, pasta. We didn't make the pasta. Chat, we didn't make pasta. Well, it's okay because pasta tastes like... Wait, really? So what kind of pasta is this? Is this feta cheese? Feta cheese. Feta cheese. Okay. It's feta cheese. All right. Zoom. Okay. When the moon hits you, I like a piece of pie that's summer. I'm sorry, can you hold it? Or put the pork in the pasta? No. This works. Okay. If I'm able to flip this pasta. Okay. And you have a twitch prime. I know you guys have twitch primes. You're sitting on them. You forgot you even have them. Okay. You might as well use them on somebody. Why not here? Okay. If and only if I'm able to flip the pasta properly. Oh my gosh. We're going to lose all of our pasta. One. Five second rule. Five second rule. Five second rule. Five second rule. Five second rule. This is gigantic. This is like really gigantic. Oh, frick. That's really... Oh look, it's like a... Oh my gosh. It's like a pizza commercial. I think it looks good. Yeah. I'm like it's got its own like. Mission accomplished. Yeah, I think this is good. I think it's really good. Yeah. Okay. Taste test, chat. Now we have to see. So this is chicken parmesan with mozzarella for some reason. Oh, the chicken is very tough and hard to cut. Very good. Does this look clean or cooked? You didn't get... Oh, you have to get some of the sauce and like the cheese. Yeah. Chat, once again I have impressed the world of my culinary expertise. And I hope you guys learned something today. We've been snacking. Snacks.  What are you eating? This is... If you eat the same amount that you ate today, that's for each meal, that's going to be like for a week. And I'll eat it... Yeah, I'll eat it over the course of the next week. I don't know.  I'm just not going to eat more today. Don't eat too much. Have you learned something today? Yeah. And chat, I'm glad you learned something today as well. The way that you beat the eggs, that was really good. So that... You know why I could do this, are you? Why? Because I used to be from Hawaii. And I'm a shocker. You used to be from Hawaii? Yeah. You're not from Hawaii anymore? No. Well I played a character that was from Hawaii. Can you just eat pasta? What? So is the only thing that is like... It's crunchy. Yeah, is the only thing like with undercooked or overcooked pasta? It doesn't even matter. It's just like... No, it doesn't. It's just personal preference. Okay. What do you think is like chicken? Or are you like... Yeah, what if you get sick? I don't know. I don't think I'm going to get sick from this. But I understand like... No. You're like it's overcooked? Then you get diarrhea. No. What? I don't know.


Moving on can be one of the hardest things to do, especially after the first failed relationship. We're not going to tell you it gets easier, nor are we going to tell you there are plenty of fish in the sea. Heartbreak hurts. What we will tell you though, is eventually the hurting will subside. Maybe not now, tomorrow, or next week. But things will start to look up again as you spend time growing with close-to-lipped ones and rediscover yourself. Are you ready to tie the loose threads once and for all? Here are 7 signs you're ready for closure. 1. You've accepted that things are no longer working out. You're not clinging onto any more false hope, nor are you considering this last meet-up as an opportunity to fix things. You've come to terms with the fact that it was either bad timing, incompatibility, or the lack of compromise that has prevented the relationship from going. You're not looking to justify your actions or criticize your partner anymore. You just want answers. 2. You're ready to take responsibility. Some might argue that you'd be wasting your time trying to find closure. So why is it, for many, that finding reason is still important? It's simple. Psychologists believe that only after understanding the situation, people can restructure and understand the past, present, and future better. In a large part of this process also means taking responsibility for your actions. You realize that you too took a part in this relationship and you're willing to trust yourself to make better decisions. Before we continue the rest of this video, be sure to subscribe to our channel for more content, as well as share this video with others. With your help, we can reach more people and provide our support. Now on to point 3. 3. You've stopped blaming the other person. You've moved past the bitter feelings of resentment. You realize that blaming the other person isn't going to change anything. Relationship experts believe that many people go into a relationship without assessing their own values. It's only along the way that when those values clash with another, that the relationship often suffers. You want this closure to highlight those moments of disagreement and accept the two of you have different values instead of resenting them for it. 4. You've stopped blaming yourself. Self-blame can be common, especially if you don't have a strong sense of self. It's hard not to blame yourself after being vulnerable and intimate with someone when you put so much emphasis on what you gave. But psychologist Alicia Clark states, self-forgiveness helps you get to the bottom of why your relationship failed and prepares you for your next relationship. You're not perfect, so don't beat yourself up over it. 5. You want to take important lessons from what went wrong. When you're ready for closure, you no longer necessarily want to react. But rather, you'd like to process the situation from an objective point of view. You realize that the only way to go forward is to assess the mistakes and become proactive in the future so as not to repeat them. 6. You understand that the pain is inevitable, but temporary. You might still cry at the thought of your partner, and that's okay. Even years later, it might still hurt when a certain memory is triggered. That's why healing is such a complicated process. It's never straightforward, never what we want. But once we've begun to recognize that it's not going to be forever, you're already moving on. 7. You're ready to hear things you might not want to hear. What does it mean to let go? A lot of it comes down to openness. Being aware of the loss, but not letting it consume you and hearing things that will hurt but are necessary. The truth isn't always kind, easy, or convenient. But how we welcome it? That is ultimately always our choice. So ask yourself, are you ready for closure yet? Want more content on relationships? Check out and subscribe to Rolachi. Funny, relatable, and down-to-earth, she shares her coming out story, flirting tips, and much more. If you liked this video, be sure to give it a thumbs up. As always, thanks for watching.


Ukraine to begin ground operations to liberate Crimea. Karilla Budanov, head of the Defence Intelligence of Ukraine, has vowed that individual strikes in Crimea will not be the end of the matter as there will be a ground operation and the recovery of our territories. He said the operation of Defence Intelligence of Ukraine, supported by the Ukrainian Navy in Crimea, is essential to make people there believe that victory and their liberation are just around the corner. The history of the National Security and Defence Council of Ukraine Olexi Danilov spoke about the details of the operation carried out by the Ukrainian intelligence on the Crimean Peninsula. He said that the most modern weapons were used during the special operation. We carried out a very strong operation. The other party has no opportunity to prevent the execution of the tasks that we have set before us. According to Danilov, the security forces have conducted various special operations before, but it is impossible to give information about all of them. I can only say that the war will not end until all our lands, including Crimea, are liberated. He added that the peninsula would be liberated militarily if necessary. If the other party does not leave our lands as soon as possible, it will be so. Recently, Ukrainian forces have carried out their most complex and ambitious operations to date against Russian military facilities in the occupied region of Crimea officials in Kiev have set. Special forces landed on the western shore of Crimea near the settlements of Olenivka and Mayak in a joint operation with the country's navy. While performing the task, Ukrainian defenders clashed with the occupiers' units. As a result, the enemy suffered losses among its personnel and destroyed enemy equipment, Ukrainian Defence Intelligence said. While they were there, the Ukrainian unit also raised the national flag. That added.