Okay, we're going to get started. Taylor, could you please turn on the recording? Ready to go. Okay, I'd like to thank everyone who's joining today. Welcome to today's CNCF webinar, using machine learning for autonomous log monitoring. I'm Sanjeev Rampal, a principal engineer at Cisco and a cloud native forum ambassador. I'll be moderating today's webinar. We would like to welcome our presenters today. Larry Lancaster is the founder of Zebraim and Gavin Cohen, Vice President of Marketing at Zebraim. A few housekeeping items before we get started. During the webinar, you are not able to talk as an attendee. There is a Q&A box at the bottom of your screen. Note, this is different from the chat window. This is the Q&A box. Please feel free to drop your questions in there and we'll get to as many as we can at the end. This is an official webinar of the CNCF and as such is subject to the CNCF Code of Conduct. Please do not add anything to the chat or questions that would be in violation of that Code of Conduct. And basically, please be respectful of your fellow participants and presenters. With that, I'm going to hand it over to Larry to kick off today's presentation. Hey, thanks so much. Hey everybody. So this is really an exciting opportunity for me to talk about something that I think whose time has come and that's autonomous log monitoring. So with that, let's get right into it. So machine data is my life and I've spent most of my career sort of taking telemetry for products in the fields and then turning that into tools and business intelligence and deliverables back to end users and so on. And so what I'm going to be talking about today is kind of motivated by that history, having sort of delved in and built sort of platforms on top of machine data so many times. You know, I kind of got to the point where I was feeling like, you know, this should mostly just happen automatically at this point.  And so then I'm going to talk a little bit about sort of what motivated Zebrium today. So really it started for me sort of looking at the state of log monitoring, which to me kind of leaves a few things to be desired. But the biggest problem from my perspective is kind of twofold. One is sort of the mess of the data itself in the sense that, you know, it's not structured sort of formally with a grammar and so on, log data. And so it's hard to build on top of it because you end up in a situation where you always have to be sort of, you know, maintaining things and you have to be aware of the semantics of the data that you're working with. And so it's a very manual process. So when I look at when I look at sort of what comes out of that, you know, in terms of monitoring solutions. I see that for one thing, sometimes it ends up being slow anyway to figure out, you know, what's happening when something when there is an incident, just because you kind of have to go digging through logs yourself anyway. So that's, that's annoying and suboptimal. The fragile part I touched on, but this is actually, I mean, I'm not sure how many of you have sort of, you know, had to ever build sort of scripts or reg X's and parsers or tools on top of log data. But like, you know, what'll happen oftentimes is, you know, you'll have something that's working, and then a developer does a really nice thing than that developer maybe someone somewhere else in another ecosystem, because you have a deep stack or they may be closer to you but they may not owe you a notification and they'll do something really nice like fixing a spelling mistake. And the next thing you know, stuffs breaking and so there's, there's all this kind of stuff that happens that that that makes sort of dealing with logs and using them, sort of a manual process. And finally, alert fatigue is is another and I think this kind of stems from, you know, given how little sort of visibility into the actual semantics of what the data is talking about the tools have. Typically what you'll have is people set up alerts on okay, you know, if I get, if I get more than, you know, 1000 errors an hour, then page me or, you know, I was talking to one guy who was kind of like yeah, you know, we have these alerts set up and I like I basically look and I say, you know, if if it's noon and I've gotten more than 5000 alerts, then I know something's wrong if it's less than 1000 I know everything's okay. And so that's kind of the state of things that that that kind of annoys me. And the reason is that if you think about it at least for me I mean logs have always been used for root cause. It's almost always the case that that you're going to go to logs at some point in fact if it's a new sit if it's a new problem if it's an incident of a new kind. I think thoughts are very good you'll end up in a log file, but why aren't so given that I mean clearly they have the information that we're looking for so why aren't they better at helping us monitor things in the first place. And I think I think it kind of drill it kind of boils down to this manularity when I say logs are stuck in index and search what I mean is there's a person searching right so in the at the, there's a problem with that. I mean it hasn't that hasn't always been a problem especially when it's kind of your app you understand things. You're very infinitely familiar with the stack. You know you can quickly search that through things and figure out problems. So let's look at that model so so 20 years ago when I started in the valley there was kind of a shrink wrap model. Of course it wasn't you know always delivered by box but you get the idea you had an incident and there was a user or a customer or you know maybe a few users at one customer that had hit a bug and there was a support department. The world was completely different back then. And so you'd had an incident on a monolithic application. There may be up to 10 I mean 10 would be pushing it log files that people would need to be familiar with and look true. And you would have them indexed and you would search through them and you would figure things out and that was great. But things have changed. I mean so nowadays you've got an incident that can affect tens of thousands of users. You've got dozens of services at least you've got you potentially a thousand log streams if you count all the different containers and services that you're running. It's kind of a zoo and yet with all the pressure of you know 10,000 times as many people anointed you you're still stuck indexing and searching through the data to try to figure out what's going on. So to me this is kind of unacceptable. So I believe that the future doesn't have to be like this. I feel like we've kind of gotten to the point now or at least to a large degree. We can let systems help us kind of pinpoint the root cause indicators in the vast swaths of log data without us having to go through the same process that we've been doing for decades. If you think forward 20 years from now I do you really think that we want to be doing this 20 years from now my answer is no so let's get started with that. What I wanted from a tool is something that will kind of characterize incidents before I notice that's kind of a like a really ambitious statement. So let's let's talk about a little bit about what that what that means and what it what it's taken. So what it means is, you know, sort of automatically to detect incidents when they're happening so things that are weird start happening.  He's looking around. He starts seeing things go, hey wire, typically he'll he'll have a good sense that things are going wrong. Even if you may not not know what there may be he may be capable of figuring out that hey something's going on here. So let's start with that. Let's let's automatically detect incidents without a whole bunch of alerts and rules being configured and so on. And then let's go find stuff that's germane to root cause indication within the logs that that we found and I think what's interesting from this perspective is that as we've accumulated probably 100 and I don't I want to say 110 120 real world incidents across over 30 stacks that people have been generous enough to share with us. We've found that there are some sort of fundamental ways that software behaves when it's breaking that lets a system go in and find clues for you to to root cause that. I talked a little bit about why it's so hard to do this from from the perspective of monitoring software. But essentially, you know, you've got ambiguous parses. You've got formats changing. Another problem is needing experts to interpret the data.  So what do what do I mean by all that.  Some of those may be, you know, also repeated indices log some may not depending on your configuration. But what what you'll what you'll find is that sort of the stuff that you'll find tools giving you sort of connectors and pre built sort of ways to get at that log data. They're mostly concerned with the prefix, which is kind of the stuff to the left of, you know, where to the left of the log line so you'll maybe sometimes you'll have kids or function names will have, you know, like timestamps you'll have severities you have some of these are really long and complicated sometimes they're very short and simple. And that's all very important information. So we need to grab all that. But everything to the right also needs to be structured if you want a machine to be able to tell oh this. This is a number in here and it's going up and it's not usually this higher or you know this very particular kind of event is happening a lot a lot more often so you need to kind of you know programmatically get into that semantics of that data and that's kind of what's what's been hard about it. So the first thing that we do is we we structure the logs to a relational level where if I wanted to run queries about what was going on in my logs I could do it. Now we don't have you do it. We have a database on the back end that has that information and and customers can get access to it, but that generally that's not that's not what we're what we're doing we build stuff on top of that. But the point being you know, if you imagine in your mind sort of a table. That gets created from a certain kind of log. So here's a very simple example of a very specific kind of log. And you can see that you can see that, you know, over time there's some numbers changing and it's kind of clear to the eye what those should be called. And what we want is we want we want software that basically creates a table just for those kinds of things at least logically creates such a table and then has columns that are typed for each of those. Sort of variables and and based on this now you can start to do more interesting anomaly detection from the user's perspective. I think what's most important is that you don't want to have to.   You don't want to have to get something wrong or right. You don't want to have to go look and make sure somebody else has a connector for X because for your application logs at least nobody's going to have a connector for X. And so you want something to you want to system that can just get in and brought the grammar like a person would and and then iterate on based on its understanding of that grammar in the background that you're having to worry about it. And that way you can embrace free text logs you know structured logging is cool. We certainly you know can can can rock that but it's annoying. I think sometimes at least from a developer perspective that you know it's really hard to read structured logs for a human. And there's really no there's no need that we should have to retranslate our entire infrastructure for machines. Why can't they just translate it for themselves. So you know a big chunk of your stacks always going to be free text logs and we believe that that's important and valid. So once you've done that now you can do anomaly detection on that data. And so like I was mentioning earlier we kind of have this sort of ways of looking at the data once we've got it structured properly that has yielded amazing results for us in terms of stuff that just tends to happen when things break. And and people always ask oh well what kind of you know what kind of models do you use and so on. So without getting too much into it what I would like to say is we don't we don't do any well so in this part of you know in this part of our software stack we're not doing any deep deep learning because this is all kind of real time. You start in we start ingesting logs in line we structure them in line everything just kind of you know gets gets better in the background there's no batch processing. It's not going to cost you an arm and a leg to run the service but so so what the way we actually are able to pull these out is by looking at point process statistics on the event type so if you think of each event type. So in a given log file or maybe a thousand unique event types right those tables I mentioned there may be a thousand of them virtually speaking kinds of things that can happen in that are expressed in the logs. So if you think of each of those as sort of like a like a like a point process where it happens with certain particular frequency or it happens with particular values or it happens in relation to other kinds of events and you build up that matrix. You can very quickly sort of narrow down coincidences that are anomalous and that's kind of the approach that is yield yielded amazing results for us. Again we don't to just to do not just the structuring with the anomaly detection we don't want you to have to tell us too much about application behaviors. So in our you in our UI of course you can set up alerts you can do all that stuff you can you can actually set up a very complex alerts if you like on this event has to happen and then that kind of an event with you know with the same. You know the same pod name or not or you know this parameter has to be higher than that within the event and there have to be three of them and you can do all of that. But you shouldn't have to do it to start getting anomaly detection working to start getting autonomous monitoring on top of your stack. And the great thing about taking this generic approach is that it works great on anybody's app. I thought I talked a little bit about there are other people kind of approaching the same space and I think it's a fascinating space there's a lot of brilliant people out there now looking at ways to structure this kind of data. There's sort of I guess what I call a community of academics that look at it one way and there's there's some folks in industry to look at it different ways. I guess the deep learning one for me was interesting because I kind of have this I have this story. So I went so there was a there's a large sort of tech company that you guys would all know their name and they have a CTO of sort of their services division and I went and spoke with him and he said yeah so this is a problem for us. So we've got all these logs and we've got all these you know different products and we we gather all the logs together and and so we decided we wanted to go do some some learning on that on that data and try to understand what's happening in in customers environments. So so what we did was we went out and we bought all of our senior engineers DGX one workstations and we sent them for training on deep learning and then they start to set them loose on it and what ended up happening was after six months we kind of abandoned it because we found that they were having to spend all their time structuring the data rather than actually building models on top of it. So it's kind of been it's kind of been my my learning that if you structure the data right you have a lot of options and you don't have to jump for the most expensive trendy one right away you can try other other methods as well. So we tried to do sort of a Swiss Army knife machine learning approach. It's interesting when you look at log data and you might find this interesting so like if you look at log log data typically you'll find like for example if I just take a terabyte of some of some applications stacks log files you know out of some environment and I look at it. Typically about half of the event types I see within that corpus I'll only see once or twice. So what does that tell you it kind of tells you that if I've got this vision in my head where I'm going to onboard someone and I'm going to get start looking at their data and all of a sudden I'm going to have this massive corpus of data that I've learned over what the you know exactly what that thing looks like and and all of that and all of the different permutations and distributions of that event type. I've got another thing coming it doesn't work that way. And so so kind of what you need to do is, basically we have kind of a four stage pipeline, which depending on how often how many times we see an event type will do a different stage of that pipeline will have the primary effect. So there there is a layer that if I've only seen the event type once and there's numbers in it. I'm going to assume those are parameters until it's proven otherwise. The next step is basically reachability clustering like these lines are kind of alike each other. I'm going to I'm going to assume that they are until proven otherwise. And once I've had a few examples, then there's sort of a naive base classifier that kicks in with a global fitness function that basically says, Okay, here's the kind of blob of stuff that's related to each other and here are the columns and then we become really sure of it. So on the back end we kind of shuffle things around into these buckets and then eventually it sort of hardens into a structure. The great thing about doing it this way is that you start out with something that works but it gets better the more data that you feed it. And the great thing about using sort of point process statistics to cross correlate among event streams is that the anomaly detection gets better the more complexity you have the more cross correlated streams that you have So with that, I'm going to hand it over to my colleague Gavin who is just an absolute whiz with the demo and he can do it in a sort of a time efficient manner. And then we're going to come back and we're going to we're going to have answer some questions. So I'm going to go ahead and stop sharing. Okay, just making sure everyone could see my screen. This is Gavin Cohen. So assuming everyone can see this, what you're looking at here is the overview screen that appears immediately after logging in. Now just to set some context for what I'm about to demonstrate, we've deliberately ingested just a tiny data set. So 24 megs, 180,000 events. And there's absolutely zero manual configuration that took place. Yes, and no one built rules. There was no pre learning. You know, there was zero knowledge of this data set until it came in. And essentially, our, you know, ML went through the, the events. And what we uncovered from an overview perspective is a bunch of exceptions and events that have error or high severity, but they're really interesting stuff here. There were 155 anomalous events. So events that broke pattern compared to what we would expect based on that small data set. And then one incident and the incidents are the things that we really care about. These are the things that bubble up as correlated sets of anomalies that we believe anomalies. They're not happening by chance. They're happening because there's, there's something changing in the behavior of the software. So what I can do here is click on the incident and you're taken to sort of a root cause description. So it seems reasonable postgres stuff, which is what it calls it. And that comes directly out of one of the events. And if I click on it, you get the detail of what we found. So let me explain this a little bit. The data was collected from an Kubernetes set of pods running the Atlassian stack in AWS. And what we found is in this pod postgres master, we see a couple of messages saying the postgres stopped. And then in a different pod, we're seeing this sort of correlated set of events coming from JIRA. So a different application. And in particular, this one that says it's, you know, it's, it's having postgres postgres issues connecting to the database. So clearly related set of events. So out of the 180,000 events that we ingested, we literally just detected five that encompassed this incident. And it turns out this was exactly the problem that occurred in this situation. Something shut down postgres and immediately after the rest of the applications running in different pod started noticing. So that's what we see that we've detected. Now, if you take this a little bit further, we make it really easy to sort of confirm the diagnosis that we've found and to maybe troubleshoot more or find out what really happened. So with that, I click on browse the incident. And I'm taken really into this sort of interesting kind of almost log manager view, but I'm filtered at the moment. So what you're seeing at the top of the screen are a set of visualizations of the data set. So the entire space of this data set encompasses 180,000 events. And then we break down some visualizations and I'll come back to those in a minute. And because I clicked on the incident and filtered on just those five events that make up the incident. Okay, so we can see them all now together, much like they would look like if you correct those lines out of the different log files, in this case two different log files syslog and the gyrolog. From here I can do sort of two, you know, one click workflows. The first one is peak. And what that does is lets us look at the surrounding events or that event in context of the source of this. This log stream. So it's the particular syslog stream that came from just that pod, which is the where this event originated, and I can see all the other events surrounding it maybe that yields more information about the, the incident. I can also go back to where I was, and I can do something different, which is I can unfilter so I can turn off this incident filter that we have on now. And now what we're seeing is we stay in the same place, but we're seeing those incident events surrounded by all the other events from all the other pieces of the environment. So in this case you see other log files by confluence and if you scroll around, you'd see all the other Atlassian components spinning out messages, but now you can see the incident in the context of everything. And so this again is another mechanism just to quickly kind of understand and troubleshoot what what occurred around the the incident itself. Now, maybe just to kind of peek under the covers for a moment. There's this pretty cool visualization of the data that I've just brought up here we call an x-ray. What we're doing is on the x-axis is is time and the y-axis is the event space spanning all the different log sources. And we're drawing kind of a colored rectangle everywhere that we find an anomaly represented in the location on the y-axis to where it came from or what particular events it came from. The brighter the little rectangle, the more anomalous the event. So the most anomalous are these sort of very bright white colors that you see just above my cursor, my mouse pointer. Now what you see here is very typical. There are always anomalies. So there are always going to be events that break pattern. And you don't want to alert on those. You don't want to create incidents on those because there's really not enough context to say they are or they're not a problem. Sometimes they're problematic. Sometimes they just events that break pattern or that, you know, new events occur for whatever reason. But if you sort of go to this section here, which is where we found the incident. You see this really tight band of correlated anomalies. And that's kind of our trigger when we see sets of very high likelihood anomalies, the very bright rectangles correlated across different either parts of the application or different log files or log types or log streams. That's our indication that there's an incident. All of these things broke pattern. They're all anomalous. They're tightly correlated. There's some very high probability anomalies in here. And that's our incident. So that's how we pick these five out of 180,000 events to make up this incident. We saw this, this sort of band and we pulled out what was most anomalous then that became the event. And then the root cause is identified kind of by the leading edge of that. The first sort of anomalous event that happened that seemed to trigger everything else. And so if you remember in the event, we showed you the root cause Postgres stopped and then the symptoms, which were in this case Jira noticing that couldn't talk to the sequel. And actually, if you go further and you look at some of the yellow, the other anomalies around it, you'll find that they, they relate to some of the other applications that also started having problems once Postgres stopped. So that gives you a good sense of what's sort of happening under the covers. The very last thing I'll show you sort of speaks to what, you know, what we did to get to this point. And Larry sort of spent a bit of time in his presentation talking about how we structure the data. But there are a few interesting things you can see here. If you look at this log line as an example, wherever there's a blue piece of text with discerned that there was a variable part of an event. So in this case, all these blue pieces are variables. And in particular, so this is the Postgres stop message. The word stopped is actually a variable, meaning we've seen an event of this type somewhere else where then there's a different value for stock. So what I can do is I can chart it. And what you see nicely here is over here we get on, you know, at 417, the stopped Postgres.  So the same events type that we've categorized through the machine learning with different values for that variable. To get this, I didn't have to parse anything. I didn't have to tell our tool to, to, you know, what to look for it automatically parsed out that as a variable because it saw a bunch of similar events that had the same structure. That's one idea. Obviously, you know, to make to aid in what we're doing here. You also need to be able to do things like search. So as an example here I can, I can do sort of, you know, full reg X searches. I'll go here and I'll search for the text. milliseconds. And sorry, I missed types something there. And what I'm doing is I'm getting taken to an event that the fight where where there's a match for the text by search bar. And once again, you can see here's an example of the end event that it found in the search. The blue is the variable text. And this time you see, you know, there are a couple of metrics that it's found as variables. So I can pick one of them. I can do my display charts and you get sort of an interesting plot of that value inside all the events of that type and how it changes. And I might want to look at, you know, this one that looks like an outlier and go and, you know, click on it and I can get taken there and and so on and look around. And this is all around being able to learn the structure of the underlying events and then be able to pull out this data without having to to manually build any parsing rules. So there's a whole lot more, but I'm going to stop here and hand it back to Larry. Thank you. Hey, awesome. Gavin, thanks. Let me go ahead and share my screen again. Then wrap this up. So, so let's talk a little bit about where we're at right now. So, so we're picking out application incidents Kubernetes incidents and even some security types of incidents. This seems to be working pretty well. So recently we've had some exciting validation so my data reproduced a slew of real world incidents from so they kind of manage Kubernetes clusters among other things that they do there. It's amazing company, but using litmus, which is a tool that they're involved in. And we were able to pull out those incidents that they recreated 100% of them and pull in a root cause indicator and put it into that incident page without anyone having to tell the system. Anything. So this vision that I've outlined for you while it sounds ambitious is actually coming true. So that's been very exciting for us. Next stop for us is to bring in more stuff. So it's so one now that we've got sort of sort of a baseline of autonomous monitoring that seems useful in the sense that if if I weren't me I would want to use it. So the next stop is what, okay, so now what, you know, things so incident, sort of the, the sort of the evidence that you would use to root cause incident doesn't stop at the log file. Sure, it's a very rich source. But, and it was probably a great place to start but but the next thing we're doing is bringing in Prometheus metrics. You know, if you're, it's very easy to deploy in Kubernetes, especially we deploy a scraper and then we look for anomalies in those time series and cross correlate them with log log events so that's going to be in a very soon upcoming release. And I think what that's kind of what we're doing there is we're, you know, we're saying kind of this should be a one stop shop for incident root cause detection for the unknown unknowns for which you may not have, you know, created rules or so on and so on or, you know, maybe it's not reasonable to do so. It's, it's, it's that kind of thing that that we feel it's time to do. So, so that the machines can start helping people do their jobs, and people can up level and do more strategic work than digging through log files. So thank you very much for your time. Here's my contact information we'd love to hear from you. And we're going to open it up for Q&A now. That was great. Thanks, Larry and Gavin. So we now have some time for questions. Please, if you have a question, drop it into the Q&A tab at the bottom of your screen. I see one question there from Nikhil. So Larry the question is, what kind of what is the actual learning algorithm that is used are you using some kind of neural net. Right. So I guess I touched a little bit about upon this during my presentation. So there's really kind of two separate, you know, sets of analytics that we do sets of machine learning. The first is of the structure of the data. And so I talked a little bit about the Swiss Army knife that we use there. And so there's sort of a continuum of approaches that we use that apply more depending upon the frequency of given event type. So the first stage would be heuristics that relate to sort of nesting, nesting indicators, numbers, special types that that you might you might imagine such as, you know, we might so, you know, floats versus ants and so on. So a lot of stuff like that. The next step is sort of reach ability clustering, which takes sort of a more global view of the lines that have been seen. And then the next stage is a naive base classifier with a global fitness function. And then finally, when we're going back and we're kind of sort of amending the structure learning we've done. We like to use LCS for that. So LCS is actually kind of the state of the art for for learning blog structure. It has some weaknesses in low cardinality data. And so for me that's actually kind of like, it's kind of like polish on a car. So it's the last thing that you do. And so with the with that sort of palette of tools, we found that to be very effective. And then and then there's the question about the question about the anomaly detection. So as you saw, there's a couple phases there. So, so the first phase is kind of determining that something is anomalous in and of itself. And when I say in and of itself, I mean that an event of a specific type happened anomalous in isolation and that will have to do with for if it's an event type where you have lots of examples, you have a good sense of, of periodicity of distribution of values of parameters within that event. So you can speak specifically to that. And then, and there's other things. So for example, for us, severity is a free parameter. So you may see an event type event of certain type happen that you may see happen with a difference very. So there's various dimensions upon which that anomalousness can can be can be sussed out. But then the interesting stuff stuff happens. Finally, when you look at these event channels as independent point processes and you develop statistics from those processes. In terms of their auto correlations, their cross correlations, and also the correlations of their activity as a sequence of events versus the values that you see in the parameters. And what you end up with then is something where you can really, really start to hone in on incidents. So hopefully that gives you a good explanation for for what works for us. Thanks, Larry. Maybe I'll tear up a question from myself here. How do you see the Zebrian product. In combination, you mentioned you're going to be ingesting Prometheus metrics. So what would be your target configuration there in terms of does it coexist with the Prometheus based stack or an elastic search or, you know, if kind of a stack. So what would be, you know, where do you see this coexisting with those technologies. Yeah, you know that it feels so I'm not sure if you mean that in a tactical way or in sort of a product positioning way so maybe technical like what would this be designed to complement one or more of those technologies. Yeah I mean so so really I think what we're trying to do the value we're trying to deliver is, you know, yeah sure you know you can have a you can have a log manager. You can have all that functionality but let us do the work to so that's a completely different value prop right and so part of that making that better is to take data wherever we can get it. And to apply it not to the problem of being a log manager and not to the problem of being a metrics alerting dashboard but to the problem of root causing an incidence. So, when you look at it from that perspective, you know you start thinking a little bit less about sort of well how, you know, because nobody else is doing that they're not trying to be that you might say well there are normally detectors in metrics in the metric space at least. And that's true. I'm not sure how far I would take that in terms of root cause detection for new issues if I don't have logs. So I don't really view those as sort of competitive and I really think that sort of like someone who's using Prometheus just to browse through their environment. We may be happy to do that we may take their Prometheus configuration if they share that with us and and use that for our collection so that we know, you know, I mean we're going to want to know what's exporting we're going to want to have access to all of that and of course Kubernetes makes it very easy to get that information but in general we're not trying to come in and say oh just use us you know that's not that's not where we're coming from in the end, you know if you find that you don't need to buy a certain other tool that you know is costing you money, because now you've got something that that will display the data for you will let you search the data and chart the data and all that and it's finding incidents for you that's, that's great I would be like success for us but that's, I don't think that's really where we're focusing we're more focusing on building the value of finding the root cause and giving it to you first. Excellent thank you. Any more questions. Well looks like that's it so thank you Larry and Gavin for a great presentation. Thank you to everyone for joining us the webinar recording and the slides will be online later today. We look forward to seeing you again at a future CNCF webinar. Thanks and have a great day. With that we are signing off.


Welcome back to my channel guys, it's your girl Coco styles and on this channel We talk about beauty fashion and lifestyle, but in today's video. I'm going to show you guys my super cute white sneakers that I purchased from a brand called Coyo and I have been very undecided on which ones I wanted it was either the Coyo's or the Common Projects and I just wanted a sleek pair of sneakers Nothing with a bunch of logos or anything because I wanted to try to wear them with like trousers shorts and skirts and dresses and stuff just on like a chill day or a relaxed day, so I'm gonna jump right into it. I'm gonna tell you guys I struggled with trying to find What size to get because I didn't want to order something and have to have that, you know send it back I'm glad I went with a size 7 because I kept hearing that they do run rather large And that is what I got here So it does come in this box right here. I hope you guys can see that It's kind of bright in this room. Just a simple box and It comes with a really good Dust bag Wait, this is a really good bag. I think it's super good. Let me see if you guys can see this I hope you guys it's so bright right here. I'm so sorry guys It just has the Coyo symbol on the front and then it looks like their logo here also in the back and then I'm gonna try to get some close-up pictures of the sneakers because They're super cute. I'm gonna see if I could I'm gonna show you guys them on my feet and Then give me some close-ups So this is a size 7 I wear size and it's funny because I have wide feet But I'm typically an 11 in shoes, but I'm really a 10 and a half I get an 11 because my feet are wide and I never find wide shoes But for what I reason the 11 works for me. So I'm glad I got a size 7 The These are really cute guys. Most people wouldn't pay this much for a pair of sneakers like this however, I You know, I did find some white sneakers that I liked but they weren't as sleek as this as these are They're made in Italy.  It's the same exact thing I'm just gonna pull up on this stuff I don't know. I need to get a good close-up so you guys can see this and Let me see if you guys can see The coyote in the back Do you see that it's there and then Here's the sneaker just has a little sticker here that says 40 Yeah The tongue is a very thin. It's a thin tongue Okay, which I like and the again the symbol is here Let's see what else It does say made in Italy on the bottom. They said that this is calf skin, but I keep hearing that it's not Inside of the shoe is full leather, which I absolutely love It's full leather on the inside And it's a little bit of suede in the back for cushion, which I absolutely love too The shoe strings are the reason why I have them because they were so strung up so tight They're very sleek sneaker guys And I'm probably gonna do a video with styling them with trousers and like certain things Just like a chill dress down day If you didn't really want to wear like sandals because I do know sometimes wearing sandals You could actually if he can get tired of that So I'm gonna show you guys these are my feet and then you guys let me know what you think about it when I try them on and again, this is a size seven and Let me see what else the tongue is really thin Actually, the tongue is like really thin and it's it's all leather also Yeah, but again, if you if you decide to get these I Would say size down Watch it watch a couple of videos and see what people are saying A lot of the guys that I have seen with them. They have like more of a narrow foot I have white feet. So I don't know the seven were perfect for me I think if I would have went to a size eight They would have been extremely too big, but Here they are I'm gonna try them on let you guys see it and I'd be right back So here the sneakers guys on my feet They are very very comfortable. The shoestrings are Like a nice they have like a nice little light coat on them They were a little stiff inside of the sneaker holes because they had them strung up really really tight But they're very actually they're very comfortable guys So given the fact that I do have wide feet Even though the front is very narrow I think from my feet they fit me really really well and again I did get a size seven and if you are a size eight, I would recommend you guys sizing down I am saying size down because I have wide feet and The seven fits me good And I still have some room. So if you are an eight in men Or an 11 and women, I believe that's how it is You can definitely get a size seven and you will enjoy these sneakers guys. All right, so here's the look so let me know what you guys think about those sneakers and I will actually do another video again, like I said on styling them with a dress maybe some trousers just something For a chill day. So if you like videos like this, please comment below And if you stay to the end of this video, please come at cocoa We see you and don't forget to hit that like comment and subscribe and guys Don't forget to follow me on Instagram at cocoa underscores fouls underscore and why so I will see you guys in the next one. Bye


Imagine attending your first figure skating competition, you've been training on your jumps and rotations, mastering your half-lets and axle, and you're feeling pretty confident. The music begins and you perform to the best of your abilities, doing everything you were taught in lessons. Then the judging comes through, and the marks are low. Disappointed, you go to ask the three judges for more feedback. You enter the room and it turns out that your first judge is someone you recognize, a novice who has attended your skating class for less time than you and wasn't selected for the competition. The second judge, a friend of the woman who runs the rink and has never seen skating before, but said she would help out. The third is an experienced skater who had a big fight with your coach five years ago. Are you any less disappointed by your low scores? Have you truly learned anything about your skating skills and abilities? The biggest criticism of debate judging is that, at its worst, it offers debaters experiences like those of our imaginary skater. Anyone who has been debating for a few years will have a story to tell about a debate where the judges got it wrong, according to the teams in the room. But what is remarkable when you consider how subjective persuasion can be is not how widespread these tales of injustice are, but how rare a tiny portion of the results of competitive debates are officially challenged by debaters. Through a stable body of rules, judge tests, trainings, and feedback over long periods, the debate community has managed to bring the subjective judgment of judicators closer together. Today we will cover the basics of judging in debate. We will discuss the common types of judges in paradigms, look at the process for fairly evaluating a debate round, before finally walking you through the process of filling out a ballot. In debate, you're going to find yourself arguing in front of a lot of different types of folks. Let's start with some of the common types of judges. Generally speaking, you were likely to run into three types of judges in competitive debate, flow judges, comms judges, and lay judges. Understanding these three categories can be useful, but be warned that overgeneralizing also causes trouble when it comes time to adapt. First, the flow judge. Flow judges preference the line by line on their flow sheets. This means you can typically speak a little faster in front of them, but focus on your organizational pattern. Making sure to give both organized roadmaps before you start and signposting constantly during your speech will be critical. Most flow judges also make efforts to protect the flow. This means that they may choose not to flow or may even disregard new arguments made in the final rebuttals. Second is the comms judge. Comms judges preference delivery and persuasiveness in their evaluations. When debating in front of a comms judge, it is important to bring up fewer arguments and make sure to speak slow and clear. Comms judges take a more holistic view of evaluating rounds, but this does not mean that you can just abandon the line by line of the flow. Try to balance working through the arguments while putting an emphasis on persuasiveness. The last major category of judge is called the lay judge. Lay judges by definition are individuals who have no experience participating or judging debate. In competitive debate, lay judges can be somewhat common. In fact, the IPDA style of debate actively encourages the use of lay judges to ensure that the event does not get overly fast and technical. When debating in front of a lay judge, there are a couple of things that you can do to adapt. First, avoid overly complicated argumentative strategies. Procedural arguments or critiques will likely be met with more confusion than understanding. Next, try to split ideas up into smaller, more manageable components. Next, try to emphasize the reasons why you are winning. It will be less obvious on the flow of a lay judge that you are upholding your burden in the round. To be honest, most lay judges will take notes on key arguments opposed to flowing the debate. Try to focus on making offensive arguments if possible. Next, make sure to roadmap and signpost a lot. This repetition will help the lay judge manage the arguments, particularly if they are not flowing the debate. Last, try to connect your arguments back to the ballot. It's pretty easy for debaters to overwhelm a lay judge. If you clearly explain why each argument you make is a reason that they should vote for you, then you would make it easier for them to do so. Debating in front of lay judges can be challenging, but the good news is that folks do not remain lay judges for very long. In fact, by watching this video, you have taken the first step towards becoming one of the first two types. In addition to the three types of judges, you will also discover that judges tend to develop a paradigm over time. In debate, a paradigm is an overarching theory about what a fair debate round should look like. Many tournaments request or even require judges to provide a written paradigm before the round begins. If you plan to judge debates, you should consider writing one yourself. If a judge does not have a written paradigm, it's common for debaters to ask general questions about them before the round begins. Knowing your judge's preferences can be extremely helpful when it comes to adapting to their preferences. Now that we've covered a bit of the background on judging debates, let's talk about some of the important guidelines that judges need to follow.  There are a couple of things that you can do to help accomplish this. First, make sure to respect the debaters that you watch. While I'm the first to admit that debate is just a game, it's one that competitors have poured a lot of their time and energy into. Having control of the ballot gives you some power. Be fair how you exercise it. Try to keep an upbeat attitude. Remember that the only instance in which you should interrupt the debaters is if they go over the time limits. I find that setting an audible timer to go off at the end of the speech does the trick. When the debate is done, make sure to provide some meaningful feedback. This should always get written on the ballot. Make sure that you also include a well-informed reason for a decision. It is your ethical responsibility as a judge to explain how you arrived at your decision. Once the ballot is filled out and if the tournament allows it, you should also be ready to share your feedback and decision with the debaters. Second, it is important to leave your own bias out of the decision-making process. In debate, the competitors have little control over what side of the issues they debate. Their job is to present the best possible arguments for the side of the topic they were assigned. Your job is to decide which team debated better, not which team happened to cater to your pre-existing opinions about the topic. When you are asked to judge a debate, you need to attempt to be as impartial as possible. This means to evaluate the round you witnessed. This means no matter how much you disagree with a debater's argument, if their opponent mishandles it, then you must give it the full weight in the debate. Remember, it's a game, but if you vote for a debater who is not winning an argument, then you are harming the fairness of the activity. Third, you should evaluate rounds based on hierarchy. In a debate round, there are certain types of arguments that must be evaluated prior to others. If the round you watch contains theory or framework arguments, then those must be decided before the substantive issues of the debate. This is because theory and framework arguments center on the fairness of the debate. It would be unjust to award a win to an affirmative team who was not following the resolution but had bigger impacts on their advantage. Evaluate procedural arguments like topicality first, then, if you can get past them on the flow, move on to the substantive arguments of the debate. Last, make sure you follow the debater's criteria for evaluating the debate. For example, in most policy-based debates, you may hear debaters argue that the round should be evaluated through the lens of net benefits. If both teams agree to that criteria, then it means you need to look at your flow at the end of the debate and see if the affirmative team has proven that the good of their plan would outweigh the net detriments and leave the status quo better off. If, at the end of the round, it seems like the status quo is the same or would be worse, then you would have to vote for the negation. Occasionally, you may find yourself watching the debate where neither team has provided a criteria. If this happens, then you must intervene and judge the round. This is a weak form of judge intervention, but the debaters have left you with no other choice. Try to be as fair as possible. Now that we've discussed some considerations of being a judge, let's look at how you would accomplish it in an actual round. Before the round, when you get your judging assignment, be sure you know the specific event you are judging and where the room is located for that event. Ask any questions that you may have about the event to the coach or sponsor who brought you, a tournament official or another veteran judge. Go promptly to your room assignment and be sure to start on time with the correct students assigned to your room. If you are judging with more than one person, be sure not to start the round until all judges are present. Be sure to ask the tournament staff if you are judging as part of a panel or if you will be the only judge in the room prior to the start of the round. During the round, it's imperative that you give the debaters your undivided attention. Please silence your cell phone and instruct any audience members to do the same. You should actively listen and keep time, following the time limits. Use the ballot provided to write your comments and remember that your feedback will be read by students, their coaches, and is often shared with fellow teammates or other contestants. Comments should be constructive in nature and help the debaters understand your thoughts as their judge. During the round, you should keep track of the major arguments by flowing the debate. Keeping an accurate flow of the debate is really the only ethical way to evaluate the arguments at the end of the round. Once the round is ended, you will determine the winner. A winning debater would have done a better job articulating their position and refuting their opponent's arguments. Consider the major arguments in the round and how they were refuted. Which argument or arguments do you feel were established throughout the debate most completely and convincingly? Make sure that you provide this information when writing your reason for decision on your ballot. Different tournaments use different ballots. At some tournaments, the ballots may be provided to you as a sheet of paper. At other tournaments, you may be asked to pull up a website to enter your ballot electronically. Regardless of the modality, ballots tend to ask judges to fill out a couple of key things. First, make sure to provide any round information asked. This typically includes the round number, the debater's names, and the sides, your name and affiliation, as well as the resolution being debated. At the end of the round, you will need to indicate which team won the debate. This is either done by circling the winner or checking the correct box or drop-down option. Next, you will need to provide both quantitative and qualitative feedback. Most ballots will ask you to provide a rank and speaker point score for your debaters. Ranking means giving the best debater the one and the next best debater the two, and so on, until you've ranked every debater in the round. Speaker points are a quantification of how well debaters spoke in the round. Most ballots will provide an explanation and limits for what speaker points should be. This is a common rubric used in NFALD and NPDA styles of debate, where scores tend to range in between 1 and 30. In IPDA style debate, the following guide is provided to help judges calculate a score out of 40. Some tournaments have speaker points that go up to 100, so make sure that you are following the directions on the ballots. Next is the qualitative portion of the ballot. Here you should seek to leave some constructive feedback to all of the debaters in the round. Make sure that you try to be encouraging in this process. The final part of the ballot is the reason for decision, usually called the RFD. This is where you need to provide a reason via the criteria of the round why you voted the way that you did. This is a good opportunity to make sure that you are fairly evaluating the debate. RFDs should be specific and reference the content of the debate. It's not acceptable to simply say that you thought one team debated better without explaining why. Judging is a critical part of the debate activity. It's important to take the process seriously and do your part to help the debaters improve. Today, we went over the basics of judging and debate. We discussed the different types of debate judges, looked at some of the key responsibilities in judging, before finally covering how to fill out a ballot. Debating may be a long cry from figure skating, but getting a fair win can be pretty rewarding regardless of the type of competition. Thanks for watching. This video series is written and produced by me, Ryan Guy, with the help of a wide variety of scholarly research and open educational resources. A special thanks to Neil Harvey Smith, whose 2000 work gave me the attention-gitter for this video. For more information on the references and materials used, see the description page on YouTube. This video is published under a Creative Commons license. Please feel free to share, use, and remix its content.


Welcome to the YouTube channel. I'm super excited. You know why? Because I got a solution to all your problems I mean, I feel like the videos that I make in Africa makes a lot of impact in people's life Can you let me know the kind of impact that my videos have made in your life? You know what anytime that I wake up people test me email me Asked me that hey Maya.  It's calling me. I'm filming a YouTube video, man. I'm so sorry Whoa Hello, hello, hello, what is the call? Listen, I have to see you You have to see me to see you right now I just send you my location. Yes, I'm actually in term of coming Can I send you on WhatsApp because I need to continue filming? Okay? No problem I'll see you soon. All right Okay That's an African-American from her voice is like I need to see you soon. I hope I've not done anything wrong I mean, these are people that get inspired and move to the Madeline because of my videos Some of them come to visit because of my videos and I just hope She's okay. So just let me wait till she comes back and I'm gonna continue the video. I want to introduce you to my friend That was fast local woody that was so fast. Yes Yes, it's right by motor junction. It was very quick very quick. Yes, you know, you know the motorway now. I know now How long have you been in Ghana that you know the motorway? I've been here for about three four weeks three four weeks How is Ghana so far? I love Ghana.  If I panicked you something is wrong. What is wrong local? I don't want to go back home Really so you want to move to Ghana I want to move to Ghana everything's going well, I love it here Back with the retreats So local is saying that she's doing fab loss with retreat. You know what she does. What do you do by the way?  I thought he was supposed to go back to America now that you want to live in Ghana What is the problem? What do I have to do now? Now you have to find me a place Yes, I mean a place to live in You have any specification I want a walk-in closet and a fabulous bathroom.  Move move forward. You see this unit right here. You know how much this will go for 400,000 That's double the price This is just $250,000 local I can see the dollars in your bag Maybe I need to show you when you are interested and I take the bag and I give you the house Okay, let me see the closet first and then we'll see if we can switch to the bag for the house I know that I will convince you with this house right here because I mean you said 400,000 Yeah, I'm giving it to you for just 240,000 dollars. That's amazing. Just go in and give me back my money You know the money's in the bag, right? The money's in the bag. Okay Yes, you know for me trust is everything Integrity means a lot to me and you know, I trust you. That's why I came to you Trust me I know you have money in the bag That's the money that I want to take. Okay, but I want you to feel secure. So first of all, I want you to meet the developer of this whole project Excellent. Are you okay with that? Hi, Woody, how are you? Are you expecting me? I'm told I wasn't. This is a big surprise Hello, I called Woody in a whim. I told him I had to see him. I Don't want to go home. I don't want to go back to America I say you want to stay in Africa? I would love to stay in Ghana. And Ghana specifically, you're welcome He's a developer behind the project. Oh, excellent. Do you have any questions to ask? Oh, I want you to tell me what's unique about your property. Okay Well, that's the broad question But let me take you around the project so that I can explain to you how unique the project is. Okay So this is a gator community, right? It's called La Rocha, which means the solid rock Okay, and in this gator community, we have two villa types. We have villa type A and villa type B So what you see here is your villa type A, which is 234 square meters That one on the construction right there is villa type B. That's 194 square meters Both of them are three bedrooms and they all have An outhouse, right? The difference between type A and type B is basically the size of an American. Can you tell me what an outhouse is? Okay, it's like a maids quarters Oh, excellent. So each one has a maids quarters outside of the actual bedrooms. Oh, that's great So some people call it a four bedroom, but we call it three plus one Okay, and we also have our apartment complex also coming up right in the corner And that's four apartments with two bedroom, two bedroom luxury apartments also coming up Now this, like I said, is a gated community So we also have a communal pool Okay And a gymnasium for the residents The gym is near and dear to my heart Oh, that's near and dear? Yes Well, it's a health as well Exactly, exactly And you have your rooftop lounge also You create an area where you can chill, relax The good thing about this development is you have 24 hour security 24 hour backup power And 24 hour water backup also Which is great because we want you to live and be comfortable within the space No problems whatsoever You're secured This is home, this is Africa, this is Ghana So you're always welcome We also have a commercial area right at the front We're gonna have supermarkets, restaurants, etc Right at the front also All in the community? Yes, all in the community Oh, that's amazing So you don't really have to go out Everything is in one space for you Yeah, so this is La Rocha But for now I think I want to show you what Villa Type A looks like I can't wait to see it I'm so excited I want to know how many people will be staying in here How many townhouses are we gonna have here? So we have 12 Only 12 Only 12 For this particular development No, you have to Because we're running out That's why I brought my money That's why I brought them Oh, that's the money bag That's the money bag Oh, excellent Is this one ready? That's ready, it's ready to go No, don't change your mind I can take the bag I'm not gonna, let me see it I want to see the closet Let me see the closet Let's see the closet Okay So the Villa Type A We have eight of those villas And then in Type B We have four Excellent Only four As you can see You can park three vehicles Comfortably I don't know if you drive a big Chevy in America I don't know what kind of car you drive But I believe You know, we can fit three cars Okay, excellent Yeah Alright, Lako Yes Welcome to your new home Oh, this is amazing This one? And the color scheme It matches I think it's meant to be Yeah, so you asked a very important question What makes this property unique? Well, we carefully selected Everything that you see in here You know, we don't compromise on quality And that's something that we stand for Our motto is Building your perfect home Is our priority So we do everything within our part To make sure that We come up with a product That the buyer Or whoever lives in here Will be happy with the property So far, I'm happy You're happy? That's good I have to see the closet as well But so far I'm happy We'll leave a surprise over to upstairs So this is an open plan Living area That's your main living area With your dining right here And then you have your open plan kitchen Okay, this house comes fully fitted You have your refrigerator Your oven, microwave, stove And everything But it doesn't come with a dishwasher But for you, Lako, if you want a dishwasher I'll give it to you Thank you I appreciate that And that will come with no cost Remember, you heard that, right? I told you it's going to cost 240, right? Yes So you take 5,000 dollars 5,000 That's so expensive That's so expensive You know, this house actually goes for 270,000 But we call it the Wudimaya Special Oh So we actually sell it for 240,000 Wow So let's go into the guest bedroom So all the bedrooms are on suite Okay, that's your bathroom Right there And this is your guest bedroom downstairs Okay All the bedrooms come with a closet Okay The two bedrooms, the one here And upstairs at the wardrobe this size And the master bedroom has a surprise for you Okay That's a surprise And this is your guest Oh, a guest bath Nice Very interesting lighting features Okay Quite cool for makeup and, you know Yeah All the good stuff For my makeup artist Okay, so we're going to go upstairs Woody, I'm going to reserve the second unit for you The second unit, eh? Yeah So I can stay close to your home So we can train often if you're close to me No, I'm tired of training Before I show you guys the rooms at the top I just want you to know that your money is in safe hands And before I do that I need to convince you about who this developer is You know, I know a lot about you But people watching us for the first time Don't know who you are Sure Just a brief intro about yourself, man Sure So my name is a doctor Emmanuel Kojo Jones-Mensa I'm an entrepreneur here in Ghana I operate in three main industries Real estate and construction industry The oil and gas industry And agricultural industry You know, I actually used to live in the UK Okay Yes, I lived in the UK for about 10 years And then I left the UK, came back home Because I had a lot of opportunities here in Africa I just thought I had to contribute my quota To the development of Africa So if you look at the things that I do They are all inclined or in line with development So that's just a little about Doctor Emmanuel Kojo-Jones-Mensa But a lot of people know me as Kojo Jones And, yeah, I mean That's a little I could say I'm a happily married man, I'm sure A lot of people know We saw the wedding We saw the wedding I think that's what people want to hear We all saw the wedding But that was a very large wedding now It was, you know, but I didn't expect it to be that big But I just feel You know, I've been there for a lot of people Right And they just felt like Okay, this is the only way we can pay Kojo back And show our appreciation So they came all out for me And I think it was amazing Which means it was your friends That did the wedding for you I see friends and family You don't need friends like that Friends and family I mean, it was up to the roof It was exciting It was a brilliant experience I think it showed the cultural How rich our culture is here In Ghana, in Africa And I think that's, for me That's the element that I like to portray the most Yes, we have a lot of wealth in Africa But we have to display it the right way That's incredible Yes, display it the right way And not forget about Impacting the lives of people also Because at the end of the day That's what we are in life for To impact lives But I've seen a lot of your philanthropic work The Bohos in different communities I've been following what you're doing You have, you have, you have You're right So that's actually my baby That's something I'm really passionate about These are things that I do And I get real fulfillment It's touching lives of people So I have a foundation called the KJM Foundation And what we do is We give water to less privileged communities We impact a lot of schools Through education And also youth empowerment I know you do a lot of youth empowerment Things yourself And I think that, you know The future of Africa is very bright But we need to really get the youth to work And to understand that These opportunities are there for us to take And I think, you know Equip them, empower them, teach them And show them the way A lot of people, I mean My inbox day in, day out Dr. Korjo, show us the way Show me the way And the only way we can do this Is by bringing them together Teaching them And assisting them also financially Because that's the biggest challenge We have in Africa Financial assistance For startups, entrepreneurs And I think if we're able To find a solution to that I think we'll have a great continent And all the other continents might be, you know Right behind us I think we need to find time And I mean Have a proper conversation No, yes, yes Absolutely Because I know you also have a hotel That I think in future We might talk about that Yes, yes But for now We are here for this estate That's right Is this your first ever Gated community? No, no, no This is actually the third Yes, yes What was the first one? The first one was Encantement The second one, East Lagoon And this one is here It comes to 23 Why here? Well, I told you You know, there are great prospects In this area I told you they're building a stadium You know, for all African games And that tells you that A lot of development is going to come Once the stadium is built So, you know, as developers We pick the nugget here And then we place our development Where the next big area is going to be And also We're looking at creating Different products For different people So you see in Encantement The product is very high-end East Lagoon, also high-end This is more mid to high And then we are gradually getting from Moving from mid to high Down to mid to low So that we can give everyone An opportunity to own a house Own an apartment And I think that's the dream That's the African dream That we're trying to create here at Africa In Ghana, yeah If this is 240 Yeah, this is 240 This is 270,000 What about my price? What about my price is 240,000 For Villa type B It's 200,000 What about my price is 170,000 Oh, wow And then for the apartments It's $150,000, Wodemaya price is $120,000. Only for Wodemaya. Only for Wodemaya. And because of this, come get your own as soon as possible. Okay, let's start here. You have a family area here. Nice. You can decide if you want to change it to an office. But we made this very flexible, but ideally it's supposed to be your family area. A space very separate from your main living area. So you can spend quality time with your family here. An interesting feature just to give it, you know, some finesse. Yeah. You know, which is great. Nice built-in. Yeah. They have another bedroom here. Like I said, all the bedrooms are also here. Oh, this is another, okay, excellent. Nice size. Yeah, nice size, second bedroom. And the wardrobe. Wardrobe. Yeah, happy so far. I'm happy so far. These closets aren't big enough for me. So other than that, let's see. Okay, now all the curtain, the main curtain here is automated. Yeah. So you push the button and you get the clothes. Oh, that's nice. That opens as a smart home. As a smart home. Yeah. So you don't want the lights coming in. You can just close it. But I think for now we'll leave it open. So the master bedroom has a very similar feature which I'll show you. Oh, okay, I can't wait to see. So come, let's see what the master bedroom is. What do you find what you're looking for? So far, so good. So this is your master bedroom. Very nice. The view is nice. Yes, that's the balcony. We will come back to the balcony. But you have your TV points right here. The TV goes there. You relax and then watch. Oh, you have more built-ins in the master bedroom. Yes, you do. Very nice feature. We do. So this is your walk-in closet. I'm sold. You're sold? I'm sold. Let me see. Udi, can you give me that? I'm sold. Hold on, hold on, let me see if it fits, let me see. There's a lot of space. I'm sold. There's a lot of space. Yes. Udi, can I take the background? No, it's okay. Thank you. But wait, I also wanted a fabulous tub. This checks all the points. Excellent. All the points. I'm happy, I'm happy you liked it. So let me see. And then you have your bathroom. Oh, amazing. Okay, good job. Absolutely amazing. So, a shower. And you have everything. Yep, that's it. Absolutely amazing. But this, wow. You can fit two people in there. Okay, nice. Two people. I'm ready to give you the balance. Wait, wait, my bucket looks 350, so I gotta take out the balance. Oh, okay. And give you the 240. But then you can have the bag witty. You have outdone yourself. Yeah, that's the balcony, right? Yeah, that's the balcony. Like I said, you also have to make the curtain tear in the master bedroom. Yeah, I think we're ready to go to sleep now. It opens up the same way. So blackout shades, which is important because the sun rises very early in Africa. It does, it does. And you have a balcony, that's it. I'm sold with the closet in the bathroom. The balcony is just a bonus. Anything from here on out is just a bonus. Oh, this is amazing. So there's a very decent space. Once the development is done, you can imagine what the views will look like. Absolutely. You know the beautiful thing about this area, right? This comes in 23. It's that they're building the Olympic Stadium. Oh, wow. So they're gonna be hosting the all-African Games. Donna is hosting the all-African Games and they're building a whole stadium with a lot of infrastructural stuff in there. So that gives this area a lot of value once that stadium is up and running. Which I believe I think in about four to five months should be done because we should be hosting the Games very soon, you know? And that's the plus you get with this property because once you buy it, you get that property and appreciation over a period of time. Within a very short period of time. Now it's only this unit that is ready? Now this is the show house is ready. Is my unit ready? Calm down. Okay. Your unit is done. Thank you. Yeah, so the other ones are under construction and the apartment is also... So if someone is interested, if... Are they supposed to pay down? Yeah, they're supposed to pay a deposit of 30% 30% of the amount. And the good thing also is that we have a payment plan for two years. Oh, wow. Yeah, so you can spread the payment over for two years. Please. Oh, so I don't have to... No, no, no, no, no, no, no, no. Okay. Okay, okay. My unit is ready. So it's okay. I wanted to tell you about it. You know, so that's the two years you spread it across.  You know, we're very flexible. So we're looking for 12 people? 12. No, we're not looking for 12 people. So we're looking for 11 people. I'm sorry. We're looking for 11 people. 11 people. The apartment also has four apartments. Four apartments. Yeah. First come, first serve. First come, first serve. I think first come, first serve. I don't want to say anything bad. I mean, locally sold. I'm also sold. So I think... So you reserve the villa too for you? Please. But I'll give you 20% from the beginning. It's what you're buying, so it's fine. 20% is okay. Yeah, that's fine. Let's make it happen. But I want to see the boys' quarters. You want to see the outhouse? Okay. Okay. We'll see the boys' quarters. Oh, boys' quarters. Okay. Here you have it. It's close to the sea. Yes, we are. The breeze. Yeah, the breeze. Yeah, yeah, yeah. Okay. So you have a backyard, you know. And it's a perfect size. Perfect size, relaxed, chill. After a long day's work. So the outhouse is right here. Oh, very nice. And I like how it's separate from the rest of the house. It is. And you have your shower. Yeah. Probably, it doesn't lack even in the boys' quarters. Exactly. So you have your room here. Oh, okay. So currently using this as an office. Okay, so you could possibly do that if you didn't want to use it as a... Yeah, we could use it as an office. Use it as an office. Yeah, yeah, yeah, yeah. I don't want to use it as an office. Excellent. Excellent. Here's the bag. Here's the money. Take it. I'm sold. Nice doing business with you. I thought the man was supposed to... He has to go to you and come to me. I'm not going to give me a position. I'm not going to give you a position. I'm not going to give you a position. I'm not going to give you a position. Business with you. Thank you so much for buying this. And this man will go a long way. I'm not sure of this. There's money in there. There's money, yeah? There's money in there. I've come a long way to build it. Don't worry about it. Okay, pleasure doing business with you. Thank you for my new home. Thank you, you're welcome. You're welcome, you're welcome. Woody. Yeah. I'm waiting for your deposit. You're 20%. 20%? You know what? I got so many people watching this video. Right. If you should tell them something, I will convince them to buy this minute before they're going to sell it. Well, I think especially for those who are looking to come back to Africa, the opportunities are here. But if you're moving back to Africa, one of the things you need to think about is where you're going to live, where you're going to stay. And that's what Empire Domos is trying to provide, the right solutions for whoever is looking for the perfect home right in Africa. We have different products. We have properties that are worth 1,500,000, 250,000, affordable luxury. Whatever home you're looking to get in Africa, Empire Domos is the go-to developer for you. We're welcoming you to Africa. We want to see you here in Africa. We want you to come back home, especially those in the diaspora. Come and let's build Africa together. Because this is all we have. Apart from Africa, we have nothing else. So come back home. And we're ready to have you. Thank you. Thank you all so much for watching. I have the money in my hand right now.  But all you need to do is to reach out to them. Let's make the dream come true, man. 10, 12 people. I think I believe that 12 people will click on this video. And all the 12 people are going to buy it. So I'll come back for more money then. I'll come back with a bigger bag. Bigger bag? I will definitely come back. Don't forget to like this video, share, and subscribe. I'll see you all in the next one. Thank you.


Let's head to the pitch. Bemidji Brainerd rivalry first with the girls meeting. Warriors attacked early and often. Regan McElfress, the Point Blake shot here, but big save by Ali Beard than later. McElfress again, look at this rocket. But Beard once again there for the stop. Jacks with a great chance later. Anna Schuchman to Kelly Christopher. But Molly Rudolph comes out and plays it perfectly. Warriors would add three goals and hit off late. Hold off late Bemidji Rally. They are now 10-in-one on the season. If you've enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland Public Television.


And the snow to give you my first impressions of the Hoka Arahi for there it is as well behind me on the shelf my first stability shoe of 2020 you know that I am more of a neutral running shoe kind of guy but I will dabble every now and then for a shout out to all the stability runners out there that need some of that stability through their running shoes okay quick side story before we dive into my first impressions by the way this is not my full review that'll happen after 50 miles I had surgery in college on my right foot to release my fashion tendon I had plantar fasciitis for years in college it was really bad and it just would not go away so I had surgery and which is why in my my opinion my thesis as to why my right foot overpronates a little bit more than my left foot if you watch the full reviews on this channel you've seen my right foot overpronate just a little bit more so anyway every now and then I do like to test out stability shoes because of that reason okay today's run 13 miles all right there it is on your screen in kilometers 745 a mile what I like to call my steezy pace it was perfect pounding ground out there and the soft fluffy snow it was amazing I bet I think I saw one other person out there for 13 miles so I love those kind of runs and also the last it's crazy but the last stability shoe that I tested it was about crazy enough it was about a year ago it was the Brooks Ravenna 9 what I I actually ended up returning it because it was a little overstabilized meaning it was not it was not loose actually here we go the twist test it was not loosey goosey enough through that twist test for me I like a little bit of mobility through that midsole even if it is a stability running shoe okay let's dive into it we're looking at a drop of 5 millimeters okay 32 millimeter stack height in the heel 27 in the forefoot for that 5 millimeter drop for that weight we're looking at 9.6 ounces in men's size 9 or 8 ounces in women's size 8 so pretty lightweight for a stability shoe usually stability shoes are a little heavier because they have to have added guidance through that midsole what a lot of running shoe companies called guide rails you've probably heard you maybe have heard that term before it's basically well it's a lot of it's a it's a piece of plastic that can run along the side of the midsole right through this area on both sides just to add that stability or what are what's also called a roll bars so that you're not over pronating or supinating too much through your gate cycle for the upper on the Rahi for its leaner than the three last year's iteration and in my opinion it's a classic hoka upper it feels like a hoka upper it looks like a hoka upper it's an engineered mesh pretty breathable I must say on today's run it felt breathable especially through the tow box I felt the air coming in my toes they didn't get too cold out there but it definitely was very breathable through the engineered mesh the tongue is not gusseted just so you know it's not gusseted to the outer wall of the upper and the heel counter nothing nothing to write home about again pretty lean through that heel counter what was I gonna say it yeah oh yeah not plush that's a good way to put it the upper is not plush at all so if you like a little more extra cushion through your upper this might not be the shoe for you okay moving on to the midsole and the outsole of the Rahi for pretty cool stuff going on here I'm gonna I'm gonna combine them together because they're working as one so do you see the J shape out here on the outsole so this orange EVA foam is firmer than the white foam okay and that firmer foam creates the stability for this shoe alright so notice notice that they on the medial side so the inside of the shoe the EV the firmer orange foam wraps around wraps around the heel but then it stops on the lateral about half way down the lateral side of this midsole and that is what creates it that is what prevents you from over pronating through your foot strike I think it's kind of neat I haven't seen so it's different than a lot of other stability shoes that have guide rails or roll bars through their midsole's Hoka has this what they call their J frame stability so I think it's I think it's interesting and I must say the first two miles of today's run I definitely noticed the stability it felt like I felt it a lot in the heel right here on the inside the medial side of my heel but then after about two miles of running today it went away I forgot about it and I was like okay it's opening up a little bit I didn't feel like it was overstabilized if that makes sense that's one of the reasons that I don't really enjoy stability shoes is because I feel like the stability is it's telling my foot where to strike rather than me telling my foot where to strike and I like to be a little more in control of my foot strike if that makes sense so anyway that's the J frame stability that runs through the midsole and the outsole here I think it's a I don't know so far so good I like I like what Hoka is doing with that J frame technology one last note on the outsole so there is exposed EVA foam but it's hard to tell because it's all white but there is also zonal okay zonal rubber right where my finger is at right now there's pods of rubber to help protect and basically increase the durability of the outsole of the Arahi 4 for fit I went true to size no issues there no slipping through the heel if you need a little wider toe box you might not you might want to look at a different lineup of stability shoes like a Brooks lineup I you know Hoka is known to be a little more narrow especially through that toe box I didn't notice it for me but just keep that in mind if you need a wider toe box on the comfort scale for the Arahi 4 we're gonna go medium all right medium it's not plush through the upper and with the stability built into into the midsole it's definitely a little more of a firm landing okay just keep that in mind my positive for the Arahi 4 it's got to be that J frame it does not feel overstabilized for a drawback I don't really have one at this point so that's a good sign we shall see after 50 miles now when I talk about durability in my full reviews and my first impressions it's always a prediction or a thesis because I can never take the shoes all the way to 400 miles 500 miles because there's too many shoes to test out there but my prediction for this shoe is that 400 to 500 mile range okay that's my prediction based off of my first run we shall see after 50 miles how the upper how the outsole and how the midsole is holding up and how will I use this shoe moving forward crazy enough I think it's gonna fall into more of the middle distance to long run category meaning 15 to 22 miles I it's it's it's got potential to be like a tempo shoe but I think the midsole is just a little too much for me to be a to be a tempo shoe so I think it's gonna fall again into that long run category now is it worth the $130 price tag I think they nailed it all right maybe man if it was 120 they definitely would have nailed it but 130 is not bad for a stability shoe considering I think again that midsole I think you're gonna get about that 400 to 500 mile range out of the Rahi for and on to that question of the day you better believe it it is time for the stability runners out there to shine down in the comments here we go what is your go-to stability shoe right now and why this is gonna be a great education for me as well because I'm just it's like stability shoes are just a little foreign to me I just don't get it I don't get a chance to run in them as much as neutral shoes so I can't wait to read your comments let us know what you're running in right now and why and there you have it first impressions of the Iraqi for I'll get you that full review after 50 miles I think there's a good chance we're gonna get to 50 miles in this shoe how long it takes I don't know but as my volume as you know I'm marathon training right now and so my volume is climbing pretty good you know next week I'll be in that 80 to 90 mile range so I have a good feeling we'll get to 50 miles in the shoe sooner rather than later all right that is the vlog for today I know it's a little shorter but we're gonna keep we're gonna cut it there and onward and upward all right all right everyone we're gonna toss it back to my full review on the right of the Hoka Carbon X that'll be on the right and then on the left I'll find another Hoka shoe you know what we'll toss it back to the Hoka Carbon Rocket full review that'll be on the left all right everyone thanks for being there thanks for watching as always see beauty work hard and love each other see you tomorrow


Although state governor wrote to me Akira Delu yesterday received his Oshun state counterpart Mr. Uega, or it's all at the government house, a lack back at the state capitol. Governor Oshun was in a karate commissary with the governor Akira Delu over the last Sunday terror attack on the war. Governor Akira Delu, while thanking the Oshun state governor for his visit, acknowledged several calls since the incidental code, and said the last Sunday attack was better imagined, stressing that the security architecture of the country must be adjusted. According to him, I'm attacking most of the strength and to meet the security challenges in the region. Hello, hope you enjoyed the news, please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


St. Nusha has been sustaining efforts in managing the national COVID-19 response over the course of this year. A core component of this response is screening and testing for the virus. A total of 1,955 tests have been carried out to date, and as of July 10, 2020, St. Nusha has recorded a total of 22 confirmed cases of COVID-19. And 19 of these cases have fully recovered, with three currently in care and progressing well. On Thursday, July 9, 2020, St. Nusha received the first international flight since the border opening on June 4, 2020. A total of 131 passengers travelled into St. Nusha, which were a combination of both nationals and visitors, as guided by the established protocol for all arriving passengers was screened by the health team of the Iwanora International Airport Public Health Facility before entering the airport terminal. None of the passengers displayed respiratory symptoms nor had fever. A total of six PCR tests were done at the airport on arrival, and these travellers were unable to travel into the island with their documented results. Passengers were later filtered to one of the locations established for the quarantine period. Many processes were put in place to ensure the efficient processing of the arriving travellers at the airport health facility. The processing of the 131 passengers of Thursday, July 9, lasted for about an hour and 30 minutes in total. A debriefing meeting was held immediately after the process was complete, and this involved representatives from the Ministry of Health of the St. Nusha Air and Seaport Authority and representatives of all of the other relevant agencies. This allowed to identify the strengths within the current process flow, as well as to identify areas requiring improvements. These improvements will focus at supporting the smooth flow of the passengers, which includes increased signage, dedicated lines for passengers with different conditions, and the use of a numbering system to provide some order in attending to the travellers, an increased number of booths for the processing of health screening. As we continued into the phasory opening of the country, we continued to advise the public to maintain the recommended public health measures to reduce the risk of transmission of COVID-19. In the event of the importation of cases, we will continue to provide regular updates on the national COVID-19 response to the public.