Welcome to, hey, it's Jeff Gehrling, already here this morning. Welcome to vlog Thursday, and it's number 351. I am at Vid Summit, so I'm keeping this kind of short.  That's just what I was trying to figure out. This is how many people are using the Huvva app, not the full amount of attendees. I've heard a couple of people say we have over 3,000 people here at Vid Summit, but at least signed up just for the app, for the conferences, 2,420.  We, me and Jeff had a conversation. We've been sharing some of the fun details we've been learning here at Vid Summit, and you can go to VidSummit.com and figure out more about what it is. Essentially, it's a creator meetup for lots of creators, primarily YouTube, but there's other people here creating other content for other things. Definitely really fun. I'm learning a lot. I really like getting to meet people in person. There's as much as I'm a technology-driven person, if you will. I think there's a lot of nuance left on the table, if you will, that I would describe that you miss when you just do a Zoom or some type of video conference meeting. Sitting down at the table and meeting people is always a little bit better, or maybe a lot better. That's why I took the time to come down here to Dallas, Texas. I'll be here a few more days, and if you're here at VidSummit, say hi. A few people said you looked busy. I said, what do you mean I'm busy? I'm here to meet people. I should get a shirt next time. I've seen some people say, like, hey, I'm a video editor under a shirt, and I'm like, I should just get a shirt so I'm here to meet people, maybe. You know, I don't mind people coming up, talk to me, asking questions. That's really why I'm here, is to hang out, do some learning, collaborate with other creators, and my voice sounds done for. I know it's day one, and my voice has gone well, technically, because it was the first day, was the kickoff party, then day two. I probably will be voiceless by Friday. So, yeah, and Jeff's right. There is no time I can think of where I'm at a conference where I wish the music was louder. I can think of many, many times. Yeah, 90 dB in the main hall. I can think of a constant time when I want the music quieter, when I want to have a conversation. I've never understood this because this is not just this conference. This is the conferences you go to for tech. They have the same exact problem. You go to a tech conference. I was at a Comtea conference in Chicago. I wanted to leave because the music was so loud. I don't know, we found a corner where the music wasn't loud. There was speakers they put over there. I don't know if someone disconnected the speakers or they broke, but I was just like realizing more and more people were huddled over in this one area, which made it a little bit louder. But I laughed because the reason so many people over there is because we just wanted to talk to each other. And cranking the music up, I'm like, cool that you had a live band. Thank you for accommodating people who want to dance. Awesome, that is, it was a big venue. We didn't need speakers outside of the live band area. And yeah, that's just, that's some of the challenge. Like it causes me to lose my voice because we all have to talk at this level. Then my ears are ringing. It also causes mentally me to be so overstimulated. I'm happy to get back to my hotel room at some point because I'm like, I just need quiet. I just, but it's been great. It's been fun. Especially I get to meet not just Jeff. I'm to meet someone else. A few people might recognize. I had a good conversation with Network Chuck, super cool guy in person. One thing is I've talked to Chuck more than once outside of his videos. He's much calmer in person. Let's just say that if you look at Network Chuck's videos and you're like, he's really hyper. I think he, and I've seen the jokes. He's had too much coffee or whatever. But if you meet him in person, he's very, he's much more chill person. We had some good business conversation. And yeah, it was, it is nice talking in some of these people like a one-on-one in person like that. I also got to meet and he's also a fellow IT, well was a fellow IT business owner, which was my friend Pete. He has also turned cool YouTuber. If you look at notabusinesscoach.com, he's got his business coaching stuff, which I think he still does some of. He, young guy, awesome, that he kind of converted, you know, I kind of jealous because I wish I would have started YouTube a long time ago. I feel like the old man here, because I definitely, it's not that there's not other people my age, but I am definitely, I'm definitely on the other side where this is the majority younger person conference, if you will.  My only complaints besides the music about the conference, so I'll throw those out there for anyone wondering, the ticketing system is atrocious. They need to fix that.  The conference itself is good. Just the, how you get the tickets is a mess. And that's my only really big complaint about the conference. They're not too big of a complaint. It's a complaint though, because I get aggravated greatly when it comes to disorganization. But at least I meet lots of cool people that's been fun. They have like a vendor area. So I got to talk to, I'm using StreamYard for this. I got to talk to the people at StreamYard. That was pretty cool. And so meeting some of the different vendors and things like that, it's not like the vendors you meet at a, I'm gonna stop wobbling as much if I move this over here, I think. There we go. But, oh yeah, I got a StreamYard duck. So that was kind of cool. Actually, one thing I'll say for the people at StreamYard was they really engaged, wanted a lot of feedback on people that used it. So I had a good conversation with like two or three of the people. And when it comes to branding, that was actually kind of neat. They even had, the girls had earrings that were little ducks, the StreamYard ducks. So I was like, okay, this is kind of like everything they had was well branded. And I thought that was, I thought that was actually kind of cool. Other companies when they have like a good plan like that and everyone was, as you usually find at a booth, everyone was really nice, hoping they'd come up with some new features and stuff like that. Onto the tech side though, one of the big reasons I'm coming here is always to think about storytelling and strategy. You know, I've seen Jeff Kearling commenting and I imagine a lot of you if you watch any tech videos, Jeff Kearling comes up a lot and I think he does a really good job of putting together a narrative and story. And I've been slowing down a little bit in the videos because I wanted to really think about how I put together some of the story. I just want to improve that. So stopping and thinking as opposed to the brute force of Tom just hits your cord and makes a lot of videos and kind of narrowing that down. I'm actually going to a class today on script writing so I can figure out how to write a script. I'm terrible at it. And my videos not really scripted, they're bullet pointed. So sometimes in my teleprompter, so I get some long model number right. I'll have a bullet point and maybe a model number of a part, but I don't really write out the script as well but I'm working on like, what's the happy balance for myself? And that way my content has a more flow to it. There's actually one of the reasons I stopped doing some of the wiring and catering videos is I felt some of the ones I did, one took me extreme amounts of time to put together to try to do it. And I'm like, how can I make that process a little bit more concise and then bring it in the story? I actually like some of the older videos I did but I also know some of those videos took me like two weeks of work. And it's challenging and maybe that's the stuff I need to split out and find an editor for is figuring out how to sort out. The problem is this is where the challenge always comes in. If I want to do a wiring type of video with an editor, you have to have the editor understand where to insert which part of B-roll. So you have to very clearly label all the B-roll for this is what this part of the project is. And that's what makes it really challenging on there. So it's definitely all the little things you have to think about as a content creator to put this together like the behind the scenes. So this is, I did say it did say Tech Talk Live Q&A but I'm actually gonna keep this relatively short because I have to go to a, well, I wanna get back to the conference which starts in about half an hour, huh? 45 minutes. 45 minutes. And we gotta grab breakfast. Oh yeah, we gotta grab breakfast as well. But I am, if you are a creator that is here, please, I would love to connect with any of you and if you're not here, you're like, hey, I'd like to work with Tom on some tech topics. I'm a really easy person to get a hold of. There's a contact form on my site. There's Twitter. I don't give out my email because I just invite spammers. But the contact form just goes to HubSpot and lands where it's supposed to land. So if anyone contacts me through that but the other places that we're fine, whatever, LinkedIn, Twitter, I have all those links down below. So definitely if you wanna reach out, connect and collaborate on things. But I'm also gonna be connecting, hopefully with a couple more people here to do some content. I've never really, it's funny because Chuck said he's one of the million to a video with me but I'll be honest and me and Chuck, we kinda laugh because we both have the same thing. Like, hey, cool, we'd both do tech content but what do you wanna do together is what sometimes the question is. And that's always my challenge with collaborations is what should I collaborate on? Talking head is easy where I've joined like Jeff on his live streams where people have joined my live streams and that's perfectly fine. But the harder part is like, what would two creators create together? I don't know, unless we are physically building a project together, which isn't always the case, it's hard to say. So nonetheless, I'm gonna keep this short because I do gotta get breakfast. The first talk is one that's kind of interesting. I will, I see, it's not really my place to do chair reviews and but why is it important? We all have to sit in chairs as tech people. I actually think Jeff came up with this, ZFS road trip to level one techs. Yes, I actually think Wendell had mentioned this and before I get to the cheer thing, Wendell had mentioned talking about, hey, why don't I create a storage place for other creators to store their data off site? And with prices going up with cloud storage, why not use Wendell? And Wendell's got a place and Wendell doesn't live, he's in the Kentucky area. So me and Jeff, who live opposite of the Kentucky area, could meet up and me, me, Wendell and Jeff doing a ZFS collaboration, I think that'd be fun. I think that'd make a fun off site storage and that might be a good collaboration video. Plus, I've met Wendell in person several times. I actually, I'll go further. I've been to a conference a couple of times with Wendell and sat around and just talking to him. We usually with a group of people for like three or four hours, which is just really cool. Wendell's an awesome person just to talk tech with for two or three hours. I was wondering if we should hit record on that because he's such a wealth of knowledge on things like that. But yeah, that might be a collaboration. Back to the chair thing. The first person I'm talking to this morning has an office. If you type in any high-end chair reviews in office furniture reviews, this particular person comes up, but they have an interesting kind of problem. They have a lot of views on their chair videos. People don't subscribe. And why would they subscribe? I don't subscribe. So he's gonna offer a lot of the insights of how he built a company reviewing office furniture because it's a critical thing. I need a nice chair. I bought a really fancy chair based on the video. I used their affiliate link, but it's not something you subscribe to. So it creates this kind of strange niche in YouTube where you can't really ever get a high subscriber count but you get a high view count because I was looking and they did a great job on title. If you type in and just want easy ways to find them, I feel like type in best chair to sit cross-legged in and that's how I sit frequently. So like they knew they were building the video for someone like me. And I think I like a high-end chair and I always use low-end chairs. One of those, well, I don't want to spend too much money on the wrong chair. So I keep using these cheap chairs that kind of fall apart over a year or two. But when one of them finally fell apart again, I was making noises. I'm like, all right, time to invest in a good chair. And I didn't want to get another Herman Miller air-on chair like I had before, so the cheer guy. So I'm going to actually get to meet, that he's the first speaker and I find it to be an interesting thing. So I think it's going to be kind of cool. Oh, let's see, I answered two more questions here before I go to breakfast. If I were getting into more areas, is it possible now to have off-site storage? Yes, I have been thinking about as well, can peer with me for free traffic. Yet, the internet keeps getting slowly faster here in America. All my European friends are laughing in one gig symmetrical transfer. I'm maybe not everywhere in Europe. I know I've had a lot of commentary from people that a lot of areas have faster. It's very wildly different depending on where you live here in the US, whether or not you have a faster, slow internet connection. But yes, they are slowly catching up and this is going to make it a lot easier for you to transfer. And for some people, there's a collaborative. I've seen more people asking this and I think this might be a fun topic is how to back up your NAS to your friend's house. And that saves you on cloud. You can set up something encrypted. So you don't want the goals as I would define them because this is a video topic I've been thinking about is how to back up to your friend's house but also have it encrypted. So you do a trade because your friend wants to back up to you. You want to back up to your friend. You can figure out how many people you want involved in this but there's methodology which you can not only do this, you can do this and have it so you don't have each other's data. Like it's encrypted before send. So you have securely backed it up. There's no way if your friend tried to pick through things they would have access to your data but now you both have created an offsite backup and you've saved yourself a few dollars in cloud fees. So that is a topic I've been thinking about and it might be a pretty good video topic. But with that being said, what do I have here? All right, making sure I have all the messages taken care of. I'm going to go to breakfast, leave your thoughts and comments down below and we have to wait for all the big eyes, piece of milk us and milk us with that before they start milking asymmetrical speeds. Yeah, there's some pain in it. And the one thing I mentioned and one of the things about doing it with a friend is usually your friend lives and granted this is not data center level backups, they may live geographically close. And that's what me and Jeff would do is the backups would be you seed the backup. So you drive to your friends to do the initial seed but then your data rate at the rate you are creating data means it's not too terrible. So cool, I got 12 terabytes or 18 terabytes to backup. That's a lot of data at first but if you're only creating a few hundred gigs a week or whatever that number might be that's not too hard to transfer and synchronize. And if there was ever one of those incidents where well I've completely lost my data, I have to go to a offsite. I can drive over to my friend's house and grab the copy and get it because obviously downloading it would take a long time. So there's a lot of merit to that. It's something I think is going to be a fun topic but leave all your thoughts and comments down below. I will possibly do another live stream on Saturday when I get back. And if you're at VidSummit and Dallas say hi I will be here all day today. So these last day of the conference. All right and thanks.


G'day. My name is Sam. I'm from the University of Melbourne in Australia. I've got two other people with me. I've got Matt here from Rackspace and Blair here from CERN. We're here to talk to you about cells, which is a concept in NOVA. This talk kind of really came out of one of the operator sessions when someone said, look, Rackspace is the only people using this, so we're here to give out use cases of cells. I guess not everyone probably knows what cells are for the only initiated. It's a way of segregating or sharding your NOVA installation into maybe a couple of hundred compute nodes, and it just makes your scale a bit easier. I've got this thing here. So how we use cells in Nectar. So Nectar is a federal government funded project started in 2011. We have eight institutions around the country. We've been running OpenStack in production since 2012, early 2012, with Diablo. When we upgraded to Essex, we moved to a cells environment. This was kind of before cells was actually in the code base, so we kind of took a little bit of a risk there. Worked with some Rackspace developers, found a random GitHub branch somewhere and merged it in a way we went. Thankfully, that got merged into trunk, and now cells is actually in OpenStack. The idea of the Nectar compute cloud is to try and get the compute near the data and the compute near the research tool. Again, we wanted to kind of federate that from a user's point of view to have one single cloud and kind of an easy interface for them to get into. We've currently got just over 5,000 users using this, which is getting up there. So we've got eight institutions around Australia. I don't know if you know Australia, but it's quite a big area. I think the pink dots are actually our cells, as they are, and the other color dots are some of the larger HBC and research tools around the country. So we use cells to kind of federate all these research sites around the country. They all have different hardware, different people, different administration domains, so it is a challenge in that sense, and with the main aim of making it easy for the users, there's one interface, one dashboard they can log in and they can launch over all the sites around the country. So we run a parent cell as well as most of the other OpenStack infrastructure, like Cinder, Glantz, Cylometer, Heat all centrally, and then each of the nodes is really just tasked to running compute nodes, really, and some non-overscheduler infrastructure. In that sense, we kind of have a core kind of knowledge base in one site, and then the other nodes don't really need to have as much knowledge base in our OpenStack, so it's been a bit of a low operational cost for us in that sense. Each site tends to run one cell, but some of them have multiple data centers, so they've got almost like a three-tier hierarchy where some sites will have two data centers, two cells. In terms of how we with cells, we kind of treat cells as a behind-the-scenes kind of thing, that's an operational deployment kind of mechanism, and in terms of what a user sees, they see available designs. I mean, these roughly match up to cells, but they don't necessarily have to. So how big are we in terms of scale and architecture? We're the smallest, we're kind of going from smallest to biggest, so you'll get more numbers. So we have six sites in production so far, we've got about four and a half thousand instances running. In the end, I think we'll almost be up to about 40,000 cores, and 1,000 hypervisors are spread out. We tend to have around about 100 to 200 hypervisors per compute cell, that seems to be a nice number in terms of splitting up the rabbit and the database connections, and we should probably have about 10 compute cells in total by the end of this year. So some of the pain points in cells, so cells are still deemed as, I guess, an experimental feature. Some of the issues we have, some of the scheduling problems, so if you launch an instance and it goes to one cell and some reason the cell doesn't, it's broken, it's not going to try a different cell, it's kind of dead in the water there, and also some of the information that the scheduler has is not really enough to schedule a certain cell. It might be advertising a lot of memory, whereas what I'm asking for is compute cores and it'll send you to the wrong cell. One of the other things is we don't have any people using cells, and that can be tricky when you're trying to get help from the community in saying that we have a pretty good relationship with these guys in Rackspace and CERN, and so we kind of have a bit of a cells. We compared notes, what, 17 times in the last two days? How do you do that? Something like that, yeah, we're all doing the same thing. Upgrades, so actually upgrades for us at the start, they were very painful, but in a sales environment now you can upgrade cells kind of one by one, so when we moved from Havana to Icehouse we upgraded our top-level cell, which was running Icehouse, and then we had all the other cells underneath Havana, and then we could slowly, you know, okay, we'll pick the cell and we'll upgrade that to Icehouse, that's working, okay, then we can move on to other cells. That was really good for us, we didn't have to do a big bang approach, and we can have kind of a test cell to upgrade first, so that was really helpful for us, so that's not so much a burden anymore. So some of the things we've been working on that aren't in the codebase at the moment, and we've, Matt will kind of talk a bit more about that later, as things like security groups syncing out there. Some of the things around EC2ID mappings, if you're familiar with them, you need them in the API, in the metadata, an instance might want to know it's AMI. Availability zone, aggregate sport, there's still really no support for that yet, but there are kind of ways around that, we can come and talk to us if you want to know about that, and same with our flavor management, so that's something that's been on the agenda too. So some of these things that we've done, we kind of have to make an assumption that there's only one parent cell. That's the end for me, I'm going to hand over to Blair Moe, and you can tell me about his side. So my name is Blair Moe, I work at CERN in the cloud deployment. So what is CERN? CERN is the European Organization for Nuclear Research, it was founded in 1954, and lab sits in the border between France and Switzerland near Geneva. The organization has 21 member states, and well, our mission, CERN mission is to do fundamental research. So what CERN looks at is looking for answers for fundamental questions like, where is antimatter, how the universe works. Quite ambition, in fact. So for this, for all of this scientific research, CERN provides different infrastructures, like a network of particle accelerators. CERN runs the biggest particle accelerator in the world, the Large Adrenal Collider, that is a ring of 30 kilometers, born 100 meters underground, and of course other infrastructures like cryogenic labs, and of course computational resources that we made available to scientists all around the world. So to give these computational resources in a more efficient way to our scientists, we started deploying a cloud infrastructure that is in production since July 2013, based on an open stack. At that time, it was the Grisly release. Since then, we provided two upgrades from Grisly to Vienna, and last month, we just finished the upgrade to Icehouse. In our cloud infrastructure, we are running two virtualization technologies, basically KVM for our Linux compute nodes, and Hyper-V for the Windows compute nodes. The infrastructure runs in two separated geographical data centers, one in Geneva, where we have around 94,000 cores and more than 200 petabytes of data storage, and a more recent one that we have in Budapest, Hungary, that we have available 21,000 cores there. So since we have this cloud infrastructure, we have been migrating all the applications, all the services that we have at CERN to run on top of the clouds, and we are converting those servers to be compute nodes on open stack. So actually, at the moment in our cloud infrastructure, what we have is 75,000 cores available, and we are running around 8,000 virtual machines. You can see that the number of virtual machines is not so big. The reason for this is that these virtual machines are machines with a lot of cores to process the LHC data. So why CERN choose to deploy cells in the cloud infrastructure? So several reasons. So we run our infrastructure in two different data centers, but we don't use the concept of regions. So we hide these from our users. We only want to have one end point. So the data centers are completely transparent for the users. The other reason is availability and resilience. So when you have thousands of resources, it's important to split our infrastructure in smaller chunks, because if something happens to your database infrastructure or rather MQ clusters, you don't want to, that your cloud is completely affected. The other reason is also to isolate different use cases. So as a private cloud, we have different use cases, and we want different hardware, heterogeneous hardware, and we want to spawn instances from special projects in special hardware. So cells is one way to isolate these capabilities. In terms of architecture, we have one API cell and eight compute cells. The size of these cells range between 100 compute nodes to 1600 compute nodes. Well, the reason we have so few large cells with so many compute nodes is historical reasons. So what we are doing now is to split these large cells that we have in the smaller ones. In fact, we believe that for us, cells between 200 and 400 compute nodes is the ideal fit. Also, we have the internal concept of shared and private cells. So we call a private share cell when only a few projects can spawn instances there. This has to do with capabilities of the hardware that we have there. For example, we only want that special projects go to compute nodes that have SSH cache, for example, or they are backed up by diesel generators in case of power failure. And then we have the shared cells where anyone can spawn VMs there. We don't have any scheduled restrictions. So of course, if we use cells, there are some features that are not available. And these are the ones that we miss more. Security groups, if you are using Nova Network, security groups are not available. So when we started the deployment of our cloud infrastructure, initially, we had a beta cloud, a small cloud with few under nodes that we opened to our users to get early feedback. And at the time, our users started creating VMs. And you need to tell them that, well, you cannot SSH your virtual machine, you don't have access to your web application because you are not creating the rule on security groups to have access. So you need to do that. When we deploy our production service, of course, security groups were not there. And users were already relying these features and using them. So this was really frustrating for them. Other missing feature is the flavors. So as a private cloud, we have special use cases, so projects have special flavors. And Nova allows that so you can create a flavor and dedicate it to a project. However, if you use Nova API, you are only interacting with the API cell. The flavor is not propagated to the children's cells that the driver uses to respond to the instance. Of course, you can think, well, I can deploy also Nova API in the children's cells. And you can do that. However, if you then run the same command, same API call in the children's cells, the flavor ID there will be different from the top cell. So they will not match. And you can create a big mess with that. So what we do now, we need to sync the different databases manually for this in order to work, basically. Aggregates. So the case of aggregates, the problem is a little bit different because the top cell API is not aware of the aggregates. Only the compute cells are aware of the aggregates. And this causes some problems because if the top cell is not aware of aggregates, the scheduler, the cell scheduler is not aware of aggregates. So, and also if you want to have availability zones in your cloud, you can't because an availability zone, in fact, is one aggregate. So you, in fact, need to tweak Nova in order to have this feature. Also server groups. This is a feature that landed in Nova in Icehouse. It's really interesting for us. It's where you can define your policy affinity for your server groups. And, well, it's completely, it doesn't work if you have cells. The self scheduler, as Sam said, well, is a very limited scheduler. Doesn't know a lot of information. And one also another problem that it has is if it selects one cell as a best cell, it will always select that best cell if the resource consumption doesn't change. So it would be nice to have some randomness that if a cell is misbehaving, to go to a different cell. Cellometry integration. So, cellometry is important for us for monitoring and I've all metrics. And if you want to have it with cells, it's challenging. One of the reasons, the main reason of this is that the cellometer compute agent needs to query Nova API in order to know which virtual machines are running in the compute nodes. And the Nova API is running in the top cell. However, the problem is that the domains in the compute nodes are matched, died these in the children cells. So the agent, the cellometer agent will not be able to identify the virtual machines that are running there. So as a consequence, you will not get any information. Of course, you can work around on this as we did. So you need to set up Nova API on children cells. But also we need them to set up a keystone for that. And also a glance for that. So it's a lot of work. It will be nice to improve this integration. So what are the certain challenges concerning cells during the next months? Until the end of the year, we're going to receive more hardware, more deliveries, and we're going to add into our cloud more 74,000 cores. Basically, we're going to double the capacity of the cloud. So the problem is there are so few use cases running cells. So how are we going to organize this? This new hardware? How many cells are we going to have at the end? Considering our expectation to have 200 nodes per cell, so we are expecting to have more than 30 cells with all hardware that we have. So then the question is how can we manage all these cells considering the lack of experience and use cases on this? So let's see. So Matt, now we'll talk about Rackspace. So as they said, my name is Matt Van Winkle. I'm an engineering manager at Rackspace. My team is pretty much charged with keeping the public cloud up and running, which means we don't sleep a lot. For those who don't know much about Rackspace, which you probably do, it's a hosting company that's been around since the late 90s. We got to cut our teeth on dedicated hosting, web hosting, those kinds of things. We offer private clouds, the public cloud. We let you stitch them together, lots of fun things like that. As far as our cloud itself, our open-stack-based cloud has been around since about August of 2012. It's currently in six geographic regions. There's a map up there. We do upgrades from Trunk fairly regularly. I think we're running on one, a poll we did around August, September, right now. We've got a poll in testing that was from mid-October. So we try to stay pretty up to date there. All of our compute nodes are Debian-based. However, we run our compute nodes as VMs on our hypervisors. It sounds crazy. It doubles the number of nodes you have to manage. And it does post challenges, but it actually allows us some flexibility we really enjoy. And then just to give you some rough numbers, I don't usually calculate these, so this math is probably off a little bit. But we have tens of thousands of hypervisors spread across those six regions with, what, 330,000 or so cores. And I've calculated just over a petabyte of RAM under open-stack management in the public cloud. So unlike these guys who have to go out and get funding for new hardware, I have the reverse problem where, because people pay us for it, I have a supply chain department and finance department that are continually landing new gear in my data center. So I have to figure out how to get it online as quick as possible. And we're running somewhere just north of 150,000 virtual machines. That was as of a couple months ago. So these numbers change all the time. So why do we use cells? One of the big reasons we use cells, aside from just kind of the natural sharding, is that we actually offer several classes of flavors, we call them. If you go to our website, I think you'll find general purpose, you'll find high IO, you'll find one called standard. We just launched some workload optimized flavors. So they're diskless hypervisors that support either high CPU or high memory. And so one way we kind of manage the growth of each of those is by grouping them into cells. On top of that, like I said, we have a constant stream of hardware coming in. And so far, based on the way certain things work that we're testing like live migraine and some other functions, we like to keep the cells homogeneous. So while we have multiple flavor classes, we have multiple suppliers within those flavor classes. And so you basically want to have one specific hardware type in a cell. In the future, when host aggregates and some of those things work a little better, that might change for us. But that's a big reason. Network actually, things outside of OpenStack end up being the biggest sizing factors for us. Obviously, public IPs are interesting, and we have to make sure we've got those sized in a way that customers can use. But we actually find drives the most around our sizing is our private IP space. So we allow customers to connect between Rackspace products over a private network. And a lot of times we size the cell based on an efficient use of that space. Because even though it's private IP space, it's still limited. And we want to make sure that we're being as efficient as possible within each region in allocating that. We've learned a few other things. I think the 200 to 400 sweet spot we learned was good because with OVS, there's actually problems with broadcast domains getting out of control of certain tendons start doing bad things. And so it was easier to kind of, we had some get as big as 600. We kind of started paring those down. In general, it's a two level tree. Like someone described, we have global APIs and resources in every region with anywhere from three to somewhere to 30, 35 cells in our biggest regions right now. Let's make sure. Since Belenro brought up the point about private versus public cells, all of our cells are available to all customers based on the flavors we're trying to build. We obscure that right now. We are looking at potential availability constructs. I'll use that generic term down the road. But we have tested and validated the ability to do private cells as well, where we combined a cell to a tenant. So we've had a couple of potential customers that have come in and said we really want to do this. So we have the ability to do that if necessary. I'm not going to repeat too much of what they said. We're seeing some of the same problems. I'll call it on the scheduler side. If you work with, does anyone work with cells that's in the audience besides my guys? When you actually attach a cell to a region, you basically link it up in a database and it's good to go. And there's really no way to throttle it. There is what they call waiting, which says you can relatively say these cells are more popular than these cells, but it's still a function of math and available RAM. So the short version of that is if something breaks, it's hard to stop sending bills to the broken cell. So there's a few functions like that we really need in the scheduler. And then, of course, we're running Neutron in a very large deployment. And people ask us how we did that, since it doesn't support cells. The simple answer is lots of duct tape. We had to get in there and make it work. So obviously, getting cells to a very fully feature complete status and NOVA puts pressure on the other projects to figure out how to support it. So that's a big driver for us. Some additional challenges, like I said, we're constantly sort of evolving our product line. And so with each new sort of flavor offering comes the potential for new hardware, new cell sizes, some of those kinds of things. The multiple vendor sources I mentioned already and I'll be honest, we're still learning kind of the exact math that goes along with the global services, so the regional level services as cells scale out. What I don't have today is an easy formula that says for every 10 cells, I should have X number of API nodes, X number of glance nodes. We're still learning kind of as we go. And while cells gives you the ability to sort of charge your database and spread the information across all these little databases, in its current implementation, there's still a master global database that's got a copy of all that information. So you still kind of end up with a large, unwieldy database if you're not careful. Case in point, we just got through doing some pruning in a couple of regions for anything that was a deleted instance record because NOVA likes to hold on to those for a long time. That was older than 90 days and I still have several hundred thousand instance records in those regions. So the reason I bring that up is because there was two design sessions this morning about cell sort of feature completion and where are we going with cells and how do we make it a first class citizen. And the bottom line is the NOVA dev team has looked at it and says it makes a lot of sense. It allows for NOVA to scale very well. It solves some problems that are out there, but it needs to be sort of fully into the mix. So I think there's a lot of work to be done. I think we're talking several like we're on K right now, so I wouldn't be surprised if it's in or in before they're fully done, but the sessions went really well. I think there's already some targets for the K release, which includes getting the upstream gates fixed and functioning with cells and then we'll sort of move from there. The nice thing about it from a to tie it back to the database piece is right now if you run cells, not only do you have this big global database, you also have a middle sort of RPC layer because you've got a cells service that runs at the regional level with its own rabbit MQ and they sort of help pass this message down to the cells and then the cells have an additional cell service, the rabbit MQ and the scheduler that we all sort of expect to see. The idea with where we're going now is to try to get rid of that middle RPC layer, make the database at the global level really a mapping that says this instance is in this cell and then sort of let the APIs know how to go from there and talk to the levels below. That's the very simplified version of what took place this morning and I'm sure there'll be some evolution of that. I think we all walked out of it feeling pretty good about how it went. Seeing how there was some worry that we would get into this thing and it would be decided just to chunk it out the door and the three of us would be really, really sad if that happened. I think the follow on, the three of us are also involved in a large employers working group that's meeting tomorrow so I think one of the things we're going to try to do is put some specifics around some of these features that we want to take them right back to the devs for that particular process. I will say if you're an operator, as the operator movement sort of continues to grow, find a working group that matches what you do and get involved because we're seeing real turnaround right now in the process of being involved in the design sessions, going back to as a working group, coming up with specifics and turning it right back around to those devs. Really good stuff. I think we actually have time for questions so go for it. Can I have questions for your first speaker? Sure. Yeah, sure. Yeah, good presentation. Just one question. You said about 1,000 hypervisors with 40k cores. Does it mean the 40 cores per hypervisor? Yeah, it probably works out to that. We have some hypervisors that have about 64 cores and some that have 24 but I guess on average, it probably works out to be about 40 years. Okay, so this is real data. Is it? Was it sorry? I mean, I just doubt 40 cores for hypervisor. We've got some brand new gear that's 40 core processors. Yeah, they're out there. Awesome. We have some AMD hardware. We've got 64 cores and hypervisor. Is it in production? Oh, yes, yeah. Okay, cool. Thank you. I have a question for the first speaker. You said your Cinder is in the different locations centralized. What about the Cinder back end? What is the latency from your cells to your back and over ice because how are you managing that? Okay, so we have Cinder API and Cinder scheduler that's central. Okay. But then all the Cinder volume services are all dotted where the cells are. Does that make sense? So the actual storage is within the cell itself. Makes sense. Yeah, so there's no, you know, you can't attach a volume from one cell to another cell. So how did you, did you end up changing the Cinder scheduler to mount it or? No, there's no modifications to Cinder. Cinder has a notion of availability zones already and we've kind of matched them up together. Yeah. I was wondering if you have any advice for moving from a cloud that does not use cells and sort of uplifting that to, you know, now using cells or did everything you guys do was all net new? I've been cells since the beginning, so what do you guys take? We actually were not at cells at the start. So we started in Diablo and then I think when we upgraded to Essex we moved to cells. It wasn't actually too much of trouble. You just have to kind of build the top level API cell and then kind of just sync a few things up to that database and then you're, you know, you're essentially good to go and just not that easy, but you know. I think that the answer that's going to change too as time goes on because there's going to be a release of OpenStack in the near future where when you install that release you've got cells. Now the default will be effectively one cell kind of transparently, but we just don't know the timing on that. So if you need it now, then I think you kind of have to go that route. If you think you're going to need it in a year then you might be able to get it without a whole lot of extra effort on your part. And going on that route it will actually, there will be a migration strategy for people who are just using a standard stock standard Nova without cells to move to the, you know, because it will be the default. Thanks. Do you know, Andy, have you heard? I'm not completely familiar. I don't have as much direct involvement with Neutron. I think the general premise from the last summit and it's probably carrying over a little bit this way is we just still have a lot of base functionality to fix. I think people from RaxBase brought the idea to it and they're still in the let's get the house kind of the ship in order and then we'll come back and talk about, you know, the fancy furniture and the things like that. So yeah, so yeah. So from a plumbing perspective our cells translate to dedicated VLANs and so Neutron can kind of assign information but the network piece is sort of established for it. And if you want more details, Andy right here in the front row is the good person to talk to about that. Punt. Oh, sorry. I attend an event of Huawei. They are spoke about cascading and do you know about it and do you think that OpenStack will be in that direction or it continues with the cell, if you know. So we were all in that session and I think the specific need they were looking for was really more of an application level thing. It was the coordination of clouds. It could be argued it was cascading but when we really dug into it, I don't even know that it was that. I think Sam's kind of doing a version of cascading with cells today. But I don't see it diverging. The Nova Devs are pretty dead set on getting cells into a version that is kind of the default install. Now how you chain multiple clouds together after that I think is there's a number of ways we can look at it. But I don't know if you guys have any thoughts. I have a question for the first speaker. How did you upgrade your data centers, your cells and keep the consistency between all the data centers? Well for starters we didn't have consistency so we kind of took a staging approach for upgrading OpenStack. Really we just put a bit of planning. We made sure we could run different levels or different versions of OpenStack in different cells and so it was really we kind of took, I think it was a week or two. We upgraded the first cell and we just left it running for a few days to make sure everything was fine and then once it happened we kind of did it one every couple days. If I came over it was just that we had a small five-minute outage on our cell when we did that. And for rice bake, do you have any problem with floating IPs? No because we don't use them right now. Plain and simple. I mean we're working on it so we're going to have to figure that out but there's actually some plumbing related issues that go along with that from sort of how routing is done for us because again we're part of a larger company so we have backbone groups and stuff like that so that's a TBD but today we don't we don't do that. Okay thank you very much. For us it's by region so each region has its own API endpoint. Now you guys can. Yeah so we all only have one API endpoint. Then we have some schedulers some filter on the cell scheduler that a user a special user with a role can specify data center but these are very specific guys. We do OS aggregates for the cases that we don't want to set up a cell with a few nodes that have that specific capability so we aggregate the nodes with that capabilities in one more large cell and the OS aggregates is basically that capability and then we we filtered the request the request that we allow for that for that project to go to that cell and then inside that cell the scheduler selects the specific aggregates so is the way we use aggregates. Anything else I think we have. Yes here how do you guys manage flavor how do you create them in production. So in case of CERN flavors basically we we don't use the API for the flavors we if the request is accepted we we have a CLI that we update the database directly. Okay yeah same here yeah just copying it's good how it's got dumped and then pop it in the other things like that yeah okay thanks I think that's all great all right thanks thank you thank you


Everybody for us here up in 2022 Bowman inception baseball hobby eight box half case number 13 Check the results anytime late sports cards calm. Thanks everybody and good luck one sec here Yeah, James woods a killer To be pyres in this I believe I think most of it's on card. So it's probably not a while ago I said believe he's pyres of this I think that's what I saw earlier Because along card obviously it takes longer to do All right, here we go eight boxes So again two autos per box the only non-on cards I saw were the Jersey earlier So those are the base class say some of them will have first Bowman as well Oscar Coloss Westcath Colton Couser 32 and 99 Orioles on the looks like a blue parallel. You get the check what's up So don't butcher parallels and stuff here. Do you see you're not in this one Joshua? I think you bought into 14 and 15 was that it maybe some other one Alejandro PA auto to 250 for the raise. Mmm. Hi It's not the next one. I think Dylan says Dylan's checking right now. Maybe it's 15 and 15 and 16. I Think it was just 15 and 16 left.  I think I pulled that Noice Yeah Okay, here we go The best it's in this break. Come on illegal. Come on illegal cut it out, man We're already 12 minutes behind I haven't sleep recaps on some of these might help us out Wilson, I just thought it was the sideline might hurt us need First bowman Jonathan met he a cardinals 63 a 75 Cool thing about this is every spot kind of has chases though because all the prospects are pretty solid Go cardinals Got a Christian of a caro auto eight of ten Holy cow Eight of ten nationals.  I Believe it is right Yeah, short print Wow That's so sick. Nats is a monster right there.  This is pretty sick to silver auto 17 of 100 pirates Lonnie white jr Do these cards pop I was watching earlier at home. They are way different in person They have like almost like a 3d effect. You can kind of see it on the camera, but holy cow. Look at that part. Oh sick Hey, it happens manny it happens Yeah, it's not difficult to skunk in this but it's also not not hard to have a good break in this as well like it's like You know goes both ways James Wood first again Yeah, James Wood should be pyjamas and this is on card autos clear Watson Ryan Wreckley Giants first number 31 of 75 Giants Ryan Wreckley George Valera blue auto 87 and 99 for the Guardians Guardians we foiled there and A head for Perez Brewer's auto number six out of 50 on the gold Just gold to fit you right now There's gold and gold pattern too. I don't know that So there's gold gold pattern too, there's different patterns for some of the colors Ryan Bliss Danny DeAndrade with a Matt McClain 31 of 50 first bowman Reds Her initiation is that what it is? Is that what it says? Inauguration that's what it is inauguration Cunha Merrill we've got a Darrell or an A's auto to 250 for the Orioles and Brewer's auto South Fray like a 75 dude.  I know it Arias Prieto nice a Rosamite Quintana to 25 Nationals again vocaro Yeti Cappy Phillies Ethan Wilson auto 242 of 250 and Oh, these are cool. This is primordial prospects auto Henry Davis 62 of 99 for the Pirates. These are really nice He's looked like those Phoenix autos star signs These pop pop surprise took a while for these to fill earlier I guess it's because Matt and Sam were both breaking the section all morning And it was also a daytime.  Can't believe he's six seven now. He's like Stanton size like O'Neill crew size Diego Cartaya 31 of 75 Dodgers. I think he might be more he might be a better hitter O'Neill crews though at least more like discipline got a Conor Norby auto for the Orioles 4 of 250 Orioles doing really well And Cardinals Michael McGree v. Auto 6 out of 50 on the gold This this is a lot better than it was last year. I would say Cars just look a lot better. There's a Meyer West Kath 77 99 for the White Sox Dustin Harris Rangers auto 6 of 75 and Primordial prospects auto for the Brewers Henry Mendez 87 of 99 We recap everybody not gonna call it numbers. Just names here Ryan spikes Conor Norby Darrell her nays Ethan Wilson Alejandro PA Lonnie white jr. Yassa Mercedes Henry Davis George Valera Colton Couser South Relic Michael McGree v. Hedbert Perez Dustin Harris and Henry Mendez and then the short print auto to 10 Christian vocaro big card Nats Thanks again everybody number 14 is coming up


What happens if you nuked the backrooms? I mean, think about it. What if a nuclear warhead was dropped inside of backroom level zero? Leave a like right now if you think that it would blow up the entire thing, and subscribe if you think it would barely do any damage, because the backroom's is too strong. So, let's test it out. Here I have my nuclear warhead. Three, two, one. It looks like it blew a hole in the roof. Let's check what happened inside. I mean, it didn't really disintegrate anything. Kind of just a few walls in the ceiling and a little bit of the carpet. Oh, and that dead thing there. So I guess that answers the question of...


Officer Candidate School. It is where college graduates and prior enlisted Marines prove that they have what it takes to be a Marine Corps officer. Right now our main mission is to train, screen and evaluate potential Marine officers and make sure that they have the leadership ability that we would want of a Marine officer. Being a leader is one thing but truly being responsible and understanding and caring for their Marines is probably the most important thing that they're about to undertake. These candidates are tested physically and mentally. A lot of PT, a lot of fast eating the chow, a lot of knowledge, just learning everything to become the essentials, the basics of a Marine officer. This year marks the 75th anniversary of Officer Candidate School at Brown Field. That first class was the first example of great Americans who went on to do great things. And that legacy has gone on to today. It's the screening, it's the evaluating of these young men and women that is so important. And we've done it over the past 75 years which has made us the legendary fighting force, probably the best in the world that we are. The next step for these newly commissioned officers is to finish the basic school and learn the skills they need to lead the future of the Marine Corps. Reporting from Quantico, Virginia, I'm Corporal Aria Herrera.


The hard question, it's super Saturday for me at London 2012, I mean just that day was just absolutely phenomenal and seeing people like Jess and Mo and Greg Brotherford just back to back to back, that's definitely one of the highlights. When me and Tom won gold in Tokyo we were part of Magic Monday where I think there was a lot of gold medals, that was a really inspiring moment for me. I guess I had to be London 2012 Olympics, home waters for us in sailing, home turf amazing experience. Well I think either getting a hundred on debut because that's the step into the unknown, you're not quite sure if you're good enough and then winning the ashes, those are the moments that I hold closest to me definitely.


Hey, I don't know if it's me, but the day that I posted the video about tops going public with mud right here, but then an hour of me posting that video, the stock shot up and then the next day it shot up even more.  Could have been, could have been me. Yeah, I don't know. I ain't say this NFT stock can make us very, very rich. Hey, what's up, YouTube? I'm Zeke and welcome to the Dream Green Show. In this video, we're going to discuss on whether you should buy tops going public or not. They're merging with the spec MUDS. I'm going to give you everything that you need to know in this video. Could the stock continue to shoot up in the near future? Because I made the video and everything about that company just seemed, everything about this merger seemed right for me and it shot up. And the price is really high right now and a lot of you guys are wondering, is it too late for me to get in? Well, I'm going to do a complete market analysis and tell you guys exactly all of the mud plays in this video. This stock, I think it got the potential to make me very, very rich in the near future. But before we dive into it, this video is brought to you by Weeble. Sign up now by clicking the link down in the description. Deposit $100 and now you'll receive two free stocks valid up to $1850. With those two free stocks, you could keep them inside the platform and decide to use it or you could sell those two free stocks and withdraw all of your money. It's literally free money. Also, I left a link down in the description to Coinbase. That is by far the easiest way to buy and sell cryptocurrency. But enough talking, let's go ahead and dive straight into this video. Welcome back dreamers. So if you want to get in on tops early before they officially become a publicly traded company, they are merging with the SPAC, which is a special acquisition company. It's just a blank check company in place waiting for a company that's private to go public. And tops is merging with MUDS Muzz. That is the company that you want to buy right now, if you're trying to get in on tops before they go public. So in this video, I'm going to give you guys all the backstory, all the back information. Then we're going to do a technical analysis on tops. And then we're going to look at why I think tops has the potential to 10X in the very near future due to their new NFT cards. Now, I used to collect baseball cards back in the day. I got a King Griffey Junior card. I got some Allen Iverson basketball cards. I got a Jeff Bagwell card. I mean, I'm from, you know, the Houston Astros. Anything Astros, I got. So all of these cards I used to always collect. And now that they're starting these NFTs that sells for millions of dollars, you bet your butt I'm about to get in on it early. You, so man, let's just go ahead and start diving straight into the information. Here we are dreamers on April 6, MUDS, a blank check company announced that there was going to be merging with the 80 year old company best known for his sports cards and bazooka in his bubblegum, which is tops. And they also make the ring pops and the big chew. Yes, that's tops that makes all of that. Right. But we're really focusing on the baseball cards. Now it was initially overlooked by investors. The entity was traded as a moderate premium to the deal level of $13 versus $10 due to the knowledgeable gap in understanding the transformation nature of tops business from the updatation of non fungible tokens NFTs. Now, when they first said they was going to go public, nobody really cared at all. It's like, okay, they're going public. But when they, they, they announced their business structures that they're going to start making NFTs, these baseball cards, NFTs, that is what changed the game. These NFTs have since hit the market and have generated extremely strong interest from collectors. And should these trends persist, this will easily multiply tops valuation. So tops is one of the few publicly traded companies that has the unique opportunity to transform their business value, the use of NFTs to investors. This is a significant upside that you would get when you invest in tops practically for free. Okay. So you guys don't know what the, the NFTs are. Let's go take a look. Here we are on the tops website, tops MLB. You can hit start collecting. And they had sell some packs for $5.99 and you will get six digital collectible cards or you could buy a 45 digital collectible card pack for $100. And inside these packs, just like regular baseball cards, you could open them up and hopefully you'll get the, the best, rarest baseball card out there that you could resale on eBay for $10, $20,000. That's how the old baseball cards used to work, right? Well, now they're digital and all of these digital cards host the exact same value as the cards that you used to open up in the, in the physical pack, except now they're digital and you can do even more with them. So here's a look at some of their NFTs that they have. I mean, these are absolutely stunning with the moving background.  These are just the moving backgrounds. These are the epic exclusive. We're going to take a look at the legendary exclusive in just a second. But these cards are already going for over $100,000.  That means whenever these cards are being sold and are resold, the, the creator of these NFTs are getting a cut. Let's just say 10%. Let's say someone wants to buy this for $10,000 tops will get 10% of that. And then let's say this card get resold again for a million dollars and then get resold again for another million dollars. Like this card we transfer from owner to owner to owner. And every time it transfers from owner to owner to owner, tops get a 10% cut of that NFT. Now with physical baseball cards, tops is not getting any cut. They print out the card and the card get resold for $10,000. Tops don't see any of that. But when NFTs tops under the smart contracts, it's going to get a piece of that revenue every time that card, that collectible card is being sold. Now if I were to look at these NFTs and they would have looked pretty weak, I would have, I would have said nah, but these are absolutely amazing. Let's look at one more. Let's look at the legendary exclusive. There we go. These are the holographic packs right here. So you could buy a pack for $5. You could buy a pack for $100. And hopefully inside one of these packs, you get one of the moving backgrounds or with the holographics on it like the card lose have. Or you could even get a signed one from the player itself. Now some of these cards is absolutely stunning. Now what makes these cards so rare is how many of the NFTs are produced. All of these are under 20. That means only 20 of them will ever be made. But these are the ones that are the most expensive. Most NFT cards go for maybe around $4 or $5 per card. But the most expensive ones, the rare ones is going to go for $100, $200, $300, a million dollars per card. And just remember every time that million dollar card is being sold, tops, the company that we're investing in is going to get a piece, a cut of that money. All right, so let's do a quick market analysis on mud. Right quick, M-U-D-S, remember this is the stock that you want to get if you want to get in early before tops go public. And then I'm going to tell you guys exactly how tops is probably going to play out after the merger date. All right, so let's pull out the Fibonacci sequence. You guys already know I love the Fibonacci sequence. If you are not subscribed to this channel, make sure you hit the subscribe button and hit the notification bell because YouTube is acting real funny right now. So we're going to go ahead and pull out the Fibonacci sequence and retrace it all the way up to the top. So tops have went all the way up to an all-time high of around $17.87. And right now it's pulling back to around $16.30. The RSI is pointing down, and the Magdi is also pointing down. That means it's probably going to pull back within this range. Is it overpriced right now? The hype? Probably the hype from my video. I'm not going to say it from my video, but the hype has blown this stock up. But the hype has blown this stock up to shoot all the way up to the top. It's a little bit overbought right now. It's probably going to pull back. So another great entry point for me would be $15. If you hit $15, I'm going to pick up another 30 or 40 shares or so and then wait for tops. It's probably going to trade anywhere between $14 and $17 for the next couple of weeks until it starts to get closer to its merger date, which is late Q3, early Q4. Now, leading up to the merger date, tops is going to explode. It's going to shoot way past $17. You're going to make so much profit. It's going to explode tops. Now, I'm not a financial advisor at all, but I believe I've seen it with ChargePoint. I've seen it with all these other specs, but leading up to its merger date, tops is going to explode. It's going to explode through the roof. It's going to blow way past $17. It's probably going to be around like $25 to $28. Then after the merger date, it's going to pull back. It's going to pull all the way back down to around like $15 or so, maybe $17. It's going to pull all the way back. And then that's just where we're in it for the long run. But that's on normal specs. That's on normal mergers. It could be completely different. Because remember, tops is one of the few companies that's actually going public with an NFT model. So this golden zone right here would probably be a great entry point. If you want to keep up with this, make sure you hit the subscribe or subscribe to my Patreon where I post technical analysis inside of the Discord. And that link would be down in the comment section. So from this technical analysis, we can see that the RSI is pointing down. I mean, it's not overbought or oversold, but it is pulling that back. But the MACD did cross right here and it has a major pullback. So we'll wait. We'll wait. And it's so far away from the 180 day EMA line that we'll wait until it starts to catch up. And I think it should pull all the way back to around the $15 price point. That would be a great entry for us. So yeah, guys, anything can change at any moment. Maybe a million, a couple of million dollars is sold and tops valuation shoot through the roof due to the NFT. So if you want to keep up everything to do with tops, make sure you subscribe to this channel. I'm very interested inside of this thought. So I'm going to keep up with it. If you want to keep up with it too, make sure you subscribe and also hit that notification bell because like I said, YouTube back been real funny. And I think I know why. So go ahead and hit that notification bell because that helps me out more than you can even imagine. And also if you want to keep up with tops and my plays on where this company is heading in the near future, I'm very excited about this company. I love cryptocurrency and all the technology that has to do with it. So this is one of the companies that's going to stay in my portfolio long term. But other than that, I'm Zeke bringing you the dream green show and I'm out. Peace.


Welcome folks, we have the Dow Industrial right now trading up 106 Nasdaqs up 98, S&P up 23. Go contract down $8.30, trading at $18.82 an ounce, we have silver up about $0.08, $22.81 an ounce of light sweet crude, down to $1.98, $91.69 a barrel, notes and bonds. 10-year note, up 9 ticks, trading 107.28, the 30-year flat at $1.1316, and here you go, Kingdolls down 435 ticks, and that's why you see the S&P up at 106.230, Euro 105, Yen's trading at $149, British Pound is at $1.21 to $1 at US Dollar. We get over and take a look at the S&Ps, so let's put the spy up first and take a look at her. So we get down to the breakout area, that's the bottom line, the huge expansion of volume. Now the problem is that now you're going up with light volume, so this could be setting up a larger ABC structure down, yesterday we come down with 104 million shares, you're going to be going up with about 80 to 8, and we'll see, we made it to 430, right now you're only at 428. We go into the end of the X100, we take a look at the Qs, and what the Qs have done, same type of setup, this is interesting though, the Qs got a little more volume going for them, they do, so the Qs, we got down with 56 million yesterday and the Qs could do 56 million today, now that and the Qs, what happened with the Qs also is that it got back inside its higher range, so that's a big positive right there, big time too. We go into the notes and bonds, so if you take a look at the note and bond market, you know, this thing has been a one way trip on the way down, you're ejected lower price today at 10707, you're at 10728, you've done 212,000 contracts, so that's a lot of contract volume, you know, so we'll see if that area is going to get tested once again, and then if we go to the good old Kingdala, Kingdala, it's pretty amazing, rules the markets in a monster way too. You can see Kingdala, bottom line, we hit a high out here yesterday of the 1068839, bottom line is down 440 right now, it's giving the markets a breathing room. Stay right there folks, come right back.


It's no longer what have you done for me lately, it's what have you done for anybody lately. And, you know, we're at the day and age, we open up our cell phone, we can buy anything from anybody, so now what is going to be the reason we're going to make that decision? What are you doing with somebody else? Don't get me wrong, I mean, you know, I'm sure that our parents said that we were horrible, right? Our generation. And listen, the millennials, there's two types. There are the ones that feel they're entitled, they feel they need to get a billion dollars for their idea and they don't speak to anybody. And then there are the other ones who, they're working all day all night because that digital device is stuck to them, they're trying to save the planet that we ruined and they want to make sure that if you're doing something, you're educating and or giving to others. You know, some of the other things I'm seeing in other generations is they don't carry any inventory. You and I, you know, we used to move atoms, they use bits.  They don't want to own anything. They also, they also care about more of an experience than anything else. They'll pay for an experience. They're not the bling, you know, people anymore, you know, everybody's talking about bling-bling, they're not. They don't care about that. They want to travel the world.


Let's look at some factory footage, Adriana. Yes. New York City factory footage without a time lapse from the Disney headquarters that's, it's gone from large to loud. That's its thing now. Initially it was just large and now it's very loud. And then one other note, because I have to fill up our show with things so I can send links to people later. So we don't have hand models. Any hands that you see in any of our photos, it's people who work at Adafruit. Some of them have cool tattoos and scars and nails and all sorts of stuff. These are real people who really work here. Stop saying we have like actors and hand models and no one really exists and birds aren't real and stuff like that.


The third phase leaders of the presidential amnesty program PAP are trying to close the information gap between the ex agitators and the amnesty office for better understanding and smooth sailing of the process in order to put an end to years of protest. The 90 Delta X agitators stated in a meeting held in Yanogua that most problems encountered during the program leading to incessant blockades of the east west road bordered on ignorance, comprehension and the absence of a proper information distribution channel from the amnesty office to the ex militants. The third phase leaders say they are now willing to fill in the gap and ensure that the aims and objectives of the presidential amnesty program meant to bring peace and stability to the 90 Delta can be achieved even as they expect the federal government to carry every one of them along. A lot of persons have been misled to carry out actions negative actions against the office and against the program. We as critical stakeholders we don't want anything that would jeopardize the effort of this program because we stand the chance to benefit more and if we are jeopardizing this program by our own self it means we cannot this program cannot be a successful one. An advice for my third phase delegate and other beneficiaries in the program is that always liaise with the leadership at your domain. Anything you don't understand liaise with your leadership to a large extent the cost of the blockade or steady protest of the east west road precisely in Myanmar is because the people lacks information. People are not giving the rightful informations to the delegates so by so doing everybody who is not properly briefed will take hunger to the road. But today as I speak to you for a very long period of time since we have taken actions a very long period of time the issue of blockade in the east west road is minimized and to a large extent within the shortest time it will be a team of the pass who engage the authorities by dialoguing. Then we are still clamoring for increase of stipend since 2009 is same as 65,000 Naira then they also got it wrong by not making provision for the leaders the leaders and the beneficiaries are receiving same 65,000 Naira stipend and that is the reason why most of the leaders tempers with their followers stipend. Now you are paying a leader 65,000 and he has other followers with the federal government fail to capture him during the time of this armament so how can that leader cater for those persons that are left behind so the only way for him to get it right is to collect from these persons that are being captured so they can reach out to these other persons. If we look about the data as is you will see that so many people have not been carried along today we are calling upon no other person than the IA of presidential amnesty which is buried in Jumo to look into in every area every look out from every state nobody should be marginalized everybody should be carried along we want to also thank him for this turn up of the loan scheme he has brought into the amnesty program today we are thanking him but we want to want him to also dig where to go into this scheme to dig where to make sure that every individual in this program being carried along 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


Hi, my name is Karan Deep Singh, I'm an assistant professor of learning health sciences, internal medicine, urology and information at the University of Michigan, and I'm excited on behalf of our research group to present ClinSpacey, an R package for clinical natural language processing using Spacey, SciSpacey and MedSpacey. Historically, clinical NLP has been fairly java centric. Most of the existing technologies have relied on the Apache unstructured information management architecture, also known as UIMA, that was adopted from IBM. And two prominent examples of UIMA-based software include MetaMap and Apache C-Takes. MetaMap was developed by Dr. Alan Aronson at the National Library of Medicine, and Apache C-Takes was developed by a team of researchers at the Mayo Clinic, and then eventually turned into an Apache project. While both of these tools are really powerful, they require a lot of configuration, and they don't seamlessly integrate with data science languages like R and Python. A couple years ago, a Python package became available that has been turning things on its head in the space of natural language processing, and that was Spacey. So in contrast to UIMA, Spacey was relatively easy to set up. It was available readily in Python, supported multiple languages, had multiple pre-trained pipelines available, had easy integration with neural networks, and came with pre-trained word vectors. And with the rise of natural language processing in the space of machine learning and neural networks, the ability to have pre-trained word vectors was a huge plus that simply wasn't there in other clinical natural language processing pipelines at the time. So with all these kind of features, and with the fact that it was fairly fast in the way that it was built, it started to see a lot of wide adoption in Python. And Spacey can do a lot of things that clinical NLP tools simply just can't do. In contrast to the UIMA-based tools, which were fairly difficult to set up and required a lot of dependencies to be kind of figured out, as well as the right Java version, Spacey was fairly easy to install. You can install that right from within PIP and Python to the extent that anything in Python is easily installable. This is about as easy as you can get in terms of Python installation. There was also already interfaces opening up from Spacey to R. And this was evident in the Spacey R package. That's fantastic. And also in other R packages like CleanNLP that support interfaces to Spacey. As I mentioned earlier, Spacey supports entity vectors or embeddings, which we'll talk about a little bit more later in the talk, and support for multiple language models. And all of this has led Spacey to have a fairly fast and growing ecosystem. So if you go to the Spacey website and look at their universe, you'll see that there's several packages out there available in Python and other languages that are built on Spacey, including CleanNLP, which was one of the packages I mentioned earlier that provides an interface to Spacey from within R. However, Spacey is not sufficient on its own for ClinicalNLP. If you look at this output here from the Spacey R package that's wrapping Spacey, you'll see that the Spacey package is great for identifying tokens, identifying lemmas, part of speech and entity, but it can't recognize clinical entities. So you can't see it here, but the basic English language model that comes with Spacey isn't specifically built for clinical or biomedical text. So for example, it's not going to parse chronic kidney disease as a single entity, and rather it will give it separate entities. It also can't map phrases to the unified medical language system or UMLS codes. UMLS is a metathosaurus, basically you can think of it as a dictionary that wraps other dictionaries that tries to tie together multiple different commonly used clinical dictionaries. And so without support for UMLS, Spacey just can't cut it as a alternative to existing clinical NLP tools on its own. It also can't recognize negations and hypotheticals, and if you've read clinical notes that there's lots of situations where things are used either hypothetically or phrases are used with negations. For example, part of the work up for someone who comes in with let's say shortness of breath is to figure out if they have chest pain. So it's not uncommon to see an assertion of a sentence saying the patient denies chest pain, in which case we assume that the patient does not have chest pain. Similarly, if someone comes in comes in with chest pain and they get a CT angiogram, the reason for that CT angiogram might be to rule out a pulmonary embolism. The phrase rule out PE doesn't mean that they have a PE, it just means that a PE is possible. And so it's uncertain whether or not they have a PE. Sci Spacey and Med Spacey are additional Python packages that add functionality to Spacey that help quite a bit. Sci Spacey was developed by the Allen Institute for Artificial Intelligence. It brings biomedical language models to Spacey, including entity vectors and embeddings that are specific to clinical terms and biomedical terms. Sci Spacey also includes a UMLS entity linker, which means that it has the ability to link phrases to UMLS codes. This is exciting because it means that assuming that the mapping is done correctly, that the phrase for CKD would get mapped to the same UMLS code as the phrase for chronic kidney disease. Med Spacey was developed by a highly experienced research team, or highly experienced research team, including folks at University of Utah. And it incorporates several pieces. The pieces that we use in Clean Spacey are the context algorithm, which detects negations and hypotheticals, as well as references to family members, as well as a sectionizer, which tries to figure out which section of a note a given concept or entity is found in. For example, a patient who has a medication listed under the medication section of a note might just mean that that is a medication that patient already takes, whereas a medication listed under the plan would mean that that's a medication perhaps that's being newly prescribed. But Psi Spacey and Med Spacey also are incomplete. Neither package directly converts annotations to a tidy data frame format, which would be one row per entity. There is a Python package called Deframesy, which does this, but not anything in R. And neither package nor Spacey itself supports transformation of data from a one row per entity format to a one row per patient format. So if you have a data set that looks like this with two patients, one of whom is 58, the other is 42, with systolic blood pressure and a phrase containing their history. If you're going to build a prediction model out of this, you're going to need to convert it to a format where each of those entities in the history column becomes a separate column with a count of how many times that showed up. So neither Psi Spacey or Med Spacey can do this out of the box. And notice here how the patient has no hypertension, and we want that to be recognized as a HTN value of zero, even though the phrase HTN or the word HTN shows up in the history. So this is where it's important for the negation detection to work, so that we can not include things that are negated, or if we want to include them, that we can set that manually. So the goals of Psi Spacey are pretty simple. We want to make setup and installation easy for all the Python components. We want to make annotation of clinical text simple. We want to make the results tidy. We know that processing of many items can be slow, so we want to make it easy to write the results to file and make it easy to then pipe these file names directly into other functions. And then we want to be able to create this one row per patient format by binding the kind of results onto the original data frame. And this binding took inspiration from the tidy text bindTFIDF function, which binds the term frequency, the inverse document frequency, and the TFIDF onto the existing data frame. Except in Clean Spacey, we have two functions that do this. One function is called bind Clean Spacey, which supports one column per entity. In this case, for example, diabetes would be a column, hypertension would be a column, and the values of those cells would represent the counts for each of those entities. And we also wanted to support entity vectors so that we know that if a patient has diabetes and hypertension, each of those map to a certain dimensional vector. And we want to be able to take the averages so that we can have essentially one row per patient as our final format for modeling. And then, of course, we want to make working with UMLS data easy if your machine has the appropriate RAM to be able to handle size spacey's UMLS entity linking requirements, which, it turns out, is actually 12 gigabytes. All right, so let's look at Clean Spacey under the hood. So once you've installed Clean Spacey and you library in Clean Spacey, you'll get this message where Clean Spacey is just telling you that by default, if this is your first time installing Clean Spacey, that when you initialize Clean Spacey, it's going to install Miniconda and install the Clean Spacey conda environment as well as all the dependencies within that environment. If you want to override this behavior, you can. But we're going to go ahead and just use the default Clean Spacey init function to get things initialized. So what does Clean Spacey init do under the hood? It makes setup and installation easy. So first, it installs Miniconda. Or like I said, you can override this if you want. It configures a new virtual conda environment called Clean Spacey. It installs the correct package versions, which is no small feat. There are multiple version dependency issues between Psy Spacey and Med Spacey. So for example, Psy Spacey supports Spacey 3, but Med Spacey does not. So Clean Spacey init takes care of all those versioning issues, including the correct versions for the Psy Spacey language models. In general, you should install Clean Spacey from CRAN. But right now, the latest version of Miniconda, which installs Python 3.9, is actually incompatible with Spacey 2.3.0. And so for right now, I would recommend using the GitHub version of Clean Spacey. But this should be fixed in Clean Spacey 1.0.3, which should be submitted to CRAN relatively soon. So once you run Clean Spacey init, you'll see it working its magic. Here it's installing all the dependencies. And several minutes later, it finishes up installing dependencies. And it loads and imports Psy Spacey, Spacey, and Med Spacey into a pipeline, and loads up the default large scientific language model. All right, so the first goal of Clean Spacey is to make annotation of clinical text simple. So if we feed in just a simple vector, this patient has diabetes and CKD stage three, but no hypertension. We get back a data frame where you can see that it's one row per entity. Here, there's no mapping to it. UMLS code, it's just the entity and the lemma. And then some facts about it that come from the Med Spacey package around whether it's historical, hypothetical, or negated. And if you look down here, you can see that hypertension is picked up as being negated. If you feed Clean Spacey a vector with multiple items in it, then by default, it'll assign different Clean Spacey IDs to those multiple items. So here, you'll notice that diabetes, Melodis, and DM actually get assigned different entities because in Clean Spacey's or rather in Psy Spacey's default language model, those were assigned two different entities, even though they might be referring to the same thing. You can also choose to give a data frame right into Clean Spacey. And if you do that and you provide Clean Spacey with a DF underscore call argument to tell it which column contains the text data, it'll actually process it and give you the same exact output as we got before. This is beneficial because then you can actually refer to identifiers that are already in your dataset. So for example, if you had a row ID like the ID column here and you provided the argument of DF underscore ID, you could actually process the output the same way except now the Clean Spacey ID refers to the existing identifier in your dataset. There's two types of diabetes here. There's type 1 diabetes and type 2 diabetes in medicine, which is not what this is about. What we're trying to disambiguate is DM from diabetes. And there's really two ways that we can do this. One way we can do this is to map all entities to UMLS-Cueys, which are the UMLS codes. Or we can try to map the entities into a vector space where hopefully DM and diabetes models are sitting close to each other. Let's first try mapping all the entities to UMLS codes. How do we do this? We first turn the linker on. This is the UMLS entity linker by rerunning Clean Spacey in it. Make sure you have enough RAM available before you try doing this. And once we've turned the linker on and then we run the same function earlier with Clean Spacey and pointing out which column contains the text, you can see that we get this huge mess. We get PT maps to Portugal, PT maps to Positron, emission tomography or PET scan. The one thing PT doesn't map to is patient. Diabetes mellitus is correctly mapped to the definition. And if you look at HTN and CKD, here it's pretty cool because you can see that it correctly maps those to hypertensive disease and chronic kidney disease, respectively. But do we at least fix the DM issue? And it turns out we didn't. So DM here is picked thought of as being potentially referring to dexamethasone or dextrometherfam, but not to diabetes mellitus. So entity linking isn't perfect. It's slow. We can remove some extraneous matches by restricting by semantic type, but lots of mismatches remain and it struggles with abbreviations and disambiguation. So my personal preference is to keep the linker off, but it depends on the task. So how do we put these predictors in a model? ClinSpacy return to one row per entity output. But to build a model, we want a one row per patient output. To put our predictions in a model, we can now use the ClinSpacy init where we'll turn our linker off. And then we'll use the bind ClinSpacy function. The bind ClinSpacy function does exactly what it sounds like. It takes your ClinSpacy output and then adds a column for each one of those entities that it identified. And in this case, it uses the lemma. That's why everything is lower case. And then those numbers represent counts. Because things only showed up either zero times or one time, you're seeing only zeros and ones. Again, it didn't quite solve our diabetes mellitus and DM issue as we expected, but otherwise, this is pretty close to what we wanted. We could also use embeddings here. And embedding is basically a multi-dimensional representation of an entity. You could think of it as points on a coordinate system. And the thought process here is that similar entities, like diabetes, mellitus, and DM, should be close to one another on this coordinate system. Here's an example of a three-dimensional word embedding where you can see that man and woman are close to each other and king and queen are close to each other, but also king and man and queen and woman are also close to each other, or at least in the same direction as each other. So these word embeddings can capture really intricate relationships, not just in terms of the meaning of a word, but also its tense and its relationship to other words. Clint's Basie includes two sets of entity embeddings, the size Basie entity embeddings, which are available when the linker is off, and the Cuey-Tavec embeddings, which are only available when the linker is on. Because size Basie embeddings are returned as part of the spacey pipeline, we need to explicitly request them when running Clint's Basie. So if you look at the code below, you'll see that if you want the size Basie embeddings, you have to set return size Basie embeddings as true. And you'll notice that the first three of 200 embeddings are shown in the screen below. Here, they've been rounded off to two digits, although the actual embeddings are several digits longer. We turn these off by default for speed memory reasons, but you can pretty easily get these as long as you specify this argument in the Clint's Basie function. Even though DM and diabetes melodists were assigned separate UMLS codes, when we look at them in the entity embedding space, and these are size Basie embeddings, we see that they're actually fairly close to one another. This is the code that shows you how you would include just the first 10 embeddings as predictors using the bind Clint's Basie embeddings function, mapping the output and then linking it back to the text data so that you can kind of join those results back to the original file and then end up with one row per patient data. And then this also shows you, I won't go through it, how you can save your results to output files to save memory and also to be able to then pipe in those output files directly into the bind Clint's Basie functions. And this is actually my preferred approach. Clint's Basie is a precursor to recipes. Because things take a while, I don't recommend that you use it inside of recipes, but rather that you pre-process text before you send it to recipes. And for more details, feel free to take out, look at our GitHub page and our package documentation. Thank you.


Hi, I'm agile leadership coach Jeff Watts and I just want to tell you about this cool deck of decide cards that I created with my good friend and agile coach Paul Goddard. These decide cards are a great way to help teams improve their ability to make decisions as a self-managing team. It's a bit of a game-like process, similar to planning poker or delegation poker if you've ever played either of those. And it allows teams to iterate through their decision-making process, being agile about it. Self-organization is a great idea and it's core to agility, but quite a lot of teams will struggle when it comes to making decisions as a team. Quite often it's the loudest or the most senior person who gets to decide or maybe the team end up with a majority vote style result or some other equally ineffective method of making a decision that takes ages and is really painful. Now that's totally normal because making decisions as a team is not something we're particularly experienced in. Often a manager is paid to make decisions, but that's not very participative or engaging. So this pack of decide cards will give your agile team a structure and a process to reach consensus in a healthy and quick way. Each of these decks contains eight sets of decide cards and some instructions and they can be used to help teams discuss a proposal by stating their current position before deciding what to do next. Basically, you just set a short time box so that you can discuss a topic and it could be anything from how are we going to design the new architecture to where should we go for our first team social event after the latest lockdown restrictions are lifted. After that time box is up, we ask one member of the team and usually we recommend the quietest person because they're usually the best listener. We ask them to summarise the discussion that they've heard and then put forward a proposal based on what they've heard. And then we ask each team member to pick a card that represents their level of support for the proposal and then as a team we discuss the results and we might repeat that voting process if necessary. One team member might defend that idea because they think it's pretty good. Another team member might inquire about it because maybe there's some aspect they want greater clarity about. Someone else in the team might concede because either they don't mind either way or they don't really think it affects them. Well, another team member might want to iterate and so they make a counter-proposal or suggest tweaking the proposal slightly. Of course, someone in the team might think this proposal violates one of their or the team's values or maybe they just generally think it won't work so they could deny the proposal and some people might endorse it straight away. After we voted, we could set another time box. We could answer the questions around clarification, perhaps we could cover the concerns and the counter-proposals and then vote again. What we've found is that teams who use these cards benefit from increased engagement in the decision-making process because everyone's involved in it and as such, you get greater buy-in to the decisions that the team makes. The team will explore a wider range of solutions and ultimately it takes less time for them to get to consensus. So why not decide to try decide cards and let your agile team iterate their way to successful decision-making?


All right, we are back in our. Colab file, but we're talking about normal distributions. And in this final video, we're going to be talking about how you calculate the p value from the normal distribution. So the p value from the normal distribution is calculated by taking the sample statistic subtracting the null value that we assume and dividing by the standard error. So we're going to continue to work with that data set that we developed during the significance level video where we drew red cards. And so if we want to determine the p value of this sample statistic, which helps us to reject or accept our null hypothesis, we need to calculate the Z score. So we can say Z is data p hat. So this is our sample statistic minus 0.5, which is our null value divided by the standard error, which is Z, E, R or S, E, R. And so we can run that. We can then print Z. And so we can see that it is 7.76. And now this value in and of itself is not. It's not entirely representative of anything that has to do with the p hat. Rather, it is used within a normal distribution to calculate the p value based off of the left or right tail test. And so in effect, you can look at where this Z value lines up within a normal distribution and everything to the left of that becomes the p value. The proportion of data to the left is the proportion of is the p value for a left tail proportion of data to the right is the p value for right tail test. And so normally we would look at the actual data, but if our data is large enough, if our data is normally distributed, we can assume that the Z value will work. And so if we were doing a left tail test with this Z value, we can say stats.norm.cdf and we can just give it Z. And so we can see that on the left tail test 99% of this data was above our Z value, which means we would accept the null hypothesis. For a right tail test. It's the same formula. Except this time we need to multiply our Z value by negative one. Essentially, we just need to flip the sign of the Z value to do a right tail test. And so here we can see that it is close to zero. So in this case we reject the null hypothesis in favor of the alternative that in this case, if we scroll all the way up to our significance. Our alternative test was that the proportion of red cards is greater than 50%. And so that is our right tail test. Then our two tail test is stats.norm.cdf same as before. We need to do whichever one was smaller. So when we do a two tail test, we calculate both sides and we double whichever one is smaller. And so our smaller tail test was right tailed. So we do negative one. And we say times two. And so here, if we were doing a two tailed test, which is essentially will say not equal in the coding form. We can say that we would still reject the null hypothesis that the proportion is not 50%. So this can be used when your data set is sufficiently large and normally distributed to quickly conduct a hypothesis test.