Hi everybody, we're back. This is Dave Vellante of Wikibon.org. I'm here with Jeff Kelly. We're here live at the Vertica user event. We're here in Boston, Massachusetts, the Western Hotel and the Waterfront. It's been two days of talking to practitioners and partners and HP executives. Brian Weiss is here. He's the Vice President of Marketing of Autonomy. He's a CUBE alum. Brian, welcome back. Good to see you again. Thanks a lot, Dave. It's nice to be here. Yeah, so we're seeing, last time we talked, you know, we talked about the deliberate attempts to integrate autonomy into many, many parts of the HP portfolio. We're now seeing the results of that. We heard today in the keynotes, Robert Young-Johns, George Kadifa, talking a lot about Haven and giving some demos. So we're really starting to see this strategy come together. And you know, I said to my colleague, John Furrier, when you're on the outside looking in, you know, and there's all this noise going on in the marketplace. They say, what the heck's going on inside there? But inside, you guys were doing some really deliberate planning, doing road maps. I mean, it's very clear now that we're seeing the results of that planning. So talk about where we're at and how do you feel? Yeah, you know, this is really exciting for us. You're right, we've been through that sort of year of deliberate strategy and planning about how we're going to take these very, very unique technology assets that we've got within Vertica for managing just excruciating amounts of extreme data, all of machine data. And then you've got the autonomy portfolio, which is fundamentally something very different. It understands the nuance and the nearness and the sort of things that human brains do that maybe databases don't. And so it's always been incredibly apparent to us that there's an unbelievable amount of synergy there, the drive of value that probably most business applications can't conceive of. And certainly in the face of the types of data we're trying to get in the value again. So, you know, with Haven as a platform strategy, really it represents our commitment to get that unique value both integrated and out to the market, whether it's as a development platform or underpinning applications that'll tell you not only what happened and how fast it happened and, you know, but as well as what it means. So those two things together. How many widgets, et cetera. And, you know, it was interesting to see George Kedifus slide where he gave a little history lesson on sort of the transaction piece of the business. And now, you know, there's a 10X growth there. And then all of a sudden he's got this 100X vision. And the thing about that, I wonder if you could comment, is a lot of that value, he talked about monetization, a lot of that value creation is, it's kind of fuzzy, you're drawing inferences, it doesn't have to be like perfectly precise, you know, a false positive, it doesn't kill you, you know, it's not like a zero in a bank account. And that's where the values come up, and that's where autonomy shines. Yeah, and you're right. So, and I think, you know, for those of you who didn't see the slide that you were talking about, it's really the movement of being able to provide insight and analysis on data that fits in a Rona column. And then the next sort of wave of that is that your computer can actually do things that are very similar to what you and I do with our brains, which is, yeah, it's not the same, but it's close, right? So I want to know all of the, you know, I want to not only have phone calls that came in between 10 and 11, if there's 20 million of them, but I want to know what they were about and more of the people mad. And if they were, I want to know the stuff they were mad about, but, and show me things, don't just give me the answer that seems right, show me the next most likely answer. So it's a question of the, you know, the data talking to you in a way and putting those two things together means that compute systems will do something very different. Very, very different.   So how is that affecting the way in which customers are changing their processes specifically to take advantage of that? I think that's really interesting. It's part of, I think it's part of why everybody's so hyped up about big data in general. So when we used to deal with the volumes of the data, it was all about, well, it costs a lot to store it and it's a pain and I don't know what's in it. And now what customers are saying is, I've got all this data, how do I get the, I think there's a lot of value in this information. If it only could figure out a way to get it out. And one way to get it out is to hire room pulls of people and have them read the documents and tell you what they're about but you can't do that. And so the realization is you can't do that human task on the amount of data we're generating or the types we're generating. And if you could, you'd be in good shape. Like a lot of great value you can find out. People are beginning to realize that it's not as possible but it's happening. And so we see that all over the place. People come to us with a big data problem and they say, I want to know how my customers feel about X, Y and Z and I'm going to get that data from YouTube, from Twitter, from social media, from surveys. And I'm basically going to couple that with information that I'm getting off of telemetry coming from their cars. I was going to say, Jeff's going to get into use cases with you but we saw that at Discover in June in Las Vegas with NASCAR. Yes. And I had some great extensive conversations with those guys in terms of how they were making decisions and virtually in close to real time as possible as to what videos to keep up there. They were taking down videos and getting instant feedback. No, get that video back. Exactly. And they're almost like a command center. And they could see the results to the bottom line. Right, right. And it goes well beyond just saying, I want to know how many times people are saying NASCAR on Twitter. It's actually what are they saying where they care about and what are the main ideas and how they matter. I mean, there's a whole nuance to it that is very hard to get to a traditional computer. You're right. It's like, hey, people are talking about us. Golf claps. What do you do with that? Right. You know, you got a lot of mentions. Okay, good. But what did they say? Like tell me if I've got a problem. I mean, a great example I'll give you of, you know, I did a presentation today on how we're being used in some movie studios to analyze information out there in the world before a movie launch. So what are people saying? How are they saying it? Main ideas? How are they responding to our actors? How are they responding to our ads? Right, are we doing the right thing or not? Right, that kind of thing. And you can imagine the sources that you- Is this going to flop or is it going to hit a home run? Yeah, or, and by the way, I'm leading up to my release so I still can make changes. Yeah, right. Like I can still adjust, you know, which actor I send to what location depending on how they're trending and how do you get that data, right? So there was this case- You can move the needle with that data. You can absolutely move the needle on it. But there was a good use case where we were doing that and it just happened to be after a film had released and we started seeing these Twitter patterns of, it was a kid's movie and the kids were like, okay, and then we saw things like crying, children screaming from the theater, running and it was geo-targeted and we knew where it was coming from and somebody had accidentally loaded up the Ghost Rider trailer for this kid's movie. And so you go in to see a kid's movie and there's melting faces and skulls but in a particular, some maul and so that was, of course, they were able to see that and spot it and get ahead of it because along with what they were tracking, they saw these other patterns coming out of it. So Brian, I'm running to a panel with my colleagues Vinny, Mersendani and Kurt Monash. We're going to go talk about big data. So Jeff's going to take it from here, talk about use cases and other examples. Really great to see you again, always a pleasure. So yeah, so before we move into some of those use cases, I wanted to follow up on your answer to the last question about, so you've got these insights, right? The movie studio example, I think is a good one. But we often hear about, and this relates to the, you can't take the human out of the process, how do you help customers not just understand the data and the insights, but then actually take the next best action to improve the situation if it's, in the case of movie studio example. Yeah, there's two parts of not taking the human out. The machine's never going to make the decision for you. What it's going to do is surface, it's going to be machine assisted, if you will. It's going to surface intelligence that you might not have been able to get from data that which is too big for you to read by yourself. But you're exactly right. And one of the things that we're focused on with Vertica and with the Haven platform is not just, let me show you a picture of what's happening, but let me give you the tools to take action on it. And I think that's one of the places that, people get excited about big data and it's, but they're going to be told something and then what? Now what? Where's my ROI? Right, super. So if I, for a lot of the things that we're focused on doing is saying, let's take this insight, let's take the information and let me take the people associated with it, whether it's the influencers who are tweeting the most about it or whether it's the customers who feel a certain way about a topic or a group of folks in a certain geography and then I want to take that, cluster those individuals, put them into a workflow and start a campaign around them. Say for example, when I'm marketing, right? I can take directs action on that, target a group of people who happen to be talking about this subject, but I had no idea it would be hitting my screen when I woke up this morning and it's about my movie, right? Or I can market this particular way to this. So we're looking, it's important for us to see and drive workflow out of it that delivers results. So not just dashboards. Right, so it's a lot more than just saying, here's some sentiment people like this or don't like this, it's directing next best actions to actually. Well, so let's take it a step further, either, right? Another core part of, and you heard about it a little bit this morning in the security part of the presentation, that we're doing is, we're taking the ability of something like Artsite Logger, right? And experimenting with the fact that we can now take the events that have been logged, which tell us what happened and when and some information about it. And we can also then look at the information itself. So maybe I'm interested in people who send data outside of my environment within 30 days of leaving the company. Maybe it's something I'm very interested in if I'm a pharmaceutical company or anything else. And so, well, while traditional systems can tell you that's happening, they maybe tell you what's happening a lot and they can't tell you what the document's about. So maybe you send an intellectual property out, maybe you did it on whether you were mad or maybe you just sent your soccer schedule. So here's another point where Idle can come in or Autonomy can come in and go, actually, no, we'll have the computer read the document and compare it to things that we know to be very likely about intellectual property that maybe you've given us. And we can come back and say, okay, red alert, documents are leaving, that's fine, people send them out all the time, but this particular set of documents is actually very risky because we understand what that is and there's no way you could do that without a person reading the doc. So that's kind of the type of place where or we can monitor the social media sites where people who might be talking about your company and perhaps intending or plotting something. Before a document goes outside the firewall, we can monitor that, not only the activity, but also what's being said and what it might mean for you. So it's a great example of where we're taking this security part of Haven, right? And looking at ways to create that synergy that's not possible with traditional tech. So as a marketer, what are some of the challenges that you have? Because we're talking about actually using computers to kind of mimic some of the things that human brain can do. And I'm sure there's some skepticism out there. I'm not too far with that, right? I don't want to, let's be very clear here. We're not talking artificial intelligence, but it will do things that are similar to the inferences that you and I would do. So not necessarily AI, but it's still pretty sophisticated stuff.  So as a marketer, how do you tell that story? How do you get that across to a skeptical end user who wants to believe what you're saying, but is saying, is this science fiction kind of stuff? Yeah, so the best way to do that is to talk about the way customers use it. And we can give you clear examples. And we have lots of demonstrations that will show you in real time what this like news. Pick out all the key ideas and news. Actually, we released a website that's completely public. Anybody on the planet can get to it. It's called news slash spectrum.com. And it uses autonomy to segregate the news automatically. It reads all the stories and decides whether about sports or politics, entertainment, and it drives the threads and the ideas within them in the same way that it would if I'd hire a roomful of people to read all the news and categorize it for me. So we do things like that, but really what's very interesting is to take the use cases where customers have taken what is a very expensive human problem like. You're a law firm and you need to read 10 million documents about a case on behalf of your client, which you're getting paid $350 an hour for, and find the relevant documents. Now what's relevancy? The judge does not say I want you to find the documents with the word bridge in them. The judge says, find me the relevant documents. Here's the case. So you have to do this exercise. And so it's a great example of where what we can do is say, all right, well, first of all, have the computer organize all the data. Put it into clusters. If this thing's about bridge, but it's bridges that go over water and not bridges as dental work and bridges a card game or bridges a musical form, there's all different contexts of that. And so we'll divide it up and say, no, it means different things. And if I then feed the machine an example of five things that are relevant, by the way, I don't have to tell it why. I don't have to say it's relevant because it's got this word and this word and this word. Nope, things like this. This counts. And it can do a nuanced analysis of the patterns in that and say, okay, great, here's all the ones. I'm going to train it again. You classify four more. So I can turn a review cycle. You know, for an attorney who's very expensive and I can use the machine to effectively do the lawyer's work. Of course, you're never going to say, okay, just let the machine run and submit. What you do is they have this iterative quality check. Is that relevant? Yes, no, okay, computer, that one doesn't count. This one does. Go again, but you can increase it by 200 fold. The speed of review and the quality of by 200 fold. So that's a significant improvement in the productivity of essentially that example of a team of lawyers going through these documents. Well, now they don't have to spend as much time doing that. They can spend times and more on higher value activities. Higher value activities, that's right. And it's going to save you money because as you said, the lawyers are very expensive. And you know, people are skeptical about this. In that business, in that industry, there's a lot of like, is the computer going to get it right and how do I trust it and sort of these things have gone all the way up into the courts who have evaluated this type of machine-assisted. They've actually come down and said, no, this is much better than people. And lots of, like, you get to be timid with people, you're going to have a margin of error. And you're going to, you know, but actually the computer does a better job in many cases of finding what you need, at least getting that first cut of relevance. Well, that's a key kind of line across when the computer is better than the person. That is a lot easier to sell that. I think a lot of what, in the answer to a question like, how do I know it's going to be, look, you can't, it's never going to be perfect, but neither are people. And so the probability modeling that we're doing to come up with this is probably what you're looking for because we think it's related to things that are similar to things that you've told us, or it's all, we've got to be good enough. We've got to end it well. Well, that's the thing, people aren't perfect either. And that's some people, I think when people are evaluating this kind of technology, they hear about it, they take, they have that assumption, well people always get it right. And that's literally not the case. So an improvement over what a human can do is where you kind of cross that barrier where this is really valuable. But plus, even if you could get it right, like you look at automatic document categorization, right? I mean, what it would take you to do that would be, we're going to read every document. That's crazy, you can't do that. I mean, that's what we do keyword search for. Okay, I can't read all of these. Find me the one with the word bridge in it and give them all of those. Okay, there's a bunch in there about card games. That's no good. But you see that perfect being the enemy of the good a little bit in here and realize that we're just like, you don't have any hope otherwise. Because you're not going to get through these 15 million documents unless you spend $20 million. And that's what the judge says. No, the computer's a lot better. Right. So we're here, there's a lot of Vertica customers here. Talk a little bit about how you're kind of talking to these customers about some of the things they're doing. Some, I imagine are autonomy customers as well, but not all. How do you kind of communicate that, the value proposition to some of these customers here that are using Vertica? You know, I think what's, people often ask me, okay, what's the difference between idle and Vertica and how do we work together and what do we do? Look, Vertica is an extraordinary engine for blazing fast query on excruciating amount of data. To be able to load and query that amount of structured information and the amount of time it can is, and that's not what autonomy does. You could do that, but you wouldn't be even anywhere near. It's not what it's built to do. And what autonomy is built to do is give you a different answer. So Vertica's going to give you the answer based on the structure of the data and the query. Autonomy's going to give you that answer and it's going to say, and by the way, here are the next things that are most likely probably related to what you've asked for based on the patterns that we've inferred out of the information. And so they kind of go hand in glove. Like I can take a use case problem. I mean, I gave you the example earlier of someone, I've got 40,000 phone calls that come in between 10 and 11 and they're this long in between this period and they come from this GPS location and they're this customer and that's all metadata and I've got those, but how many of them were upset customers? In my call center, right? And by the way, what did they talk about? So I can analyze that data and say, okay, great, now I've got 40,000, now I've got another 50 or 60 more data points that I've used autonomy to tell me what's happening and I can now, now I've got a much richer view of what's happening. That's an example of combining a structured analytic query with sort of a deep conceptual one. And it works great. I mean, it's great for things like audio and video and really hard sources to get. Well, that's, yeah, when you start talking about audio and video, now, you know, that's really, the amount of data being created in that genre, it's just outstanding. It's unbelievable, the amount of data points inside of video, so we do this deep video indexing. Interestingly enough, when you look at video, I can do a search on YouTube or something. You're searching for the label. You realize that you're not actually searching the video content. If I do a video about rabbits entitled dragons and you search for rabbits, you're not going to find my video, right? So what we do is actually watch the video and we look for the words on the screen, the words that are being said, the stuff at the bottom, the shape that's in it, and if it changes and, you know, rabbit-shaped things, it'll find rabbit-shaped things. So I think there's a lot of richness in that because we have a history in video surveillance and facial recognition and those sort of technologies, which are the same thing, it's pattern. Does it match or does it not match or is it close enough? Because it's never going to match. If it's never going to match, what's close enough? Right? It's close enough and then it gets the job done and that's where the value comes in. Exactly, it's the signal from noise and that. So in your answer to the question, working with Vertica is very exciting because we generate a lot of data. We generate, and so we can give that unique answer, but we can also then pair it with, you know, all that extreme structured information which gives us a really, I mean, it gives a holistic view of the big data problem. Fantastic. Well, Brian, we are out of time. Brian Weiss. My pleasure. Vice President of Marketing and Autonomy, thanks so much for coming on. Thank you, everybody. Nice to be here. We'll love to have you on again in future events, looking forward to it. Everybody, stick around. We have more coverage today. We're going until mid-afternoon today, covering all the action here at the HP Vertica Big Data Conference in Boston and we'll be right back. Thanks.


Hi, my name is Marek Malusz, I am an Academic Worker and Researcher at the Department of Psychology at the University of Ostrava in the Czech Republic. My today's talk is about the dark therapy chamber-rest research, how we do it in the Czech Republic. It's similar in a way to the flotation rest, that on the other hand it's really very different and perhaps that it's something very important and useful to people's life.


So how would you sum up the show in 20 seconds or less? It starts in Oxford and where it's set now and so it's humans but we suddenly quickly realize that there are also creatures living in the world which are demons, witches and vampires and the story centres pretty much around a witch called Diana Bishop and she suddenly meets a vampire called Matthew Claremont. The relationships and the chemistry between Matthew and Diana are so important to this show and how did you build that chemistry? Well I suppose there's two parts to that. One is that it's inbuilt in the novel which is great but then you have to find two actors that might be able to do it and you have that weird thing where I was sent off, I got given the job which was great, it was very excited and then I got sent on a plane out to Los Angeles. Then you do, it's kind of like speed dating from what I've been with the same girl for 15 years but from what I hear speed dating's like and I sat in a room and there was one person that we really wanted to have the job and that was Theresa, particularly as I saw a film that she'd done with Andrew Garfield on the way out on the plane and I thought who was this girl, she's absolutely amazing, I hadn't seen any of her work before.  I was like busted. So we've just got on really well straight away and I always think well isn't it just about learning your lines but apparently chemistry is something slightly untouchable and I hear we have it so yeah here's hoping that people enjoy the show. How did you bond when the cameras weren't rolling? We swear a lot English and Australian so I mean I suppose there was that too but she also she's had children pretty recently and I've got three and so the minute that they get to each other it's just that oh here's mine, these are my kids and I was like oh here's mine and and she was still breastfeeding and doing all of that stuff at the same time so we were just having a bit of a giggle about you know how when you do that things can change in size so yeah no we got on very well and we're still very good friends after six months of filming together that's good. What's your favorite thing about working with Theresa? She's so positive I think that's you know she's like an earth mother as far as and she's like I mean her husband ended up leaving to go off and do a job which he wasn't meant to be doing but she said of course she should go and do that and so she was working these 14 hour days really hard whilst and she had a nanny during the day but obviously her little ones had to come in and be breastfed at many different times and she was learning all the lines and doing the main part but the minute her day finished she had to go go home and be mum was obviously my kids were back at back at home with their mother and so she was just had so much energy and I think English is slightly more cynical perhaps or maybe it's just me and so she would keep me buoyant and happy and for most of the time and and she's just brilliant actress so all of those things together make it fun to go to work really. It sounds like the cast really got on really well what's your funniest memory from filming? I can't think of anything to my every day which is very there were certain scenes that Theresa because some of this novel is obviously very it's very much sort of fantasy based and some of it I found quite amusing just you know having to meant there was a day when I was filming something about she had to tell me what she saw in a in a in the book of life when she when she took it out with Ashmole 782 and and she goes and there's this amazing drawing of an alchemical child which was a baby which is upside down and I was like and I just started laughing and I found it I actually couldn't laugh I couldn't look at her throughout that scene because I just such a bizarre thing to be talking about and be excited about a baby upside down. How were you with all the magic jargon? I mean luckily for me I didn't have any incantations because that's the witches so I was pretty pleased about that because that does seem like mumbo jumbo some of it but then you actually have to deal with the actual tech I thought you were going to go with the technical difficulties that actually put like portraying the magic on screen so there's various things that are connected to like almost invisible wire which have to be tugged at certain times to make it look like it magic exists they've done really well actually because that's the thing isn't it when you have a fantasy novel it's very easy to write it it's a castle and it has six towers and it's huge and it's everything's vast and then you're like I know but we've only got a budget that's like hmm so it's it's it's quite a it was a very difficult thing to to put to make flesh if you like and I think I think we've done a really good job. The books have such passionate fans I mean have you had any memorable fan experiences yet? There were a bunch actually who who happened to be in and around when we were filming in Oxford because you know you get a lot of visitors from from abroad and from America particularly up there if they aren't actually already enrolled on campus and so we would often get set and there'd be people that would be super passionate and we'd only just started filming really so we sort of go out and speak to the fans when they seem like a really nice bunch because sometimes you never know what kind of fans you're going to get and it's not necessarily it can be a little bit daunting but if you get like big groups of people coming and being like so how's it going you know and it's it felt like it was a bit too early to talk about like when we think it's going okay um but they put a bit you know they've seemed like a pretty erudite intelligent bunch of people so we'll see if we can please them with with the adaptation. Looking back at your past back catalog of movies we love Watchmen I'd love to know if you'd ever be up for doing a sequel. No I don't I don't anything so I just think that they they're doing a they're doing a television series of it now I think that will do it better than again it's that thing of doing a novel versus a film and I always think that I should have been that they should have been an actor who's a little bit older so maybe maybe now I am the right age to play it so maybe maybe I should. Okay so now I have some fan questions that have been tweeted in the first one is what surprised you most about your character? Well I suppose the fact that he's so reserved in general um and he has such a checkered past you know but the fact that he and he's he has he has problems controlling himself he does suffer from this thing called bloodlust which we learn about later um but might as well throw it in now because no one really understands what that means probably but um but the fact that he falls for her so quickly I think considering he's his main thing is to try and understand why creatures are losing all their powers and suddenly this witch comes along and witches and vampires famous and don't get along and that he there's this sense of inevitability um or that that you know for a strong man he seems quite weak in some ways. What's the major difference between creating a TV show versus making a movie? Well this is a TV show obviously um so uh time generally uh and and also the fact that there's a nature of but due to it being adaptation things change like continually throughout when often oftentimes when you do a movie it's fairly set people have been working on it it's two hours you know the the structure doesn't change that much maybe a tweak here or there whereas this was a living breathing thing and Jane Trante is our producer sort of prides herself on that she does not stop and will re-edit and tweak and change lines and blah blah right up until literally the day it goes out so I wouldn't be surprised if yesterday she was tinkering with something because that's that that that there in lies that sort of bigger difference but it's quite nice also when you're doing when you're doing a book it's nice not to have to do it in two hours it's nice to have it unfold like the chapters of the book itself so you have more time on screen so in some ways you can do things and much more subtly. Have you read the books and if so uh we're watching the show be different if you know how the books end? It's funny Jane didn't want us to read the book to start off with and then we just felt a bit naked about it we're like well I need to know the mythology and everything that's going on so we sort of said look we pleased and she was like okay um so read one and I haven't read two or three yet because uh I don't want to ruin the surprise I'm I know that I'm gonna have to read two very soon because we start it again in March. What's your favorite thing about your character and do you have a favorite scene? I think the stag hunting was quite fun so having the possibility of incredible speed and agility as opposed to the actual of doing it which is up on a mountain in you know in the Breckin Beacons and you're and like my my wonderful director at that point was saying uh in the terms that you could possibly run a bit quicker look a bit more agile and you're like well I'm running up a one in two gradient and there's potholes everywhere but I'll give it a go um so yeah we got challenged um but yeah it's nice to have that kind he doesn't fly but he has some pretty good um you know physical strength. How did you prepare for your role? I read the book and I we chatted out but you suppose that in some ways you can't really prepare until you meet the other actor that you're going to spend the rest of your time with so spending really good amounts of time with Theresa and also working with Jane Tranta who does no stone unturned um she's a real inspiration as a producer so you know you do your homework you turn up and do your best like going to church once a week do your best. If you could ask your character one question what would you want to How do you take the left side of a golf course out of your swing?


Hi guys, welcome to a day with my dog. Pike and I are headed to go get Starbucks, of course, first, and then we're getting her shots. So she needs rabies, and then there's this like what other one that I forgot, but I got a text and it was like, oh, she's due. So then I'm like, all right, so we are going there. Her appointment is at 11.45, but I left a little early because I wanted to get Starbucks and stuff and kind of just relax in the car because I'm gonna go to the Starbucks closer to our vet. Yeah, she has no idea what's going on. Tomorrow morning, she's getting groomed, but I don't know if I'm gonna vlog that because I kind of want this to just be like a day vlog because tomorrow we're focusing more on Valerie's birthday weekend. This is kind of just like an empty day, not like an empty day, but I'm gonna be like resetting for the week, for my work week because we're going to a Disney resort. We're gonna have like a little Disney sleepover tomorrow and I kind of just want that footage to be its own thing. This is just gonna be a little day in the life with my dog vlog that rhymed, I love that. Sorry, Piper. The noise is probably a lot, sorry guys. Yeah, it's a dog weekend too, along with Valerie's birthday weekend because she's going to the vet and then tomorrow's the groomer. So she'll probably hate me. I swear, every time I take my dog out, it's like for unfun things, like sometimes we do a little Starbucks run. Hi baby, aw, Piper.   She lives a pretty good life if I do say so myself. So, but it's a fun ongoing joke that I love saying, like oh my gosh, this dog's always pissed. All right, so I got the beverage. Piper, I told her that tomorrow we're gonna get the pup cup. Isn't that right, baby? We're gonna get it tomorrow. All right, yay, we got a cute little pick for the thumbnail, my poor dog. You know what, Piper? We should film a Q and A, right? Yeah, no, I wish she could talk. This is gonna be a little sappy, but I couldn't have asked for like a better dog, honestly. Like she is such a good dog. Like when I got her, I don't know, maybe I'll tell you guys quickly the story about how I got my dog. So basically I have always wanted a corgi and I was like been set on a corgi for years and years. I played in 10 dogs and that's why I wanted a corgi so bad. I always said if I got a corgi it would be like in 10 dogs in real life. So I was just so set on getting a corgi.  Like I don't know, there's just something about her. So then when she started dating Sam, if you guys know, like they're engaged or whatever. I'm talking about her, like she's like Kim Kardashian or something. I don't know, I don't really like follow Kim that closely. I don't know anything about like a lot of people when it comes to like certain YouTubers like I do, which is weird, but you know, I like them because they're not like major superstars or like really, really well-known, but they're still like well-known to an extent. But anyway, when she started dating Sam, he had a Pomsky and I was like, I just fell in love. Like it's a miniature Husky. And I've always thought like Huskies were gorgeous, but I don't think that like they were meant for me because they're like larger dogs. I was looking at breeders and stuff because I wanted to get a puppy and Valerie wanted to get a little kitten. And you know, it just was never the right time to get a dog. And I was talking to a breeder, some other state. And basically it just didn't really work out because of timing wise, we were getting ready to move too. The timing was just bad. And basically once we got moved, they were, my parents were like, once we get moved and like settled in a little bit, we'll get a dog. So I don't know, it was a Sunday morning and I was scrolling on my phone. I was in Pomsky groups in Florida and just like normal Pomsky groups. And I actually got in trouble in one for like asking people like where they got their Pomskies because I wanted one that looks like a miniature Husky. Because the breed isn't like a standard breed, it's not regulated by the AKC or whatever. Because it's not like a regulated breed, there's so many like people that breed them. And then they come out and not looking like, I feel like a Pomsky, they can come in different colors, but like what I want is the Husky mask. Like the face to me like matters with like a Pomsky. So personally, that's just my personal preference. So I was just very picky. That's why I was asking people and they were telling me and then I got in trouble for one in one group and I'm like, okay, not, not it. But then I saw when I first opened Facebook, I saw this picture of Piper and she was like just in a little backyard and it said looking for a new home for this girl. And I was like, what? And it was like two years old and my friends in school back at the time when I was in like search tech school, they were like, why don't you just not get a puppy?  So when I found out she was like two and she was like potty trained, good with kids, everything, she was just a mom of a couple breeds, like a couple litters, I should say.  So they were looking to just find her a new home and I'm like, why would anyone want to re-home this girl? But, you know, they had a lot of other dogs. Fast forward, I messaged the girl and she was like, where are you located? I'm more of like in the Orlando area and I'm like, oh, that's great, me too. I'm in celebration. And she was like, I'm in St. Cloud. So Sherry and I drive to meet this little girl and she was a completely different dog. She was very, very lethargic. Granted, she had just given birth, but, you know, she's very lethargic. Her tail was down and Sherry was like, why is there a tail down?   And the only thing we want from you is to pay to get her spayed or neutered. I think it, I don't know, I think it's spayed. I'm not that big in like the animal world. So clearly I wouldn't know, but they were like, if you pay us like $315 just to have our vet like do it, then we'll, you know, then she's yours. So then we write them a check and they have her spayed or neutered basically a week from when we like agreed. And then they kept, well, like the girl kept pressuring us to kind of like take her before May 1st. And I was like, no, it has to be May 1st because like that's when we agreed. So there was a time and like, there was this day that I was like about to not, I feel like not get her cause I was just going to pull out the deal because like this lady was like pressuring me. And she was like, in a way, she sent this like long, long message. And it was like, I've never had anyone send me that long of a message ever. And I was like, all right. So then I was about, I was already having a day that day. I remember my friend Kyra will tell you, like I had already just had a bad day that day. And I like on this on top of it, I was about to just say, just forget it. Like I don't need a dog right now is like financial burden, whatever. But then I was like, you know what? Just keep calm. And I'm like, it's May 1st. So I don't know, somehow she like agreed to it. She used the excuse of like, we want people to like take their dogs sooner because we want them to bond with the dog. And I'm like, she would already, it's not like she's a puppy. Like she's an adult dog. Like I don't need to start bonding. It doesn't matter like what time I need to like start bonding with her, but whatever. I just had to include that because I was like, that's weird, but fine. Fast forward the day that I got her. I didn't want to go alone.  And I was going to be driving to the girl's house like where I had been. But she was like, no, it's more convenient if you meet me at this gas station. So I literally picked up this dog at a gas station. And I'm like, I feel like I'm doing some illegal trade because I have this dog now given to me in a parking lot. And then Kyra went home, I took her home and she was like curled up in a little ball. And she didn't have a leisure collar. Luckily Kyra because she was a vet tech for so many years and stuff had like this little leash type thing, but I had no leash for this dog. I feel like they put something in her for that was like, it was like a dry shampoo. And I just did not like it. And it was literally when I had pet my dog, like my hands were swollen. You can go on Piper's Instagram and see. It was just like hives, but I immediately gave her a bath and she like completely wet the whole bottom of my bathroom floor. You can see that on her Instagram too. I think it's under the home tab. But yeah, that was the story about how I got my dog. She was a little baby getting re-home. She was a mom's ski. Her puppies are the cutest things ever. You can see them on her Instagram as well, but she had like four puppies. And oh my gosh, she's, I don't know guys.  Like I couldn't have asked for a better dog. Like she was crate trained. I can literally sleep in until like 11 or just be in my bed and she will not make a peep in her crate. I don't know, how did I get so lucky? She's just such a good dog. So anyway, that's gonna be it for this clip. Hopefully I can get some little clips in the vets. I don't know, it's weird. I'm thinking about taking her to a different vet, but they take her back and like leave me out in like the main area. They just take her back and like do whatever.  It's like super weird, but you know, whatever. I'll probably end up switching vets. But yeah, I definitely love this girl. Whoever said like Pomskies are like hard to train and hard to like, you know, they're just like too hyper and stuff. I don't, I think it's really based on just personality of your dog because she's great. Every dog's different, just like every human. So you can't really like put a label on any type of breed. I definitely first see a Corgi in my lifetime. But the Pomsky, the Pomsky was just calling me. So, and it was just meant to be and my parents love her. So, all right, that's it for this clip. That's the story about how I got my dog. All right, Piper, she's lounging, but we're here. And who, she's, come on, we're gonna go in the vet now. Just got out of the vet and she got her stuff updated here. That was my total, but she got her vaccines and I'm very happy that they have this, but she got a little rabies tag that I'm gonna put on her collar now. I didn't realize that dogs got these.  And I'm like, oh, well, I didn't get one when I got her, you know what I mean? But the vet gave me this. So thank goodness now, she's legit. So now I'm headed to town hall because I wanna update these like forms for Piper. So she can go to the dog park and hopefully I'll have access. I know I'll have to renew the pass pretty soon, just like my ID for the town, like to get into the stuff. I'll have to update that soon, but hopefully they'll already have like the files for my dog, so I won't have to like, you know, renew that part as well. So I'll only have to renew just the me part and my dogs will be fine. And it's kind of like on my way home. So that's what we're gonna do now. We are here at Wolfgang because I wanna give them her updated vaccines. Yeah, she doesn't like this place either. Oh, you show dramatic, show dramatic. All right, so slight change of plans. Valerie is not cooking. Where are we going, Valerie? I guess it'll make me feel bad. The weather is not good enough to cook. No, it's too windy. Too windy and it's gonna rain. So we're going to go to Longhorn. Yeah, we had a great meal there, so I'm excited to have a steak and fries. I forgot what else. I'll probably have to check on the vlog because that's how I always remember because I just go back through my videos and be like, oh, what did I get? Because I just remember having a really good meal there. Okay, yeah. That's fine as long as I don't have to cook because I don't think I can. If we're grilling out, it's not gonna happen. Yep.  It's a little bright there, a little bright, sorry. We need a few minutes. All right, so I'm continuing on this vlog because we're still with the dog. Like I said, in the beginning clips, she has a grooming appointment and this poor girl, I swear, has no idea. Actually, I think she does know but she loves going out with me, so she's torn. She's like, I hate getting groomed but I wanna go for a ride. So we are going to get her groomed. I might just do the mini groom. She had already been de-shedded last time so I'm thinking that she'll just do the mini groom today which only takes like an hour and then we're gonna get some Starbucks and stuff. On another note, I don't know if I'm gonna do a vlog or just a TikTok of Valerie's birthday. I normally always vlog it but I am basically not spending the night at the Disney Resort because I have to take care of the dog. So I'm just gonna stay here but I'm gonna like partake in the birthday festivities. So I might just do a TikTok. I'm already a little bit ahead with the vlogs because of the Get Ready With Me video that I wasn't planning on filming. So I don't know. I haven't decided if I'm gonna vlog or not. I might vlog and then combine the footage with like next Friday's video. That's what I'm thinking I'll do because I know for real, like I will not have enough footage because I don't vlog a whole lot like when out and about. So that's what I'm thinking. And now I'm just thinking out loud so you guys can enjoy my thoughts. Finally, for once we're kind of like on time. Normally we're like in a huge rush to get to the groomer but like they're always like running behind but the last time when I was like two minutes late they were like waiting for me. I'm like, of course this would happen. This would happen. It is a beautiful morning downtown celebration. Piper loves to be out and about and here's her favorite restaurant. Come on, Piper, let's go. Okay, of course Piper had to take her poop in the road but I pulled her over to like this area. Well, not this one but on the other side.   You know, like she's just not having it. At least she went to the bathroom like here instead of the groomer because that would be embarrassing. Piper's clearly at her favorite place. She hates the groomer. But we love Adriana, don't we? All right, so I got the Starbucks, got my drink and then also got a cake pop because I was feeling a cake pop today. Okay, if I like had the money, this is something I actually would probably buy. The buddy, the elf with the etch-a-sketch. How funny. Here she is. Adriana. Brother did now grinding. If I try to clip it before she pees out. Oh, okay. Oh. That's funny, right? Yeah, she's odd. She has weird tendencies. Thank you. So Piper is looking better than ever and she smells so good. That is going to be it for this dog vlog. I hope you guys enjoyed. It was another fun little different thing to throw on my channel and I don't know. I like doing different things. You guys know that. So if you guys enjoyed, give it a thumbs up. Don't forget to subscribe. Do it for Piper and I'll see you guys in my next video. Bye.


We're remaking a coffee table. Yes. We're remaking a coffee table So we originally saw this coffee table at world market Cameron coffee table the Cameron coffee table It's country style table. It was like 280 bucks at world market, so we originally built it last year since then we built a lot of stuff and we've been standing at Briar smoke this coffee table was stand gray And it just looks like a big sore thumb in the middle of the room So we took it apart and we're going to Refinish it we did a lot of sanding and now we're going to show you how we put it together. All right, let's get started Let's get started. All right. This is a pretty big project. So our materials list is pretty big We'll slide that in right here And then we'll drop the cut list in over top of it so you guys will have that too We'll give you a minute. It's big Top support two legs and a 28 and a half inch top support board using our 1 by 3 and 28 and a half inches and our 2 by 4 is a 16 and a half inches We're going to inset this top support three eighths of an inch and then attach it to the 2 by 4 using the pocket hole screws Side done side to almost done race using your 2 by 2 These you'll put the pocket holes down Facing underneath of the coffee table and then they will be flushed to the front of the support But an inch and a half up from the bottom Did you get all that got it flip them over? We're going to attach our 39 and a half inch One by three side graces and then you're gonna set these inset these three eighths of an inch as well All right almost done Step 4 2 by 2 supports you want the pocket holes Facing up while you install so that they will be facing down when it's turned right side up Now we're gonna add the bottom support 2 by 2 brace and it is 43 and a half inches long and I'm pretty sure Just to make it fit. Yeah, you just need some love and then it's just gonna go flush to the bottom 13 and a half inches on either side Hey supports, we'll use the one by fours at 31 and a half them with an inch and a quarter inch brad nail Okay, so we're going to use sleeves of nails as spacers. So just a little bit of a space In between each of these boards to keep it exact So it's got a little tiny head on it. So it makes like a perfect spacer Attach attach these top supports 29 and a quarter and they are gonna come in from the side of the 2 by 4 11 and 5 eighths almost done Almost done step 8 is to make the top. So let's take this down so we can show the top We drilled all those pocket holes I showed you earlier and we just glued and screwed them together That's it. We did facing down and we leaned on top And we leaned on top of them while we were screwing it in so it'd be nice and flat on top Don't get me wrong. We had to do a lot of sanding after that too So this is using six two by sixes at 48 inches. Oh, right Okay, so step nine is to attach the base to the top So we're gonna put the base on the top and center it All right, it looks like we lift the three quarters of an inch Overlap from the top all the way around inch and a quarter pocket hole screws. This thing is going nowhere Let's start screwing. Oh, we need now a sanding and staining camera on coffee table coffee camera Copy table coffee Cameron copy coffee table market Cameron copy coffee table. Wow