Life is a winding road Oh, tellin' where it goes Driving through days and nights Won't stop for traffic lights If I'm fallin' down, I will keep on searchin' for my highs You can say I lost my mind I will keep on holdin' my head high Even if the sky's fallin' down Jumpin' from cliffs so high Trustin' our wings to fly Okay, so for breakfast, Ash has requested a cereal bar. So I made these last week at the end of the week. And they're just, they've been in the freezer. Basically, they're Cheerios, Trader Joe's Cheerios. Rice cereal, peanut butter, honey, and then I put a little bit of chocolate chips in them. And I just keep them in the freezer. And then I just cut them apart. And they look like this. So peanut butter, honey, Cheerios, rice cereal. So like, I'm talking about rice Krispies. Rice Krispies, chocolate, and honey. I don't know if I said that already, but it's only five ingredients. And then like I said, I just keep them in the freezer, and then I cut these off when he's ready to eat it. So he's having this and a yogurt for breakfast. We're crashing down but we get up and start from the ground. Hey bud, say hi again. Hey bud. Push the blue button. Push the blue button. Over here. Open it and say hello bud. Okay, guys, in the car, we have everything we need for public, what are we calling it, public going outing? I don't know. Hand sanitizer, which they're now saying doesn't really work. we've got some disinfecting wipes just in case we touch anything we need to disinfect afterwards and then I have a whole ton of gloves in my bag um those are just like black gloves from the police station um I have masks coming in the mail my mom actually made them so they're gonna be pretty cute but anyway we are on our way to preschool I don't think I'm gonna stop for coffee on the way back because I think I'm gonna drink a pre-workout today instead of coffee that will be my caffeine intake for the day my home classroom and I have been sitting here for probably about an hour and a half now since I last talked to you guys and so what I've been working on is I've been uploading a video lessons to my classroom youtube channel I guess it's just it's not like a public youtube channel it's just the one that goes with my school email and all of the videos there are unlisted um but so I just wanted to quickly share with you guys like the expectations for teachers in my school district so I'm gonna put you down and kind of talk you through it so our school district pushed out a digital learning expectation plan for the teachers um the first week we were posting one lesson slash assignment for each subject and those subjects were reading writing and math only and then a social emotional component so some sort of like activity for the kids to do that is for their social emotional learning this week though we got an email um pushing out a new plan which is going to be two lessons and videos for each subject and they have added science and social studies into the mix so instead of three subjects we're going to have reading writing math science social studies and fcl so the students are going to be given 12 assignments per week things that they need to turn in uh that's two per subject and then we are supposed to make video lessons for each one of those assignments um one video can be filmed by us and made by us and then the second video can either be made by us or we can find something online to support the students with their work um so a lot more pressure on us than the last week the first week um and then we're also supposed to be planning for two weeks at a time so um our lessons are I guess our activities our assignments have to be ready and posted by today Wednesday um for the next two weeks of online learning and then our videos don't have to be live until the Monday beginning so um basically what that means is I have assignments ready for the week of April 13th and for the week of April 20th but my videos don't have to be ready and live until that day the 13th and then the 20th so the videos I'm filming kind of as I go a little bit slower so every week I film like a welcome video where I just welcome the kids to the week I talk about a couple things that are different I talk about like what they can expect from the week and then I kind of guide them through like I do a screen recording on my computer and just show them everything that they're gonna have for the week um and kind of just explain it and then as a team we decided to divide and conquer so I am the ELA teacher uh the digital ELA teacher so I plan post and teach all of the ELA lessons so it's kind of nice because instead of filming like 12 videos a week I only have to really film like three or four um and then my team teachers one's doing math one's doing science one's doing social studies and then um the one who's doing science is also doing SEL so we've kind of divided and conquered um we just send each other all of the assignments and the links to all of our videos and things that we've made and then we each share everything on our Google classrooms so that has really helped um alleviate some of the workload because as you can imagine 12 assignments creating posting and making lessons for that many per week would be really hard to do um the only thing that I worry about with this much workload is that our district says that we should only be giving our students about an hour of work a day and I think that the amount of work they're asking us to post is far more than an hour a day so I'm just going to suggest to my students that they do one subject per day um like maybe they'll do reading on Monday maybe they'll do math on Tuesday science Wednesday social studies Thursday and then Friday they can do their SEL um which is like you know like a fun emotional type activity and then they can do their PE and their music videos our PE and music teachers are also posting videos every week for our students to do so I'm kind of thinking that if they do it that way they'll be able to manage everything and make it an hour of work a day um but our principal is so amazing and he's always telling us just do what's best for kids and if you feel like what you're posting is too much for them then cut it back but do what's best for them so we're pretty much done with the next two weeks and then after that I think we might cut it back a smidge um but as of right now I do have about 15 students logging into google classroom or at least have logged into google classroom whether it's from a computer a tablet or a phone um and so far I've only got about five active students which means that there are five actually turning things in so um that's really positive especially since in our school district a lot of diverse needs a lot of kids don't have technology a lot of them don't have internet um and so we did push out a lot of packets for kids who had a phone I suggested that they get a packet so that they can do the work and then just post a picture to google classroom instead of trying to function google classroom on a phone because it is a little bit harder especially for a nine-year-old um so that's kind of what I'm doing I've already filmed I filmed it yesterday my welcome video and my eLA video you guys can see my little anchor chart is still out so I kind of just film right here and then today or tomorrow I think I'm gonna film my holes read aloud so um I know this isn't a third grade level book but it is a really fun read aloud and I promise them that I would read this to them before the end of the school year and so I wanted to oh I wanted to my crown fill I wanted to make sure that I read that to them before the end of the school year so every week I'm doing four chapters so I need to film that probably today or tomorrow post that and then all my videos are done for next week the 13th of April and then I will start filming for week three which is the 20th of April um so that's what's going on I've had coffee this morning I've had my skinny tabs as you guys saw I need to get hot to trot I'm drinking my water because it's been hard to manage like so many beverages in my day um but yeah that's how we're kind of doing things here um and for my boo boo classroom what are you up to trying to get a police hat a police hat from chase yeah oh are you gonna work out I want to work out too I eat some prots prots what's a pro it is teach me yoda jocados chocolate way protein banana and peanut butter wow it sounds delicious carbs and protein all in one you're off today huh yes you want to go watch them all american oh that sounds good why is my oh see so see what had happened was I put I switched it because cash was helping himself to the pantry you get me right well I accidentally put this facing the wrong direction so you can take it off and fix it but it doesn't bother me I had to do it quick before I got caught by the toddler it still works all right guys so I'm officially done working out um if you want to do the workouts that I do I'm following this girl on instagram her name is I'll show you guys um her instagram k campfit um it looks like this k campfit um I really like her workouts before all of this staying at home stuff started she did a lot of stuff in the physical gym and now that she's home she posts a lot of at-home workouts that are really easy to do so I just did a back and biceps workout now I'm going to make lunch and I think I'm gonna make a sandwich like a basic sandwich okay so it has been quite some time since the last time I saw you around lunch time since then um I haven't really done a whole lot in our day we um showered we watched all american which is a show on netflix that we're both really in love with um we watched what like two episodes of that um we went to pick up cash from school we went and picked up a drive-through order from coals cash needed some new shoes and some new clothes he's like outgrowing everything um and then we came home we went for a scooter ride which I will insert a clip of that here we're getting ready for bed cash is just watching his favorite little show he wanted to watch a show tonight instead of reading books so we're going to do that hi and we're ordering food to be delivered because we do not want to go out what buddy where's what though it's under your blanket babe anyways we're gonna go ahead and end this vlog tonight um I think after we put cash to bed we'll probably watch more all american um we're ordering our favorite little mexican place it's called marino's really good um and then I think we're gonna just watch some more tv probably either all american or we're on the game of drone season one we just started it so um so we'll probably just watch that and then go to sleep and start our day all over again so I hope you guys enjoyed this day in the life video and I'll see you in the next one bye guys


Law perm cat vs. Selkirk Rex cat. What are the differences between these two cat breeds? Law perm's outstanding feature. Of course, this is coat, which has loose, bouncy curls, making it light and airy to the touch. Tighter, longer curls surround the neck, forming a rough and clustering at the base of the ears. The whiskers are also long and curly. The coat comes in two lengths, short and long, and any color or pattern. Tabby's, tortoise shells, and red cats are most common. Long-haired law perms have a plume tail. The coat's length and fullness may vary seasonally. It sometimes parts naturally down the middle of the back. The short-haired law perm does not necessarily have a rough, ringlets, or earmuffs, and the texture of the coat may be harder than that of the long-haired law perm. He has a tail with fur that looks wavy, more like a bottle brush than a plume. The short coat may also part down the middle of the back. Kittens may be born hairless, with straight hair, or with curly hair. Both long-haired and short-haired kittens can be born in the same litter. The law perm's head is a modified wedge shape, meaning it is slightly rounded. The medium-sized to large ears are slightly flared. Long-haired law perms have full furnishings and a covering of fur on and around the outer ears that resemble earmuffs. Expressive eyes can be any color. Imagine a medium-sized to large cat with a heavy bone but muscular body, a rounded head, and a soft, wooly coat of loose curls that can be short or long in any of a multitude of colors. What's the Selkirk Rex? Kittens come out curly, go through a straight stage during adolescence, then mature into plush, dense clumps of curls when they are about two years old.  The difference in coat length may not be immediately obvious without first observing the rough and tail. Short hairs have a rough that is the same length as the fur on the rest of the body and plush curls on the tail that lies compactly. A long-haired Selkirk has a longer rough that frames the face and plummy curls on the tail that stand out rather than lying demurely in place. With either length, the coat may appear most curly around the neck and on the belly and tail. The coat comes in any genetically possible color or combination of colors. If it's possible for a cat to be purple polka dotted, you will find a Selkirk in that pattern. The eyes, too, can be any color. The clever law perm has a sense of humor. Even described as clownish, he is something of a mischief maker who makes talented use of his paws to open doors, swipe things he wants or tap you on the shoulder for attention. He's not clingy, but he likes to be with you and will follow you around, sit on your shoulder or the top of your computer, or sit in your lap, whichever option is most convenient for him. He is moderately active and enjoys retrieving items that are thrown at him. Despite his reputation for getting into things, the law perm is pleasant to live with. He rarely uses his voice, and he is affectionate, gentle and patient with his people. Most are also welcoming to visitors as long as they were well-socialized as kittens. Selkirk is the relaxed Rex. His Persian and British shorthair ancestors give him a placid, cuddly nature, but he is more active than those breeds and likes to play.  Tolerant and affectionate, the Selkirk is not always a lap cat, but he likes people and other animals. With his sociable nature, he dislikes being left alone for long periods. Keep this fun-loving cat entertained with a fishing pole toy or flashlight beam, and he will be thrilled. Ignore him at your peril. He won't hesitate to demand attention in a small, quiet voice if he feels that he's not getting his due. Hello. This video is sponsored by Beemix Pets. Are you looking for high-quality cat collars at an affordable cost? Check out BeemixPets.com. Use coupon code KittenLife, to get 20% off. Law perm's unusual coat is easy to care for and usually doesn't matter or tangle if it is combed or brushed one to three times a week. Longer coats should be groomed more frequently. Use a comb with revolving teeth for best results. It will go through the coat easily without straightening the curls. To keep the cat looking is curly best, mist the coat with a little water or fluff it with damp hands to set the curls. The coat sheds little, and shedding is further reduced with regular brushing. If small mats develop, brush them out gently with a slicker brush or greyhound comb, stainless steel with narrow teeth at one end and wider teeth at the other end. If you give the law perm a bath, press a towel against the coat to soak up moisture and then let him air dry in a warm, draft-free spot. Using a blow dryer will give him a bad case of frizzies. Selkirk Rex has a dense coat that should be combed a couple of times a week to prevent or remove tangles. Rex do shed, so regular combing also removes dead hair that would otherwise end up on your clothing or furniture or hacked up as hairballs. Gently running your fingers through his coat will help fluff up the curls, just about the easiest styling you can do. A bath is rarely necessary, but if your Selkirk gets dirty, use a moisturizing shampoo and let him air dry for a few hours in a warm, draft-free place. Using a blow dryer will give him an appearance more like that of a pool. Brush the teeth to prevent periodontal disease. Daily dental hygiene is best, but weekly brushing is better than nothing. Wipe the corners of the eyes daily with a soft, damp cloth to remove any discharge. Use a separate area of the cloth for each eye so you don't run the risk of spreading any infection. Check the ears weekly. If they look dirty, wipe them out with a cotton ball or soft damp cloth moistened with a 50-50 mixture of cider vinegar and warm water. Avoid using cotton swabs, which can damage the interior of the ear. Keep the litter box spotlessly clean. Like all cats, they are very particular about bathroom hygiene. It's a good idea to keep a law perm and Selkirk Rex as an indoor-only cat to protect them from diseases spread by other cats, attacks by dogs or coyotes, and the other dangers that face cats who go outdoors, such as being hit by a car. Law perm and Selkirk Rex who go outdoors also run the risk of being stolen by someone who would like to have such an unusual cat without paying for it. Cats are obligate carnivores, meaning they eat almost exclusively meat. Their diet should consist of no more than 5% carbohydrates and most of their protein should come from an animal source. It's hard to accomplish this nutritional requirement by providing only dry food as most commercially available dry food contains a lot of carbohydrates, fillers, plant protein, and other ingredients that are not natural for a cat to eat. Another important consideration is your cat's water intake. Cats in the wild get most of their hydration from their diet, not by actually drinking water as most people think. Some cat food is about 70-80% water, while dry food is only about 10% water. Although you can make up some of the water lost by providing a bowl or fountain of water, it's still not usually enough to cover the difference. If your cat is healthy and doesn't have special dietary requirements, then mainly wet food with dry food being supplementary and raw food being optional will likely be fine. Unless your cat has an exceptional ability to self-regulate its eating, it's not a good idea to leave out unlimited food all day especially dry food. It's okay to leave dry or wet food out all day if you use measured portions that make sense for your cat's weight and activity level. The easygoing but playful law perm and the gentle but social Selkirk wrecks are well suited to life with families with children and cat-friendly dogs. They can learn tricks, enjoys interactive toys, and loves the attention they receive from children who treat them politely and with respect. Supervise young children and show them how to pet the cat nicely. Instead of holding or carrying the cat, have them sit on the floor and pet them. Please introduce any pets, even other cats slowly and in a controlled setting. If you enjoyed this video, kindly press the like button. Also don't forget to subscribe with notifications on, so that you don't miss out on videos like this. Thank you for watching.


Welcome to another edition of RCE. Again, this is Brock Palin. You can find us online at RCE-cast.com where you can find links to our Twitter, our blogs, all that fun stuff. Once again, I have Jeff Squires from Cisco Systems and one of the authors of OpenMPI. Jeff, thanks again for your time. Hey Brock, how are you? It's getting to be fall here, which means it's getting to be the ramp-up time for supercomputing. Right, so once again, I believe we both have booths this year. I am not directly participating in anything, but a number of us will be participating in panels on arm for scientific computing, machine learning, GPU computing, a number of other different little things that we've dabbled in over the last couple of years. So this is probably the most involved year we've ever been with supercomputing for my group. Yeah, that spans a wide gambit there. And I'll be having the usual OpenMPI Birds of Feather session with Dr. George Basilica from the University of Tennessee given the state of the Union of where we are in OpenMPI and where it's going all these things. So love to see all of you there. But enough about supercomputing. Let's talk about today, Brock. Who do we have? Okay, so our topic today, we have Dr. Brian Granger to talk to us about Jupiter. So Brian, why don't you take a moment to introduce yourself? Yeah, thanks so much for having me here today, Jeff and Brock. And I'm a professor of physics at Cal Poly State University in San Luis Obispo. And my background is theoretical atomic physics, but for the last, I don't know, decade or decade and a half, I've gotten very involved in open source software development originally in the scientific computing space, sort of before data science was a thing. And then as the entire sort of universe has shifted around this new idea of data science, a lot of the open source projects I'm working on are right in the middle of data science. And but also continue to be relevant in traditional scientific computing as well. That includes Project Jupiter. I'm one of the leaders and core developers of Jupiter and I Python. And then also in the last year, I've been working with Jake Banderplass on a new data visualization library called Altair. So that's kind of funny because actually my degrees in nuclear engineering got into scientific computing, started off with the classic scientific computing. In the last couple of years, with the rise of data science, I'm doing a lot more data science infrastructure for people in social science, health, IoT and engineering and then everything else. So it's funny, kind of parallel tracks. Yeah. I think there's a lot of us who've taken very parallel tracks in that respect. This is amusing. I'm the odd man out in this podcast here where I'm the only pure computer engineer. We just consume what you do. Yeah, it's apparently so. Yeah. So, Brian, can you give us a little bit of detail? We're here to talk about Jupiter. What is Jupiter? Yeah. So, Jupiter is a sort of offspring of I-Python. And so I can start with a brief history of I-Python. It started in 2001. Fernando Perez, a classmate of mine at the University of Colorado, a graduate school, started I-Python originally as an enhanced interactive shelf for Python. This is sort of in the very early days of Python starting to be used in scientific computing. And Fernando had long been a user of Mathematica, as I was, and really missed a lot of the sort of niceties that Mathematica offers for working interactively with code. And so he started I-Python in 2001 to bring that to Python. And for the first roughly decade, it remained a terminal-based interactive shell for Python. And then in 2001, we built a web-based notebook. It was part of I-Python at the time. And over the following years, we abstracted out an architecture that allowed other languages to plug into that web-based notebook. And Jupiter was born as sort of the language-independent part of the overall effort. And so today we refer to that notebook as the Jupiter notebook. I-Python continues to exist as one of the language extensions, or as we call it, a kernel for Jupiter, which provides basically the Python language support for Jupiter. So where did the name Jupiter come from? And particularly, it's got a slightly odd spelling. There must be a story behind that. Yeah, it was a long time for us sort of hunting and trying to find a name that met the basic constraints of something that was open on domain names, open on GitHub, Twitter, et cetera. And so that once you impose all of those constraints, there's not much left. We also wanted to name that sort of knotted in the direction of our scientific computing heritage. And so you've got Jupiter, Galileo sort of built into the name. And then also the fact that even though Jupiter is language-independent and support, we support many different languages, our Python heritage as well. And so the sort of changing the I in the name Jupiter to a Y is where we ended up. At times we've said the name also sort of has fragments of some of the main languages we support, namely Julia, Python, and R. So you could think of Jew, pi, ter. I think that's a little bit tongue-in-cheek. But if your listeners want to take that seriously, that's also completely fine. I think some people in our community, that's the folklore that's emerged around the name. And I'm completely fine with that. So for those who haven't seen this before, why would you not just use a Python, Julia, or R, ID? Yeah, that's a great question. And it's a question that is becoming more difficult for us to answer. And so let me first give the answer, looking back. The main thing that the notebook, the Jupiter notebook provides is interactive computing. So when someone is running a computation and while it's still running, writing more code, submitting it and looking at output. And so it's really optimized around that type of workflow and that in combination with a more of a narrative format. So the Jupiter notebook is actually a document that a user would create that mixes live code with narrative text, LaTeX equations, visualizations. And so at the end of it, not only have you sort of been able to do your interactive computing yourself, you end up with an artifact that is can be used to reproduce the work, share the work with other people, etc. And so traditional IDEs don't offer those two aspects of sort of reproducible interactive computing. And then what we refer to as a computational narrative that could be shared with other people. This is the concept of literate programming, right, the idea that you can have all of your documentation and everything embedded, but it's more for learning rather than actual documentation. Yeah, we like to use the word computational narrative rather than literate computing. The original vision of literate computing was slightly different in that it wasn't designed to be an interactive experience. It was more that you would have a single file that contains the source code and the documentation, and then later run various post processing steps to generate documentation or source code. Whereas what we're talking about here is actual an actual live interactive computation in the middle of a document that honestly looks more like a Google Drive document or a Microsoft Word document than a traditional IDE. So yeah, this actually kind of strikes me and I'm going to date myself a little bit by making this reference, but when I was doing research for this interview and looking into what Jupyter is and how it works and whatnot, it kind of struck me that this seemed like a Google Wave for science, particularly with the timing back when you started this in 2001 or so, that Google Wave was the predecessor of Google Docs and Google Sheets and things like that, where they kind of introduced a lot of this technology where you could even have multiple authors working in a single workspace at the same time and everybody gets a simulcast of exactly what's happening all throughout and stuff like that, but you've taken that and run with it even further to actually put computation in there. Is this, was there any relation to the Google Wave ideas at all or were you before that or did it happen at the same time or is that even an apt analogy or am I just speaking nonsense here? That's a great question. I don't remember the exact year of Google Wave and to be able to sort of put it in the context of our thinking, but sometime I got involved with IPython development around late 2004, 2005 and originally I had started to do work in more in parallel distributed computing in Python and part of what Fernando and I started to realize was that the interactive computing experience was really something that we loved and other people loved, but companies and efforts such as Google and Gmail were starting to come out and show that the web could be used for more than just static content and so the idea of a web-based notebook was something that we started to talk about very early on in 2005 and it wasn't a single thing, but it was really a constellation of new collaborative web applications that people were starting to build. Google Docs is one of those Gmail. The social media side of it was probably less of an influence. I think for us at the time social media was more entertainment than sort of a productive work tool, but that sort of broad new direction that many people and companies were taking of building rich collaborative interactive web applications was definitely something that informed our thinking and planning and direction of IPython and Jupyter. So while you were speaking there, forgive me, I did go off and research. Google Wave looks like it came around in 2009, so you were several years ahead of that, so quite the pioneer, but let's flash forward back to today. So from your definition here, it sounds like you're not even intending to be a competitor to an IDE. This is an entirely different paradigm of doing work. Is that an accurate assessment? Increasingly, those lines are being blurred and let me sort of describe what we found over the years. So we've really focused on interactive computing and what we've seen from ourselves and our users is that eventually those interactive computations start to look more like traditional software engineering. So eventually you need to pull a function out of a cell in a notebook and put it in a standalone file and start to write documentation in a test suite and package it. And it's been pretty painful to go through that transition from an interactive notebook to more full-blown software engineering. And in response to that, we're actually building a new user interface for Jupyter called JupyterLab. And honestly, when most people see it, they say, hey, wait, that's an IDE. And so it's definitely something that is becoming much blurrier in terms of traditional IDEs and then interactive computing. And I think the way we're casting it now is that if by IDE you mean an interactive development environment, then yes, we're willing to commit to that. The traditional notion of an integrated development environment that's focused on more software engineering type of workflows, that's not there. There's many other IDEs that are much better than that. We're really focused on the interactive portion of the development process. So if it's being less used for interactive and people are really kind of using it for everything, does running a kernel inside of Jupyter introduce any performance overhead? No, not really. So for your listeners, the word Jupyter kernel is what we use to refer to the separate process that we send network messages to and that process runs code, the user's code in a particular language. So there's a Python kernel, a Julia kernel, an R kernel. And the only thing we're sending to the kernel is a string of source code that that process can then interpret and run. There is a small amount of overhead to just sending that small string of source code over. But once it starts running that code, there's essentially zero overhead. We have, for example, a C++ kernel and once it gets that network message that has the source code, it compiles it and runs it and it's full blown native C++ performance. So how would you say the most common way people kind of start with Jupyter and end with Jupyter if they're using it in their project? Yeah, I think the sweet spot for Jupyter right now is people that are doing a wide range of tasks in scientific computing and data science where maybe they're running a simulation or they're loading a data set, cleaning the data set, processing the results of a simulation, doing statistical analysis, doing machine learning based on data sets, and then doing visualization in an interactive context. And then at the end of it, wanting to have something that they can share with other people and communicate their results. That's sort of the core use case for Jupyter, even as we support more traditional software engineering type workflows. So you kind of already talked about the history between Jupyter and iPython. Is Jupyter still primarily used by Python or is it kind of evolved into one of these other kernels becoming more popular? Yeah, that's a great question. And there's a research group at UCSD that has recently scraped all of the notebooks off of all the public notebooks off of GitHub. I think there's 1.2 million Jupyter notebooks on GitHub. And they're starting to look at these notebooks to help learn about how people are doing interactive computing. And I'm pretty sure they were the ones that mentioned that of the existing notebooks on GitHub, I think it was something like 97 percent, mid 90 percent were still Python. Now, it's very possible there's some sampling bias there that other communities are not putting their notebooks on GitHub like the Python community is. But based on our observations, there's still a very large fraction of our user base is Python. That's also helped by the overall popularity of Python in this space. Is another use case, because you mentioned Leitech in there as well. Have you seen anybody write a paper specifically in Jupyter and have their graphs and charts and what not be active computations so that they could produce, say, a PDF that actually represents an integrated set of work rather than, oh, I got to have my scripts over here that generate my PDFs of graphs that then get slurped into Leitech and blah, blah, blah, blah, that whole kind of thing. Has it been used to create publishable results like this? Yes and no. So the narrative text in a Jupyter Notebook is marked down and even though we support Leitech in the marked down cells, marked down is a little bit too limited to offer sort of full blown publication content. It lacks a lot of features that you need for that. And because of that, the main way we're seeing people use this in a publication context is as sort of accompanying material for a formal academic publication. That's being done quite often. So a great example of that and it's a perfect day to mention it is the LIGO collaboration which discovered or observed gravitational waves and actually won the Nobel Prize for that just today that was announced. They, anytime they have an observational event, they actually publish a Jupyter Notebook that reproduces all of their analysis that goes into the associated peer-reviewed publication. And that type of usage pattern is something that we're seeing quite often and that a lot of academic publishers are quite interested in. So let's talk about the guts in a way this works a little bit. You mentioned a couple languages but out of the box, if I install Jupyter, what language is slash kernels does it support? So the Jupyter itself, actually that's a really good question. Today I think if you, for example, installed Jupyter with Pip or Konda, the only kernel we will install is the Python kernel that we build, the IPython kernel. And then any other kernels that you would install beyond that, you would have to install separately. And the reason we've done this is that we ourselves, that sort of the core team, core Jupyter IPython team, only maintain a very small number of kernels. Most of the kernels built for Jupyter are developed by third parties. And so it's completely up to those third parties how you would install those. Also the many of those other kernels are written in other languages that have completely different packaging systems. And so it wouldn't make sense to Pip install an R kernel. R has its own packaging system and the R kernel is shipped using that packaging system. So what are some of the common kernels that are out there? Yeah, so the Julia kernel is quite popular. Julia was actually the second language to have support for Jupyter other than Python. So that kernel has been around a long time. It's fairly mature. And the core Julia team has sort of been using and promoting Jupyter for quite a long time. And other popular kernels, the R kernel, there's an open source R kernel that is people are definitely using. Other sort of broad areas that we're seeing is a movement towards JVM based languages that a lot of people are interested in for tools like Spark. And there's a number of Scala kernels. And then also actually JavaScript kernels. There's a couple of different JavaScript kernels for Jupyter. And JavaScript is a great language for working in the web. And we give users, being a web application, we offer users a lot of the niceties of being in a web-based environment. So you can use libraries such as D3JS to do a visualization if you want. Has anybody come up with, or any of the commercial languages supporting Julia? There should be things like Matlab, SAS, S plus. Anybody like that? So do any of those have Jupyter kernels? Is that more the question you're asking? Yeah. Yes, actually. So I'm pretty sure that IDL, which is sort of an old school interactive computing environment, used a lot in the astronomy community. They, as far as I know, ship a Jupyter kernel. And then SAS as well has a Jupyter kernel that they're shipping. There is an open source Matlab kernel that is available. I've not used that myself. I've had some students that have tried it and said, it's okay. You can use it, but it's definitely not sort of a first-class kernel. We would love to see MathWorks take on that and build a really nice, robust Jupyter kernel. And that is something we're hearing from our users, that a lot of people are still using Matlab and want to keep using Matlab, but they want to integrate with Jupyter and get the Jupyter notebook format and a lot of the other benefits of the overall ecosystem. So for creating these kernels, you said they're a separate process. What's the mechanics? How do you get from the web front end to the kernel and back? Yeah, that's a great question. The kernel is defined by the network protocol that it speaks. And the transport layer that we use for kernels is called 0MQ. 0MQ is a message-oriented layer on top of TCPIP that we use. And the way that kernels talk over 0MQ is basically through JSON messages. And we have a formal specification for the types of messages that the front end would send to a kernel and then also for the types of messages that a kernel would send back to the front end. And as long as a process uses 0MQ in that way and speaks, sends and receives the right JSON messages, it can be a valid Jupyter kernel. And there's a lot of flexibility within that that exists. But that's sort of the minimal notion of what a kernel is. So then a kernel is not even a plug-in. It's just a standalone entity. And as long as it listens and speaks in the right way and you just tell the Jupyter core like what TCP address and port it's listening on, you're good to go. Is that correct? Pretty much, yeah. The kernels are registered with the notebook server by dropping a small JSON file in one of a couple of different configuration directories. And that JSON file essentially has the command line program to run to start that kernel. And so it's a very, there's no sort of language to language calling. We literally just, you tell us what process to start and we will start that process and assume that it speaks the right network protocols. There is a way for kernels and the notebook server to agree upon which ports are being used as part of that. But it's all a fairly simple system. So then let me ask my own bias here, being an HPC MPI kind of guy. Has anybody written kernels that front a back-end HPC cluster using MPI or some other parallel technology so that you actually have a Jupyter notebook either launching or controlling or directing some larger computation that's running either a small or large size HPC job? Yes, definitely. And there's a couple of different ways you can architect that. One is that there's no constraint over the type of code you run in a kernel. And so, for example, if you're running the C++ interactive kernel and you want to start to just use MPI in that context, you could do that and it should work fine. Now, with that said, Jeff, I'm sure you know, there could be a lot of subtleties about how MPI processes get started. And so, if you wanted a kernel that really did that well, you'd need to think about that sort of bootstrapping phase that MPI does. But there are examples of that. One other project that exists within the IPython organization is something we call IPython parallel or IPyparallel. And it actually exposes a Python API for talking to basically MPI clusters that are separate from the kernel.  The kernel would be running interactively on that head node. And so, it's not doing anything computationally demanding, but then the user might start a large parallel job with Python and MPI and then be able to steer that interactively from that Python notebook. So that is one of the very early use cases that we had in mind for this. What about something using one of these new web stack orchestration engines, like Kubernetes or Rancher or Mazos, where you could actually say, start my big thing over here, it listens on this port and I can run Jupyter locally? Is anybody kind of doing that? Almost like I run Jupyter locally and when I'm ready, fire up this thing into cloud or something like that to do the heavy lifting? Yes, definitely. The biggest place we're seeing that is in the Spark community. So there's a PySpark client library and that library knows how to communicate to Spark clusters. And there's a couple different ways of doing it. Either the kernel can be started as part of the Spark cluster. There's a new REST protocol for talking to Spark called Livy, L-I-V-Y, I think it is. But that's definitely one of the usage cases that we see in that's happening a lot in the large companies that are offering sort of turnkey Spark deployments is they're sort of packaging that around to Jupyter notebook based front end for their users. So what's Jupyter Hub then? How's that coming to play with all of this? Yeah, so the original Jupyter notebook is a single user web application. So it's something that users tended to start just on their local machine. So they would type Jupyter space notebook at the terminal on their local laptop. And that starts the Jupyter notebook server. And then they use the software through their web application, but it's just talking to this local server. Jupyter Hub is an organizational multi-user version of this that basically takes care of spawning single user notebook servers on behalf of different users. It handles authentication. And then there's a proxy layer that routes the traffic to the appropriate single user process. So it's a think of Jupyter Hub as a multi-user organizational implementation of Jupyter. Is it possible for does Jupyter Hub understand batch systems or cloud orchestration APIs or anything like that? So we can kind of spin these things dynamically because I know there's other tools already do this like the tech visualization hub at the Texas Advanced Computing Center allows you to submit a job that spawns Jupyter for you and reverse proxies it back. And we actually support that at Michigan too on our cluster. So people don't have to make their script or anything. So does Jupyter Hub have that built in? Yeah, really how I look at Jupyter Hub is a set of building blocks that you can assemble in different ways for particular types of deployments. And for example, one of those building blocks handles authentication. And it's an extensible API. So if you want to authenticate with OAuth, you can plug in whatever OAuth system you have at that point. Another building block takes care of spawning individual single user notebook servers. That's also extensible. And so the simple default one just starts a local sub process on the server. But there's different people have written spawners for different batch systems for Kubernetes, for Docker and so on. And so that's something that a lot of work has been put into. And I think the largest scale deployments that I know of these days of Jupyter Hub support many thousands of concurrent users. And I think the largest ones right now are using Kubernetes to manage the sort of spawning and load balancing and auto scaling of the system. Now, something you mentioned and alluded to earlier in the conversation here was about the efforts you guys have encountered upon for developing a community around Jupyter. What can you tell us about that? For example, you just had Jupyter Khan in August. Can you tell us a little bit about that? Yeah. So over the last few years, we had started to experiment with different events to bring together Jupyter users. And we had had a number of Jupyter Day events as we were calling them all over the world, really. And we had started to observe that there were a lot of users. And our users have really amazing things to share about how they're using Jupyter. And part of what's fun about it is that the really diverse ways that people are using Jupyter ranging from social sciences and humanities to traditional physical sciences to data journalism. And Jupyter Khan was our first sort of larger conference to bring as many Jupyter users together as we could. So yeah, that was just this past August about, I guess, just over a month ago now in New York City. And we had around 700 attendees. And it was organized, co-organized with O'Reilly Media and also the non-profit organization for Jupyter, which is the Numfo Foundation. So what's the easiest way for someone to get started with Jupyter? Yeah, most of our users probably install Jupyter through the Anaconda Python distribution. That's really the easiest way to get a working Jupyter installation that includes all the other dependencies that you will likely want to use along with it, different visualization libraries, scientific computing libraries, machine learning. And so the Anaconda Python distribution is probably the most common way that people get started. Another increasingly common way is organizational deployments, where someone within an organization deploys it on behalf of the rest of the organization. And at that point, you're typically the users pointed towards the URL for that deployment that you could log on with whatever credentials are set up for the deployment. What's the strangest thing that you've seen Jupyter used for? Yeah, the strangest thing. Let me think about that a little. We usually like to ask this question for most of our guests to kind of emphasize the way in which software and even science itself escapes out into the world and then gets used for these sometimes wacky or crazy imaginative ways that the authors and developers just didn't intend at all. Yeah, I mean, I think, I don't know if strange is quite the right word, but one usage case that I don't think we had in mind back in the mid 2000s when we got going on this journey was usage in data journalism. I think I was ignorant of any work in data journalism happening at that time. There may have been. Again, it could be just that I wasn't aware of it. And so the idea that journalism teams would have computational folks involved who are using a tool like Jupyter and doing machine learning and data science and data visualization is something that we've been extremely happy to see. But it's also something that I think has surprised us in the best possible way of being surprised. And so that and honestly, I think part of the fun of it is that the organization that has done this most successfully is Buzzfeed, which is not usually pictured by folks as being a serious news organization. But there's a fantastic data journalism team at Buzzfeed News. Jeremy Singer Vine is one of the folks that we've interacted with a lot. And at this point, as far as I know, anytime they publish an article that has data behind it, they share their analysis and the data set on GitHub, and they're publishing that as Jupyter notebooks. And so they're really setting a very high bar for openness and reproducibility in data journalism. So that actually raises a fascinating question. What license do you distribute Jupyter on under? And does that carry through to the work that is published by Jupyter notebooks? Yeah, we use the three clause revised BSD license. And it's a very liberal license. And that's a choice that we made very early on. We wanted people to be able to use Jupyter in pretty much any way they wanted, whether it's for non-profit work or academic research or even for building for-profit companies and products around it. And so there's no constraints on how people license Jupyter notebooks themselves. They can license those notebooks essentially using any open source license or not even an open source. You could write completely proprietary Jupyter notebooks and that's completely fine. Okay, so what about the Jupyter organization as a whole? You said you started as IPython and you kind of made it abstract. How are you guys organized? Yeah, so we are now a part of the NumFocus Nonprofit Foundation. NumFocus is a 501c3 non-profit that's home to a number of open source projects in the Python R and Julia communities. So a lot of the other open source projects that users are using when they're using the Jupyter notebook are also part of NumFocus. And we have a fantastic development team working on Jupyter. Our project is sort of led by a steering council, as we call it, of 12 individuals that have made long-term significant contributions to the project. And then Fernando Perez continues to be the BDFL for the project. But it's a very large and significant effort by a lot of different people. I think we have somewhere on the order of 25 full-time people plus hundreds of other part-time and occasional contributors to the project. So it's really a large community effort at this point and many, many countless people making contributions to the project. And we're really grateful for all the work that everyone's doing. So thanks very much for your time. Where can people find out more about Jupyter and get started? Yeah, we have a website at jupyter.org. And that's probably the best place to start. There's links there to installation instructions as well as our documentation. The other place that would be great to go to learn more about the project and how it's being used would be to go to the JupyterCon YouTube channel. We have videos of all the keynotes and all the sessions there. And I'm not sure all of the sessions are uploaded yet, but they were in the process of finishing those uploads over the last week. And there's many really good talks that are on YouTube for free that anyone can watch and learn more about the project. Okay, Brian, thank you very much for your time. Thanks so much for having me on, Brock and Jeff. And yeah, thanks for what you do. All right, thank you.


So let's take a look at a couple of different methods of multiplication, and we'll start out with multiplication by the area model, because this is a fairly important and fairly powerful method of doing multiplication. And it's based on two principal ideas. First off, the area of a rectangle that's A units wide by B units high is the same as the product. So that means that if I can find the area of the rectangle, I can find the product, and vice versa. Now, that by itself isn't particularly useful except areas a conserved quantity. And what that means is that if I break an area, if I break a region into several pieces, the area of the region is just the sum of the areas of the individual pieces. And that's really useful because it means I can find whatever area I want to, however complicated the region is, I can break the region into a bunch of bits and find the areas of each individual bit. So let's take a look at a problem. We're going to use an area model for this. So our first step is we want to draw a rectangle with sites 24 and 8. So there's our rectangle. And if I can compute the area of this rectangle, then I also can find what the product is. Now, the important thing here is we don't just find the area of this rectangle by multiplying 24 times 8, because that defeats the purpose of having an area model in the first place. The key idea here is we want to split this up into a bunch of pieces and use areas we are comfortable calculating. So I want to break this rectangle into a couple of pieces, and the only requirement for those pieces is I should be able to calculate what the areas of those pieces are. So let's take a byte out of this rectangle here. Let's keep the height of that rectangle 8, but let's take a chunk of that with 10. And because the rectangle is 24 across, I know I have some extras left over. Let's take another chunk of size 10, and let's see that's 10 and 10. That leaves me four left over for this last bit. And since area is a conserved quantity, I can find the area of the whole rectangle by finding the area of each of the individual pieces. So this first piece here has area 80. This is 10 by 8, also area 80. 4 by 8, area 32. And the sum is the area, so the sum is the product, so I'm going to add those areas together. The product is the sum, and 80 and 80 and 32 is 192, and there's my product. Do we have to do the split this way? Not at all. We can find it using an entirely different split, and again, it depends on what we feel comfortable working with. So we can use any split of the factors that we want to. So again, we'll start off with our 24 by 8 rectangle, and suppose you know how to multiply by 12. You memorized your 12 times tables. Well, you can break off a chunk of 12, and there's my first bit, and again, the rectangle is 24 wide, so if that first bit is 12, it turns out the last bit is also going to be 12, and you can say, ah, I know how to multiply 12 times 8. 12 times 8 is 96. And so I have the two areas, and now I can just add the two areas together. The product is the area is the sum. So I'll add them together, and I should get 192 once again. Just as a quick review of how we might add, this is almost 100 plus almost 100. This is four short of 100 twice. So this sum here, 200, but I've gone too far, subtract 8. That gives me my 192. Well, you can also find products of multi-digit numbers. So here's an example, 125 multiplied by 38. So again, we'll set down our 125 by 38 rectangle, and again, it doesn't do us any good to just multiply these two numbers to find the area to find the product that's defeating the purpose of using the area model in the first place. I want to break up the factors into pieces that I am willing to work with. So what might those be? Well, let's start with this 125. So maybe I'll take a first piece that's 100 wide, and there's still a bunch left over, so maybe I'll take another piece that's 20 wide, and then that's 120, and the last bit is going to be a width of 5. And so now I have 1, 2, 3 areas, and they're a lot easier to work with. Well, nothing in the rule says you only can split the top. I could also split the side as well. This length of 38, maybe I'll split that further. That's going to be a length of 30. I like working with 30, and then leftover length is going to be 8. So here's my setup for the multiplication, 125 times 38, and it's worth noting that what's going to make this easy is that any rectangle whose area I have to find, I can find by multiplying two numbers, both of which have a single non-zero digit. Now, I didn't have to do it this way, but it's convenient because if I know how to multiply one digit numbers 20 by 8, then I can find the product fairly easily. So let's go ahead and find what those areas are. Useful idea for things to come later on. If we're interested in estimating the product, notice that this rectangle here is actually the largest. It has the greatest width and it has the greatest height. So if I wanted to estimate the product 125 times 38, I could look at the biggest piece, which is 100 by 30 or 3,000. So as a quick estimate of what the product is, it's someplace in the vicinity of 3,000. Well, if I want to find the actual product, I need to find the remaining areas. So this rectangle here, 20 by 30. This rectangle here, 5 by 30. This rectangle here, 100 wide by 8 high. This rectangle here, 20 wide by 8. This rectangle, 5 by 40. And those are the areas of the individual pieces, and the sum is the product. So how you add them together, it depends on how you want to add them together, but we might add them this way. I might go to this first set of rectangles here, 3,000 plus 800, 600 plus 160, 150 plus 40 gives me 190, and I'm going to add these three numbers together. And skipping over the details, I get 4,750 as my final sum and product.


It's the metal universe, right? Yeah. And if there's none, there's a sealed case in the back. I think we have some. I think the two are up front, actually, so thank you. Nice. What's up, everybody? Happy Sunday, happy Mother's Day. Hope you all are doing well. We've got Sunday Fun Day group breaks. Let me do one thing on my phone here. I think I'm going to do something on my phone here. That's the attempt. Barcelona won La Liga. Can you believe it, Jake? Raise one. That's unbelievable, actually. Yeah. It is very unbelievable. Judgeful out to the warning track. We almost lost again. Judgeful out to the warning track. I think I like the last out or like the bottom. Yeah. It was pitching. I don't know. I know. I wasn't watching. I just saw the notification. So what's up, everybody? How are we doing this evening? Oh, man, we got game seven on. Been a great game so far. Game seven vibes. Got the biggest game of them all tonight. Red Sox and Cardinals. Got to avoid the sweep. Got to avoid the sweep to a last place team. You never want that. 12 by the Reds last year. Eflin got the win for the Reds. I knew I should have started him in fantasy. What's up, Steve? 16-3 twins. What the heck? Whoa. Who are they playing? Playing the Cubs. Doesn't matter who you're playing. 16 runs is insane. That's just a throwaway game, then. All righty. Well, we're ready for our first break here. Have you guys seen the Sunday Fun-Aid Mixer lineup? It's kind of bonkers. Have you seen the Jake? I took a quick peek at it. I saw we had the good old 2020 Flawless in it. 2019 Flawless Foto. There is what else is there? 2015 NT. Yeah, 2015. You want some famous James? Go. Famous James. Oh, boy. Todd Gurley. Wow. Super Mario. Super Mario-ta. So prison basketball, all your teams are on sale. Like, very on sale. Most of them were like $15, $20 off. So let's get those filled up. Baseball's sold out. There's more baseball breaks in the morning with Matt. Just a few minutes. We'll get started on time here with the first break. Skybox, metal universe. We'll pull another Gretzky auto. I still can't believe that. Pull a Gretzky auto here? Yeah, on like Thursday. What? That's crazy. It was awesome. Now we need to pull an MJ. Never pulled an MJ auto.  I have only been breaking for a handful of years. Not even a handful of years. What's up, Alvin? Kaminaro. I imagine Kaminaro is. Let's see. Let's check out his MILB stats. He's currently hitting 367 with an 1126 OPS. What level is he at right now? He's at, I think, high A. Yeah, yeah. Well, he will not be at high A for a lot longer. He's about to be to double A at some point. He's going to be the majors.  Like high A is like not the highest level, but still like that's really freaking good. A little over 100 at bats. His career minor league stats, almost 500 at bats, 945 OPS. The dude clearly needs to be playing in double A, even if he's 19. It doesn't matter. Like, you have to move. Like, I'm sure in like a month or so, he'll be up there.  That's kind of unreal. He wasn't even drafted by us. Sorry. Probably he needs it. Was he drafted or was he signed? Or signed, yeah. Sorry, he's signed. I was going to say, if he's DR, that's still insane, man. Yeah, obviously the OPS, he's hitting for power, too. He's just, hey, he's doing everything. So I was cleaning my water bottles. Sorry, I got this one today. My water bottles at home. This was cheaper. This liter and a half was cheaper than a one liter at Publix. I don't know why. It's probably because they never sell these. Nobody wants to buy a one and a half liter water bottle, typically. I was like, OK, I'll do that. Evion, delicious. Keeps on Jake to give me the discount at Publix. He never does. I probably still can't even use this one. Publix employees don't get discounts. They don't, yeah. Don't listen to that false information. No, I know they don't get discounts. It's kind of insane that you don't get discount where you work, especially like groceries, like crazy. Anyway, so just to hit just football and basketball efforts on everybody, Prism teams on sale. There's one left on Prism Choice. Got Contender's Optic Football, Six Box Obsidian and Prism Mixer, and then we've also got NT Collegiate before the 30 Box Falters Treasures Football Mixer, which has a box of 2020 flawless football, 2015 NT football, 2019 flawless football first off the line.  That's like 10 boxes that are at least to granted value. Oh man. That's a crazy mixer, man. That's silly. That's silly. That's legit now. This is legit now. Yeah, it was. Yeah, it was, yeah. Yeah, it was, yeah. Eternal Water Bogo. What's Eternal Water? Do I just, do I just know the... It's from the Holy Grail. Eternal Water. Oh yeah, I know what that is. Okay, the weird shape bottle. You know what it is, Jake? There you go. Yeah, I've seen it. I'll have to get some of that. I've had to lift many of those for customers. Yeah, I'm gonna go there. If you were still working there, I'd be like, sir, can please help me lift this. I'm gonna take Jake back to the public that he used to work at. I'd be like... Everybody remember this guy? Remember this guy, Employee of the Month right here? I was never Employee of the Month. Well, we got him now. Surprisingly. We got him now. Everybody watching Game 7? Oh, it's time to start. Time to start. We talk about Game 7 in a moment here. Let's do it. Everybody forced here up in 2021. Upper Deck Skybox Metal Universe Champions Hobby, one box number 47. But I just realized that that's not where I wanted it quite. There we go. That's much better. 2021, Skybox Metal Universe Champions Hobby, one box number 47, Random Pack Style. Randomize right now to see who's got which pack before we open the box here. There are 15 of them. There's three stacks of five. So the left stack will be one through five. Again, top down. And then six through 10, 11 through 15. So we'll do it. Good luck. Thanks for joining everybody. We're gonna go seven times on the random. The names and then the numbers. Same amount of times. We'll put them side by side. Names first. One, two, three, four, five, six, and seven. Felipe down to George I. And seven times on the pack numbers. One, two, three, four, five, six, and seven. 13 down to 15. Okay, here we go. We got Felipe, 13. Jerry F. with nine. George I. 10. Brian R. with eight. Oliver, one. Ryan P. 11. Jason S. Six. George I. 12. Chris P. Four. Justin O. with five. Matt Z. two. William S. seven. Kevin L. three. Justin O. 14. And George I. 15. He's posted. Let's get to the opening here. It's a good game so far. It's a good game so far. All right, game seven. I didn't realize, so I saw that stat earlier that like Philly and Boston have played more game seven against rather than anybody else in NBA history, which is kind of crazy. It's like seven of them. Didn't know that until this morning. That probably happened a lot in the eighties and nineties. Here we go with pack one. Fresh foundation, Adley Ruchman. And an Adrian Wojnarowski auto. Wojbaum. Wojbaum. Wojbaum. Wojbaum first pack of the night. Let's go. That's how we do it. That's pretty sick. Pack number one, Oliver H. with the Woj. That's awesome. Pack two. Intimidation nation, LeBron James. LeBron James. Pack three. Yes, that is good. It's an auto. Yes. Fresh foundation, Devante Smith. Anci Fatih and a Roberto Campos Skybox Premium. Trey Lance. DJ Boston, Zach Wilson. Mack Jones, Blast Furnace. Matthew Wolf. Got Fresh Foundation, Justin Fields. Pack six. See ya. Oh, we got something nice coming up here. Fresh foundation, Jalen Waddle and a LeBron James. Gold PMG. Woo-hoo-hoo, 19 to 100.  Pack number six, Jason S. Gold Precious Metal Gems, LeBron James. Big congrats on that. There's kind of a soft corner here. It's not major, but I'm gonna send a box wrapper so maybe you can try to get that one replaced. If there's a card that you can get in a really nice condition, that would be the one ideally to do it. Pack number seven. I think Sixers weren't even in the NBA until like the 70s, right? Or were they a part of the merger? Mojave King, Fresh Foundation. Intimidation Nation, Kaminga. I believe it has LeBron Autos in here. Oh, we're going eight now? Yep. Pack eight, Tralance Metalized with a Allen Iverson, Skybox Premium. Number nine. So I might get, yeah, it's a great start to the night here. Chloe Kim, Fresh Foundation. Giddy and Ben Simmons, Blast Furnace. Number 10. Pull the Woj Auto. Woj Bomb right away. Gretzky, Sky Brown. Number 11. John Taffer. There's Jake's Boy. Coleman Naraz. And the Dyka. Nice, Josh Giddy. Reaching for the stars. Question. Oh man, now I feel pressured to know the answer. Pack 12. Arkweald, Scotty Barnes. About that, 13 here. More rushing, right? Gretzky, Skybox Premium. Just Flash, Portnoyant, even. 14. Blast Furnace, Bryson Dechambeau. Oh, I don't know the answer to that, Micah. I honestly don't know. I think you can do it there. I don't know if it's like different process or what. That would be a PSA question. I'll send, this is Pack 15, by the way. And that will do it. Thanks again, everybody. We are on to the next one. Pretty good first break right there. Lebron Drinks, it's impossible. Yeah, there's a lot of, what do you call, pop culture people in a Skybox Metal universe. So that is not so random considering that. Skateboarders, snowboarders, skiers, golfers, basketball, football, baseball. Who else is in there? John Stafford, there's musicians in there. Jake is in there, I think. Yep. Still haven't pulled one of his cards, this is crazy. Just cannot seem to hit me. I can have Sean hit you. He already does that in the regular. Oh geez. That's not true guys, it's a joke. It's a joke. It's a joke, it's a joke. It's not true, it's a joke. The one-on-one Jake. We can hit the one-on-one Jake. There's some choice tier to sold out. Okay, about ready to get started on the next one here. I'm gonna type in the Prism Choice Random. Let's get these breaks filled up, everybody. Prism, basketball teams are all on sale. Every single team is on sale. Got that huge mixture at the end of the night. 2020 Flaalas, 2015 NT, that's right, 2015. 2019 Flaalas first off the line, some 2022 Immaculate first off the line and Honors first off the line. 2021 Prism first off the line. Just a bunch more, man. It's the biggest mixture that we've done in a few months for football. What do you do this time? Barcelona won the Liga, Arsenal choked away, the Premier League championship. Crazy times out there, Jake. Pretty crazy, pretty crazy. Dude, Brighton beat him three-nil. Oof. Oof. Oof, big oof. That is, that's a colossal oof. I don't think anyone's ever said that before. Colossal oof. Colossal oof.  Bunch of nonsense. Hope everyone's having a good Sunday. Mother's Day to the Mothers. Especially the collecting moms. The moms of collectors. Celtics buzzing right now. Dude, the garden is hot. Al Horford looking like Prime Rodman on defense right now. Oh my God, what are they doing? Philly is just, Philly, oof. Talk about throwing away a chance at something. Yikes. All right, I don't know, James Harden. You should probably step out on the guy that's shooting the lights out right now. I don't know, man. All right, let's do some more Skybox. Everybody forced here, ripping 2021 Upper Deck Skybox Metal Universe Champions Hobby, one box number 48, random pack style. We're gonna randomize right now to see who has which pack before we get started. There are 15 packs, three stacks of five. So the left stack is one through five, top down, and then six through 10, 11 through 15. That's how we'll do that. Good luck. Five and three, eight times. That's true, Lane. That's very true. Magic legend, Doc Rivers. Eight times in the names, one, two, three, four, five, six, seven, and eight. Oliver H down to Angelogy, and eight times on the pack number. One, two, three, four, five, six, seven, and eight. Eight down to 14. All right, there you go. With pack eight, Matt Z, nine. Brian R, 15, Matt Z, two. Lin Xu with seven. Matt Z with six and 10. George I, three and 12. Lin Xu L, four. Laura Q has five and one. Angela with 13. George I, 11, and Angela with 14. Posting the results right now. I'll drop you a quick link and then we'll open the box here. Let me drop you guys a link for Prism number 71. All teams on sale in Prism. And if you buy a team in the three breaks, 71, 72, and 73, then that is a full case worth of that team. That's a sealed case spread across the three breaks, so. Jake's here tonight, so we'll probably pull some absolute heat out of the football mixer. Maybe we'll get another Steph Curry reaction. If we're lucky. Bro, last week, last week, close, it was close. What did we pull last week again? Oh yeah, that's right, I was moving. I was like, what, I don't know. I wasn't here, that's fine. I watched it get pulled though. Pack one. Yeah, I think so, Matthew. I think so. Sad to see him be like that. Jayne Springer reaching for the stars. Pack one. Just sad. Pack two. Toby Miller, Fresh Foundation, Trevor Lawrence. Blast furnace Bryson to Shambo. That's a Donnie's boy. Anybody like Bryson? Pack three. It's kind of weird, right, Anthony? Get a contract, not looking good for the Mets. Three. Hal Pitts, Michael Jordan, Skybox Premium. Pack four. Najee Harris, Matt Jones, Metalized. Naomi Osaka, Intimidation Nation. Osaka, pack five. Teofimo Lopez, Adley. Number six. Lexi Thompson and a Teofimo Lopez Auto. There we go, I don't think I pulled one of these yet. Good stuff there for pack six. Teofimo Lopez Auto. Looks like my abs, actually. It's pack seven. It's all the crumble that I eat. Oh yeah. Did you try them this week? No, I saw them. Oh man, I actually ordered some late last night. Ben Simmons, Blast Furnace, Holy Crap. The lemon cheesecake, unreal. Really?  What is the mom's recipe one? That's a pack number eight. I'll tell you after pack eight. Okay. A Cocoa Goff Fresh Foundation, Jalen Green and Ben Simmons, Skybox Premium. It is, it was an oatmeal cookie with chocolate chips, peanut butter chips and caramel with like a little bit of sea salt on top. Dude. Like, I think I should have just ordered a six of those and a bunch of other ones. Pack nine. That's right, Sermon, Arkwell, Michael Jordan. Number 10. It is pretty impressive, Anthony. I mean, he has been dominant for like a decade straight. Okay, we're on Laura. All right, number 11. I'm the proverbial mother of the breakers. So you're wishing me happy Mother's Day. All right, this is outrageous. A B.J. Boston, it gets ruby to 150. B.J. Boston, illegal. Out of all the things that you've said that are outrageous, that one, that one's pretty high up on the list of most outrageous. Back number 12. B.J. Boston, Shane Wright, Matt Jones, Blast Furnace. Jake, were you really nice to your mom today? I was. That's good. Pack 13. I got our gift in everything. Boston Wolf, Schifrin, Kominga, Isaiah Todd, Skybox Premium. 14. Yeah, dude. So my mom lives in Arizona. Yeah. Pack 14 here. That was an Amio Saka on the Fresh Foundation with an intimidation nation on Tsufati. You followed this team for so long, illegal. That's not time-out worthy. It's just like, like LOL, very hard-worthy. Just a ridiculous Pack 15 there. That'll do it. Thanks again, everybody. We're onto some Bowman now. So my mom lives in Arizona. I gave her a call. I sent her card and flowers and all that stuff. But her long-time boyfriend, I think, I heard to see her red hot chili peppers tonight. Really? Yeah. And they spent like a whole week just doing stuff. I was like, yeah. Then I'll see her around my birthday, actually, for family reunion-type stuff. So that'd be sweet. Yeah, dude. She get to go see the chili peppers. It's pretty dope. That was one of the first concerts I ever went to. It was actually with my mom. We went to go see the red hot chili peppers. That's pretty cool. Yeah.  I think I was in, like, 10th grade or something. No, I think I was a freshman in high school. She read what illegal wrote in chat. Oh, man, you're going to love it, especially you. It's literally directed at you, actually. Going to enjoy it. The sixes are going to have to start knocking down some shots. They're themselves for their sons to be doing their job for the next two years. They need the legend Pete Fairbanks back as their co- Can throw a strike. That would be ideal. I got to get out of my starting lineup, though. I know that. Yeah, dude. It happens every year, illegal. One or two people go down, and then we're just stuck with, like, two or three, like, good to talk to your pitchers, and then the rest are all, can we put pieces together? Maybe you guys should just spend some money on some, like, players. Extended springs. You got her. Rasmussen. Is that all the money that you spent? What's those two guys? We don't have much that good to go around. That's what I'm saying is, like, maybe they should move to Orlando. Better for you. Oh, dude. If they end up coming here. I would probably get, dude. I'm getting season tickets. I would split season tickets with you. I would hire them because they send them back down for, you know what, so, not because he's... All right, y'all. Sunday, fun day mixer. You got six box obsidian and prism as well. I'm going to update your spots right here. I don't know when that... Then we'll do the random. When that manipulation is going to end, and ever since he's been sent down, he's been getting shelled, so not great. They messed them up, man. Hopefully soon, but I doubt it. Okay, let's random now for 2023 Bowman Baseball Jumbo, A-Box case number 49, random divisions. Good luck. It's a one and a four, five times. On the names, one, two, three, four, five. Alvin S, down to Eric C, and then five times on the divisions. One, two, three, four, five. AL East and the AL Central. That's true. We definitely helped ourselves out. Alvin S, to the AL East, Chris G, AL West, Matthew B, NL West, Daniel L, NL East, Jeffrey H, NL Central, Eric C, AL Central. Let's get those results posted. We're starting a moment here. No, I didn't see that, Anthony. Who was it? Do you know who it was? And I guess what level was it, would be our next question? Is it like a known name? That's what I'm wondering. Is it like, it's not Myer? Is it like Raffaella? Is it like just some guy we haven't really heard of? Was it Roman Anthony? It was Raffaella. It was Raffaella. In Portland. Yeah. So he's in double A. Jesus, dude. So I guessed correctly. Two for three for two walks. We got on base four times. And stole seven bases. That's awesome. Seydan, Seydan Raffaella. Seydan. AL East is good. You got Spencer Jones in there, Roman Anthony. You got the Orioles rookies. You have Jackson Jackson Holiday has autos in there too. Race of Kaminaro. And then the Luches have Bonilla. They might be the most well-rounded division in the product. Every team legit has a chase in that division. Like a big one. A Seydan. Not fair to have a Seydan on base. All right, I'm going to take.