All right, we'll get started next with Chris Hall to talk a little bit about deep-scale learning. So go ahead, the court is yours, you got the timer. Test, test, test. There we go. Awesome. I haven't done a university lecture for quite a while. I feel like I'm back at college. So my name's Chris and I originally trained as a lawyer, legit, and as a data scientist. And then about two years ago, I had a mid-life crisis and went back and retrained as a helicopter skiing guide. And can anybody tell me the difference between a helicopter skiing guide and a large pizza? Nobody. The difference is that a large pizza will feed a family of four. Ah, so you stop doing that. But in my spare time I do quite like to climb mountains. So that's looking down towards Vancouver, and we actually climbed up the top of that with our skis, ride bikes with my little daughter in taupe, and kayak down waterfalls. That's about 10 minutes from my house in New Zealand. So I work for these guys. And I've only worked there a couple of years. And interestingly enough, these guys are now these guys.  Because I like taking the piss out of all of my friends who tell me they need a Mac to do open source development. And the reason I'm presenting off my Windows machine is I presented it at the TensorFlow user group last night, and then the guys from Google were kind enough to say, thanks Chris, they took me out for dinner and I said, do not let me forget my laptop at the restaurant because I have a habit of doing that here in Singapore. I'll be picking up my laptop from the restaurant at Maple Tree Business Park on Monday morning. So we're presenting on Windows and remote into my Linux workstation at home. So sorry about that. Originally, as you can probably tell from the accent from New Zealand, but now I live in this place. Although if you do it properly at scale, it's really this place. But that's kind of cool. I can ride the bike around the whole island and things like that. The project I'm going to talk about today we did in this place, which is Korea. Fantastic. So at Microsoft, I run a team called the Commercial Software Engineering Team for the region. And we build ship.  We built this out with a major customer in South Korea. They would be the biggest of what they do in South Korea and they don't make either cars or television sets or cell phones. So we built this project out with them and I thought I'd come and talk to you a bit about it. This is not a deep learning session. So you don't really need to know about deep learning for this session. It's really a deep learning meets high performance computing session. So we're really talking about how can we take deep learning and apply high performance computing techniques to train things at massive scale. So the project was with this big firm in Korea. And they have built a Korean language chatbot that uses a deep learning conversational model to provide the answers. That deep learning model is for those who are who the deep learning nerds. A few, there's one at the back. You're talking next, right? It's an LSTM based or current neural network with attention. And just to geek out, it has about 80 million trainable parameters. And to give you some idea of scale, Baidu's deep speech version 1 had about 100 million trainable parameters. Deep speech 2 had about 300 million parameters. So it is, by global standards, a very large neural network. They had this already built and they said to us, we've got a problem which is actually we can't train it quickly enough. So it was taking several days to train the model. And now that it's in production, they want to be able to run it every day, catch a feedback from the mistakes that it makes and then retrain it reasonably quickly at the very least overnight, although ideally potentially train it intraday. So the model is built using a high level machine learning framework called Keras. Who uses Keras? A few people, fantastic. And it uses TensorFlow as the back end for doing the actual training. And this is a bit of a story about how we went and allowed this to run distributed training using Keras. So Keras is from some folks who work at Google. TensorFlow is obviously from Google. It's a tool called Horobot. I'm going to talk about and we ran this all on top of, I think, Batch AI, which is a service in our cloud that allows you to run really big high performance compute workloads. It was originally written down in New Zealand and Wellington, New Zealand by a company called the Green Button, who were a bunch of folks who spun out of Weta Workshop who made the Lord of the Rings. And you knew that given ours from New Zealand at some point, this presentation would talk about Lord of the Rings. So what are the reasons that you want to think about doing distributed training for your machine learning models? I already put them into three buckets. The first is you want to run more models. So this is typically at experimentation time. This is typically your data science folks wanting to do things like hyper parameter optimization, test new features, blinking. It's on the blink. Is it going to work? Let's try that again. Okay, I won't touch it too much. So you want to run lots of models. You want to try different hyper parameters. You want to try different model architectures. You're typically running smallish levels of training on top of those models. It's quite experimental, but you want to run a lot. So typically you'll run some sort of a cluster, distribute those models to be trained around the cluster, and capture the results. The next one is you need to run the model training faster. This is typically a combination of I have too much data, because we have this explosion of data. One of the things that's really empowered deep learning is the fact that we've got shit tons of data to work with now. Or you've got too many trainable parameters or too complex a model architecture. These funky new model architectures, VGG, if you've heard of VGG, from the guys at Oxford, is a particularly nasty model to train in terms of computational complexity. So a common approach there is what we call data parallel. We train the same model on multiple machines, and then either share the weights, or average the gradients as part of the training process. And we'll talk about that one shortly. And then the last one is you've got really, really big models. Typical candidates here are really large recurrent neural nets. Speech models, language models tend to be very, very large. And in some cases they're too large to fit into a single GPU, and therefore too large to be able to train the same model across multiple GPUs. So what you have to do is to break that model up, put portions of the model on multiple GPUs, and effectively flow both down through the GPUs during forward prop, back through the GPUs doing that prop. We're going to talk primarily about the middle one today. So of these sort of three distribution approaches, we have a thing called model parallelism. That was that last one I talked about, where we chopped the model up, train it across multiple GPUs. We have data parallelism. We were training the same model at the same time on multiple GPUs, and then sharing the weights or sharing the gradients. And then we have hybrid architectures, where we might be doing data parallel across multiple machines, but on each machine we have multiple GPUs, and within that machine we're actually having to do model parallel, because again we've got two larger models. The whole model parallel thing, Eberson flows, particularly as we sort of have this race between models getting larger, and the amount of RAM in our GPUs getting larger. There's a quick show of hands, who's got a GPU in their machine? Who's got more than one GPU in their machine? Yeah, just a couple. So this is really talking about how do we enable training across machines with more than one GPU? And also how do we enable training across more than one machine with more than one GPU? And to give you some idea of scale, we're looking at training anywhere up to 100, 150 GPUs at a time. So reasonably large scale training. So we came into the project, we were working with TensorFlow, that was kind of the default. We did try some other things, tried changing out the back end underneath Keras, but we stuck with TensorFlow. There's a couple of approaches to distributing TensorFlow. The first is to use a thing called TensorFlow distributed, which is a native part of TensorFlow. It's really good at doing things like model parallel. It can do data parallel by using a parameter server to update the weights. There are some challenges with both of those approaches. The first is that with model parallel, you've got a significant amount of rework to actually map your model into the physical topology of the GPUs that you've got. That's worth doing if you really need to have model parallel.  We really just wanted to make the thing run faster, as quickly and easily as possible. We didn't really want to go spelunking into a whole lot of code that had been written by our Korean customer and had lots of comments in Korean language that only some of us knew. So we just wanted to do this with as few lines of code as possible. So just before we engaged, the folks at Uber announced a thing called Horavod. Horavod is a data parallel framework for TensorFlow that basically manages doing the weight updates or the gradient updates during the training process. So as you go through, you do your forward prop for a mini-batch and then your back prop for the mini-batch get your gradients, and then we need to pass the gradients around all of the other GPUs on the same machine across the network so that they can all average the gradients and then update the weights accordingly. And it implements a thing called the Baidu, or the MPI ring all reduce pattern, which was originally sort of pulled out into deep learning by the folks at Baidu. So we'll talk a bit about that first. So once you start scaling across... you start with a deep learning model and very quickly you go from having a deep learning problem to I've now got a big compute problem. So we start to solve the big compute problem and very quickly you go from I've got a big compute problem that I can now solve with a crap ton of GPUs in the cloud to having a high performance networking problem because ultimately I have to communicate between those GPUs.  Who can tell me the problem with this approach is I start adding more GPUs? So it's less about memory, it's more about bandwidth. Yeah, so my communication cost scales linearly with the number of GPUs that I have. So it starts to have real problems when I want to run 50, 60, 70, 80, 100 GPUs. So what the folks at Baidu suggested is that we use a thing called what they call it ring or reduce. It's really a ring, scatter, reduce, or gather. So it's been pretty common in high performance compute for a while and basically what this does is it has a pattern for passing data around this ring architecture and that means that at every step each machine can send and receive at full bandwidth just to its neighbor in the ring. So it's bandwidth optimal by taking full advantage of the bandwidth of all of the GPUs. So the way this works is that it's effectively a way that we can sum up large arrays. And if we're trying to average gradients for a deep neural network, summing up large arrays is kind of useful because that's really the gradients. We sum those up and that lets us get the average. So the way this works is you take your array on each machine. So each machine's got its own copy of the array. You chop it up into K chunks where K is the number of GPUs that you've got. And then we start passing these chunks around the ring. So at each step the thing to note here is there's only one little gray arrow going from to and from each GPU. So we pass it around and then at each step we can add the data that's been passed around or reduce the data that's been passed around. So ultimately we then end up with each GPU having one chunk where it's managed to reduce all of that data and sum all of those arrays. So that's the scatter reduce portion. So that's one trip around the ring but again we're using optimal bandwidth the whole way. Then we do an all gather which is basically taking that data and we pass these chunks of data again between the GPUs and ultimately end up with a copy of every complete chunk on every single GPU. Again it's another trip around the ring but bandwidth optimal the whole time. So this gives us a really efficient way of passing these gradients between GPUs and the great thing about this is it does not in terms of computation cost the number of GPUs that we have is not relevant so it doesn't scale in terms of cost with the number of GPUs. So that's really important because we want to add lots of GPUs. So to do this we're using this thing called Horobot. Horobots are distributed training framework for TensorFlow which is a source under the Apache 2 license from the folks at Uber. It implements that scatter reduce all gather pattern and it does so using NVIDIA's Nickel 2 library and is able to take advantage of RDMA if you've got either InfiniBand cards or RDMA over converged Ethernet. So ideally what you want to do is you want to have a cluster of GPU machines and you want them connected by really high performance interconnects. Anybody lucky enough to have a DGX-1 from NVIDIA? They're really nice. Anybody want a DGX-1? You must want a DGX-1. I've got a customer who's looking at buying one at the moment. 150 grand. Very nice. They always have this thing called TensorFlow Fusion and TensorFlow Fusion allows us to batch up gradient tensors before we send them across the network. So you'll find if you're training really skinny deep models, things like ResNet, you can end up having to pass around quite small tensors and you start getting hit by communications latency. So the latency as you go and do a lap around the ring starts to become a problem if you're only doing quite small tensors. So what they do with TensorFlow Fusion is they bin up 64 megs or so of gradient data before they pass it around and then split it back out once it comes back around the ring. They have this cool thing called Timeline which does distributed logging of the execution and then they have a way of rendering that in Chrome's viewer. Can I just ask my friendly AV person? I do also have... Do I? I've got VGA as well, maybe. No. I thought I had a display port. I've got... It's just I don't...  Well, let's do this. Let's go Windows P. Extend. Let's chuck that up there. So you want it to go to 720, yeah? 12V by 720, yeah? Okay, let's try that. Really minimal changes to your TensorFlow program. I'll show you shortly and you can just go pip and install Horavod or condor and install Horavod and off you go. So the way this works really the most important thing to understand is that it's just a distribution framework for the optimizer. So it rats your standard optimizer and it manages all of the distribution of gradients to do updates across the network. It uses NPI, but really just for discovery and coordination of the process, because it primarily uses Nickel for the scatter-reduce all-gather. You don't have Nickel because you've got Windows, or you just don't have that capability, then it'll fall back to using NPI. I spoke a bit about TensorFlow, and I'm just trying to skip through that. In terms of scripting with it, pretty simple. Want to try that one? Oh, yeah, yeah, that works. So import the packages, initialize Horavod, so what that does is it does discovery of all of the local GPU nodes and all of the remote GPU nodes over NPI. Pin each process to a specific GPU, so you do local rank, so get the rank of the process and then pin it to each of your GPUs. Specify your epochs in terms of the size of your Horavod network, so basically the number of GPUs you're working across. Build your model just the same as normal. Create a standard optimizer of your choice, adjusting the learning rate based on the size of the network. Wrap that with the Horavod Distributor Optimizer, compile your model, and fit your model, and this callback here is used to broadcast all the initial weights around the network. That's really important because otherwise if you've got different random weight initialization across the different nodes, you may end up with your average gradients not necessarily all pointing downhill, so they could be pointing away from each other, and it doesn't work very well. In terms of training, a few things to look out for. Pre-process your data. Ideally before you spin up your major cluster, if you've got a large amount of data, you may want to do the data pre-processing on the cluster and stage it into a temporary, stateful store. This is important because your cluster's potentially going to have uptime and downtime. If you're running 100 GPUs, you will have downtime. That's so planned for that. Shuffle and sample the records. Broadcast initial weights out to all the nodes. There's some guidance here on, in terms of mini-batch sizing and in terms of learning rate. Papers there from Facebook and the folks at Google. It will work with TensorBoard. It will work with Model Checkpoint, and if you're going to do that, do that on one specific GPU just so you don't get race conditions. I'm not going to do the demo, but I've got a repo that you can pull the code down from later. Super simple. In terms of the perf that the folks at Uber came up with, it has significant performance benefits when you start pushing out to really large numbers of GPUs, because the problem that you've got is your communications time starts to outweigh the time you spend actually training on mini-batches. In terms of how other frameworks support it, there are some other options in TensorFlow that provide this all-reduced pattern. There's one from Baidu. We found that the Uber one was a bit easier to use. CAFE supports it natively using Nickel. It also has a nice thing called one-bit stochastic gradient descent, which is an aggressively quantized way of sharing gradients. And an even nicer thing is that last Friday, that Friday before last, on the 15th, we changed the license for one-bit stochastic gradient scent from being a somewhat proprietary license to now being covered under the standard CNTK license, which is an MIT license. So that's a really nice thing to be able to share at the open-source conference. Chana provides a thing called Chana-MN, Chana-Multi-Node, which also uses Nickel. One thing to note, there doesn't support floating-point 16-bit arithmetic yet, and you want to be using Fp16 generally for training your models. MXNet does not provide Ringel reduce. It uses a parameter server. So you'll probably see similar scale challenges to what you see with distributed TensorFlow. Torch provides a couple of options. One is a super simple multi-GPU using shared memory on a single-node approach. The other is a full multi-GPU multi-node approach once again using Nickel, and Tiano is dead. A few other things to note. Who's thought of building a GPU cluster? Who's thought of using GTX cards in their GPU cluster? Because they're nice and cheap. Who got a mining cluster at home? Bitcoin miners? No Bitcoin miners? Surely. No Bitcoin miners. So, if you are looking to build a cluster, you need to be aware that on the GTX cards Nvidia do not enable a thing called GPUDirect. GPUDirect allows high-performance communications directly between the GPU and IO devices, such as storage and more importantly, InfiniBand network adapters. The reason this is really important is in this Ring All Reduce pattern, you are limited by the slowest connection point between two GPUs. Without GPUDirect communications from a network card to the GPU, you need to come through the switch through the CPU into the DMA driver space for the IO driver, then back through the CPU to get copied to the DMA space for the GPU driver, and then from the GPU driver back through the PCI bus into the GPU memory. I know this is kind of super-nerdful, but again, when you start training at NASA's scale, every piece of performance is costing you significant amounts of money. With GPUDirect, communications can go straight through from network card into GPU memory. In terms of optimal GPUs to be using, ideally, if you can get hold of them, you want to be using the Volta Generation GPUs from Nvidia, which have extremely high-performance FP16 flops. Haven't played yet with Google's TPU. They want to give me some cloud credits to play with it. That'll be nice. Don't know if we're allowed to do that. We're allowed to do that. But, you know, the Volta's kind of the gold standard Nvidia card for training deep-learning models. In our cloud, we have a SKU called the NC24RS underscore V3, which has four Volta V100s and FDR, 54 gigabit per second in Finnyband. So that works really well. We also have the Pascal Generation P40 cards that are worth thinking about for model inference, because they support really high-performance int8, and you can quantize your models down to int8. One of the things I'm a massive proponent of is to use spare capacity in people's clouds. We have a thing called low-priority VMs. Google have preemptible VMs. AWS have spot instances.   So you need to think about how am I going to go back to a checkpoint and restart it. Ideally, you'll use some sort of workload manager, BCOS, Azure Batch, AWS Batch. There's a whole bunch of different options. So we're obviously using Azure Batch.  You're running really large clusters with lots of nodes. Think about running spare nodes, so that when your job gets killed, you can immediately restart it with a hot machine, rather than waiting for another machine to boot up. Why? Because you've got 20 other machines sitting there doing nothing until that new machine starts up. Spear capacity VMs, we run a fixed price for low-priority VMs. So the fixed price on the NC24 RSV3 is about a buck 22 US per hour for four-volted GPUs. Not sure if you think you can get that cheaper anywhere else, but I'm pretty sure you can't. So frankly, there's no point in building your own big machine learning cluster when you can get them for a buck 22. That said, I do advocate having a powerful local workstation with at least one ideally two consumer-grade GPUs so that you can test everything locally and muck around and kick it up to the cloud. We use a single batch AI, the sample codes in the repo. It's a HPC job distribution engine, and then we've taken that and built AI-specific stuff on top of it that makes it really easy to run any of the major AI frameworks, either natively on the VM or inside Docker containers. It's sweet. It's really well. Some final tips and tricks when just watching the clock here. For best price performance, use Linux. You should not be using anything other than Linux to train your neural networks. From both the price point of view and in terms of availability of the necessary drivers. Key drive, key thing you're going to miss on Windows is support for the Nickel V2 framework, which provides and video-optimized all reduced to go across network boundaries. Remember that every second of less than 100% utilization of your network and all your GPU is costing you money. So really focus on optimization. Scale up, then scale out to multiple GPUs, then scale out to multiple nodes.  Start by going to the biggest GPUs you can buy, which at the moment is V100s. Then get as many of them as possible running in a single machine. Then go out to multiple machines containing as many GPUs as you possibly can. Use the largest possible batch size you can get away with without affecting accuracy. Why? Because it reduces the number of times you need to go from training to doing a ring or reduce and back again. You are only as fast as your fastest GPU and your fastest interconnect. So don't try and do this with heterogeneous GPUs. So if you've got like a 1080 and an old 940, it's a really bad idea to use those two together. Likewise, don't build a network that has different interconnects. Ideally, you want everything as fast as possible and interconnect with the lowest latency and Finneband is great. Another thing to watch for is anything that can make mini batches take different lengths of time. So if you've got weird network architectures or weird approaches to mini batch sizing, that can make certain mini batches take longer on some nodes than others, you are going to be wasting GPU clock cycles because you're going to be waiting for every single node and every single GPU to finish its training to do the gradient update. There are some asynchronous approaches that you can look at, but it just starts making things hard. Use a job management, cluster management tool and that way you can take advantage of these low priority VMs. So again, we've got low priority, Google have them, AWS have them. Probably the only sales pitch I'll give you today is that you won't find anywhere remotely as cheap to buy Volta 100 capacity at the moment than what we've got. GitHub repo, WACCA-ULDNZ, WAC high scale dash D and N dash training. If you want to really nerd out, there's like a 200 slide tutorial from the Hot Interconnects conference last year from the folks at Ohio State, go Buckeyes.  And a good discussion from last year's GPU Tech Conf on ways of doing automated model parallel work distribution. And I note that next week, I think is this year's GPU Tech Conf. Is anybody going? Maybe wish they were going. So folks, with that, I'll thank you very much for your time and attention. So it was interesting content. My team at Microsoft loves doing hard stuff. So if you're an interesting major customer or a high potential startup and you want to build cool shit, I'd love to talk to you. Thanks very much. Thank you, Chris. In the interest of time, no questions, but if you have questions.


I'm basically what you call, a collector. I collect Disney movies and place them on shelves in my man-caped downstairs. There are many racks full of Disney movies, ranging from Snow White and the Seven Dwarfs to Toy Story 4. Trust me, I've become the movie's version of YouTuber Cinemassacre. To continue being a Disney fanatic, I decided to go to Disney's Hollywood Studios, a theme park at the Walt Disney World Resort near Orlando, Florida. With so many Disney vacation packages and options available, there's always a new family experience to discover in Florida, from kid-friendly adventures at Walt Disney World Resort, to grand oceanic escapes aboard the Disney cruise line. The resort has several expansion projects planned or ongoing, including Star Wars, Rise of the Resistance attraction, is due to open at Disney's Hollywood Studios on December 5, 2019. Disney Rivera Resort, a new Disney resort opening in December 2019. Mickey and Minnie's Runaway Railway, is replacing the great movie ride, opening at Disney's Hollywood Studios in Spring 2020. A Tron Lights Cycle Power Run at Magic Kingdom, is due to open in 2021. Expansion at Epcot, includes new attractions related to Guardians of the Galaxy, Cosmic Rewind, and Ratatouille, and a newly designed entrance due to open by the end of 2021. Reflections, a Disney Lakeside Lodge, a new Disney resort to be opened in 2022. Flamingo Crossings, a shopping complex similar to Disney Springs, opening date to be announced. Star Wars, Galactic Star Cruiser, a new Disney resort, opening date to be announced. Mary Poppins, will be celebrated with a new attraction at the United Kingdom Convion at Epcot's World Showcase. I went to Disney's Hollywood Studios with my friends, Scott, Tavis, and Zachary, and rode some rides there, my favorite being the Twilight Zone Tower of Terror. Then we went to Hollywood Boulevard to hang out. Hollywood Boulevard, inspired by the real street of the same name, serves as the park's main entrance and operates in the same vein as Main Street USA at Magic Kingdom. It is lined with theme streetscape facades and venues selling Disney merchandise and park services. Guests enter through the main entrance gate, which resembles the pan-Pacific auditorium. Near the park's gate is a recreation of the crossroads of the World Tower. Live street entertainment and seasonal parades travel down the main street throughout the day. At the far end of Hollywood Boulevard, stands a replica of Gromans Chinese Theater. Near the entrance of animation courtyard resides the Hollywood Brown Dirty Restaurant, a themed replica of the original Brown Dirty Restaurant in Hollywood, California. Then, we went to Echo Leak. Inspired by the similarly named neighborhood in Los Angeles, is designed to mimic the suburban California crazy form of architecture from Hollywood's golden age, and is anchored around the area's namesake Leak. Echo Leak includes three major attractions based on characters and films created by George Lucas and produced by Lucasfilm. Star Tours, The Adventures Continue, is a 3D motion simulator ride set in the Star Wars universe. The Jedi training, Trials of the Temple, a live-action stage show, invites children to become Padawan learners and receive lightsaber training from a Jedi master. The live-action Indiana Jones' epic stunt spectacular re-enacts various scenes from Steven Spielberg's Raiders of the Lost Ark, while illustrating how professional film stunts are performed. The Hyperion Theater houses for the first time in forever, a Frozen Simulon Celebration, a musical show based on Disney's 2014 animated film Frozen. The adjacent ABC Sound Studio building showcases Star Wars, Path of the Jedi, a short film retelling of the Star Wars series. Behind these buildings lies a subsection named Commissary Lane, that connects Hollywood Boulevard directly to Grand Avenue and bypasses Echo Leak altogether. In this area resides the ABC Commissary and the Sci-Fi Dine-In Theater Restaurant, a dinner theater with a retro-style theme featuring vintage car-themed tables, and a large movie screen featuring continuous clips of science fiction films from the 1950s. Grand Avenue is themed as a gentrified historic district, inspired by the real location of the same name in downtown Los Angeles. The area is anchored by Muppet Vision 3D, a 4D film attraction starring the Muppets from Jim Henson's The Muppet Show, that is presented at the Grand Arts Theater within the Avenue's Grand Park, itself also inspired by the real park of the same name. Grand Avenue is also home to Pizzarizzo, a Brooklyn-style pizza restaurant owned by Rizzo the Rett, Mama Melrose's Restaurant Italiano, and bassline taphouse, a modern California-style pub. The main street of Grand Avenue leads into a recreation of a figural street tunnel which connects to Star Wars Galaxy's Edge. Grand Avenue was originally planned as a themed area called Muppet Studios, following Disney's acquisition of the Jim Henson Company. In addition to a Muppet Vision 3D, this area was to include a themed restaurant, and a Muppet Dark Ride parody of The Great Movie Ride. The deal fell through after Henson's death, and only Muppet Vision 3D was developed. The realized Muppet Theme section became a part of the park's former streets of America Area, which encompassed several attractions, including an urban street denomination of New York City and San Francisco. The area's namesake street facades were formerly the Park's Working Backlot set, which was originally a component of the park's inaugural studio backlot tour, and opened to pedestrian park traffic in the mid-1990s. This area closed on April 2, 2016. The Muppet themed areas, and a single remaining block of the streets of America facades were re-incorporated into a Muppet's courtyard, which served as a placeholder designation until Grand Avenue was completed in September 2017. Star Wars Galaxy's Edge is set within the Star Wars universe at the Black Spire Outpost Village on the remote frontier planet of Batu. Attractions include the Millennium Falcon Smuggler's Run, a flying simulator attraction that allows guests to pilot the Millennium Falcon through a customized secret mission on behalf of Hondo Anika and Chewbacca. The forthcoming Star Wars Rise of the Resistance will be a dark ride that places guests in a climactic battle between the First Order and the Resistance. Restaurants and shops include August Cantina, Saddy's Workshop, and Droid Depot. The land opened in 2019, replacing the park's streets of America section. The 14-acre area cost an estimated $1 billion. Toy Story Land is inspired by Pixar's Toy Story series. The 11-acre area is themed to Andy's backyard with three attractions, each hosted by characters from the series. The attractions include Toy Story Mania, an interactive 4D attraction inspired by classic Carnival Midway games, Slinky Dog Dash, an outdoor roller coaster, and alien swirling saucers, a spinning teacup ride. The land opened on June 30, 2018. Toy Story Mania was originally a standalone attraction within Pixar Place, an area dedicated to films and characters created by Pixar, resembling the animation studio's Emeryville, California campus. Pixar Place was also the home of Luke Sojunier, a six-foot-tall audio-animatronic version of Pixar's Deskland mascot. The movie and character performed periodic shows throughout the day and evening across from Toy Story Midway Mania. Located just outside Toy Story Land is the former Pixar Place area, now known as Municy Berg, an Incredibles-themed area featuring a merchandise location titled Municy Berg Gifts, an outdoor vending cart known as the Neighborhood Acre, and the Edna Mode Experience, which features an Edna Mode meet and greet, and displays of Incredibles costumes in the queue area. Animation Courtyard is home to attractions based on films and characters created by Walt Disney Animation Studios. Its entrance is marked by a square studio arch. This section of the park originally was the starting point for the studio back lot tour. The former Magic of Disney Animation building hosts Star Wars Launch Bay, a Star Wars exhibit featuring behind-the-scenes props and character meet and greets with Darth Vader, Chewbacca, and BB-8. Mickey Avenue, a subsection of Animation Courtyard, is home to a walk-through exhibit, Walt Disney Presents, which explores the life and legacy of Walt Disney through photos, models, artifacts, and a short biographical film narrated by Julie Andrews. The Courtyard section also hosts two live shows. Disney Junior Dance Party entertains guests with characters from the Disney Junior series, including Mickey and the Roadster Racers, Vampirina, Doc McStuffins, and the Line Guard. Across the Plaza, voyage of the Little Mermaid uses glow-in-the-dark puppets, lasers, music, projectors, human actors, and water effects to recreate favorite scenes and songs from the 1989 animated film. Sunset Boulevard, inspired by the real thoroughfare of the same name, was the first expansion of the park, opening in July 1994. The focal point of Sunset Boulevard is the Twilight Zone Tower of Terror, a thrill-ride based on Rod Serling's The Twilight Zone. Located near by is rock and roller coaster starring Aerosmith, an indoor darkened roller coaster themed to the music of Aerosmith, with three inversions and a high-speed launch. Sunset Boulevard has two outdoor amphitheaters and one indoor theater. The covered theater of the stars hosts Beauty and the Beast live on stage, a stage show featuring highlights from the animated film. The larger open-air Hollywood Hills amphitheater is the home of Fantasmic, a night-time show featuring Mickey Mouse and many other Disney characters in a story with fireworks, lasers, and water effects. The completely indoor Sunset showcase hosts Lightning McQueen's Racing Academy, and features Lightning McQueen and other cars characters teaching rookie racers about the lessons they've learned when racing. While at Sunset Boulevard, there was a guy with a DVD case in his hand. He wore a black baseball cap and glasses. We knew this guy at high school, his name is Earl, and he was the nerd from biology class.  We walked up to Earl and we said hello. Earl claimed that he downloaded a completely different version of a Disney movie someone made from the deep web, which is shocking to us because we never went to the deep web before. Earl continued to claim that he had seen a different version of the Disney film, Bolt, and downloaded it onto his DVD-RAM. He wanted to give it to me, and said that it's better to have me upload it to YouTube or Facebook, to show people the dark side of Disney. At first, I didn't know what he meant by the dark side of Disney, but then I suddenly knew what he meant.  For instance, in 1938, the Walt Disney Company sent a rejection letter to Mary Ford, stating that girls are not considered for creative positions. The letter was rediscovered in 2009, when Ford's grandson uploaded the image on Flickr. The letter received greater attention on January 7, 2014, when, after congratulating Emma Thompson for her best actress win at the National Board of Review Awards, Beryl Streep referenced the letter. Referencing Thompson's film, Saving Mr. Banks, Streep responded. It must have killed Disney to encounter a woman, an equally disdainful and superior creature, a person dismissive of his considerable gifts and prodigious output and imagination. In response to Streep's statements, many Disney scholars and artists defended Disney, including Disney legend Floyd Norman, who said, Much has changed, and changed for the better. Other journalists found the speech ironic, noting that Streep just finished filming the then upcoming Disney film, Into the Woods. The Walt Disney Company has also been criticized for the lack of feminist values seen in the older original Disney princesses. Snow White in particular is under constant criticism for her lack of feminist ideals. The film Snow White and the Seven Dwarfs, features the main protagonist who at the time, fit the domestic and docile expectations of women in the pre-World War II era.  Through her actions portrayed in the movie, she draws on the traditional femininity that was encouraged in 1930s American culture. During the Great Depression, women were encouraged to return to the home and care for the household, a theme that is widely displayed in Snow White and the Seven Dwarfs. However, with the second resurgence of Disney movies, known as the Disney Renaissance, beginning in 1989 until 1999, Disney transformed the dam's go-in distress into a strong woman with a desire for adventure. This new approach ushered in a decade of go-getting, pro-active heroines, who possessed character traits that coincided with the new era of acceptable sexual roles in a society where women hold the same jobs as men. This is evident in princesses such as Ariel from The Little Mermaid, and Belle from Beauty and the Beast. When we got back to our hotel rooms, we just relaxed and watched reruns of Wizards of Waverly Place on Disney Channel. I was still holding on to the DVD Earl gave us, and I didn't have time to notice the writing on the DVD cover. That read, Bark of Insanity, in Black Sharpie. I guess Earl decided to give this thing another title instead of Just Bolt. This interested me, this is a dog pun of, Spark of Insanity, and it gave me tons of questions like, will this be any good, or is Earl right about this being found on the deep web? After the show was over, I asked my friends if we should watch Earl's CD on my laptop, and we looked at each other for a minute, and agreed, seeing that this is Earl we are talking about and that he downloads weird shit all the time. We placed the CD on my laptop, and on screen was the movie Bolt. The movie started with a white shepherd puppy named Bolt, being adopted by an 80 year old girl named Penny. Five years later, Bolt and Penny star in a hip television series called Bolt, in which Bolt uses various superpowers to protect Penny from the TV shows main villain, Dr. Calico. To gain a more realistic performance, the show's producers have deceived Bolt his entire life, arranging the filming in such a way that Bolt believes everything in the show is real, and that he has superpowers, including a devastatingly powerful sonic scream, called the Super Bark. After a cliffhanger episode causes Bolt to believe Penny has been kidnapped, he escapes from his on set trailer in Hollywood, but knocks himself unconscious, and is trapped inside a box of foam peanuts that are shipped to New York City.  It's just like the 2008 film, and I thought to myself, why is a movie like this doing in the deepest bowels of the internet? Soon enough, my question was answered. In New York, Bolt resumes his search for Penny, and quickly finds that his superpowers are useless. He encounters Megans, a feral cat goobolies pigeons out of their food. Bolt compels Megans to guide him back to Penny, the latter being convinced her captor is a lunatic, and the two start their journey westward by truck. Meanwhile in Hollywood, Penny is distraught over Bolt's disappearance, but is convinced by the studio to continue filming with a less experienced look like dog.   We were blown away at the sudden turn of events, I mean it did go from 1 to 100 very quickly.  And starts laughing out of surprise. But what confused us was that, there was no music. Literally. No. Music. But that's not the strangest part. None of the voices matched up with the characters. Bolt didn't sound like his original voice actor, John Travolta. In this scene, he was voiced by another person, specifically, a college student. However this anonymous voice actor was, he did some pretty talented voice acting, he sounded like he was extremely pissed off with Megans for whatever she must have done. But, what did she do to set him off? The sound effects of this scene were realistic stock sound effects you'd find on YouTube or sound board online, and the sounds of Megans' anguish war from the sound recordings of someone crying or screaming. Again, you'll find this somewhere on YouTube or whatever. Getting back to the movie, Bolt is being shown by Megans how to act like a cute but needy dog, obtaining food for them both at an RV park. They are joined by Rhino, a fearless hamster and fanatical Bolt fan. Rows and wavering faith in Bolt, substantiates the dog's illusions about himself, but allows Megans to figure out Bolt is from a television show.   We knew that this was now getting to adult for little kids, and we stopped the movie right there and pondered about what we saw. I looked at Zachary, who replied quietly. Wow. We took a break and ordered some pizza, and we continue to talk about what happened on that CD, with some ice cold beer. As our pizzas arrived, we felt like we needed to watch the rest of the movie. I was hesitant at first, but Tavis was the person to put it back on. Bolt, freeing from the patrol van by Rhino, finally realizes and accepts that he is just a normal dog, but regains his confidence after Rhino, oblivious to this revelation, exhorts him to heroism. They rescue Megans from the shelter, and as they continue west, Bolt and Megans form a close friendship, in which she teaches Bolt how to be an ordinary dog and enjoy typical dog activities. The movie was normal, as you'd see from the original film, but what caught us off-guard was that the musical number, barking at the moon by Jenny Lewis, was changed to The Headless Waltz by Valter. Megans make plans for the three of them to stay in Las Vegas, but hearing Bolt is still drawn to find Penny, tells him Penny is only an actor, and humans never truly love their pets, and eventually betray and abandon them, as happened to her. What happens next, really blew us away honestly. The music stops again, and Bolt began to growl. But this time, he was shaking, and filming at the mouth, and glared at Megans with an expression of maniacal rage. Megans were now backing away with fear in her eyes. It stayed like this throughout the entire scene, until it cuts to another scene. Bolt reaches the studio and finds Penny embracing his lookalike, unaware that Penny still misses him and her affection for the lookalike is only a part of a rehearsal. A broken hearted Bolt leaves, but Megans, who was supposed to be on a gantry in the studio, and the roof, wasn't there. The music stops, and he then looks up at the studio and has that menacing glare again. This time not growling or filming. Bolt rushed in during the studio's screening, and attacked the cast members, the special effects crew, and shockingly enough, Penny herself.  By now the scene fades into view, and Bolt goes back to Las Vegas, and by now we saw what happened to Megans.  She was covered in bruises and scars, her eyes swollen and purple, and missing a few teeth. Her eyes welled up with tears, and she was saying, God, get me out of here, which is a recording taken from Stephen King's huge-o. By that time, we now knew what happened, but didn't want to say what Bolt did. Because, by this time, you already knew what happened. He did some very very upsetting and anger-inducing stuff to her, that it would even make Jeffrey Donger himself look like the sanest man alive. By this time, the film ended with a quote. It is not the bruises on the bodies that hurt. It is the wounds of the heart and the scars on the mind. From Aisha Mirza. We ejected the CD and placed it back in the case. I got on the phone with Earl.  The conversation went like this. Hello? Earl, this is Tyler. I need to talk to you about the movie you downloaded.  Is it that bad? That's bad. Ugg. Look, I didn't mean to give it to you.  Ugg, Jesus Christ, Earl. Why us? Can't you do it yourself? We don't need to be your little test monkeys. Just upload it yourself. You got a YouTube channel, right? Yeah, but my computer got a damaging virus after I downloaded the movie onto the CD, and I don't want to damage my computer any further. That's why I'm asking you guys to do it for me, just until I boot up my computer again. No way, dude. I don't want the same virus on my laptop. Besides, I upload some pornography to which I masturbate. Dude, I'm a Christian. Sorry, but that's the truth. Anyway, I understand your concern, and you don't have to upload it if you want. Just put it in your collection, that way when you do view it again, it would be a sight to behold. I'll think about it. Thanks. I hung up the phone, but relieved that Earl was as shocked about the film as we were. Months later, we went back home to Atlanta, Georgia, and I put the CD among my other collections. I did think about viewing the CD again, and this time, recording the full film with my video camera, but I decided against it, thinking that no other person should relieve the same moments we went through. Besides, if I did that, then that'll be going up against YouTube's community guidelines, and I do not want a strike on my channel. The CD is still on my rack to this day, and I still don't know if I should upload the film online. Who knows, since YouTube's going down the drink, maybe it'll gain a million views, and stay on the platform. Because after all, it is from the deep web, and stories about the deep web have been growing more infamous ever since. I still remember the contents of the movie and let me tell you, it still haunts me to this day. But still, I have done reviews of it in one of my vlogs, and this is what came up. Cats are perceived as feminine, so it's like abusing women. With that said, people would say that dogs have a real, loving, affectionate relationship with you. I've had dogs that loved me more than themselves. Cats don't do that. They look at people as a provider of resources, which is not true due to facts I picked up from veterinarians. As a cat lover, I find this film very misogynistic, which is why I said that cats are perceived as feminine, and that they get the torment they receive from men, which is why they are perceived as dogs. Cats and cats hate each other in the film industry, which has been used in so many movies, shows, and even YouTube videos. So, I guess this comes to show you that women, ethnic minorities, and even cats, have it rough in the movie business. And because of that, it makes me feel bad for them. For anyone who's reading this, no I don't know who made this, nor do I have any clues to the original creator of the film. However he is, he's damn good at CGI and voice acting. I'm looking at the CD now, and I might just upload it to YouTube, and get it over with.


Our study shows that the reduction in pollution caused by the Covid-19 lockdown in Lombardy, Italy was complicated by weather variations, but using machine learning to build counterfactual scenarios allowed us to determine that the lockdown reduced background concentrations of PM 2.5 and NO2 by 3.84 gm3, 16% and 10.85 gm3, 33%, respectively, resulting in a saving of at least 11% of years of life lost and 19% of premature deaths attributable to Covid-19 in the region during the same period. Our analysis highlights the benefits of improving air quality and the need for an integrated policy response addressing the full diversity of emission sources. This article was authored by Francesco Granella, Lara Alleluia Race, Valentina Bacetti, and others.


We're from Burke. Mason's been diagnosed with ALL leukemia, high risk. We've been here for nine months. We had half an hour to pack a bag and we've been here ever since with no home visits or anything. And we won't get home till July, so it's been pretty tough. It's a little warrior. So we've come to Ronald McDonald House to help serve these kids some dinner and play around with them and yeah, give them a little fun experience. Being around the inside of this community here is something pretty special and it's very, with a few boys from the squad, it's really nice as well. It's a really good kind of opportunity to get out there and give back and I think, yeah, really eye-opening for sure. Is that enough for you a bit more or a bit more? Obviously it's nice to see any kids smile, let alone someone you know that's been going through a little bit of hardship and stuff. So yeah, it's been good to come down here and I've really enjoyed it. The kids are in tough situations here and it's tough for their families as well. So obviously them being here, it helps a lot but also us coming here, it puts a lot of smiles on the kids' faces and it's very important to do it to be honest and yeah, it's amazing and I'm sure all us boys we enjoy coming and doing it. So it's been fantastic tonight. Unbelievable, like we're just playing football basically. Like it's nothing more than football you know and if you know what they're dealing with, it's like nothing compared to this. So it's great what we can achieve with our job and yeah and it means the world to us to put a smile on a little kid who's sick and has a moment of happiness. That's just a great thing.


Hi, let's propagate a monstera! First, find a node. This is where two or more leaves are coming out of the stem and generally we will see our herbal roots at the node. Then with your sterilized brooding shears, cut about two to three centimeters from the node and we have our cutting. Add cinnamon to prevent fungi. Fill up a jar with water and submerge your plant making sure that the node is in the water. Make sure to change the water every week and you will see roots in no time.


Hi everybody, Joe for Jaspy's Case Breaks.com coming at you with 2022 Panini Certified Football. Eight box, half case, pick your team number 10. Second half of that case we popped open not too long ago. Remember I did those squiggly marks in that video to let people know that they're from the same case here on the 25th. Sold it out straight up, no filler. I appreciate that. Thanks everybody for putting in the work. There's Jared, last bot Mojo Steelers. Thanks to him. Thanks everybody for making this happen. Break 11 and 12 are in the store right now. So if you want to run back another half case or maybe even another full case, I'm down. And down. Certified, pretty nice stuff. I think a fairly, fairly friendly price point, I want to say. Nothing too, not like flawless or anything like that. So if you want a slightly more inexpensive way to start collecting some of these guys and certify it might be it. And I feel like some of the RPAs, even some of the higher numbered RPAs that will give you a little color in there too. They're not always napkin. Sometimes it surprises you. Yeah, how much is a team Joe in this product? Joe Burrow, Joe Mix and Joe Montana. It feels just a Joe break. I feel like it'd be one of the more expensive, more expensive breaks. Expensive teams if it was just a Joe team. I'll have to look at the checklist. There is Sam Howell. 144 out of 249, Jersey and Autograph, Diego with the commanders. There's Rashad White to 399. And there's Ryan Tannehill. We've got Terry McLaurin to 99. And we've got an Aiden Hutchinson new generation rally, 113 out of two. Check that three, 49. It'll be for Detroit, Jordan with the Lions. And all card ship. There's Tyler Algear. Algear? I don't know how to say that name, 12 out of 25. But I've got him on my fantasy team. He's been doing all right. If you need like a flex spot guy or a bi-week running back, I feel like week to week they've been putting, giving them a little more work. There's Roger McCary. McCrary, I'm having trouble with names today. 49 out of 75. Rookie Auto for the Titans. That'll be for Jared. And we've got James Cook, 37 out of 249, Dalvin's brother. And if you believe Wikipedia, James Cook's middle name is Dalvin. And Dalvin Cook's middle name is James. Dallas Goddard to 349. The next box. Pace, is there a Joe Pace out there, Carl saying? I'm not familiar with a Joe Pace. Next box, McCrary Pax. Got a little basketball on the background here. Dallas at Pelicans. I think Pelicans are without, are without Brandon Ingram and Zion. And they're playing some competitive basketball here. They're tied at three, halfway through the third quarter. That's pretty good on TNT. Rex, what's going on? Gilo saying, and if you believe Wikipedia, Gilo and Dalvin Cook have the same birthday. So we've learned Gilo's middle name, his birthday. All we need is some, an SSN, Mother's Maiden. We've got his identity right here. Breaks on Gilo. The Terry McLauren to 275. There's CD Lam, seven out of 50. Pretty cool looking card there. Dallas, Brandon with the boys. Correct, Paul, you are right. Some of the gold team cards are numbered on the back. There's Jalen Waddle to 99. Yeah, I mean, I think some of these dark horses can be numbered as well. I've pretty much everything here. A lot of numbered cards and certified. Freshman Fabric, two color patch. An autograph 69 out of 149. David Bell. Cleveland, this is for you. Brody with the Browns. Got two things there. You got the bell, you got this is for you. There's Kenny Pickett. And there's a Malik Willis falling in that numbered. 88 out of 99. Malik Willis going to the Titans, that's for Jared. And the Kenny Pickett going to the Steelers, that's also for Jared. I kind of like these cards here. This sort of like a street art vibe here. That's right, only if you believe in the group efforts of Wikipedia. And there's EZE, that's how he does it. Eric Azucanama. Azucanama, 102 out of 149. Some nice colors popping in that window. That's for Miami, that'll be for Brandon.  Chris Lobby is making sure Wikipedia is well maintained. There'll be a lot of battles and the discussion tabs on Wikipedia as well. There's Romeo Dobbs, 171 out of 199. That's going to be for Nick and the Packers, piece of his jersey. It's an extra blank card here too that we can give to the shipping team. I'll use those for the shipping team. How many of you have actually donated to Wikipedia? Does anyone donate, even if it was just a dollar? I feel like I should. There was a tremor in California, Max saying. There's tremors all the time. Mac, most of those tremors do not affect us. How's it going, Mac, good to see you. I fall on Twitter, there is a earthquake bot. On Twitter, like LA Earthquake Bot or something like that.  You never feel those though. Oh yeah, we're nowhere close to that. If it was near San Jose, we're in Southern California. That's about hundreds of miles away. There's a mirror white, 128 to 349. Carl donated 6,000 to Wikipedia. Carl loves Wikipedia. There's a mirror white going to Jeffrey and the Raiders. And then we got Alvin and Bethea, defensive end, 33 out of 75 for the Oilers. Oiler stuff, of course, goes to Tennessee. That'll be for Jared. Oh no, it says Carl. There's a ModSauce Gardener, 140 out of 399. Gilo donated to Wikipedia. I feel like I use Wikipedia a lot. I feel like I should throw some money at him. Nick Chubb to 199. Some of you may want to stash Jayne Deberleys, maybe stash Jameson Williams. On your fancy roster, I believe you might make a contribution to the Lions later in the season. There's Elijah Moore to 149. There's Tyler again to 325. And we got a Johan Dotson, freshman fabric, Jersey Nautograph, 190 out of 249, Diego Bloomberg and the Commanders. There's Evan Neal to 275. There's Lamar Jackson certified stars to 99. Cameron Jordan to 275. Their kid reporter card. He used it to switch the Cook Brothers. So is it now James Cook and Delvin Delvin Cook? All right, next box. Let's see, what's going on the Hoops work? There are not too many games on it on the Tuesday, but wizards, two-on-one wizards are leading the Pistons 97 to 85. No, Luca, get up. You're okay.  That's all. Not good for the hobby, if Luca D'Angelo gets injured. Palakins are up 82 to 76. Two-on-one Palakins and the Clippers are trailing the Thunder at half time. Thunder up 49-43. And then the late TNT game. This should be a good one. Warriors at Suns, both are both two-on-one. Suns minus one and a half. This should be a good one. Yeah, the Palakins are just doing it with some balance. Yeah, seeing my cloth, a good one, man. Doing it with some balance. Corey Davis to 149. We've got Breece Hall, poor Breece Hall to 75. That'll be for Adam and the Jets. I think he's done for the year, right? With the, did the ACL was having a, a offensive rookie of the year caliber season up to now. And that's not gonna happen. There's Damian Harris to 10. And Jalen Tolbert to 325. Poor Breece Hall. Pierre Strong, Jr. Two-color patch to 99. 96 out of 99. Patriots Karen with her pass. And behind Josh Jacobs is a Devin Duvernay. One out of 99 certified potential autograph for the purple birds. Alexandra with Baltimore. Justin Herbert to 349. Certified stars, Debo to 149. Chris Olave, Jersey and autograph. Nice, 146 out of 249. Freshman fabric. That'll be for Bill and the Saints. There you go, Bill.  Piece of the game. 49 out of 75 for Miami. That'll be for Brandon. All right, we are halfway through this break. About another 15, 20 minutes to go. We'll do a little recap at the end of the video as well. How do we feel about Christian McCaffrey going to the Niners? What does that do for that team? It's healthy, maybe a lot. Thanks, Evan Sia. Niners are at Rams this Sunday. And Vegas currently has the Niners as slight favorites, minus one and a half. Christian McCaffrey will get a whole week of practice this week, get familiar with the system. God, take some pressure off Jimmy G. There he is. Might open the offense for everybody. Here's Alec Pierce, freshman fabric jersey and autograph. 127 out of 349. If you kind of turn your head and squint a little bit, I feel like this could almost be, and especially with the Colts uniforms, almost looks like Andrew Luck's autograph. That'll be for the blue horseshoes. That's gonna go to Matthew and the Colts. It was Derek Stingley to 325. And there's Chris Oleducan to 75. Oh, interesting. Debo has a hammy problem now. CMC came just in time. Now, if they're fully healthy, they don't have to really rely on Garoppolo that much. Classic sort of established the run. Open up the pass. Reach for the sky. 94 out of 199, sky more. There's a snake in my boot. It'll be for Jared and the Chiefs. What up, Terry? Terry's done with football cards this year. Everyone he's been collecting, getting injured. There's Jonathan Taylor, gold team, to 50. Yeah, injuries, hashtag not good for the hobby. There's Darren Waller to 275, like this guy. This guy's been getting injured. There's Kyle Hamilton to 249 for the Ravens. There's James Dalvin Cook to 275. Right, yeah, Gilo makes a good point. Brian Robinson Jr., in an attempted carjacking, gets shot a number of times, I think. And then he's back playing football within a couple months. Guys are sitting out because of hammies. Come on, not NFL is supposed to be tough guys. Three out of 10, Canton certified Isaac Bruce. It's for the Rams. Jeffrey with the Rams. I feel like the Rams could use an Isaac Bruce type player right now. Damien Pierce, 232 out of 349. Texan, that's gonna go to Patrick. Right, look how many times 50 cent got shot. Well, I don't know how soon 50 cent was out and performing or in the recording studio after his, we'll have to look at what his return time was. But, Brian Robinson's return time seemed pretty quick. All right, three more boxes to go. I mean, technically, Terry, everyone's one tackle away from an injury. That's the nature of this sport, the NFL. I don't know, I mean, I feel like every year we forget that there's all these injuries, you know? There is one year where every other person seemed to be going down with an ACL injury, you know? Saquon, Christian McCaffrey, multiple years, I mean. Like there's always bummers every year. There's always a poor so-and-so, you know what I mean? Poor so-and-so gets knocked out. Travis Etienne, last year, pre-season. Doesn't play a single regular season snap. There's Austin Eckler, 25. And there's Danny Gray, two-color patch. Autographs, 72 out of 199 for the Niners. Jerry, from San Francisco. Yeah, Gilo's saying they need more natural grass fields. Yeah, I mean, allegedly some of those new turf fields are, the technology has improved greatly on those, but yeah, I think I would argue that more players would want that natural grass. 92 out of 149, Johan Dotson. It'll be for Diego. And a Tony Pollard, I think an autograph. Yes, 27 out of 30. Mirror signatures. Tony Pollard, Dallas, Brandon. So up and down, sold a Bailey Zappie autograph last night for more than you expected as soon as he had a good drive. And yet the person paid. What's the controversy there? Is there controversy there between Mac Jones and Bailey Zappie? It'll be interesting. There's Drake London, 119 out of 249. Skip Schumacher is going to Miami to coach. I don't think I heard that. Brody with the ATL. And there's Dak Prescott to 275, Dallas. And it'll be for Brandon. It's true, Adam, Adam is a Jets fan. Doesn't care, he hopes they both implode. It's Kevin Harris and Malik Willis, 149 for the Titans. Yeah, having the Patriots won enough. All right, two boxes to go. Terry bought a nice RPA, hockey cards with rookie, paid on Friday. Over the weekend, the guy scores four goals in four games and the dude cancels on you, saying he didn't have the card anymore. Yeah, Yuck is right. I mean, come on, just sell it. Gotta ask him, gotta honor those transactions. That's just the luck of the draw. What are you gonna do? If the guy has a slump, maybe you can get it back at that price again, Terry. There's Sam Williams, 95 out of 149. Yeah, I'm surprised he didn't see the Zappy sale and then be like, hey, my kid clicked it, sorry. Yeah, Dallas, Brandon.  Adam, did you see where the shipping address was? Was it the Patriots practice facility? Someone there knowing something that we don't? There's Devonte Adams to 275. And there's a Mods, Soss Gardner, nice. Three color patch, autograph, 30 out of 249. Yeah, sometimes those higher numbers, they'll still hit you with some color here. It goes to Adam and his Jets. It's Jordan Davis to 275. Certified stars to 149, Debo. Taekwond Thornton for the Pads to 50. Jilo, you think it's still Zappy's job? And Kyle Hamilton for the Purplebirds to 299. The guy about the Zappy car was in North Carolina. That's Alexandra with Baltimore, gets that relic. Godwin to 349, Garrett Wilson, Lewis Seen. Yeah, I mean, as soon as Bailey Zappy got to that game, right Jilo? Like, all of a sudden they were pushing the ball down the field. All right, last box coming up. Last boxy box. Wasn't Justin though, wasn't Justin. Where's Bailey? Bailey Zappy, I just punched this up. Went to Western Kentucky University. 6'1", 2'20", from Texas. You know, and then a fourth round pick and then he might be starting QB all of a sudden. Terry's like, look at six rings. The bell check stays alive. Yeah, who knows? Haven't they had enough? That's not orientation, I don't know. Final box, ladies and gentlemen, we got more certified in the store right now. Jaspy's casebreaks.com. You loaded up two half case breaks. Break 11 and 12 will be from the same case. It's Khalil Shakir, 72 out of 99. Yeah, Terry, thank you. I appreciate the optimism for the old raiders here. Step one, you can only go 11 and five if you start winning games now and the raider surely did beat the Texans 38 to 20. So they're currently two and four. So Terry, where do you see the loss happening? It is Khalil Shakir. Buffalo, Brandon with the bills. There's Justin Jefferson to 199. Dallas Goddard certified gamers 42 out of 199. That's for the Fly Eagles Fly, Eric B with that one. Shawn White rumor has it that Patricia stacked the deck against Mac with play calls last night.  There's David Montgomery, 105 to 199. Oh right, Terry, you got an added game there. 12 and five, they're 17 now. It's Christian Watson. Two color patch in autograph, 268 out of 299. Nick Martindale, Green Bay. Kevin Harris to 299. I wish I could share your optimism, but I do not. All right, 11 and six. So you're saying they got to find nine more wins somewhere. It's Fred, Fred Warner, eight out of 30, two color relic for Jerry in San Francisco. Darren Waller to 249. And what's our last bits gonna be here? Got Calvin Austin, the third to 149. Got Jermaine Johnson to 199. Some certified stars, Michael Pittman Jr. and Antonio Gibson to close things out. There you go, my friends. Thanks for getting in on it. I appreciate it.  Let me just leave these guys really quick. Tuck away the base cards, all card ships. So in case I'm, I don't think I got everything, but in case I miss something, don't worry, everything will ship. So from the last box, from box eight to box one, we'll go in reverse, my sauce gardener, some Tony Pollard, some Isaac Bruce, Chris Olave, DuVernay, sauce gardener relic, Bethea, I don't think we've seen a train whistle hit as of yet, right? Maybe in the next half, let's see some train whistles. All right, and then we started off with Sam Howell for the commanders. There you go, gang. I'm Joe for jaspyscasebrakes.com. Thanks for breaking with me. Thanks for talking some sports with me. I'll see you next time for the next break. Bye-bye.


So the company is Empire Robotics and the product is VersaBall, it's a flexible robotic gripper. We put together a beer pong demo for CES where we show off the capabilities of the gripper. Really the intended use is for pick and place operations in industrial manufacturing. We handle objects that may be different shapes, come in different orientations or maybe you want to handle several different objects at the same location. That's when the gripper does really well. So far the machine is doing really well, it's 18 wins for the machine and two for the people.


Oh, Piper got Zomies this morning. Yeah, we took her out. She pooped, she peed, she did everything. There's Sherry. Good morning. And now it's time for Piper's breakfast. Who's ready for breakfast? I had to wipe off her paws. They were so dirty. You do that when you put them on. Who's excited for breakfast? Oh my God, she's so excited.  Okay, who's ready? Come on. Sit down. Good girl. And that is breakfast being served. Good morning guys, and welcome to a new vlog. If I haven't already started the vlog, I am on my way to Cycle Bar, which is crazy. Haven't been to Cycle Bar in like probably over a month. I don't know, but I've been waiting to kind of like get done with clinicals before I start going, but also to do a little update. I paused my membership because, today's like a free ride and I think the studio actually like created me like a new account or something. I don't know.  So I'm like, I wanna go. So basically I am going to the free ride because I want to and I really do miss it, but it's just kind of like expensive and not having like a whole lot of income. Like basically my YouTube money covered my membership, but like I have other expenses like gas and stuff that I need to pay for. And then I also want the new iPhone. So that'll be kind of expensive. Luckily Apple will buy back this one like I've said in previous videos, but I don't have another part to like help cover it. So it's gonna be like an expense except for when my big camera sells that will cover the difference. So basically I'm just like waiting for someone to buy that or like anything else on my eBay store because of other like finances, financial things. I'm like, you know what? I should just pause this membership because I'm not going as much as I'd like. Also someone commented like saying that they hope I like drive safe and stuff when I vlog. And I always do look at the road like there's a biker right there. Like I know that I am slightly getting over. So like, but thank you for your concern and everything. Also I kind of wanted to touch on that I'm almost done with clinicals. I have three cases left and I'm so excited. Of course we're gonna be going to school and everything and I potentially could go to cycle bar a lot more but that still doesn't negate the fact that I would have to pay that much money a month. So I really should have paused my account earlier. I just didn't know how things were gonna pan out. So I don't know, you live and you learn. I am on my way there now and later today, Hyper is going to get groomed. So that is going to be fun and exciting as always. Made it to spin. And I'll see if I can get a clip during class. I'm about to start my period. So I'm like, oh, I really don't wanna go and I'm tired. But since this is like a free ride and stuff, I have to go today. Okay, so the class was so good and now I'm going to get my pumpkin spice latte. I haven't had one since last year. And honestly, it's not my most favorite fall drink. If you guys have been around, you know my favorite one is like a chai tea drink because it's like pumpkin spice latte season. I have to have one. So I'm gonna have that and I'm gonna also get mother her grande americano. And here she is, my friends, the pumpkin spice latte. I did not get whipped cream because I don't really like whipped cream. It's very good. I love anything pumpkin, but I will say this tastes like burnt pumpkin. I think I said that last year. It is good. I like it. It's like bitter pumpkin because of the coffee, obviously. Must have for the season. I think I'm gonna try next time I go to Starbucks. I'm gonna try the chai tea drink. I think it's new. I don't know if I always get chai tea also during the fall season because it's my most favorite drink. It's a grande iced chai tea latte with tree pumps of pumpkin spice. So good. That's like my go to. Whether there's this like chai tea with pumpkin cream cold foam. So I'm gonna try that next time. It probably tastes really similar to like what I normally get anyway. And now Piper and I are headed to her grooming appointment. I'm so excited. She's been needing this. I scheduled it like a week late because I think like I wasn't, I didn't make the appointment when I was there last time. So she's a little late and she's definitely due for cleaning because you smell. So we're just hurrying there. Pretty much the update. We are here. We are here. Come on, Piper. You want something to drink? No? Come on. Got to see a lot of dogs on National Dog Day. Okay. So basically no grooming because they didn't have her in their like calendar to make her get groomed or like let her get groomed. I don't know. Anyway, it was a bad communication. And I think like whoever I talked to on the phone like didn't actually book it.  But we're going to go to the park instead because you know, I feel bad taking her out like not do anything. So we'll do that. And then I'm going to bathe her at home cause like she really does like she needs a bath and I was counting on today. But it's fine. She's actually gotten a lot better in the shower and stuff, not shower but like bath when I bathe her. So she's still going to clean cause her next, well the appointment I made today that now I have a text message, it's in two weeks. So all right, update. No park for Piper because her human. Okay. So I guess every so often you have to prove that they have a rabies vaccine cause I have the paw print on my little ID but that's not good enough because you need to now have a date on the card or something. I don't even know.  And every time I go to that park, there's only been probably like eight times that I've gone without any issue but I always go when it's closed for maintenance. I don't know. But poor Piper is just going to get a bath now at home. At least she got out of the house a little bit.  Hey, saves me some money, you know, going to get her groomed. I guess you just got to roll with the punches and I'll go get the ID thing like updated my little badge to get into the park later. I have no idea what I'm actually going to do that. Like their hours are just so bad.  I'm telling you, man, it's all right. I'm going to go home and relax. I was going to go to the pool, but I'm like, do I really want to do that? Probably not cause I just want to like relax in the air conditioning. Both the pup and I have been bathed. She peed a little bit. She like dribbled upstairs and it was so annoying. I had to clean it up. So that's why she's still demoted and not allowed on any of the stairs. We both have white hair. This is when I wish I had the wide end angled camera but I could just put this SD card in there but I'm a little lazy right now. Anyway, going to probably sweep the floors but I'm going to wait a minute to like cool off of it cause we just went outside cause just in case she had a finish going, I was cleaning up that poop or I was cleaning up the pee. So I just cleaned the floors. I didn't vlog that because it was literally running a Swiffer and broom across these floors. Just got done with that. Folded and put away some towels. And now I'm going to edit my vlog. It's like a night routine. You guys would have already seen it because I'm going to post it as soon as I edit it pretty much. So hope you guys enjoyed it. I do miss just like vlogging like it's 2017. So that's why my vibes are kind of like that at least with this camera because I don't know it just takes me back and I just like talking so. One eternity later. Hi guys. So it's been a couple of days and I'm actually going to a UCF game tonight. My friends were just kind of like, we're going to go and I was like, all right. So the decision was kind of made for me, which is fine. I'm sure it will be fun. I hope it doesn't rain, fingers crossed or I'm just going to get soaked. I'm just hoping it won't rain. But I went to school today because I'm done with clinicals, which is so crazy to say but since I'm done, we just like go to class and apply to jobs and study for the CST exam. It's like the big national certification test. And I have a couple like, I had a phone interview today and then I have another interview on Tuesday, which I'm excited about. I hope it goes well. But yeah, I just wanted to fill you guys in. I'm going to go get gas somewhere. I'm on my way to Donato's, my favorite pizza place because it's just so good and I'm around the area. So I'm definitely going to get the pizza I haven't had it in a hot minute. So I'm going to do that, get gas and change because I'm still in my gray scrubs. I completely forgot that stadium's at least like the UCF one. I can't bring like a bag, so no camera. I'm going to vlog on this trusty little thing. I mean, it's just a football game. So I probably will just like show my friends and then like, you know, show a little bit of the game because my battery is already starting to like go and I had to turn on my car because I was getting way too hot. So I just need to freshen up and air out the pits. Okay, she's super red because of this flight. But here's Tyra. We're at our favorite place, Donato's, the best pizza ever from Ohio. Want to promo this place because it's amazing. And yeah, now I need to charge my phone. All right, so I got the cheese pizza. Look at her. And I'm going to like devour this. So hungry. What'd you get, Tyra? I got a cast. So yeah, we're gonna change face. It's just going to be as hot as the pizza. I let her try a piece of the pizza. Oh, good. So I'm in public because Tyra and I are going to tailgate show the wine. Well, it's Champagne and then orange juice. So basically I was saying, the last time I was at a UCF game, I was telling my friend and her husband that like I wanted to be a surge tech and I was waiting to hear back from the program. And now like, come full circle. Like I'm going to a UCF game and I'm about to graduate the program I was talking about. It's just so wild. What? Yeah, yeah. All right, so Kyra and I are tailgating in the car because honestly, we didn't want to like walk down with all these cups. It's just going to be weird. So we have the stuff we got and we're going to cheers it soon. Yes? Yes. I forced Kyra to turn down the music because of copyright. And we're going to pop open the bubbly and her jacket smells good times. You know what? We're going to look back on this and laugh. I hope so. Do you know that? All right. Thank you. Cheers. So today was super fun. I had a lot more fun than I expected to have. I think UCF is obviously going to win the game. That's just my opinion, but we were like 35 to three. So yeah, it was really cool. And I'm glad like my makeup kind of like stayed on for the entire day. I'm glad I went even though I'm like not the biggest football fan or like really know anything about football. I will say it was nice getting back on the campus at UCF because it's been so long. It was cool to see the campus again and like kind of relive some memories. I made it home around 11, which is nice. I have to get up at like six, but it's fine. It's not that big of a deal because it's not like clinical. It's just class. If it was clinicals, I'd be like, I would have to leave earlier because I don't know, clinicals just like freaking drain me. I don't know what it is, but they always tire me out so fast. So that's why I like, I don't know. I literally couldn't do anything during clinicals, at least like during the week. Since I'm not at clinicals anymore, I feel like I have a little bit more like freedom when it comes to like doing extracurriculars, like going to a football game. Anyway, I think that's going to be about it for this video. I hope you guys enjoyed it. Make sure to give it a thumbs up. Don't forget to subscribe to my channel and I'll see you guys in my next video. Bye.


Good morning everyone, Basel Chapman here at this 10 o'clock Tiger Financial News Network. Market update on what we this is the Thursday the 17th of March. The Dow earlier on was down a little bit and normally what we would see after massive move in the last hours we saw yesterday about a 20% give back of some of the last hour acceleration we've we had maybe a little more than that and now we're at the point where the Dow is actually up four points at 34,064. It is testing this inside track repellent zone right here at 34,068. If it's able to get to the 34,150 area and hold that's going to be not only very impressive but what you've done is given a chance for this pink nine-period moving average to cross positive and go green and that would be a very important moment. We're looking at the S&P right now the S&P has turned around it's also up it's up six at 43,63 up above yesterday's high which is a spectacular day we don't have to go through that again that was really a big move but the 43,86 200 period exponential moving average area is going to be resistance and that also takes you into the Chapman Wave inside track repellent zone so how it handles after 1.32 o'clock this often is going to be important we'll talk about that in a moment but we'll look first look at the cues up 39 cents and 3,4067 need to get to the 34,346 area preferably by tomorrow and that'll be important looking at the IWM the Russell 2000 that is a nice move up 97 cents and 2,287 this one's starting to improve a lot it's got the lowercase h goes to a lowcase m we'll see if it's able to get to the 206 by Friday we're looking at gold gold overnight at a really strong move it's held that it's up 34,1943 so that makes the whole area of 1980 and 1990 very good support and we're getting close to the resistance area at about 1952 we're looking at the dollars quickly look at the dollar dollars pulling back just a little bit but crude oil is holding extremely well it's up to day up $6 at 101 and we've got the TLT down again heels continue to move higher I'll be back with the traditions out and don't forget to check out more people than you said