Real Conditionals Conditional Sentences express a choice and the possible consequences of that choice. There are three types of conditional sentences. Real, Unreal, and Unreal Past. The first type is the easiest to learn. It involves a present choice and a future consequence. If you drive north for three miles, you will get to Columbus. If he doesn't exercise, Fred will gain weight. If you purchase a raffle ticket, you might win a car. Real conditional sentences contain two parts, the if clause and the result clause. The if clause indicates the choice and is expressed in present tense. It indicates a choice and can be either positive or negative. If statements can also imply the opposite choice and result. If you study hard, you will pass the test. Choice and possible result. If you don't study hard, you could fail. Implied opposite choice and result. The result clause indicates the consequence or possible consequence and is expressed in future tense or with modals can, could, or might. If clause.  Result clause. You will grow stronger. If clause.  Result clause. I can spend more time with the kids. If clause. If Troy moves to Hollywood. Result clause. He might become a movie star.


Good afternoon. I'm Emily Daley, Head of Assessment and User Experience at Duke University Libraries, and I'm pleased to be presenting with my colleague Joyce Chapman, Assessment Analyst and Consultant. Joyce and I are going to share information about the in-depth mixed method study of Black undergraduate and graduate students that our department led last fall. During this recording, please post your questions or observations in the chat and I'll answer them in a live Q&A at the end of our session. First, I want to mention the bitly of our full report. We won't be able to go over all aspects of the study or findings today, so I encourage you to look at the full report if you'd like more information. Now let's talk about how this study came about. In 2016, we noticed differences in the responses from particular user groups to survey questions. We were limited by what we could learn from the survey data, but we could see that something was going on and we wanted to know more about how these particular user groups experienced the library. So we developed a series of in-depth user studies beginning with first generation college students, then moving to Black students, and then to international students, a study we have just begun planning. For each of these populations, we reviewed the literature, talked to campus stakeholders, developed research questions, and then mapped out our methodology. An important aspect of these studies is that we don't limit our research questions to the library. In order to develop a complete and accurate rendering of our students' experiences on campus, we must understand the greater context for their library experience. Clearly, of course, the library comes up naturally during our discussion groups, but we always have a library-specific question or two in our script just in case it doesn't come up on its own. In order to answer our research questions, we used three methods and sources of data, the Duke Library's biennial student survey, discussion groups, and the photo voice methodology, which I'll talk more about in just a moment. We led a total of five discussion groups with 32 undergraduate and graduate students during the fall 2019 semester. We also analyzed Black and non-Black students' responses to our biennial student satisfaction survey, which we developed in-house and conducted in January and February of this year. Photo voice is a community-based participatory research method where participants take photos in response to prompts. We then spend our time in the follow-up session viewing participants' photos and discussing their responses to the prompts. Here are a few of the prompts that we gave to photo voice participants. Participants took pictures and captions, shared those in advance of the discussions, and then those became the basis for our sessions. You'll see here that our prompts asked students to think about both their campus experiences and their library experiences. Some of the images students submitted were directly related to their experience as Black students at Duke, like this image of the program of the 2019 Black convocation on the right side of the screen. Other images, like the one on the left, were not specific to Black students, like this photo of furniture and a student space on campus. Our non-photo voice discussion groups included similar questions to those we used as the ones that we used in our photo voice. Again, we included a mix of library-specific questions and questions about students' experiences on campus. You'll find all the photo voice prompts and our full discussion group script in the appendix of our report. We gave a lot of thought to who would moderate the five photo voice and discussion groups. Duke library staff trained in moderating qualitative discussion groups are all white. Additionally, it detracts from an unbiased discussion to have library staff in the room. We knew students' conversations would not be as rich or candid if moderators or note-takers were white people or library staff, so we explored ways around this. At the recommendation of a faculty member at a nearby institution, we hired two Black graduate students to moderate the discussion groups, and we asked our AUX graduate student assistant, who is herself a person of color, to take notes. Recruiting Black graduate students to moderate was extremely successful. In fact, one of our graduate student moderators became so interested in this study that she offered to be involved even after the discussion sessions ended. We were able to hire Pamela Zabala to analyze the photo voice and discussion group transcripts. Pamela developed a report of findings that is linked here and in our full report. The research team then conducted its own analysis of the findings and used Pamela's report to supplement our work. For the team's analysis, we used a method called affinity mapping, where we closely read and tagged interesting quotes and identified themes in the transcripts. Many of these overarching themes appear as sections in our report. We also developed recommendations for improvement based on our affinity mapping and on Pamela's analysis. Let's talk briefly about recruitment. We worked with Duke's institutional research office to recruit participants. We also posted information about our study to several student group listservs. And because we hope to talk with both US students and international Black students, we worked with Duke's international student support office to recruit participants. And yes, we provided incentives, $10 gift cards for discussion group participants and $20 or $35 gift cards for photo voice participants. We also provided snacks and gave each participant a handwritten note thanking them for their time. We were also able to pay our two graduate student moderators at the same hourly rate that we pay the graduate student assistant in our department. And with that, I will hand things over to Joyce to discuss findings. Okay, I'm going to whiz through some high level findings and we hope you will go and read our full report to learn more. Our participants praised a lot of services programs and spaces at Duke University that contribute to a welcoming environment. But at the same time, participants agreed that Duke provides a less inclusive space for Black students than it does for white students.  Duke's past continues to shape the culture of our campus. Black students contend with campus culture, curricula and physical spaces that still largely reflect and center white experiences, history and values. Participants reported systemic bias and instructors behavior and the scholarship assigned and discussed in class. They experienced microaggressions in almost every area of life at Duke. And these instances of bias reinforce the idea that their belonging at Duke is qualified. So, participants had a positive view of the libraries. Black students largely view the libraries as inclusive spaces in the sense that they meet their diverse learning needs. When asked whether they feel safe, welcome and supported at the libraries, students listed numerous services and resources that they value. They reported some negative interactions with library staff and peers in the libraries. They also perceive aspects of library spaces to be unwelcoming, specifically because they center white history. They have recommended improvements, especially for our physical spaces and shared valuable insights that can help library staff, staff understand what it means to be black at Duke and ways that the libraries can make our spaces more welcoming. Students reported a general feeling that both Duke and Duke libraries will not actively hostile or racist or complicit in their silence. Participants are not convinced that Duke cares about racist incidents and believe that Duke and Duke libraries will not take meaningful action if they complain about a report instances of prejudice or microaggression. So let's dive deeper into some of the issues and I'll loosely follow the structure of our report. Beginning with, what does it mean to be black at Duke. As indicated by this quote, the validity of black students presence at Duke is constantly challenged. Many discussed how demoralizing it is when white people make the frequent assumption that they were admitted to Duke as part of an athletic program, or tell them that they were accepted as part of a racial quota, instead of on the same basis that Duke merits as other students. At the same time, black students are often unable to fade into a crowd and are forced to be perennially conscious of their race identity in a way that white students at Duke are not. Despite the time that has passed and the number of students of color who have been admitted. Duke remains a historically white space and this history continues to permeate and shape the culture of the campus. In our study were very aware of this history.  Undergraduate participants perceive that Duke's curriculum does not prioritize ensuring that all students will be exposed to diverse points of view, and talked about how interdisciplinary courses tend to be racially sacred. Students believe that if campus spaces in the libraries want to make minorities feel welcome, they need more visible signs or statements about inclusion and diversity, particularly because the default Duke spaces is overwhelmingly visible, visible representations of white people and western art and architecture. Students from survey respondents echoed those themes asking the libraries to post more visuals about being a discrimination free zone in support for various communities. In our survey, the 2020 library survey asked students whether they feel safe from discrimination, harassment and emotional and physical harm at Duke libraries as well as at Duke University in general. There were stark differences by race among the 2600 students who responded. Students do not feel as safe as white students either on campus or in the libraries. Only 34% of black students strongly agree that they feel safe at Duke University versus 71% of white students. A quarter of all black students do not feel safe to some extent versus only 7% of white students at Duke University. More black and white students feel safe in the libraries than on campus in general but fewer black students strongly agree with that statement than white students for the libraries as well. There are many examples of systemic injustice perpetuated through the curriculum. Academics at Duke are often a space where black students do not see themselves highly represented or valued. From the arts and sciences to statistics and economics, our participants reported systemic bias in a variety of areas ranging from instructors behavior to the scholarship assigned in class. In the words of one student reflected back on their experience at Duke, we were absent the scholarship and when it was there it was highly problematized. In addition to many instances of racial biases and scholarship assigned, participants discussed the behavior of faculty and instructors as it contributes to systemic injustice in the classroom. Several students provided examples such as the one in this quote. This example reflects on the damage done by professors who will be attitudes of Duke's international student population towards African Americans. Faculty are both mentors and authority figures who represent the face of Duke to their students. Their silence can speak as loudly as their words and shaping students' perceptions of the extent to which Duke, as well as academic fields more broadly, valued them. Next I'll talk about physical spaces. They communicate priorities, expectations and cultural values both implicitly and explicitly. They do this via architecture, materials in the spaces such as books, art, signs and decorations, and social groupings within a space. Students across discussion groups listed example after example of spaces at Duke, including many of our libraries, where art and architecture cause physical spaces to feel exclusionary. The photo voice submission you see here is a room in our main library with many portraits of white men that stare down from the walls. In this quote, a student reflects a general feeling among participants that if Duke wanted to do something about this, it could, but it chooses not to, which speaks just as loudly as the presence of the art and portraits in the first place. Another aspect of library spaces that students discussed was the visibility of collections and public spaces, specifically a lack of balance in the cultural relevance of materials and areas that students must constantly walk by in order to reach bathrooms or library services, or that are located in study areas. Quite a few of our discussion groups touched on a related topic, which is the lack of a library or a room or study area within our libraries dedicated to black studies. And indicating a library space to black scholarship and culture was a primary recommendation from students coming out of this study. We found that affinity spaces are critical and signal what Duke values. Additionally, person to person interactions can be critical contributors to whether black students at Duke feel welcome and supportive. We've discussed many positive interactions on campus and in the libraries with library service desk staff, librarians assisting with research, friendly security guards, housekeeping staff, academic program office staff and more. Students listed many spaces on campus that make them feel welcome and valued at Duke, but one space that is particularly welcoming and supportive is the Mary Lou Williams Center for black culture. There are a lot of voice submissions for library security guard stand out as a group that can help students feel safe and supported with just a friendly word or wave, though we found the security guards can also make black students feel and welcome. Several students made comments such as the one you see here. Participants discussed many positive interactions with library staff exemplified by the comment here. Participants value friendliness and good customer service as well as subject expertise discussions highlighted the fact that there is a delicate balance between our roles as teachers and a service providers. While many library staff are trained to teach research skills, students often approach the service desk expecting staff to help them complete their tasks as quickly and efficiently as possible. Efforts to teach them how to complete this action by themselves when they have not asked for instruction instead of just assisting them can be interpreted as patronizing or as a review for having bothered the staff or as poor customer service. And that brings us to recommendations for these in depth user studies implementing recommendations for improvement is equally important if not more important than in conducting the study and reporting what we learn. The research team developed recommendations that fall into four general categories spaces services library staff and marketing opportunities. Here are a few examples of the recommendations that we feel will have a particularly high impact on black students at Duke. Here are a few more recommendations from our report to give you a sense of the variety of suggestions. I'll move through these quickly in the interest of time but they are all listed in the full report. So what have we done since we completed this study. We shared the report first with library staff at campus partners in April. After George Floyd's death we were motivated to post our report to Duke's institutional repository and promote the study through national listservs and to share it again with campus partners. Those posts generated a lot of interest and led to numerous discussions with Duke library staff and campus stakeholders. Finally we formed an implementation team of six library staff to move forward the 40 recommendations that came out of this report and the follow up discussions. In closing, we are holding ourselves accountable to do this important work, and we have assured our colleagues that we will keep them well informed of the changes and improvements we're able to make as a result of what we learned through this study. We owe that to ourselves, our colleagues and especially to the 32 black undergraduate and graduate student participants and black student survey respondents who entrusted us with their stories about what it means to be black at a highly selective predominantly white institution in the south. And with that we'll go ahead and stop the recording and take any questions or comments you might have about the study.


Okay, I think we'll get started. So thanks everyone for coming. I know it's late in the day. It's been a long two or three days. And my name's Ian Houston. I'm going to talk a little bit today about operationalizing data science on CF. And what I mean by that is trying to move data science models from, you know, some data scientists' laptop, where it works there, and try and get them in production. And I'm not coming with like all the answers now. And, you know, some of these things, things we've tried. And some questions I have about, you know, what other people have tried. And I'm trying to figure out, start a conversation about what we should do in the future as well. Get this to actually work. Okay. Chrome not working very well for this. Okay, there we go. And so who am I? So I'm a data scientist working at pivotal labs, working with clients, doing predictive analytics and machine learning type projects. But I'm also a Cloud Foundry user. I've been using CF for, you know, two or three years now. And I got so far as I've actually written a build pack. So I've got like quite far into the, you know, process of how do you stage an app and how do you make your applications run on Cloud Foundry. So hopefully I've been able to bring a little bit of that to bear today in this talk. And what we're really going to be talking about is how do we get those sort of machine learning and predictive analytics smarts into applications. And the reason this is important is because it's kind of becoming the expectation that, you know, everyone wants to see, as Mark Benioff said recently, everyone wants to see systems and applications that are smarter, that have more predictive capabilities. And they want things scored and, you know, to see the next best opportunity, next best offer. And basically they want their applications to get smarter. You know, at the moment we might call these smart apps, these sort of things that have this extra machine learning inside them. But very soon we're probably just going to call them apps. It's just going to be expected by consumers and business users that their applications have these kind of capabilities. So what's the problem at the moment? Well, really, if your machine learning model is to provide any business value, it has to go into production. A lot of data science up to now has been in the sort of exploratory mode.  But that if that only lives, ever lives in a presentation or lives in a report, then it's not actually providing real business value.  If their output, if their work is not really making you into production, then you're not getting the business value out of that. So slide decks don't count as production as well. So that's, you know, a few people sort of think that, but it's not quite true. And a lot of people have this problem, you know, sure, data scientists have the problem. We want to see impact for the work we're doing.  Developers are starting to feel the need to include these kind of capabilities into the applications. We're seeing the sort of growth of an ecosystem. Google earlier talked about some of their machine learning APIs. So making it easier for developers to add this capability. And then you've got like CIOs, chief data officers. They've invested quite heavily in sort of big data space. And now they want to see some return on that. And, you know, the only way that the return is really going to exist is if these models get put into production, if they're actually part of the systems that are working. You know, and Pivotal Labs, we see quite a few of our clients are starting out on their journey on this path and want to implement their first machine learning models. I want to see the results they can get. So this is very important for them that, you know, what we're building with them isn't just this one-off experiment that, you know, we don't, we do it, we stop and then move on. This is something that actually lives and can contribute. So to do all of that, you need a few things. The first thing you need is you need a way to actually run your models. So, you know, this kind of day one problem. What's the first thing you need to do? Well, we need to run the predictive model. It may, you know, data scientists put a lot of effort into building it, but now it has to go and run for real. So there's a lot of things that go into that. I'm not going to talk about each of these in turn, but the kind of part of that process is loading and transforming the data and training the model. In machine learning, we normally have these training phases and scoring phases. So actually training the model. That's the actual learning part. When we actually connect it to real incoming data, and that, you know, sounds simple, but in a complex system, that could be one of the hardest parts, cleaning and transforming that data so it's in the right form to go into the predictive model. Actually, applying that model, which again, maybe sounds easy. You just, you know, got some, basically some mathematical formula, you just apply it and run the new data through it.  And then finally, you take some action. So, you know, that can take multiple forms.  You show someone the recommended video that they should watch. Or you do something else, like you increase their quality of service.  So, it's kind of an absence of action. So, these are all the things you need to do, even just to have anything in production. And I'm going to run through a few really high level kind of, maybe architecture is too grand a word for the next few slides, but a few high level ideas about how to do this on Cloud Foundry. And these kind of follow this typical way that machine learning and predictive analytics models get put into production. So, you know, the first one is kind of a scoring as a service. So, in this case, you're building your model somewhere else, maybe on some kind of big data systems, some kind of distributed cluster type thing, using a lot of data over there. The output of that is something you store, you know, on a data service, Redis or something like that in Cloud Foundry. And then you're ingesting the data in CF, you're applying a model inside an application, you know, maybe these are separate applications, an ingest one and an apply or score application.  So, this is kind of like the simplest thing you can do. You could argue that all the hard work is being done in this kind of gray box in the bottom left, which is actually doing the predictive part. And then you're just using the results of that on Cloud Foundry. Yesterday I was doing a talk about build packs, and I showed a demo, which was like a sentiment analysis model that had been pre-built somewhere else. And then we pushed it to CF and we were able to send it, you know, new text and see what the sentiment positive or negative was for that text. So, that's kind of an example of this first case. The next case is something much more like, well, it includes CF in the building part. So, this is something I kind of called CF powered learning. You need to come up with a better name, maybe. Here you're ingesting all the data through Cloud Foundry. So, using something maybe like Spring Cloud Dataflow or something to transform it and, you know, get different streams. And then you're building the model. Maybe it's on a Cloud Foundry application instance. Maybe it's a CF app controlling something else, like sending, command and control to your other big data system. And then there's still this batch update of the model that's stored somewhere and is applied by another application instance. So, that batch update normally happens at relatively low frequency compared to the new data that's coming in. But you have to, you know, start thinking about, you know, how do you do this update? How does your app know that there's a new version of the model to download or use? And then how, you know, how do you keep all this in sync? And you can imagine, you know, if you've started looking at microservices architecture, you can squint and imagine like different services being composed of the different parts here. I'm going to run through a video later of an example of a predictive model that uses this kind of architecture where we have three different microservices. So, one is the training application, one is the scoring application, and one is kind of the front end, or how do we show those results? One more sort of high-level way of doing this is to not have the batch building of the model. So, you have much more of it, it's called online or in-stream learning. So, as the data is coming in, you're updating the model as you go. And so, this poses a lot of challenges that conceptually might look simple on the screen, but it's actually probably the hardest one to do from an algorithmic point of view, because you don't get, the idea behind this is you don't get to see the data, you don't get to keep it, you just see it as it goes past you once. So, you'd have to do like a single-pass algorithm, so you don't get to go back and look through the whole data set again. So, this is, you know, Google has been doing a lot of work in this area as well, and you know, there's a few blog posts about this where they talk about, you have to be really careful about the timing of the ingest of the data, because if you don't get it in the right order, you might have to reorder it yourself in order to apply your algorithm, which is expecting things in a particular maybe date-time kind of order. Okay, so how can we do this? Well, you know, you can build it yourself. I've been using things like Spring Cloud Dataflow and Spring Boot applications to actually build these kind of infrastructures for some clients. And you obviously make use of marketplace data services. Today, we had a really good talk about, again, about Google's services in this area being available now through a service broker. And, you know, as a data scientist, I'm often using Python and building these Python models. So, being able to deploy those to Cloud Foundry is really important as well. And, you know, the official Python build pack now has this ability to deploy kind of the PyData stack, which is kind of the standard packages for Python, which it wasn't able to do previously. There's also some initial offerings in this kind of area from, you know, GE Predix, IBM's BlueMix has some of this stuff. You know, Alpine Dataminer have started looking at this. They've, you know, kind of got like a published to CF button that uses PMML, which is a kind of open interoperable data model format. PMML has its issues, and they're actually moving to a new format called PAF, predictive analytic framework or something. So, there's a few different options, probably at this point. And, you know, no one standardizes in any particular one. And building yourself for your particular use case is probably, sort of, if you weigh it up, is probably the best bet at the moment. Okay, so I'm going to try and look at this video. And so this is an application that was built by Pivotal Labs, some of our team. And it's basically an accelerometer in your phone taking sensor readings from your phone and building a model of what activity you are doing at any moment. And you can see here our lovely volunteer is going to, first of all, link his phone into the application. There's actually just one front-end app that's doing what you see on the mobile and browser, desktop browser. And it's going to use, as I said, the accelerometer in the phone. And this is an example of that CF-powered learning model that I showed earlier. So here we're training the model. So we're going to give it some data. It's going to store that data, and it's going to build a model from that data. Here he's walking in place, and you can see the kind of, that timer is just telling you how much data you have to generate before we're going to be able to recognize that in the future. He's very, very happy about this. So this is a fairly simple example in terms of the activities he's doing, but you can imagine something like a fitness tracker and using these kind of predictive models to determine, oh, are you taking 10,000 steps a day, or are you actually sitting in a cab and pretending to take 10,000 steps? So we're going to side-to-side. He's basically doing a little dance, and we're going to, it's called the Pivotal Moves app, but we could have called Pivotal Dance Moves app maybe. And what we'll get to is the point where we're able to hit the build the model button, and what's going to happen there is the front-end app is going to hand off to that training app that's sitting in the background that's actually going to do the predictive analytics. It's going to be using a random forest ensemble of decision trees to take in the accelerometer input and then predict one of these three outputs. So it's a classification problem here. So he's trained, got some training data now. We can build the model, goes off, builds it, comes back. And then we can now go to the scoring phase. So this is kind of, as new data comes in now, what happens? And you can see, as he's standing there, you can see the big caption at the top says walking in place. And you can see the accelerometer details. He switches to moving side by side. And as the scoring phase happens, it changes to side by side there. So it's determining as he's going what the activities are. So that's kind of a really good example of that kind of CF-driven learning because all of the whole thing lived there on Cloud Foundry. So that's great. We've got our model. It's working. It's in production. It's taking in real data. What do we need to do next? Well, day two, you want to update the model. You've figured out something else. You want to change the parameters. You want to use a different type of model. And you suddenly start getting a few problems. You now need to know which predictions were made with the old model and the new model. So if you're storing these for some kind of auditability later, you need to know, did you predict that this person could have a mortgage or could not? Which version of the model are you using to do that when the auditor comes along to ask you questions? And do you need to be able to continue serving the old predictions? Is it important that that old version of the model is still available to maybe consumers who, in whatever form, have built against that old version? And maybe you're building this for customers and some of them want to move to the new version. Some want to move to the old version. Kind of like Spotify bringing in a new prediction algorithm for what songs you're going to like. Maybe some people want to stay on the old one. There's a whole usual problem of dependency management. So that needs to be looked at. And also data schema changes. So is the data that's coming in different to previously? Has it changed in the underlying system? Is the data you're storing changed? Are you doing some different transformation? And can you replay the stream if necessary? So if someone comes back to you and says, okay, why did we decide that this person liked this song? This person could have this mortgage. This person's hard transaction was denied. Can we replay it so that we understand what decisions were made? Some predictive models have more explainability than others. So sometimes you can get that sort of out of the predictive model. But you're not going to need to have the inputs and you're going to need to have them all versioned. So to me, this kind of starts looking like, okay, we can't just do as was done in that demo store model as an object, a serialized object in some data store, maybe in Redis or something, then pull it out and use it again. Maybe we need something more. Maybe we need something like a model service. Something that when you ask for the results, so when you ask for the model, it gives you the right one for your use case. Maybe it's versioned. Maybe it's parsing data with different schema. It kind of has that ability to deal with legacy data. Maybe it's, as I said, serving the appropriate version depending on who's consuming it. And it's possibly storing the underlying data in some more general form or an unstructured form so that you're able to retrain and reproduce the results later on. I don't think there's anything like this quite yet on Cloud Foundry. But there's a few other things out there. It's kind of going in this direction. There's a project called Palladium from the Auto Group that did something sort of like this. You could send it and you'd get a version of your model. And Prediction I.O., which I think was just bought by Salesforce, but also the technology has just been donated to the Apache Foundation. So it's Apache Prediction I.O. incubating. They have something like this, but it's a whole system itself. So they have a server. I don't quite know what the underlying technology is, but you can implement sort of replaceable algorithms as little extensions effectively. So kind of, I think some people have looked in the past at moving that onto Cloud Foundry, like would it work in that kind of, would we be able to deploy it as a service or an application on CF? I think it didn't go very far with that one. So maybe there's a need for someone to build something more general that could be reusable in different scenarios here. So that's kind of like, you know, my hope for this talk maybe is to start a conversation about what is needed here. I have a few ideas myself, but I'd like to hear from other people who kind of need this functionality. How do we make it? And kind of more generally, what's next for doing data science on Cloud Foundry? I think we're still, you know, very much at the beginning of this journey, even just given by the number of talks at this summit about sort of data science topics. There's a lot now about IoT, and you know, suddenly this year, there's a lot more sort of ingesting of IoT data. And you know, immediately the sort of thing that screams that you then is, okay, we need to do something with all this baggage tag data, for example, that we heard about earlier. So to do that, we kind of need a few things and probably need more data services and maybe more data computation services. And so I know there's a few different projects to incorporate Spark into kind of a CF workflow and some other distributed computation systems. We need more examples of how this works, some demos, I'm trying to collect some of those and sort of make them available to the community or like just list them. And we need some examples of successful projects whereas this worked sort of hopefully next time at the CF summit, there'll be someone doing a talk about a project where they've used Cloud Foundry with machine learning and useful for the community to learn from that. And I think we also need to start building some of these building blocks so that, you know, they're a little bit easier to get started on this than not everyone's reinventing the wheel every time that they want to build something like this and have, you know, have this capability of having this model service. And so that's really all I've got. I was, yeah, this DS on CF.com thing is kind of this list of examples that I found of day science. If anyone has any other examples, please let me know either in the website or via Twitter. And but apart from that, just thank you very much for being here. Is there any one of any questions? No, I don't think there's any limit on the kind of learning you can do.  So as long as you can get the data in and do something to it and restore that result, do some training. Like, I think the limitations are there's some technical limitations. So if you want to do, you know, a very large batch training over terabytes of data, doing that on a single application instance in CF is probably not the way to do that. And maybe in the future, you know, with things like the, and what's the name of the future where you get a, you can have different zones of computational power isolation segments. So with the isolation segments, you might be able to hive off a part that's like very high powered machines for your data science work and keep your, you know, relatively smaller machines for your just your web apps. And so there's a few different things. We were talking earlier about tasks, I think as well being a really good way of like running a one off like training. And so when these come into CF proper, it's going to be interesting to see how they can be used in the data science context. Any other questions? Okay, thanks everyone.


Now, everyone watching this video right now probably has a phone in their pocket. And I find myself always whipping it out to record videos for capturing quick moments. But I've seen some amazing cinematic videos shot with just their phone. Now watching them has inspired me to level up my own mobile video making and show you how I did it. So, I challenged myself to shoot and edit a video with nothing but the phone that I'm holding right here. No stabilizers, no paid apps, just this electric powered PopTart that I use for everything except make phone calls with. By the way, hi, it's Herman here with Artlist. And for a long time, I would use the excuse of not being able to make great videos because I couldn't afford expensive equipment. But technology has come such a long way to give us new tools. And one of which is a Swiss army knife in the palm of our hands. I fix things with it. I cut butter with it. But I have never shot and edited a video entirely on my phone before. The rise of social media has also created demand for video content like a fish needs water and platforms like TikTok, Instagram Reels and YouTube shorts is the new television of our generation. So instead of murdering our wallet with this and that, why not create videos with the same device people are watching it with? Today, I'll be sharing my journey as if I had no camera, no computer and no money. But I want to create a professional looking vertical video for Instagram. Now, this could very well happen if I were to ever lose my luggage on a flight or I get my car broken into before I shoot. Real stories, by the way, but hasn't happened to me personally yet. Knock on wood. This isn't wood. No matter what phone that you're using or what situation you're in, I'm going to take you through my entire process and share the things I believe are the most important to pay attention to. So make sure to stick around and watch the final edit. But another reason to stick around is because we have an awesome giveaway at the end of the video that you don't want to miss. So right now we're at the rooftop of Vancouver Central Library. It's a very beautiful location with this amazing view. And this is where we're going to be doing our first shot. We have a lovely Tom over here who will be our talent. He's going to be the talented cardist doing the cardistry. And he's basically going to make this video kickass while I'm shooting everything in my phone. And hopefully the phone video will complement the badassness of the cardistry. Don't know if badassness is a good word, but that was pretty cool. And behind the scenes right now, we have Jansen. And I'm going to see if I can do a nice little video of him. All right, we're going to shoot that with our phone. And see how it turns out. Now, the first thing I looked for was to have manual control over my phone camera. Now, if the phone is a bowl, then I've got to be the matador. Because if my shot suddenly goes out of focus or gets brighter all of a sudden, my client would just ask their grandma to shoot a video instead. Unless this is their grandma. The settings I want to bend to my will are the resolution, the frame rate, shutter speed, ISO, focus and white balance. Now, I know this is starting to look like a grocery list, but if you already have some shooting experience, you'll know what each of these settings do and how they affect one another. But the vanilla settings I recommend for most scenarios is shooting at least 1080p at 24 frames per second with a one over 50 shutter speed.  Now, I recommend shooting in at least 1080p because HD has been the standard for a while and anything below that can start looking blurry or pixelated. 24 frames per second has been the standard in cinema for various reasons. But the main reason I recommend it for mobile video is to create some weight to your video. If you film in 60 frames per second, for example, and play it in real time, any motion becomes very fluid, which isn't what we're used to seeing when we watch movies. Having your shutter speed at one over 50 is to match your frame rate of 24 frames per second so that you can capture motion blur in a way that copies what we see with our eyes in real life. The general rule is to double the number of your frame rate to get your shutter speed. Before you start judging me for not knowing basic math, one over 48 isn't as common as one over 50, which is the closest to doubling 24 frames per second. This is the most math I've done out of high school. Now, depending on your phone, you may already have manual control over your camera settings. Now, I'm using the Samsung S20 FE, which isn't the latest phone available, but it does have a built-in pro video mode. If your phone doesn't have something like that, there are a lot of camera apps available that give you manual camera control. Because I don't want to spend a penny in this case, I went with a free camera app called Pro Cam X Lite, because going through the app store for free apps can feel like walking down a sketchy alley. But this app felt like a brightly lit up 7-Eleven that was safe to walk in. Again, with the weird analogies. There are always limitations to free camera apps, but I'm only using them for shots that my default camera can't accomplish. Now, I encourage you to try the most popular ones for your device and see which ones check all the boxes for you. All right, we're just going to switch to pro video mode and we're going to see what it looks like on camera. Can I have you take your steps to your right? Just so you're aligned to the pillar in the back. Oh, that's great. All right, 5600. We're going to make sure the focus is on him. And three, two, one, action. Although this isn't specific to filming on your phone, it is crucial to think about your camera angles. One of the mistakes I made at first was always filming at eye level, because it was my default pose for a zombie away on my phone. Different camera angles convey different emotions, and using a variety of them make your videos more visually interesting. I think the biggest advantage to filming on a phone is how easy it is to change your angles on the fly. So instead of lugging around a camera on a tripod, you are as free as a bird to move your phone wherever you like. Low angles, tight spaces, thrown into the air. This is true freedom to create. Speaking of freedom, it's also easy to add camera movement when you're shooting with your phone. Movements like pushing in on your subject, orbiting around them, or going from side to side, helps create more dynamic shots. Oh, this is way more interesting. I shouldn't push in. I should be doing this. Just parallax like this. Yeah. When I was pushing in like this, there was some parallax, but it just wasn't enough, I think. And it's also hard to get steady. But if I just kind of move and shift my body weight back a little bit, it's a lot easier. Learning something. I'm learning as I go. Remember that you're not taking photos, so make sure there's something moving in the scene if it's not going to be your subject.  But remember, we're using nothing but my phone, so I am the stabilizer. But how would I get buttery smooth camera movement when I can barely walk without tripping? The answer for me was Brandon Lee. Ivan following his work for a while and what he does with stabilizers is so magical, I'm convinced it's witchcraft. But thankfully, this magician reveals his secrets because he made a video that teaches how to get smooth handheld movements when shooting with just your phone. I encourage you to watch what he covers, but the broad strokes of what I took away was having one hand holding the phone in a stable manner, having your other hand as a support, bending your knees and walking like a ninja from your favorite anime, but at a constant speed. If your phone camera has the option to shoot slow motion, you can sprinkle in some slow-mo shots to emphasize certain moments and add variety to your video. Notice that I said sprinkle in, because a cake wouldn't be a cake if it was entirely made of sprinkles, just diabetes on a plate. The same thing goes for your video, if everything is filmed with speed ramps or slow motion. Unless that's the theme of your video, I believe no shots stand out if a certain technique is overused. But when you capture fast movement in slow motion, it becomes a surreal experience that we're not used to seeing in real life. I'll usually check what the highest FPS on my phone can shoot in and then go with that, but make sure to film slow-mo in a well-lit environment, preferably outdoors, so that you don't get flickering lights that look like you're filming everything at a rave. One of my favorite features that phones have started incorporating recently is including a wide lens. Shooting on a wide lens can give your scene a larger-than-life atmosphere and it feels more dynamic when you also add camera movement. Just like sprinkling in slow motion shots, I also recommend not abusing the wide lens just because everything may look cooler on it. I try to only use it when I want to show off more of the environment or capture a unique perspective. After filming everything on my phone, it was time to get into my favorite part, putting it together in the edit. At least that's how I feel when I'm editing on a computer, but on a phone it's a little bit tricky when I tend to fat finger everything and rely on auto-correct to convince people I'm not a toddler. Just like camera apps, there are a lot of free editing apps available. Capcut, InShot, and VM Video Editor are a few popular ones, but again, find the one that feels right when you're walking down that sketchy back alley. Now, I went with Premiere Rush just so I had some sense of familiarity in the foreign world of mobile editing. As expected, it was a bit tricky having to tap things with my finger instead of clicking. I also didn't have access to tools that I'm used to relying on, like keyframing, blending modes, or scopes to color grade with. Although these sound like drawbacks, I did enjoy the simplicity of just editing with essential tools. It's a great way to surrender perfection and rely on the content I shot. For videos that upload straight to social media, I can see this being an efficient workflow for the job. Now, no edit is complete without audio, and music is the best way to compliment your visuals while setting the overall mood. Finding the perfect cinematic song can be a huge challenge, especially if it's royalty-free music options that are free. But just like the free coffee that they give out at office waiting rooms, something about the taste is just always a bit off. Music is personally the last thing I will compromise on, so what I did was browse Artlist on my phone and downloaded the track I wanted to edit with. It was surprisingly really easy considering how I usually hate using mobile websites, and I just don't download files or edit with my phone. Now, I know I'm not supposed to spend any money in this case, but just like how I already have my phone, I already have a subscription to Artlist, so it's up to you to find the best option for yourself. Well, those are all the steps I went through when learning how to create a video entirely on my phone. So, let's check out the final video I made after incorporating everything that I shared. Being a creator, I've always been stubborn about a few things, like learning to edit on my phone or making vertical videos. It just felt like I wasn't staying true to my craft and adapting to these new trends was like learning Zoomer Slangs, like Bussin'. But this wasn't a Bussin' mentality. That's not how I use the word, is it? But change is inevitable, and as I mentioned earlier, tools are improving to make our lives easier. That's why I'm always willing to challenge myself to step out of my comfort zone and at least try these new tools that are available to us. Just like trying new food, you'll never know how it tastes before you put it in your mouth. It's like when I tried eating snake once. That example, because I'll never eat it again. The hardest part for me has always been finding out what to do. But with the steps that I shared with you today, I encourage you to just go out and try creating a video on your phone if it's something that you've always thought about doing, but you just weren't sure where to start. This is your sign to start now. See? And make sure to share the video you create in the comments below so that others can check it out as well. I always think it's important to share your work so that others can watch it and also be inspired to create. Thank you all for watching and as I promised earlier in the video, there is an insane giveaway. We're giving away three yearly creator pro subscriptions to Artlist. For your chance to win, comment with your best tip for creating cinematic videos on your phone or your favorite tip from this video. And we'll be announcing the three winners next week on our Instagram Stories, so make sure to follow us there. The links are in the description. If you haven't already, make sure to subscribe to the Artlist channel for more videos like this and the bell notification helps you never miss the next one. That's all for now. So until next time, I'll see you in the next video.


Hey, what's up everybody? So I made a video yesterday about some issues with the new 7.3 firmware update for the black magic cameras and the Viltrox adapters Several comments people, you know, say in the experience the same issues I tried several things trying to roll back the firmware on the adapter to, you know Multiple versions nothing really worked And then I saw a couple of comments that made me think maybe there's a different trick to this And that was working for some people with older versions or even with this adapter So what I tried was Actually removing the lens while the camera was on instead of the adapter The previous trick I used was to have the camera on and remove the adapter a couple of times until you could You know change the iris setting and that stopped working with the 7.3 update So that's why I posted the video But I've realized now that if I detached the lens from the adapter Sometimes three times sometimes five times one time it took eight times But eventually I will get the iris to show up The iris setting to show up so that I can change the aperture of the lens from the camera So I just want to make an extra video like this so that if anybody sees this and searching for it They'll find, you know, maybe this is a workaround until Viltrox can, you know Put out of 3.6 or whatever version that's gonna, you know work with this new black magic update So anyway, I hope this helps if you like this type of stuff Make sure you subscribe and give this video like share it with anybody that you think are having the same issues With this camera and this adapter Thanks for watching


Well, Joseph R. Biden has been president now for a full week And I thought it'd be helpful to take some time to kind of like go over all of the things that he's done I mean a lot has happened over the last eight days and You know, I want to go over the good the bad and the ugly because it's a bit of a mixed bag There's a lot of pleasant surprises, but there's a lot of things that I find downright irritating and Outrages so first of all when it comes to the good there are a lot of things that I do want to give Joe Biden credit for So first of all he surprisingly Froze munition sales to Saudi Arabia, which is something that we actually tried to accomplish under Donald Trump there was an effort led by Ro Khanna Bernie Sanders and Mike Lee and They passed a bill to stop selling arms to Saudi Arabia because if we gave them weapons at that time Those weapons we knew were literally aiding and abetting a genocide that they were carrying out in Yemen So that effort unfortunately was thwarted by Donald Trump who ultimately vetoed that legislation when it reached his desk Well, Joe Biden is now freezing the sale of weapons to Saudi Arabia This is a really positive thing Additionally, he repealed the Trump's ban on transgender Americans serving openly in the military I mean, this was brazenly bigoted and unnecessary like it doesn't do anything to make our military stronger If anything, it makes us weaker and it's just nice to see this get undone And I absolutely applaud Joe Biden for this now in a surprising turn of events He actually signed an executive order that ends federal contracts with private prisons now at face value. This sounds awesome Unfortunately when you dive a little bit deeper and look at the details It's not as sweeping as I would like and there's a lot more to be done This is basically him reinstating in Obama era executive order. The problem is that this doesn't apply to ice detention facilities it applies to DOJ Contracts so overall this affects I think less than 10% of private prisons and if you're not actually tackling ice The problem then is that we know most of the abuses occur at ice detention facilities So if we're not actually ending private prisons with regard to ice then this isn't going to do as much as we'd wanted to So, you know, he gets credit for this But it's the bare minimum and really we need legislation to abolish these private detention facilities altogether If not just nationalize them now this was part of a larger initiative to advance racial equity So he also signed additional executive orders to address discriminatory federal housing policies Recommit to respect tribal sovereign team and more all very positive steps in the right direction Of course, this is only a step forward. It isn't comprehensive Criminal justice reform that we need but still it's something now even more surprising is his call for Congress to pass legislation Ending subsidies to the fossil fuel industry and he actually signed an executive order that directs the federal government to do Just that and this comes after he unilaterally killed the Keystone XL pipeline and at a press conference He said something about this that almost made my head explode Unlike previous administrations, I don't think the federal government should give handouts to big oil to the tune of $40 billion in fossil fuel subsidies I'm sorry. What are my ears deceiving me? Let's hear that again I don't think the federal government should give handouts to big oil to the tune of $40 billion in fossil fuel subsidies That's not a deep fake. That's actually Joe Biden. Yes, Joe Biden saying that So I know what you're thinking. Wait a minute. It's Joe Biden based And look even though that is genuinely pleasantly surprising Hold because in that same press conference. He also said this and I know this always comes up We're not going to ban fracking. He just lost the left and there's the Joe Biden. We all know and hate Yeah So look he gets credit where it's due and when he does things that I disagree with I'm going to call him out for it Fracking is absolutely harmful. It poisons drinking water pollutes the environment and literally causes earthquakes studies have proven this So this is a position that I think is untenable in 2021 When we have what a decade left to act in order to mitigate extreme climate change So, you know, I'm not trying to create an overall narrative about Joe Biden I think that if he does something good, I will applaud him if he does something bad I will criticize him and there is a lot of things that he did, you know, besides these things that I'm not happy about now last week. I love the executive orders with regard to COVID-19 I like that he instituted a federal mask mandate where possible But there are some things that I have to point out that I'm not happy with so his secretary of state Anthony Blinken stated That it is quote-unquote vitally important to consult with Israel on the Iran nuclear deal. Actually, it's not Israel's right-wing Prime Minister and Benjamin Netanyahu has gone out of his way to sabotage peace talks between the US and Iran so we don't have to consult with them Israel and Iran don't like each other Israel is overly antagonistic towards Iran So I don't think we need to consult with them. What happens between the US and Iran happens between the US and Iran So I don't like that direction the Iran nuclear agreement is effectively a peace deal So if you forego that if you don't get back into it, that's a bad thing now thankfully Biden has said that he intent on rejoining the Iran nuclear agreement and Iran does seem open to negotiating with him again So hopefully Anthony Blinken what he's saying doesn't represent what Biden actually wants to do But nonetheless, this is his secretary of state. So that's a really bad sign now Additionally while we're on the subject of foreign policy Not much has changed when it comes to our meddling in Latin America because Biden is likely to recognize Juan Guaido Who was not democratically elected as the president of Venezuela? Now, it's incredibly ironic to me because He's recognizing someone who is an illegitimate president Just after Donald Trump was trying to Guaido the American election I think Emma Viglin put it best hard to slam Trump for pretending he won the election and then simultaneously uphold this fiction exactly If you support democracy Then you support democracy you can't like waiver when it's inconvenient or if it benefits us You know when it comes to foreign policy or if there is a democracy that has a leader that we don't necessarily like because that leader Isn't allowing our American oil companies to access their oil wealth It's just you know, this is something that I expected It's frustrating but that doesn't mean that we should give him a pass because this was expected. This is Bad like stay out of Latin America.  I am not a fan of Joe Biden's healthcare I think that when it comes to this issue, he is cruel and during a pandemic I think that that title of him being cruel gets upgraded to him just being a straight-up piece of shit If you don't support Medicare for all during a pandemic, then I think that that's borderline psychopathic behavior But he's watering down his own Healthcare proposal and the reason why it's getting watered down is because it has reportedly been written by You guessed it lobbyists So he's starting to signal that his administration is moving away from a public option Although that's not like a hundred percent determined as of yet But I mean, it's almost like we've seen this story before in 2009 President Obama said I support a public option And then when it came time to do healthcare reform, he didn't even propose it So once again, you know, we're seeing Joe Biden do the same thing that was done last time And of course, it's a betrayal and I said in 2019 look for Joe Biden to propose a public option Why should we believe that he supports this when him and Obama claimed to support it before but we didn't even get that We got the Affordable Care Act, which is right wing healthcare reform Originally thought up by the Heritage Foundation. So It's just it's outrageous. It is outrageous and I am not a supporter of a public option. The right answer is obvious It's it's Medicare for all But he's not even going to do that What he's proposing is to pump extra money into the private healthcare system and basically subsidize cobra This is not going to get people the healthcare that they need They might get health insurance. They might be able to buy insurance On the Obamacare markets, but guess what? That is trash Insurance people can't afford this. So if you don't actually propose something that's affordable What you're doing is effectively meaningless because in a couple of years we're going to have to revisit our healthcare system again So if you don't take at least somewhat bold steps to reform this horrible system Then nothing's going to change But on top of that people are losing their employer based health insurance during a pandemic So if you won't even be bold during a fucking pandemic that is morally reprehensible It shows that joe biden Lacks the moral character needed to meet this moment. Now. Finally.  This is unacceptable. You said $2,000 And you should stand by that but unfortunately since this story came out Well, the plot kind of thickened either even further because joe biden is proposing to further means test that $1,400 which means Less people will get access to it. It may take longer because means testing It slows everything down if you just have a universal program Then that's preferable because you get it out to americans faster And if you really want to means test it just like tax folks later next year down the line Just get the checks to people they're suffering But now we're hearing from Chuck Schumer that it may take a month month and a half before we get the checks And it just it keeps getting worse This is like one of the quickest 180s we've seen in politics in quite some time You just promised us checks and to say that now it's going to be 1,400 Now to say it's not going to be immediate now to say well, we're going to means test it This is such a bad look But again, I you know, I want to point out that There's some good There's some bad and there's some really ugly things about the biden administration I will say overall if I step back Assessing his first week in power It's better than I expected he surpassed my expectations But unfortunately for him my expectations were like below the floor Because I expected him to do not very much But when you have so much things so much crises that you have to address We need bold action and as I've said before Joe Biden is not the individual That is able to meet this moment But having said that though if he does good things he'll get credit for it and already Uh, he's proving why he's better than Donald Trump We're at least seeing that bare minimum level of competence that was lacking Throughout the last couple of years and what's especially needed During times like this a pandemic economic devastation But I mean to say that he's better than trump doesn't necessarily mean much because that's a really really low bar I think that if you put like a five-year-old In the oval office, they would do a better job than donald trump So, um, look, there it is. Joe Biden's first week You've got a lot of pleasant surprises.  That's gonna be my response going forward


What's going on? Chris from joshescasebricks.com doing 2022 Panini Immaculate Included Football Five Box Casebrick number two, Random NFL Division. This is an eight-spot break. Every spot is a division. From CJ David M. David C. Craig, Kevin, Michael, Tana, and Travis are eight friends. To our eight divisions, the AFC Northeast, South and West, and the NFC Northeast, South and West. We will do our randomizer or dice for both of these. Good luck everyone. Thanks for getting involved in this break. Let's do it. A five and a one six times. So six for both. Here's one, two, three, four, five, and last one six. Six times right here. David and David at the top going down to Tana. And our division six times. One, two, three, four, five, and six. Boom. From the AFC East to the NFC South. Now let's take a peek, shall we? So to start us off, David C. has the AFC East. David M. has the NFC West. Michael Lausia has the AFC North. Travis Melbrick has the AFC South. CJ has the NFC East. Greg with the NFC North. Kevin with the AFC West. And Tana with the NFC South. We'll take a pause here. We'll look at any trade discussions, any offers. And when we come back, we're going to rip this five box case break of a collegiate immaculate. Very, very nice. Thanks again, y'all. We will be right back. So we are back. No trade discussions. So we're just going to get right to rip in on this full case five boxes of immaculate collegiate football. Let's go, man. Very exciting. The one box break we did on the R&B. Really fun. Really nice patch. Some good autos. Let's get more of the same. A whole case of it. Who knows what's in there. One goodies and treats. We're looking at. All right. So let's get this started here. Thank you to our friends once again. David, Michael, Travis. Travis, perfect timing. Good luck to you. Kevin, CJ, Greg, Tana and David M. You ate. Good luck. Thanks again for getting this break filled out. Let's get you all some bangers. Who says no? The one thing, I'm not the biggest fan of this color. It's kind of clay looking. It's just okay. I kind of like that little Dijon mustard, that brown mustard of the NT football for 2021. That's like one of my favorite of the different colors. But here we go. Here is box numero uno. Big rookies, good vets and college legends. Big fun checklist here. Just getting warmed up. I'm just going to grab a regular one. Stickers. Yes, Sarah does. Very exciting. First up, how about paid Manning, Tennessee or volunteers? Addition 51 out of 99 by our rules. If you are retired, it goes to longest NFL career and paid. And that's going to be for the Colts. Let's get the divisions here. That NC soccer, the World Cup, that's going to be exciting for sure. So Broncos, AFC West, that's the Broncos. Colts, Colts. AFC South, that's going to chavis that last spot mojo coming in quick. Kind of redemption already. So let's grab this one. Say that one for last. First auto, Wondale Robinson, rookie for the New York Giants. That is the AFC, NFC East. NFC East, that's going to go to CJ. No, NFC East. Wondale Robinson, signature moves, six out of 99. Nice. Eight out of 10. Bailey Zappi, go tops on the card and on the signature. Bailey Zappi, rookie for the New England Patriots going to the AFC East. That's going to go to David C. That's a sweet one right there. Love when players take that little extra on the signature. Go tops. Nice. Next up, three color patch and auto. 74 out of 99. Kenny Pickett for my Pittsburgh Steelers and the AFC North. That's going to go to Michael Osia. Very nice. You know what's all about the boom. Kenny Pickett, Bae Bae for the first box. Last card. Another big patch. Going to go to Garrett Wilson, 67 out of 99. New York Giants going to go to the AFC East and David C. Once again.  This is a little thicker here. Let's start, huh? And the redemption. Good luck.  And Justin Ross is the Chicago Bears. Let me double check that one real quick. I appreciate Justin calling it from Justin to Justin. One with the guest on CJ Show with Justin. Chiefs. Kansas City Chiefs. So the Kansas City Chiefs. We'll have to take a look at what that's going to look like. Kansas City Chiefs going to the AFC West. Going to Kevin Shepard. Congrats, Kev. Boom. That's going to be a sweet looking one for sure though. With the Team Slogans. That's like the Bailey Zappy that we saw. All right. That was box one. Let's do box two. About a redemption per box. Going back to our one box break. We had a redemption. This box had, or this last box had redemption. Let's see if we're going to go three for three here on Redemption. All right. Box two. Jonathan Taylor. 56 out of 75. We're going for a little bit of a color match here with that Wisconsin. Jonathan Taylor plays for the Colts. That's the AFC South. That's going to Travis Milburn. That last spot mojo. Next up. Jared Bernhardt. 2017 National Champions. La Crosse player? I'm going to have to do a little bit of checking on this one. That's interesting. Jared Bernhardt from Maryland. Interesting. Jared Bernhardt is an American football wide receiver for the Atlanta Falcons. Previously played college La Crosse at Maryland where he won the Tier Rarenton Award as the nation's best player followed by one season of college football as QB at Ferris State. Interesting, but I guess Atlanta Falcons currently on the Falcons. Interesting. That's going to go to the NFC South, which means that Tana is on the board. Super athlete. See South. All right. Congrats to Tana. Next up. Immaculate Ink on the Richard Sherman. 10 out of 25. Immaculate Ink Auto. Sherman called it. I believe the longest tenure is going to be the Seattle Seahawks. That's going to the NFC West. NFC West and David Mollell. That's David. Let me make sure that's the case. That's the chase. That's the chase. Yeah. It's going to be the Seattle Seahawks. That's going to be his longest tenure. So NFC South. NFC West. What? Got another redemption here. 51 out of 99. Johan Dotson, RPA. I believe Johan Dotson is on the Commanders. That's two color patch and auto. Commanders. That's going to go to the NFC East going to CJ. Last card. T Higgins and Michael Pittman Jr. Okay. It's going to have to be a randomizer here. T Higgins for the Bengals. Michael Pittman for the Colts. That's going to have to be a randomizer at the end. 16 out of 25. Two color up top. One on the Michael Pittman. It's out of the randomizer. And our redemption is Rashad White. Premium Patches Rookie Auto Ruby. Rashad White. Rashad White. Running back for the Tampa Bay Buccaneers. It's always interesting to see why players are on the same front. I have to look into it though. So Tampa Bay Rashad White. NFC South. That's going to go to the Tana. Two down. They must be, they're both 2020 rookies. T Higgins. Michael Pittman of the 2019. That's part of it sometimes. Same position. Same draft. Sometimes it's teammates. Sometimes it's rivals. It could be a Whitney of things. All right. Box three of five here. First up. Herbie. Justin Herbert Oregon Edition. Boom. God. Three run bomb for Cody Ballinger. AFC West going to Kevin Shepard on the Herbie. That's 47 out of 75. Next up. Immaculate Inc. Tyler Van Dyke. Seven out of 25. Miami Hurricanes. Tyler Van Dyke. Tyler Van Dyke. I believe still in college. Still a QB. So that's going to be, it's what it says on the break page itself that all active college players are randomized at break or discretion. So I'm going to just randomize it at the very, very end. Everyone has a chance at the Tyler Van Dyke. So I'll randomize that as well. Next up. Some fridge. William Perry. 33 out of 49 on the Immaculate Inc. Red Auto. That should be the Bears. William Perry. Frigerator. Ten seasons primarily for the Bears. Give me pro football reference. Yes. Nine season with the Bears. The fridge. Be the beast. Be a big boy. Next up. Wow. Look at that patch for Isaiah Spiller. Two out of ten on the patch in auto. That's an awesome patch. That's what we're talking about. Isaiah Spiller. A rookie for the Chargers. Going to the AFC West and Kevin Shepard. Look at that beauty right there. Oh, very nice. Second to last. Back to back on the sweet patches. Here's Alec Pierce. That's one out of ten. Man. Oh, going back to back. That's what I'm talking about. Alec Pierce. I believe Colts. Yes. Colts. One more for the Colts. One more for the AFC South. One more for Travis. That last spot, Mojo. Very nice, Trav. Last one. Going to be a patch. Brian Robinson, Jr. 73 of 74. Very random. On the white jersey. Brian Robinson. I think that's the Commanders as well. Yes, sir. Washington Demander's third rounder. One more for the Commanders in the NFC East for CJ. All right, box four. Oh, God, you're right. Jesus. I totally forgot about that. Jesus, Louise. Hope the kids are doing okay. That's all I can say. Jesus. That's what I'm hoping for. We can keep doing some more of these. We definitely have the R&B spray still available if not the case. All right, first up. Promptime. Beat by Dion. A third Eon. Dion. That should be the Falcons, yeah? You know what? You're right. It's like the best dang champagne I had in my life. Five years for the Falcons and five years for the Cowboys. That's also going to have to be a randomizer to even it up at least. Next up. 17 out of 25 on the Billy Sims. Another old school guy. Immaculate Ink Billy Sims off Hado. What do you mean? Tar-Van-Tar? Not Tar-Van-Tar. What was his name? 17 out of 25. Billy Sims. Should be the Lions, yes. Jack B. Calls it. And will be correct. Billy Sims. All with the Lions. Thank you, fellas. Appreciate you. Lions. That is the NFC East. NFC North. NFC North. Right? Am I tripping? Am I having a stroke? NFC North. There we go. That's going to Greg. And another William Perry. Another fridge auto. You only got the inside fridge and then you got the fridge in the garage. 75 out of 99 on the Immaculate Ink. Next up. Jack Cone. Three-color patch and auto. Rookie 59 out of 99. The big old boy. Big strong boy. Jack Cone out of 99 RPA. Is that a Patriot? Free agent. Played it in Wisconsin and Notre Dame. Any practice squads? Signed by the Colts on the 22nd wave. Signed as a free agent for the Colts on May 13, 2022. Waved on August 30th. We may have to rock with just the Colts. So if this is the Colts, it's going to go to the AFC South. One more for Travis. What's up? Nice. Another RPA. True. Out of 99. Aidan Hutchinson. The Michigan kid going to the Detroit Lions in the NFC North. Going to Greg. Greg heating up now. Greg finally getting some love. Nice. Aidan Hutchinson. Two-color patch and auto. Last one. Justin Ross and Mike Williams. 84 out of 99. Justin Ross on the Chiefs. Mike Williams. Mike Williams on the Chargers. So actually no. Both will be going to the NFC West. No. AFC West. AFC West. Going to David Moell. And then there was one last box. Then we're going to do our three randomizers. They'll be quick. Here's this love. Here's this love. Here's this love. Here's this love. That I'm feeling. Nice case. That middle box with the two sweet patches. That's our best box in the case. See if we can grab one more nice patch like that. Maybe a little lower. We've had some of us in the 10s. Probably get some train whistles. Some you know it's all about the booms. Last box mojo here folks. Thanks again to all y'all getting involved. Let's do it. Good luck. First up. Devontae Adams. 19 out of 25. Devontae going to the Raiders. AFC West. Kevin Shepherd. That is 19 out of 25. 35. Excuse me. 19 out of 35. Next up. Another team slogan here on the Go Irish. 42 out of 99. Kyle Hamilton. We have Kyle Hamilton buzzed the Ravens first round pick. That would be the AFC North for Michael Osia. Can you give a double check though? Yes sir. Baltimore Ravens. Kyle Hamilton, AFC North. Next up. Nice. Charles Whatson. 1997 national champion. 13 out of 49. That should be another one going to the Raiders and the AFC West. Double check. See if he is beat out on years by some other team. Charles Whatson. No. Seven years for the Raiders. Six for the Packers. Then two more on the Raiders. Not all good. Raiders. AFC West. Next up. Devontae Pierce. Two out of 25 on the big gold patch there. Devontae Price. For the Colts. One more for the Colts. Have a break AFC South and Travis. Last spot Mojo. Second to last card here. Oh, look at that pretty patch. Free color patch and auto true for Sam Howe. Very nice. 40 out of 49. Sam Howe. Double check.  Commanders. NFCE CJ. Nice CJ. Nice Sam Howe. And the last card. The patch. Oh boy. So all a lot of skills players from this year. We have Traylon Berks, George Pickens, Chris Olave and John Mechie the third. So I know AFC North, AFC South, NFC South and John Mechie the third. Is that the East? Is that the NFC East quad patch? No, Texas. Another one for the AFC South. So let's get to our, let's do a little recap and we'll get to our randomizers. You know what? You're right, Paul. That is going to be it then. So Dion Atlanta Falcons. That's going to go to the NFC South. Good call. I forget these things sometimes. It's been a minute. Thank you, my friend. So NFC South. Tana gets the Dion. All right. Let's do a recap and then we'll do now three, three randomizers. Good eye. Well, Sam Howe RPA, Devonte Price RPA, Charles Woodson, Kyle Hamilton, Go Irish, Aiden Hutchinson RPA, Jack Kona RPA, Billy Sims, Brian Robinson Jr. Let's get to the sweet ones here. Alec Pierce out of 10. Awesome patch. And then one more awesome patch. Isaiah Spiller. Killing it here, man. Immaculate. Heck of a case. Thanks again, y'all, for getting involved in this. Our second fridge, another Herbie. Rashal White Redemption, Jehan Dotson patch auto, Richard Sherman immaculate ink. Jared, the funny card here. Jared Byrd Hartle lacrosse. But actually isn't enough for a player now. Jonathan Taylor, Justin Ross Redemption, Jared Wilson patch, Kenny Pickett, boom. 74 out of 99, three color patch and auto. Bailey Zappi on the go tops on the signature as well. Wanda Robinson auto and Peyton Manning. All right. So three redemptions that we have to do. All right. Let me grab these dice lists. Let's turn this into a list. And every game, this game is a game inches. So three randomizers. So for this first one, T Higgins, that is AFC North, Michael Pittman, AFC South. Cheating. There we go. AFC North, AFC South, fresh dice. We'll grab all the divisions for the Tyler Van Dyke who is still playing in college. So everyone's got a fair shot at that division wise. And then the four player with the AFC South, AFC North, NFC South. And because it's on there again, two AFC South players with John Mechie and Traylon Birks respectively, we will throw the AFC South on there twice. So we'll roll these dice. And this dice roll will be for all three of these randomizers. Everyone, thanks again for your involvement in this. Good luck. Let's do two and four, six times. So six times right here for the first one, four, the Pittman and T Higgins dual relic. One, two, three, four, five, and last one, six. Good luck. AFC North. Alright, that's one down. Next up, this is for the Tyler Van Dyke. Still a college player. So all eight divisions active here. One, two, three, four, five and last one, six. Good luck, folks. Wow. AFC South picked up here. One more. Going to Travis. That's that last spot Mojo folks is a game changer. The quad relic. That involves the AFC South, AFC North, NFC South, and AFC South one more time. Traylon Birks and John Mechie, the third representative on there. So here we go. Six times. Good luck. Last randomizers. One, two, three, four, five, last one, six. There it is. AFC South. They had two chances here. They'll take it one more time. There we go. Thanks again, y'all. Very, very fun break. I know we have one box brace of these still on the sides and we may have one more random division full case break. Very, very soon on the site. Jasperyscasebreaks.com. Thanks again. Bye bye.