Okay. Is that a pretty good volume? Oh, I could have done that. Thank you. All right. So like Kim said, number one, I appreciate Kim inviting me to give this. I'm sure she could have easily given this and I could be still sleeping, which would have been a much better alternative, but that's fine. In any case, so I saw that looking through the agenda today, I saw that there's quite a bit about therapy and surgery and what we essentially should be doing with patients. And so I thought I'd give a little bit of a historical overview. Part of my postdoctoral career, meaning the time when I was doing research, was actually a very exciting time for kidney cancer, sort of the time where we began to understand the molecular biology in which loss of this gene, which is probably very familiar to you, the von Hippel Lindau tumor-special gene, gives rise to kidney cancer. So to take care of a couple of housekeeping items, number one, kidney cancer is relatively prevalent. So these are updated numbers from 2014. As you can see in men on the left in blue, it's the sixth most common cancer. And in women, it's the eighth most common. So it certainly makes this top 10 list. And we hope that it continues to, well, we hope that it drops off, but unfortunately, as I'll show you in a second, the incidence does seem to continue to rise. Now, actually I'm showing you that right now. So the incidence continues to rise over time. So this is only up to 1997, but I believe that the trends continue even into the 2000s. A lot of this may be early detection. So there is, as I'll show you in a slide much later, there is a tendency now for us to find more smaller renalges. So as many of you know, patients go to the emergency room for many things, and CTs are essentially required when you come to the emergency room. And these are often found so-called incidentally, meaning they weren't necessarily looked for, but were seen. Fortunately, kidney cancer does not cause as many deaths as it does cases. So cancers like pancreatic cancer, there's almost a one-to-one ratio. Once you're diagnosed, the likelihood of you passing away from disease is quite high. But you can see here, it's actually the tenth most common cause of death, cancer death in males, and doesn't actually make the list in females, fortunately. And so hopefully what we'll see is this drop off, not because everybody else is doing worse, but because kidney cancer patients are hopefully doing better. So what are the risk factors? None of these are actually blaring risk factors, meaning, for example, in lung cancer where we see smoking as being a huge risk factor, this number called the so-called relative risk, meaning your chance if you smoked, for example, of getting lung cancer over a person who did not smoke is around 8 to 10. So the bottom line here is that all of these so-called relative risks, while they are significant when you study them, they're not like they increase your risk to the degree that we see with lung cancer or bladder cancer or other so-called smoking-related diseases. But nonetheless, if you sort of look at what is associated, it's clear that smoking is probably the biggest culprit, and that's why if you, when you first get diagnosed with this, your physician will likely ask you, you know, did you have a history of smoking? There's a number of occupational exposures that have also been associated, again, to some degree, lowly with... I don't know why I put obesity as an occupational exposure, actually. I'll have to change that. But nonetheless, asbestos exposure as well as petroleum exposure has been associated with increased risk of renal cell carcinoma. Patients with kidney failure, who in particular end up on dialysis, they are known, and I don't think the mechanism of action is quite known, but dialysis and end-stage renal disease, these patients form renal cysts.   We know that these sort of inherited familial syndromes, such as von Hippel-Lindau disease or tubal sclerosis disease or Burt Hog-Dubais syndrome, are all associated with an increased risk of kidney cancer that tends to happen multifocal, meaning many places within the same kidney, or bilateral, meaning on both sides or both kidneys if you have them. So there are many histologies of kidney cancer, and what that means is when a pathologist looks under the microscope at a section or a piece of the tumor, believe it or not, you know, to the untrained eye, these eyes may look very similar except the color that they look, but the pathologist can actually differentiate these the majority of the time. And the frequency or the incidence of these is in this row, and you can see that the majority of kidney cancers tend to be this clear cell type, and that's because the cells look relatively clear, and this is actually an artifact of how the tissue is actually processed. Nonetheless, we shouldn't leave out the fact that there are a number of other types of kidney cancer that affect patients and are very important, so this so-called papillary type kidney cancer can be divided into these type I or type IIs, and not only do they look different on the microscope, but the patients have a relatively different outcome. And then finally, chromophobe kidney cancer, which Dr. Rathmell had a, I would call seminal paper this past year, describing the genetics of that cancer, is another one that, you know, as physicians we don't see as much, so we tend not to think about it as much, but we now know much more about the genetics of this more rare cancer because of that work. So von Hippel-Lindau disease, this is how everything really started, and it's a familial cancer syndrome, and that means that somebody at some point had a mutation in this gene, the VHL gene. And that means that, you know, you start off as an infant, or as an embryo, actually having two copies of every gene, and in VHL disease patients have a mutation or lack, essentially, one copy, so they only start out with one. So you can imagine that it only takes loss of the other one to really give you cancer. And that was what was seen in the pattern, essentially, of patients getting these clear cell type renal cell carcinomas.   And so, Treacher Collins, who was an ophthalmologist, noted this back, essentially, well now 120 years ago, that these patients were coming in, and they had a spectrum of these two tumors in particular. He did not make the association with kidney cancer back then. But he reported this over a decade ago. And now we know that about 100 years, it took 100 years really for us to, not us, I should say, I was, I think, like in high school when this happened. But for very impart people like Marston Linnahan, Burton Zavar to really come up and find location of where VHL was and how it was associated with VHL disease. And we know that it exists on this chromosome. So if you take your cell and you sort of spread it out on a slide, you can see that there's all the chromosomes and they have this kind of banding pattern.  And we now know that the VHL gene resides on chromosome 3P. And I won't really touch on this. I don't know if others will. The interesting thing really is that a number of the other genes that we now know are involved in kidney cancer development are also found very close to here. So for some reason, unlucky to us, nature made it so that you could lose VHL along with other genes like PBRM1 or set D2 sort of all at once. And that likely is a part of why kidney cancer is developed in that fashion.  And so one of the things I did when I was a postdoc was to see, and this is not all my work, I just really compiled all this data from what was published, to see whether or not VHL mutations were associated with so-called sporadic kidney cancer, meaning you didn't have a family history of kidney cancer, you just sort of developed it out of the blue. And what you can or cannot see from where, depending on where you're sitting in the room, is that, you know, in the range of 50, 40, 60 percent of patients who have so-called sporadic kidney cancer, meaning no family history had mutations in VHL, or in the bottom panel here, you can see that they had so-called hypermethylation, meaning not a mutation, but a mechanism which just silenced the expression of the gene. And so what we then really knew was that, you know, 80 percent or so of patients probably had inactivation or loss of VHL, and this was clearly an important step in kidney cancer development. I won't bore you with all this, but I think it's, I'm sure many of you heard of this gene called VEGF, or the VEGF receptor, and these are drug targets for things like Sunitinib, Pozopinib, or Votrient, Seraphinib, Exitinib, and Bevacizumab, so we're getting many of them. And so why does that work, and how do we actually think to target that? And the bottom line is that this VHL gene that I just described sort of negatively regulates this pathway, which is so-called the HIFVEGF pathway. And so you can see under conditions of low oxygen, meaning, for example, if you have a heart attack, your heart becomes deprived of oxygen, this pathway gets upregulated, and you get high levels of VEGF. And VEGF was described at first by this gentleman, Hal Dvorak, at Beth Israel Deaconess Hospital as being a pro-angiogenic protein. So it's sort of a protein that caused growth of blood vessels. So what happens when you have loss of VHL? So either if you have VHL disease and you lose that remaining so-called wild-type allele, or if you're unlucky enough to have both of these alleles or genes, copies of this gene lost in your kidney, you basically get upregulation of this whole HIFVEGF pathway. And we believe that's why those blood vessel-type tumors that happen in the back of the brain or the eye, or kidney cancer, which is notoriously hypervascular or has a lot of blood vessels, is related to this gene. And HIFV does a lot of bad things. So not only does it turn on that VEGF protein that creates blood vessels, but it does a lot of other things that are important for tumors to continue to grow and proliferate and do things like metastasize or spread. And so back in the 90s, early 2000s, we actually had this model and it's really reiterating what I've been saying. And that is really that if you had VHL disease, you were born with one copy of VHL that was just gone. And that you're fortunately able to prevent cancer by having one copy. But if you lost that second copy, you really upregulated this protein called HIFV, which regulates VEGF, and that could cause these cysts in your kidneys that weren't frank cancer, but could easily lead to cancer. And back then, when we wrote this, you know, the other so-called hits, meaning the other genes that were involved in developing kidney cancer weren't actually known. But now, thanks to efforts from the so-called cancer genome atlas amongst other individual groups, we know that these other hits are clearly things like PBRM1 or CETD2, and they actually reside on that chromosome right next to VHL. So clinically, you know, when I was in residency, I do remember hearing that kidney cancer was the so-called internist tumor. And that's really because, you know, not a lot, but 10% of patients would present with this so-called triad, meaning these three symptoms. And that was basically flank pain, meaning you had pain near where your kidney was. You had hematuria, which is a medical term for blood in your urine. And when you went to the doctor, the doctor could feel like there was a mass there. Okay. So it was really called the internist tumor. And that obviously predates the advent of things like CTs and ultrasounds that we can now just sort of look for these things. But it was also interesting about kidney cancer. It's also called the great masquerader. And I think that that, what that means is that it fools us a lot because it does funny things. It acts strangely and variably. So as I'm sure many physicians can attest to, you have a really broad spectrum of kidney cancer, some that do quite well, meaning they don't grow very fast and can kind of sit there. Others that do more poorly, meaning they grow quicker. But it's interesting that this cancer can actually, this is primarily the clear cell type. I don't mean to generalize, but it can really spread to weird sites. You know, like your thyroid is a pretty good one that we know does happen a low frequency pancreas. I myself have not seen testes, but it's certainly reported in literature. And so the bottom line is that it does weird things. And I think as physicians and as patients as well, it's very important to keep this in mind when you see strange things happen in a patient with pre-existing kidney cancer. So I alluded to this earlier. Fortunately, even though the incidence has been increasing, if you look at some of the tumor characteristics of the kidney cancers that we're finding, they're of a favorable nature. So basically number one, the percentage of tumors that are asymptomatic, meaning that the patient didn't go to the doctor or the ER because they had flank pain, has changed, you know, back in the 1970s where it was only about 10% to now 60%. So 60% of patients now don't really have a symptom. The average size has been shrinking, so it's about a centimeter different. And then finally the organ-confined tumors, meaning it's only confined to the kidney. Well, back in the early, late 1980s was only about half of patients now. It's three-quarters of patients. So that likely explains why, despite the incidence, meaning the percentage, the number of patients in the U.S. who are developing kidney cancer is going up. Fortunately, the majority of these are probably sort of being found earlier and we're doing better with them. There's a bunch of so-called perineoplastic syndromes. So these are syndromes that don't appear to be related to the kidney cancer at face value. But if you treat the kidney cancer, many of these can get better. And so I won't get into all these, but things like high blood pressure, high calcium, weird things going on with your liver, high red blood cell count, et cetera, are all sort of a spectrum what we can see here. Okay, so when I was actually going through the historical thoughts about this, you know, it really struck me that kidney cancer therapy, think about it, it's all about keeping the faith. And what I mean by that is that the two major, I would say therapeutic advances in kidney cancer have come through persistence. And number one, immunotherapy, which I think all of you are probably very excited about and have heard about in the past year. If you look at this gentleman, and this is not to say that all the work has been done by this gentleman, but if you look at sort of him as a pioneer, this is Steve Rosenberg who's at the NCI surgical branch. I don't know when this was taken. But nonetheless, he was clearly younger here. Decades went by and he's much older now, okay? But the bottom line is, you know, back in the 80s this guy believed in immunotherapy. He persisted despite what I would, I don't have facts on this, but I would assume a perceived lull in the field of immunotherapy, meaning if he applied to the, well he didn't actually have to apply because he's intramural, but if he applied to a study section at the NIH, it would probably have sort of been thought to be out of vogue or frowned upon. But he persisted for decades and now this past year or two we now know that these immune checkpoint inhibitors are quite beneficial to patients. And so if you look at a tumor, he was very focused on this lymphocyte. He didn't give up. He persisted. And so I think this is one story where sort of keeping the faith or believing in your own ideas had paid off. And then if you think about the other thing where we've made great strides in kidney cancer, it's really anti-angiogenesis. So it's basically blocking that VEGF protein. It's inhibiting that pathway. You know, recognizing that these kidney cancers are vascular and can we modulate their tumor size by doing this. And this is Judah Folkman, who unfortunately is now deceased. In fact, I was going to the, he passed away on his way to a tumor-angiogenesis meeting. And I think, I don't know if you were going, I was there at the meeting waiting for him. He was a keynote speaker. And when I got there to check in, they unfortunately had a picture of him with the year which he passed away. And he died on the way to that meeting in the airport in Denver, unfortunately. So, but nonetheless, you know, he spent decades, so in 1971 he sort of wrote this seminal sort of thought piece in the New England Journal describing, and I actually have a slide of that, describing the fact that tumors require angiogenesis or growth of these blood vessels to continue to grow.  He put a lot of faith in this molecule called endostatin, which, when I was a fellow there, clinical trials, and it didn't look very good. But I think he, again, persisted through this and others joined the bandwagon, and now we have drugs that do a really good job of targeting this pathway. So, Judah Folkman, he focused on these endothelial cells in the vasculature. And again, I think it's really a story of keeping the faith and persisting through decades of work. And that got me to this, you know, for kidney cancer, when you think, when you, at least as physicians, when we read these articles and journals, you know, they give, like, this history of development of something, it's always linear, you know, it's like 1971, such and such to this, 2000 this, this, this. But, you know, it's, for kidney cancer, I would make it almost like a circle. You know, Dr. Folkman and Dr. Rosenberg had these ideas back in the 70s and 80s, and they sort of came back around in a better manner, okay? So, this is one of Dr. Rosenberg's, one of the first reports in the early 90s of IL-2, high-dose IL-2, and its efficacy in kidney and melanoma amongst other tumors. And what is the bottom line? The bottom line is, you know, there was efficacy, so some patients had a so-called partial response, meaning their tumors shrank. Some patients had a complete response. But the biggest problem was that there was a lot of toxicity. So, despite being able to achieve shrinkage of some of these tumors, the toxicity was really big. And so, it really became a balancing act of whether or not, you know, toxicity being on one side, much like any drug, frankly, and benefit on this side. And you can see that, you know, back then, two to five percent of patients were dying during this treatment. So, you can imagine if five percent are dying and you're only really helping five percent, what does that ultimately, you know, get you? And so, you know, you're skipping, really, from the 1990s to 2012, when these two great articles about blocking this immune checkpoint pathway came out in the New England Journal. And what is this all about? I mean, who's here heard of PD1 or PDL1? Not everybody. Okay, so that's good. So, what these are, are these are actually proteins that exist on tumor cells? I'm sorry I should have made a slide of this. But essentially, you know, there's always been this belief that tumors should be seen as foreign, meaning they're just like a bacteria or a virus and they should be eaten and sort of disposed of by your own immune system. And obviously, I know that many patients come in and ask me, you know, how can I boost my immune system? And I used to sort of disregard that, but I actually don't anymore. Not that I know the answer, but I do think about it more carefully now. So, these proteins are actually things that are on the cell surface of these cancer cells amongst other cells in the tumor. And I would equate them to being so-called don't-eat-me signals, okay? So, if this tumor is expressing PD1 or in fact PDL1 in particular, the immune cells, when they come in, they'll see that and they won't eat the tumor, okay? Whereas if you're not expressing these proteins, it's not to say that the immune system will get rid of your cancer, but I think it has a bit of a better chance to do that. And so, if you could block this don't-eat-me signal or sort of hide it or sort of erase it, then what they found was that you could actually get relatively robust responses in some patients, not every patient. And these are so-called spider plots and the middle line in all these is, well, there's no middle line here, but the middle line in both of these essentially is zero, meaning the tumor size doesn't change over time. So, this is time versus tumor size. And it goes from zero to 100%, meaning the thing essentially grew tremendously, or it went to minus 100%, which is completely shrank and it was not visible by our imaging. And the bottom line is you can see that there are a number of patients whose tumors went below the line and did quite well, even some for quite a while. And so, there's a lot of interest and excitement about this and you'll hear more about blocking these so-called immune checkpoint pathways later. So, anti-antigenesis, this is the 1971 article in New England Journal by Dr. Folkman.  The growth of solid neoplasms, meaning cancers, is always accompanied by neovascularization, meaning the growth of new vessels. This new capillary growth is even more vigorous and continuous than a similar outgrowth of capillary sprouts observed in fresh wounds or in inflammation. And so, what's quite interesting there is that he equates cancer to sort of a wound and that's something that has also been continued to be followed up in the scientific literature. So, the first drug that was used was this antibody called Bevacizumab, or Avastin. And we now know that it was able to keep patients from progressing to some degree. And now, we now have Sunitanibs, Raffanibs, and Pizopin. I can't go through all of the studies, but they all seem to clearly prolong progression-free survival and overall survival, meaning we're helping patients with these drugs live longer. And I guess one would equate, you know, chemotherapy, which is, it's not like we haven't tried this in kidney care, well, it's before my day, certainly. But, you know, that's really a carpet bombing. You know, we weren't doing a very good job of targeting tumor cells. And I would equate what we're doing with Sunitanibs, Raffanib, a little bit of the so-called targeted therapy, which is more like a drone strike, which had its own problems. I think if you get too precise, we're not doing very well either. And lastly, we've learned a lot about genetics. You know, this is the past two years have been really good. So the TCGA, which is an NCI-sponsored effort to sequence tumors, a single tumor with a ton of different platforms, meaning different omics, per se. Clear cell renal cell carcinoma, which Dr. Rathmel led the RNA expression portion of this huge group, published this article in Nature. And then, as I was referring to earlier, she took this very, I would say, orphan disease called chromophobe kidney cancer and published it, I think, just recently in cancer cell. Again, really allowing us to begin to think about what's going on in these tumors and can we start to develop therapies that are specific to chromophobe kidney cancer. So in conclusion, you know, kidney cancer instance, it is increasing over time, unfortunately, but at the same time, the tumors we're finding seem to be more benign and quite, I don't want to say benign, but less aggressive and hopefully can still be treated quite well. Much of science and medicine is rediscovery. So as I alluded to, we're sort of going back to the 70s and 80s. Immunotherapy, Dr. Rosenberg sort of put that on the map back then. Antigenesis, Dr. Folkman put that on the map back in the 70s. Immunotherapy holds great promise. I think you'll see Dr. Moshkos talk about that later on today. And while it has its own side effects, it's not without its own problems and toxicities. So I've had a couple of my own patients have issues with these drugs. But overall, I think the future looks brighter. I think that as we learn to use immunotherapy properly and potentially combine it with other therapies, I hope is that things will continue to look brighter as we go on and make our own history. All right, I think that's it for me. Thank you.


Howdy, mods fans. Welcome to this week's edition of Whiteboard Friday, and I'm your host, SCO Sean. And this week, I'm going to talk about how do you help Google crawl your website more efficiently. Now, to start at a high level, I want to talk about your site structure, your site maps, and Google Search Console, why they're important, and how they're all related together. So, site structure. Let's think of a spider. As he builds his web, he makes sure to connect every string efficiently together so that he could get across anywhere he needs to get to to catch his prey. Well, your website needs to work in that similar fashion. You need to make sure you have a really solid structure with interlinking between all your pages, categories, and things of that sort to make sure that Google can easily get across your site and do it efficiently without too many disruptions or blockers so they stop crawling your site. Your site maps are kind of a shopping list or a to-do list, if you will. Of the URLs, you want to make sure that Google is crawling whenever they see your site. Now, Google isn't always going to crawl those URLs, but at least you want to make sure that they see that they're there. And that's the best way to do that. And then through Google Search Console, anybody that creates a website should always connect a property to their website so they can see all the information that Google is willing to share with you about your site and how it's performing. So let's take a quick deep dive into Search Console and the property. So as I mentioned previously, you always should be creating that initial property for your site. There's some wealth of information you get out of that. And of course, natively in the Search Console UI, there's some limitations. It's a thousand rows of data that are able to give to you. Good. You could definitely do some filtering, regex, good stuff like that to slice and dice, but you're still limited to that thousand URL in the native UI. So something that I've actually been doing for the last decade or so is creating properties at a directory level to get that same amount of information, but to a specific directory. And some good stuff that I have been able to do with that is connect to Looker Studio and be able to create great graphs from reports, filters of those directories. To me, it's a lot easier to do it that way. Of course, you could probably do it with just a single property, but this just gets us more information at a directory level like example.com slash toys. Next, I want to dive into our site maps. So as you know, it's a laundry list of URLs you want Google to see. Typically, you throw 50,000, if your site's that big, into a site map, drop it at the root, put it in robots.txt, go ahead and throw it in search console, and Google will tell you that they've successfully accepted it, crawled it, and then you can see in a page indexation report what they're giving you about that site map. But a problem that I've been having lately, especially at the site that I'm working at now with millions of URLs, is that Google doesn't always accept that site map, at least not right away. And sometimes it's taken a couple of weeks for Google to even say, all right, we'll accept the site map, and even longer to get any useful data out of that. So to help get past that issue that I've been having, is I now break my site maps into 10,000 URL pieces. It's a lot more site maps, but that's what your site map index is for. It helps Google collect all that information, bundled up nicely, and they get to it. The trade-off is Google accepts those site maps immediately, and within a day I'm getting useful information. Now I like to go even further in that, and I break up my site maps by directory. So each site map or site map index is of the URLs in that directory. It fits over 50,000 URLs. That's extremely helpful because now when you combine that with your property at that toys directory, like we have here in our example, I'm able to see just the indexation status for those URLs by themselves. I'm no longer forced to use that root property that has a hodgepodge of data for all your URLs. Extremely helpful, especially if I'm launching a new product line, and I want to make sure that Google's indexing and giving me the data for that new toy line that I have. And always, I think, a good practice is make sure you ping your site maps. Google has an API, so you can definitely automate that process, but it's super helpful every time there's any kind of a change to your content, add sites, add URLs, remove URLs, things like that. You just want to ping Google, let them know that you have a change to your site map. So now we've done all this great stuff, what do we get out of that? Well, you get tons of data, and I mean a ton of data, and it's super useful as mentioned when you're trying to launch a new product line or diagnose why there's something wrong with your site. Again, we do have a 1000 limit per property, but when you create multiple properties, you get even more data specific to those properties that you could export and get all the valuable information from. Even cooler is lately, recently, sorry, Google rolled out their inspection API. Super helpful because now you could actually run a script, see what the status is of those URLs, and hopefully some good information out of that. But again, true to Google's nature, we have a 2000 limit for calls in the API per day per property. However, that's per property, so if you have a lot of properties, and you could have up to 50 search console properties per account, now you could roll 100,000 URLs into that script and get the data for a lot more URLs per day. What's super awesome is Screaming Frog has made some great changes to the tool that we all love and use every day to where you can not only connect that API, but you could share that limit across all your properties. So now grab those 100,000 URLs, slap in the Screaming Frog, drink some coffee, kick back and wait till the data pours out. Super helpful, super amazing, makes my job insanely easier now because of that. Now I'm able to go through and see, is it a Google thing, discovered or crawled not index, or is there issues with my site, to why my URLs are no longer or not showing in Google. And as an added bonus, you have the Page Experience report in Search Console that talks about core vitals, mobile usability, and some other data points that you could get broken down at the directory level that makes it a lot easier to diagnose and see what's going on with your site.  I know these tactics have definitely helped me throughout my career at SEO, and hopefully they'll help you too. Until next time, let's keep crawling.


What's up guys and gals laying here with late in sports cards? We're ripping 2023 tops chrome update Hobby jumbo eight box mixer number four. I'm gonna read off the teams and we'll get ripping first up Charles a twins Chin Y H Marlins Dan C Red Sox Dean E Royals Donald D White Sox Ed B Phillies Gabriel C Orioles Jeffrey H Yankees Jeremy S Astros Jerry B Mets John W Tigers Joseph G Mariners Rockies Josh Josh H Blue Jays Joshua B Dodgers Judson C Guardians Leonie G Nationals Matthew B Reds Michael K Rays Peter M Diamondbacks Ray G Cubs Riley F Pirates Scott V Padres Scott Matthew Rangers Sean H Braves Steven U Cardinals Tim W Brewers Vaughn B Giants Wayming H Athletics and Uda F with the Angels. Those are your teams. We'll get ripping here. Again, this is mixer number four, everyone. Mixer number four. We got just two teams left in mixer number five two to go in mixer number five Guardians blue auto at a 150 Logan Allen there you go Guardians Marcus Strowman Bobby Whit and Mason Miller Corbin Carroll future stars Bobby Miller on the prism Jose Altuve and Joey Ortiz Joey Vada Titans Shoehead on the refractor Adley Rushman and Casey Schmidt Hobby Harris on the prism Peter Lanza and Dylan Dodd Dylan Dodd Randy Vasquez on the purple Vandy Vandy Randy Vasquez purple out of 250 for the Yankees Little Vandy Vasquez Vandy Vasquez Riley Greene and Johnny burrito Honestly, I'm surprised. I haven't messed up more names today. I was messing up I struck out like nine times yesterday.  There you go. Rocky's very nice card there Jared Schuster Riley Greene and Oscar Coloss What happened Cameron? What are we saying wow to? Titans mookie bets Matt Olson mookie bets and James McCann wander Brett wisely Garrett Cole and Juan Fen Juan then Juan then Juan then that's I think a red wave auto I think we got a red wave auto y'all Come on Yuri Perez out of five Finger who has the Marlins? holy cow That's the biggest hit of the day Yeah Chen H GNY H that is massive I'm sorry Vaughn That is easily the biggest card of the day.  Sorry Corbin Carroll Wow Wow Wow Logan Allen holy Yeah, that's insane was not expecting that was not I saw the red wave and I was like, let's hope it's good That's good of the best probably the best name you could have for like their true rookies in this Jeter downs Not yet Cameron, I'll do it after I'm done breaking Josh young and Geo Urchella. We're a little bit behind right now. So I'm just I'm trying to Not be so late that we eat into Sam when Sam is supposed to go live Jordan Walker Peter Lanza You're done. No, it's in the garden. It took me a little while to get used to Everything that needs to be done on YouTube versus fanatics Grant Anderson on the magenta out of two ninety nine Or three ninety nine. Excuse me three ninety nine on the magenta Kody Bellinger prism Mike trout and Edward Julian Rockies and pirates that's it rockies and pirates y'all last two teams Rockies and pirates That breaks next That breaks next y'all Fresh case of jumbo Kyle Schwabber for the Phillies There you go Phillies Kyle Schwabber out of fifty thirty four of fifty nice I don't know if it'll be fifteen K, but it's definitely a four-figure card Without a doubt, it's a four-figure card, especially if it PSA 10s That is the first 88 auto Cameron. Yep, Julio Rodriguez Cory Jokes Rushman and Reese McGuire Yeah, he has no bowman auto his first rookie was in bowman chrome So his rookie stuff is extremely limited Extremely limited. So good Yuri Perez rookie stuff is going to fetch a huge premium Tatees loudest Gurria Corbin Carroll and Nolan Gorman. That's insane. I Want to pull the super of him so bad Okay, that's a I want to see one way more than pulling another super yeah Tommy Pham Sandy and Randy Vasquez. Yeah, I really want to pull catch Keep saying that their redemption same But does that mean that one's actually been pulled because I haven't seen any pull me either or any like Posted that they were pulled Dominic Fletcher purple speckle purple speckle Dominic Fletcher show him Jordan Walker and Aldemus Diaz Has anyone actually seen a The debut patch card pulled yet gold mini diamonds Trevor Williams out of 75 for Washington gold mini diamonds Trevor Williams Tyler Alexander Raphael Devers and Teosca Hernandez Aaron Judge Julio Tatees and Bryce Terrain Francisco Alvarez Jesse Winker Scherzer and Joey Weimer Yeah, there might be one of him hitting and pitching. That's probably why We're sleeving and top loading all the otanis. So You're covered either way. There's our auto leave. I stout Leave I stout base auto for the Reds Jay Ram Vladdy and Injury you the engine He had a somewhat down here. It felt like yeah Sam Hillier Oscar Colas and Devin Williams Gus Varland on the negative Gus Varland for the brew crew on the negative Titans Julio Rodriguez Corey seager Brett Beatty and Oswald Parazza Purple Noah Cindergarten. I haven't either Travis Cole Ragnz Jordan Walker and Riley Greene There we go box number three of jumbo of the break we're gonna pull one Come on. We're gonna pull or it's gonna end up being forced in like one of his two cases that he has on fanatics Literally has two cases stay and he'll end up pulling the debut patch Drew Maggie purple auto at a 250 for the Pirates Literally last team taken congrats on that one Austin Hayes Michael Harris and Sean Murphy. It's just the Pirates left y'all Just the Pirates in the next one Jazz Chisholm pass Quintina Jordan Walker and Josh young Yes, yes, it is Andy Jayrod Acuna Jazz Chisholm and Carlos Vargas Yeah, all of the late in like true logo is a super fracter all the hoodies. Yeah Nikki Lopez Adley Rushman and Lamont Wade Jeter Downs Jeter Downs for Washington Josh young for the Rangers Jayrod and Mike Clevenger Ash Bradley on the gold mini diamonds Yellow mini diamonds, maybe maybe gold gold mini diamonds out of 50 There we go rays Taj Bradley and right behind it Brandon Williamson Blue green lava lamp Grayson Rodriguez on the prism Sandy and Tristan Casas Joey bottom Louisa rise Paul Goldschmidt and Jose Quas Jake's guy Literally Jake was like I want to pull the one of one of Jose Quas. We're like why I Love relief pitchers Wandy Riley green and Mikael Garcia Yeah, Cameron. I'm sorry. I broke the rules There we go reds Aqua wave auto to 199 Spencer steer Spencer cow out of 199 for the Reds very nice hit there wander Freddy Freeman and Bryce Miller Jake you are a bot dude. That's why you're out there man sure about Casey Schmidt Max Scherzer and Jared Schuster. I still don't know what's sprinkler. Where did that come from? Carl, what did you do though?  It was cuz we were ripping Phoenix Aaron judge and we had like you're either pulled the sprinkles card Remember there was a sprinkles card. No, no, it was like a SP in Phoenix. So Mike trout Ryan. No And then it actually caught on yeah Oh Matt Mervis on the magenta purple lava lamp. There we go cubs we go Very nice hit there Gabriel Moreno on the prism Oscar Colas and Patrick Corvin I'm down with the lava lamps. Here we go. Y'all last box of jumbo for the break.  I pulled a Xander yesterday Travis I Was like unbricking the cards and I saw it and I was like, oh, this is gonna see Peter Lonzo Nolan Aronato Paul Goldschmidt and posh Mr. Posh Kibrian Hayes Coby Eilert Juan Soda and Grant Anderson Yeah, they're insane. They are insane Travis. I was Waldo Cabrera I was a Waldo Cabrera for the Yankees Castellanos Freddie Freeman and Brian Rock you Brian Rock you Brett Phillips Grayson Rodriguez and Nick Fortes Sprinkles in the juice box guy Adley they think he's like two so they keep on him juice box guy Joy All right, now that makes sense. Yeah I'm with you magenta Christian Vasquez for the twins Mason Eggler Corbin Carroll and Adley Yeah, he's a baby That's super thick We're just like I don't know if it's extra that yeah, that's so many extra cards that okay Casey Schmidt negative Israel Panetta on the refractor auto for Washington Kyle Tucker Michael Harris and Matt Mervis Yeah, that was like seven or eight extra base part Like that's what a normal pack thickness is very weird very strange Adley Mason Miller Aaron Judge and Connor Wong Connor Wong, I think so Travis. I believe so Gunner Henderson Juan Soda jazz and Jason Hayward Eddie Rosario Volpe and Bachman. Oh That would be why that other one was so thick because that was super thin And this has no base cards because they were in that other pack Matthew Beaton on the Aqua Matthew Beaton Aqua Blake Perkins on the prism Yoshida and Mitch Hannigar Yeah, that would explain that Yeah, Milan, it's crazy 100k on the Volpe Connor Capel refractor auto for the A's Vladdy J rod and Matthew Beaton. Here we go moving on to hobby y'all No JT There'll be a replay of the brakes what after as soon as the stream as soon as the stream ends Yes, so as soon as the stream ends JT, you can go to fanatics and watch back that stream Yes Crushed is hobby only Cameron and the black and white mini diamonds auto is jumbo only Yeah, JT, I think it's the whole stream. Yeah, it's the whole stream JT You do get a notification now when the break you join starts on fanatics So that is a nice change that they made Just buy one of each Cameron Just the pirates left y'all next break just the pirates Six left in number six y'all Six left in number six JT. It's gonna be Sam breaking Well technically Sam and Kyle Kyle will be breaking personals on channel two Sam will be doing group breaks here on the main channel So Kyle and Sam are breaking tonight Sam goes live at five That's why I'm kind of trying to speed up a little bit. We fell a little bit behind Trying to make sure that we can finish by the time he needs to start We got to fill up these breaks though Can't rip it. We're not full Ryan Mountcastle Adley Edward Julian Jacob DeGrom Ronald Acuna we got Ryan Felter Felter on the prism Thank You Cameron appreciate you Mason Miller Devers Juan Soto Matt Mervis for the Cubs Green Cory jokes for the Astros Green Cory jokes on Milan a lot Otani I Think that these cards are gonna sell like crazy Maybe not right out the gate, but long term like I don't think a single debut patch auto is gonna sell under twenty five hundred dollars. I Think twenty five to three thousand is gonna be the floor Doesn't matter what player it is Kibrian Hayes Bobby Miller The better the player is the more expensive. It'll be from 3k to twenty five hundred up gunner In my opinion. I very well could be wrong Castellanos JP France Bryce Harper Gunnar Henderson Titans Exactly like even Oscar Colas is gonna sell for for three thousand in my opinion Nathan Luke's on the purple speckle Auto Purple speckle auto for the Blue Jays Blake Perkins, I mean like Jordan Walker. There's a hundred thousand dollar bounty out for his one-of-one So six figures on Jordan Walker Stuart Fairchild Sandy Kyle Tucker JJ Blede refractor Gunnar Henderson Mookie Betts Titans Jeter Downs on the refractor Sorry about that Cameron Christian posh Brett Beatty and that'll do it for box one of hobby all box number two Sold out now Just the pirates y'all They've been hitting they have Yuri de los Santos. They have They have quite a few names actually They have like four rookies in this definitely should not be the last team taken Over on fanatics yesterday the Blue Jays were the last team taken all day. Yeah Yeah, I agree Milan. I would agree with that. Yeah, I would agree with that probably in that order, too It's probably gonna be close to six figures. Yeah, like not exaggerated. Yeah, it probably will be I Mean we've never seen cards like this We don't know what this is gonna do. Yeah, this is like when exquisite like first became hot And you had like logo man for the first time. Yeah, this is something very new brand new Right Grant Anderson That's fair Milan. Oh, even if I pull both. There's no way I can keep it Got to sell something like that. He was talking about like Matt that used to work here Yeah, I'm just a Cardinals fan. I feel that even as if I was a Cardinals fan. That's a lot of money I mean, I I have some cards that I've pulled.   Yeah Would I go back and take it down? Yeah, but I didn't take it at the time. I feel that you never know Loudest girl. Yeah, you you just you can't like everyone's different with how they feel about cards things like that Where you think the card is gonna go? Logan Ohapi Mitch Hannigar on the magenta purple Like if Josh Young had a debut patch and I hit it in a break. I wouldn't sell it really It would just be in my PC. See a hundred percent Grant Anderson prism Hundred percent that would just go in my PC and then when I was ready to like buy a house or something That's when I saw it when I actually need money Vinny Pasquantino But if I didn't need money, there's no reason to rush so that Riley green That's more my point like let's see how the market goes Yandy DS Brandon fought we haven't seen him in a while Francisco Alvarez and your dog. We have not seen a Brandon fought in a while We ripped 10 cases of breakers delight yesterday Travis Volpe and Did not hit one Casey Schmidt not saying 10 cases is a lot, but it's not a little Matthew Baton on the prism Pete Alonzo here. We go second half Corbin doesn't have a debut pack Neither Corbin nor Josh Young have debut patches Cory Seager Tommy Pham Tyler Anderson on the magenta for the angels Jason if you just searched 2023 tops chrome update checklist you should be able to find it Pete Alonzo Matt Reynolds on the prism Yeah, I think I do Gregory Soto Jose Altuve Jose Ramirez Cole Reagan's for the Rangers There's a checklist Sprinkles just dropped it for you mookie bets Fernando Tatis Titans Yeah, no problem Jason For the Marlins have a break Gold wave auto Xavier Edwards Xavier Edwards for the Marlins Yeah, Joshua, he's got a speed up you're right Jason Hayward prism Mike trout sold out on the next one y'all rock Six to go Six to go in number six y'all. Let's get that filled six to go y'all just a little beyond We put a red Yuri Perez, which is like arguably better than the super yeah Let's keep it going Fair fair. I won't argue with that. I agree with that. I will not argue with that one. The super definitely looks better But I would take the red Yuri over the shame the plan Sorry Jake Jake probably wouldn't know he definitely not six to go y'all Just six left We got eleven total spots for the day shift left Sam spots are flying for tonight. NT number one is already full Pretty much all of NT for tonight is already in single digits. So if you're looking to get an NT breaks with us tonight don't uh Don't wait. All right Milan. Let's do it. I hope so man. Let's do it Freddie Freeman Jordan Walker Garrett Mitchell on the green Garrett Mitchell green out of 99 Jordan Walker That's very fair Cameron Sam Bachman on the prism. Yeah, that's the other thing. You don't think about that Yeah, I like I pulled my first one. I've ever hit it. I don't really want to sell it G-man Jordan K though It's a lot KC Schmidt Brett Beatty Gunner Garrett Cole I love Bobby Whit, but he is a fan of the wrong college that killed me When he posted horns down after Oklahoma Longhorns, I was like, I can't collect Bobby Whit anymore Andres Jimenez Corbin Carroll Corbin Carroll Mookie Betts Titans Brian Rockio Aaron judge Gabriel Moreno refractor for the Blue Jays bingo There he is Mike trout crushed trout for the angels There he is That's disgusting One soda. Okay for conditions sake that makes sense Yeah, if they're all redemptions then condition won't be a factor Connor Wong If they're all redemptions, then they're all be good condition hashey or not good condition, but the packs won't mess with them Devers You're just gonna have to make sure that they're packaged properly when they get shipped to you Wandy Brett Beatty action stars Gunner Henderson Titans Ben delusio purple auto for the Cardinals Travis that's what people are saying is that they're redemptions Ben delusio and Yoshida That's what they've been talking about in chat I hadn't seen it yet, but with the people have been saying we were like none of us have seen him yet. So who knows Gotcha Mason Miller. So they are confirmed redemptions. They are redemptions Halley just came in and confirmed that dance me Swanson. So yeah, we're looking for redemptions y'all We are indeed looking for redemptions that redemption Francisco Alvarez earlier Very easily could have been a debut patch Just like that so Now for once She confirmed your confirmation now for once We are legitimately hoping for redemptions It's the only time I think I'll ever say that let's hit a redemption That's horrible. Yeah, that's sad Honestly, that's like that's really sad. I could easily see one being pulled from them being in retail is a mistake in my opinion Retail is fine to have but you should not have a chase like this in retail. That should be hobby exclusives My guess is that their redemptions will be fulfilled very quick So so they are alive in breakers delight and they are redemptions in hobby and jumbo For card condition like like what everyone was talking about so in breakers delight They are live in every other skew. They are redemptions But yeah, that in my opinion is a huge mess up by tops to put the debut patches in retail skews You can't do that. I mean they did it. It's already done, but that's a that's a mistake I disagree with that Cameron. I like breakers delight actually. I think the most of all the skews You get the most colors. I'm talking about as a consumer. I agree Austin. I agree a thousand percent That's I'm saying Lee.  Shoot That's what I'm saying That's nonsense. That's crazy you that's actually insane to me Yorda hobby hairs hobby hairs Jazz Chisholm action stores Vinnie what's a bad take that the debut patches are In retail they should not be in retail Titans Julio Rodriguez Brewer Hicklin Yeah, exactly like Austin said have specific retail chase hits But the debut patches should not be in retail Matt Mervis, but that's right. They are it is what it is Nothing we can do about it to change it Marcus Strowman. So I mean we'll just pull on anyway, Brett wisely Mike trout Nt basketball goes tonight, but there's someone fanatics right now Tyler Narcisco crook Narcisco crook there we go Red Sox Casey legumina Anthony Volpe Julio Rodriguez Jesse Winker Eddie Rosario on the Aqua blue lavalet Eddie Rosario What I mean agree to disagree Vinnie Miguel Amaya on the gold mini diamonds for the Cubs out of 50 Like there's but yeah, I won't get into it Julio Rodriguez Bryce Miller on the prism Corbin Carroll future stars Pablo Lopez Josh Young Wander Shoahit Cody Bellinger purple out of 250 gunner Dan it's not about the price point.  It shouldn't be in blasters Michael Harris not when it's like specifically one-of-one patch auto It's not like it's like a base superfractor where you're just like Printing out sheets, and if it happens to be in that pack, it's in that pack They're placing those cards in in the packs But again, it's just that's my opinion Everyone's entitled to their own Omar for the Mets Owen Miller for the Brewers Not saying retail shouldn't have chases a hundred percent Retail should have chases just not this But again, that's my opinion Sure, it would because you could still hit color. You could still hit autos. You could still hit plenty of stuff. I Just don't agree that the literal biggest chase we've seen in baseball pretty much ever is In you can potentially hit it in retail. Here we go Tyler Anderson these are all the magentas Christian Vasquez Grant Anderson Magenta purple lava lamp Mitch Hannigan Matt Mervis Ezequiel Tovar purple Gunner Henderson Noah Cindergarten Aqua Matthew Baton Eddie Rosario Aqua blue blue green Brandon Williamson Garrett Mitchell green out of 99 Cory Jolks green yellow mini diamonds Trevor Williams Gold mini diamonds Miguel Amaya Taj Bradley and our autos base auto Jeter Downs Levi Stout Narcisco crook Oswaldo Cabrera refractor autos Conor Capel Israel Panada Purple speckle autos Nathan Luke's purple auto Randy Vasquez drew Maggie Bendeluzio purple speckle Don Dominic Fletcher Aqua wave auto Spencer steer Logan Allen blue On the 88 auto Kyle Schwabber gold wave Xavier Edwards crushed Mike trout crushed Mike trout and Red wave auto Yuri Perez that'll do it for break number four y'all. Thank you for filling it up moving on to break number five


So hi, my name is Brian Prophet. I am a manager of the Community Insights team within the open source program office and what part of what our team does is gather metrics and data to try to figure out how healthy communities are and make sure that they are growing and thriving the way open source projects should. And one of the people that I work with on my team, an amazing talent, is Kelly Dolphy and I'll let her introduce herself. Hi, my name is Kelly Dolphy and I'm a data science intern here at Red Hat and I've been here for, you know, about a year and a lot of the things that you're going to be seeing today has been my work over the past six months at Brian Prophet and I have been doing. And with that, I am also finishing up my degree at Boston University in computer science. Okay, great. So we'll go ahead and get started. And the first question that we always ask ourselves here is how to discover community health and sustainability. This is something that's really important. As I mentioned before, making sure communities are healthy and growing is a big part of what we do at our job at the open source program office at Red Hat. But we're also very interested more and more lately in the sustainability aspect of a given project because as more and more businesses and organizations get involved in open source, they really want to know, you know, how strong and how healthy a community will be if they're going to invest time and money and resources into that given project. So next slide, please, Kelly. Okay, so historically, this has always been done kind of by the seat of our collective pain. We've always, you know, tried to figure out community health around things that were fairly innocuous and seemed very obvious, such as, you know, how popular is a given open source project? How many people are using that project? What how many downloads are coming in for that free software project? Because, you know, if you look at it from there, obviously those seem like really good strong signs of community strength and help. As platforms like GitLab and GitHub came into being and you could start looking at the first level activity of a given project on those platforms, you might assign things about popularity and strength from stars, such as what GitHub has. And there's nothing really wrong with any of those metrics. The problem is this. They don't necessarily give you a true signal of what the community's health is. Things like popularity and downloads and the number of stars are great. And they look awesome when you're trying to market your project and tell people how, you know, wonderful it is. And it may very well be true indicators of how wonderful your project is. But think about this. Suppose you have a project that has a bazillion downloads, and there's no indication from the user consumption side that currently in that project there might be some kind of infighting going on. And there's been a massive battle going on on the project mailing list for months. And the project is about to forge and there it will then lead to, you know, a degradation of the quality of the software that's coming out from that project. Nobody on the user side will know because, you know, it's just still being downloaded. So it's not that downloads and stars and popularity in general are a bad thing, but we have found that over time they are not really, you know, solid true indicators of how healthy a community can be. Next slide, please. So what we're doing in the open source program office, specifically on the community insights team, is we're trying to do something and move beyond those gut feelings. We're trying to deploy analytical rigor. And that we've been able to do that through the rise of two key movements within the broader open source ecosystem. One is the standardization of metrics. And we'll talk about each of these in just a second. And the other is alongside of that standardization and how we can look at all these different kinds of community projects in the same way is the evolution of tools. And that is, again, something we're going to be talking about as we move through this. So next slide, please. So right now the tools are this and these are all coming around that standardization of metrics that I referred to earlier. And that is actually coming from a project independent of Red Hat called Project Chaos. Project Chaos is a Linux foundation sponsor project that is very keen on getting together metrics that can apply to any project because in the past, a lot of the pushback has been that we have just had metrics that you can't really apply something to a project about databases versus an academic project or something different. But that turns out not to be the case. All projects, no matter how they are constructed or what they are producing, have similar aspects which can be measured. And to measure that, we currently at Red Hat and in the Ospo office are using three sets of tools. One is known as Caldron. Caldron is an open source project that is based on Grafana and Elasticsearch and tools coming in and Gribmore Labs. And these are all tools that are put together by a wonderful vendor in the open source community known as Paturgia. Paturgia has put together Caldron as a hosted service. You can go to caldron.io now and start running your own metrics against projects that are out in the open source world, whether they are hosted on GitHub or GitLab. And what Caldron does is it gives us a very quick analytical and graphical snapshot of what a Git hosted project is looking like. What are the activity levels? How many developers are there now? How many developers were there last year? How fast are pull requests being looked at for the first time? How fast are those pull requests and issues getting closed? These are all things that Caldron can do. In fact, we liked it so much that we have stood up our own instance of Caldron so that we can run our research projects that much faster. Alongside that, we are working with members of Project Chaos on another tool that does a lot of deep diving into data sets called Augur. What Augur does, it does not give us a graphical picture of what a community is looking like, but it does give us really solid connected pieces of data that show us how different contributors are working within different projects, what those connections are, how projects relate to each other, which is amazing when you are trying to look at an entire ecosystem. You can see very quickly with Augur not just the strength of that project, but what you are really seeing is how it connects to all the other projects that are related to it, which is an amazing set of insights. Beyond that, we take the information from these two tools, and then what we are doing is we build something known as community report cards. These report cards or analytical reports basically give us the graphical and the analytical tools to define how a community is doing. We can run these at any time. We are working to make them as automated as we possibly can so that somebody can run the report, look at the data, draw conclusions from the data based on what they know about the project and how historically it has been, give that information back to the community itself, and they can work on identifying those things that might be problems and need to be solved. So, a combination of these three tools are what we are using now. I am going to give it over to Callie now and let her talk about some of the other things that we are doing within our team. Now, looking at this next phase of tools, we are trying to look at and see how we can use the information that we are comfortable with and have gotten very experienced with, and find new perspectives with the new options that we have going on to our table. This goes into the next level analysis of what business needs to know about open-source communities before they get heavily involved. It is no secret that businesses these days are having a growing interest in open-source communities, but a lot of these people have a lack of understanding of the little nuances in the open-source world. You can measure health, and that is one portion of it, but it is a completely different thing to figure out how that fits into your business in a way that is sustainable. Is the community sustainable and it is something that is compatible for you? Coming from somebody who is relatively new to the world of open-source, I have always been asking the question, what should I be paying attention to whenever we start looking at new angles of the open-source communities? Where does it go from when you are trying to look at intuitions of people in the past and trying to take that knowledge and make it data-driven? This goes into our new tool that we have created with a partnership with IBM Research. This goes into three parts. The first one is the IBM Watson discovery. This looks at GitHub at a completely new way of using AI-powered natural language processing. We can start to understand the industry's language, where it is uniquely looking at how open-source keywords and different things along those lines. This is a way that we can start grouping together repositories, not by certain metrics or whether these contributors all look at it and work on it together, but seeing if they are using similar languages. Are they talking about similar things within their readme's? That brings a whole new way to group together, repose and start to see new trends that are happening across all of GitHub. The next stage of this is project debater, which is one of the ones that I am really excited about. Project debater takes in a set of data where you can find arguments for or against a certain position. For example, you can type in OpenShift, the best container platform, and you can see the arguments that are going through it and see the weight, whether on the positive or negative side, and see what people are saying about it. Start to figure out, okay, where are the different weight places that we need, whether if you are looking at it from a point of view, like what do I need to do to take a one step up, or is this something that I want to become involved in if you are looking at it from an outsider point of view? Then, from here, we can start to use the experimental method. This is whenever we start to use multiple different sort of searches with a few of our different tools to see what the impacts are of the work that you are doing. What is the impact of the different events that we are having, the discussions like the one that we are having around here, around different search targets? When we are bringing all of this together, you are starting to see open source communities in a new light and start to get a little bit of a one step ahead of everyone else in the community. Yeah, no, thank you. Taking the new tools that Callie just outlined, we are going to take all of the things that we mentioned, the tools that I was talking about, and also the new tools that we are working with IBM to create around mode and debater and Watson. Now, we are going to start looking at things that we have never been able to really do before. One of those aspects is going to be the business impact of a given community. In the past, usually measuring the return on investment for a community has been rather difficult because you know you have to have a community and be a part of an open source project and put some time and effort into that. But what is the business getting? You can say, well, we are getting a commercial product that we are selling and we are making money off of that, and that is certainly true. However, there are more things, there are more aspects that you can pull out when you look at all the different elements of working with a community. This is what our tool set is trying to do. We are going to be looking at things like if a given organization is really interested about raising a certain conversation within the broader open source ecosystem. With the mode tools that Callie described earlier, we can actually look at conversations that are going on in debt-based comments and issue trackers within the mailing list. Any place that there are public conversations that are going on, we can quickly look for the terms and the conversation that we are really wanting to see if we are getting raised. Hypothetically, if a company were trying to talk up container space and Kubernetes-based tools like what we see around the open shift ecosystem, you could start looking to see if those conversations were happening. Are they positive conversations? If they are not positive conversations, maybe there is something going on. Maybe there is a problem going on with your tool set that you were not aware of before. We can start dialing in and figuring out what those conversations are about. Also, we can look at things like how the resources can be calibrated towards community health. If we see a problem within a given open source project, we can quickly ascertain what skill sets, what resources in terms of infrastructure, anything like that, what needs to be put in place to help that community solve the problem that it is having. Another thing that these tools are going to be able to help us do is get into that element of sustainability. With all of these tools that are disposable, we can measure risk at many different levels. We can still measure the internal project health, which is what we have been doing for quite some time, with cauldron and auger, especially with cauldron. Now, with auger and mode, we can look at how that community is interacting with the broader open source ecosystem. We can really get numbers and figure out exactly how important a given project is to its peers. Again, it is not a popularity contest. We are really trying to define very quantitatively the strength of a project and also how important it is. None of us who are watching this want to see another open SSH where you have a project that is maintained by a very small number of people and yet so many projects rely on it. If those people are no longer able or willing to maintain that project, then there becomes a very large problem in the broader open source ecosystem. The other thing that we are doing here is we are trying to catch these risk factors as early as we possibly can. The earlier we can figure out that there is going to be some kind of risk to sustainability for a given project, the faster that all the businesses involved with that project can make a business decision and rescue it. You are not in constant firefighter mode. You are planning ahead and making business decisions based on project risk as early as possible. Tell us a little bit more about the other things that we want to try to do. Absolutely. The next stage of this is looking at strategic investment and taking that one step ahead. By putting together the different tools that we have here, we can start looking at new companies, new communities that are bolstered by these contributed and project data starting to see what are the anomalies, what are new players that are coming into the field that we have not necessarily paid attention to before. This can go from just being a community to being a buzzword. We can start looking at what is going to be the next containers. If we start to see buzz in different talk, whether it be on GitHub and GitHub activity, whether it be at different events, we can start to see, okay, how is this activity comparing to the prior large exploding buzzwords in the past and start to see are there similarities between the two and if it is something that we should try to get on early on. Once we start to see these certain words come out, we can start tracking their trends and looking at their GitHub data and seeing what the issues and discussions are around it. What are people saying about it? We can look at that from project debater and seeing over time, is it going up or is it going down and taking that one step ahead look to see where we can become involved in. From here, we will be going a little bit into a demo of these different projects and how they connect together. First, we will be doing a walkthrough with the mode debater tool. If there is any chance that you cannot see my screen, please let me know. Right here, we have a demo of the mode tool on the discovery side. The terms that are getting used to bring all the data into this is we are looking specifically at Red Hat, Fedora and CentOS stream. We can change what those key terms are if we start to say, okay, we want to look just in general just at containers. Right now, for this tutorial, that is the terms that we are going to be looking at and what is taken into the large set of data that is going into this. One thing that I have really picked up on is that you can start examining the impacts of events with changes of GitHub activity around that date. One example of this is the DevCon CC that usually occurs around January. We can see that in 2019, looking right after the event, we can see that there is a bolster in the amount of projects created and even more around that time leading up to the event and after the event. The amount of commits per week are on a large upward trajectory, which I think is a huge thing to look into here because projects being created that has one portion of it, but if there is more activity around the different projects that have already had a ground in state, we start to think, okay, what was being talked about during this event that is making this activity go up? Is there something new or is there something that has been on the stage a little bit but really is taking off because of the talks that are occurring at this event? That is when you can start to get into a little bit more analysis and start seeing, okay, where is the why? From here, we can also look at the growing technologies and buzzwords. In a hypothetical, this event was really big talking early on about containers. We can start looking at a subset of this data. For here, we are going to do the example of containers and start looking at, okay, what is in this set that has to do specifically with containers? We can see right here how the different data changes over time and that the subset of data that has to do with containers almost has more of a peak than even the large overall set. You can start to think maybe this has a large impact in it. That starts sending you down the rabbit hole, which really starts to bring in new ideas. You can start looking into who are the contributors' companies that are being involved in this. Obviously, a lot of these are more red hat-centric because right now we are looking at terms that are focused specifically on red hat. Overall, we can start looking at the top contributors as well who are the big players here. We can also start to see, okay, what is their activity? Are they going and branching out onto some new projects as well? It really has this branching effect that starts to give way more ideas of what is going on in our communities. From here, we have some things that are starting to spark our interest. This is when I really think Caldron starts to come into play. Here he has a project that takes in a community's repo data. From here, maybe I was thinking to myself that I want to learn a little bit more about what companies are involved in this project. We can go to our visualization tool here and create a new visualization to start to see what is actually happening here. Whenever I'm starting to look at which companies are coming at play, I personally go for the pie option because you can see what are the percentages. It's a lot more visually appealing for me, but there are so many different options here that go upon. I'd really like to use the goal tool to start to see how the activity around the commit is happening. Is it reaching the goals or some type of threshold that I have found for this community? Like I said, there are so many different options here whenever it comes to visualizations. For this example, we're going to be going on to the pie tool. We're going to be using the source of their Git data to create this. Here we have just a random circle. We now start to look at what is the aggregation that we want to use. From here, we're going to want to take a unique count of author IDs. Obviously, people will commit multiple times on different repos. You want to make sure that you're uniquely counting each time a new one comes to play. I'm just going to get a little label going for this, which is the unique IDs. Here, we're going to start looking at the buckets, which is what are the sections that we're going to want to look at. We'll go into split slices. Our aggregation here is going to be in terms, looking at the different terms that are going into the field. From there, we're going to be looking at author domain right there. This is looking at their email URL, which is not a perfect metric, but it can really just get you starting to look at an idea. I feel like that's the biggest portion that I'm getting out of all these tools, is that they're not something where you just look at them and poof, they give you all the answers. They give you a new way to think about things and start to get more data behind the intuitions that you have or maybe telling you that the intuition that you had isn't necessarily always accurate. We're going to make the size of these buckets around 10 just for making it a little bit more visually appealing. We're going to group whenever you have a bunch of emails that are random, that are not large enough. There's not at least 10 of them. They're going to be grouped into another category. We're going to update. Look what we have here. We can start to go and click and see what are the ones that are actively involved in this specifically community. Gmail is a pretty common one, which doesn't tell us too much. We can see here that Red Hat is actively involved in it. There's a lot of others. There's also these other ones that are in the corner, sews.com. I've personally never heard of it, backtick.net. There's different, smaller players on this field that you necessarily wouldn't learn about. In different communities, you might have more of your major players on hand. But those are the two main tutorials that we have to show today to kind of give idea of what these tools do and how they could be working, how they could work together. And from there, open the table for some questions and see if there's any other things that you'll like to maybe analyze with our tools that we have available. Definitely would be open to looking at some different keywords using project mode. Cauldrons sometimes takes a little bit longer to do some specific searches, but if it's something quick, we can definitely make that happen. Well, this is awesome, Kali. I really love seeing you guys using this. And for the health and sustainability of communities, it's really key. And a lot of this, the tooling here that you're showing, especially the pie chart, and that is something that we've been using in the OpenShift community for quite some time. And one of the things that I always say about and try and preach, I think I'm very preachy about it, Brian might agree with that, is that the community management and community development, we kind of think of it as an art, but this is bringing and trying to bring to bear some of the data driven approaches that we use in our sales and marketing and everything else. Why shouldn't community and open source communities have access to this to do the same sort of stuff? So it's wonderful to see you guys using all of this. The other thing that I always and I love seeing in this demo was I'm up in Canada. I used to be in Massachusetts right near you in Beverly and a shout out to UMass, and I know you're at BU, so we had a little competition going there. But is this hockey puck concept about looking for new emerging technologies? And one of the things that we've been doing quite extensively is watching the migration of resources and even our end users from different projects. So and using these same tools and the bitersia tools and the network analysis ones, I think if anyone knows me, they've seen me throw up what I call the jellyfish diagram because it's always pink and many tentacles of watching how people collaborate across communities. And these tools have been available for us for a while. But what is really interesting to me is the use of the Watson and the AI stuff to do maybe some predictive things, to do more than just watch what I call them the migratory processes. So I'd like to hear a little bit more about how the Watson part plays into this, if you can explain, because that was slightly different than the pie charts and dividing out those are tools that that I've had, but the Watson, the mode stuff is really cool. And how that and how you're working together with IBM is, if you could talk a little bit more about that, that would be wonderful. Yeah, so this project kind of evolved a little bit over the past probably six to eight months. And it started out with IBM reaching out to us just wanting a little bit more of a perspective, a community perspective that they knew they're like, we are big on the research side. We've done a lot of this analysis from the standpoint of academic papers. But we understand that that's not how and where we're going to find the useful data to understand the open source ecosystem. And so we brought in more of the community and open source perspective to start to see, okay, what should be the main things to look at whenever it comes to analyzing GitHub. And so whenever we can actually go and look back at the visualization here. So with this project mode tool, all of these terms are being grouped together by using sentiment analysis, mainly on the readme's. They're also doing a little bit of metrics on like community metrics on the amount of these repos that have community guidelines, which is something that's also very interesting to look at. But it's taking the strength of Watson, like Debater, Discovery to group people together, group repos together using sentiment analysis, not necessarily grouping you all together because of the transfer of similar contributors. And so you can't go and just look at the entirety of GitHub whenever you're looking at this like mode tool whenever you're looking at this table, but you can say, okay, I want this specific subset. So for right now, for this demo, the subset of terms that we're looking at is anything that has to do with Red Hat, Fedora, CentOS Stream, maybe we wanted to look at containers, maybe we wanted to look at hybrid cloud, maybe we wanted to look at something just looking at like Google seeing it like what is going on that's just specifically doing going on with a direct competitor VMware. And so you can go and look at the repos and group them together in a completely different way. And how you want to do that has there's a lot of flexibility that comes once you bring in that sentiment analysis portion and start looking at the key terms. So if I hear you right, this mode tool is looking at readmes and contributor guidelines. Is it looking at like the mailing lists? What are the data sets? Is it mining in the Slack, the blogosphere, Stack Overflow or any of that kind of content? Or is this strictly the readmes? So right now it's just the readmes, but the mailing list specifically is something that we brought up and have talked about really integrating in especially with the Watson debater who don't have access to an interface to show today sadly. But we can take in the mailing list data, which is actually something that I worked on like cleaning and preparing data for a different project that I was working on during my internship. And so we've talked about pretty heavily taking the tools that we already have for preparing the mailing list data and putting it into tools like this. For this demo, we aren't looking at that, but it is very it we can make that happen pretty easily. This setup can be applied to a way wider scope than just readmes. This is just where we this is the starting point. Yeah, no, this is great because like I said, what we've been doing with the detergent tools is we do we do a wide range of things with it and beyond just community health. But one of the things that the other things that's really important, I think to emphasize to people watching this is that the importance of domain knowledge. It's great. And this has happened in any data science tooling too is that if there isn't somebody with a bit of domain knowledge about the domains and how they interact, it's the tooling is really one of the is lacking. So like I've worked in other spaces, finance, accounting, auditing and stuff like that. And if you didn't know what you were auditing or what is appearing, it's pretty you can you can go down a wormhole, let's say, or make the wrong assumption. So I think one taking it step by step doing the readmes adding a new step is really the right thing to do. And as we torture you and make you learn all about open source and communities will eventually add in hopefully all of the CNCF projects, the cloud data computing foundation ones, which is really where most of the projects that I work on and interact with live and breathe, along with the OpenShift ecosystem too. So I'm really looking forward to getting this with a wider dataset. And that'll scare everybody over at IBM, you know, in terms of, you know, getting getting that set up because we have a lot of that data, but not in the semantic analysis or sentiment analysis stuff going on. And the other thing that's really, I think, important to understand is, you know, who are the and I think you have a little bit of that, like you can see some of the people's names who are, you know, leading up the repos, but is it possible here like to do analysis that drives on an individual contributor to a project or is this really a higher level thing? So to see where where's Kelsey Hightower playing in these days or, you know, what's going on, you know, in the sandbox at CNCF, there's I think there's now 45 projects in the CNCF sandbox now and, you know, that's those those kinds of trends and analysis I think are key to really understanding the entire ecosystem around some of the projects. Yeah, so so answering that question. So it's a little bit of a fine line because we're trying to so the short answer to your question, the yes, we should be able to do that between the tools we have here and also auger, which we haven't demonstrated, you know, auger is very good at like looking at an individual developer and seeing where that person has, you know, wherever they are in GitHub, it's not limited by subset it just looks for all connections and finds out where that person is, you know, working. So we can do it. We're a little bit hesitant because and how we apply that because we don't want to get into privacy issues. We've historically and you know this to Diane because your work with Patergia and the stuff that Ospo used to do, when we approached this as like a giant fire hose of information, we were always very careful to try to keep the user data as aggregated as possible. And, you know, it's always been a fine line because when we look at things like the pie chart that Callie showed us earlier where we're trying to identify domain by domain, like who's working for whom? Are they working for Red Hat or Google or Sousa? We can do that. But to refine that, we kind of need to know a little bit more about the person because like at Red Hat, we are not all required to use our redhat.com domains when we you know go participate in any open source project. So there could be more redhaters or you know at any given project beyond just the redhat.com and it might be similar for other organizations too. So yes and as we move forward, we're really trying to kind of be very mindful of individual privacy, especially, you know, we're getting into situations with GDPR and the California equivalent. And now, you know, Japan has one and Brazil has one. And I just heard, I think, you know, another state in the United States has something going. So there are a lot of individual municipalities and countries that have privacy laws in place and we have to be mindful of those as well. Yeah, no, definitely. And that's really one of the things that like you were mentioning with the bitersia and the sorting hat and the cauldron projects and stuff like that. They have been very mindful of making, ensuring that it's following those things. And that's also, you know, especially when we talk about putting some of these toolings and making them available publicly as open source projects. And that's really been key. You know, I'm always, I'm a huge fan of this stuff. So I'm totally thrilled. I really want to have you back and demo the auger stuff and take some time, Collie, maybe, and look at where OpenShift lands in this and when OKD lands in this. And this is really very timely.  And then all of the vendors who are collaborating and the value that they get from participating in those open source projects. And so I think when we try and sometimes in our jobs have to justify resources being allocated to different projects, which, trust me, happens all the time inside of Red Hat, these kinds of tools let us really help with those judgment calls about where the resources are. And as I said, I think the other big piece of this is using it to see where the hockey puck's going. You know, where are we shooting to because we're always, we're always in the present when we're doing this, we're always trying to suss out some, you know, current kerfuffle in a mailing list or something that's going on over in here because somebody's unhappy with it. And we often don't do any forward looking what's coming down the pipe. Where are our end users playing? You know, there was a great example of it a while back with a company Amadeus that is using OpenShift and they came to gave a talk on their use of Kafka, right? And it was really early, very, very early days of Kafka. But I think had we had tools like this, we probably could have seen them starting to log issues in Kafka, log, do a PR, you know, those kinds of things. And so for vendors who are looking to see where their customers are playing, where their end users are playing, these kinds of tools, not just for Red Hat or IBM, but for everybody who's working in open source. And it really helps us justify continuing to engage and do this. And as we all know, open source is part of pretty much every company on the planet these days is using something open source, whether you're making candy or manufacturing rocket ships or writing software. So this is really part and parcel of every business organization's decision making process now. And the work that chaos is doing that, you know, all of the different inner source and other communities are doing are really very, very important. So I can't say thank you enough, Kali and Brian for highlighting all this work. And I am so thrilled to see it being done and getting the airtime and resourcing internally at Red Hat. So and really looking forward to picking your brain, Kali, and running some OKD stuff there. And the other one I want to see run is you run is operators that term, the operator framework, because that is really good. It's tentacles and so many different things beyond containers. And this is where once you've got these tools refined, and we've got some processes in place for it, getting the lead project engineers who are working on it, whether it's in the emerging tech office at Red Hat or one of the project leads for one of the CNCF projects, so that they have the domain knowledge and they can tweak and see this. This is really hugely helpful for community development efforts. So again, thank you very much for coming today and putting up with some of our technical issues this morning. But we are definitely having you back, Kali. This was great. Thank you all so much for having us. Yes, thank you. It's been a pleasure. We're excited about the work we're doing and we're looking forward to showing it off again.