The powerful Yamnaya culture is one of the most important cultures you may not be too familiar with and may actually be related to. It existed around 5,000 years ago in the Pontic Caspian steppe and expanded across Eurasia over generations and through associated cultures, expanding steppe ancestry in the process. This expansion was probably very violent and was perhaps close to a genocide. It likely consisted of bands of men on newly domesticated horseback wiping out male populations across Eurasia and marrying their women. A 2017 study found that this was a male-dominated migration, as for every migrating female from the steppe, there were between 5 and 14 migrating males. You can only imagine the carnage. Subscribe for more.


One of the main question of cookies, I think that me, you, and CSI talk about is where is all the black and purple coming from? I mean, we know Urkel can kick purples and that's in my opinion at least, Urkel is the main force behind cookies and the look, the shape, a lot of it. But it's not really well known for consistently and being a true breeding purple breeder. There's a lot of green coming out of purple or purple Urkel and GDP much the same, but there is a consistent true breeding black trait in that line. And, you know, we had talked about deep chunk, we had talked about maybe it is just Urkel, maybe it's like this recessive shape being brought up and it's been slammed into each other enough that it's just consistently bringing black now.


I'm going to show how to invite graders to your assessment in CrowdMark. From the dashboard, you navigate to the team tab at the left. And then I can invite members of my grading team by email. Here are the three different roles in CrowdMark. A facilitator has full access to invite other team members, participate in the grading, access student information, and send results to students. A grader is able to participate in the grading, but they're not able to access student information or release grades to students. The printer uploader role is for someone who's not going to be participating in the grading, but they will be able to print the assessments and also upload the completed assessments. So I'll invite a grader here by email. Once we're integrated with your...


Wow and to do so I want to sing a cappella for a second it worked and we are doing a movie review and since it is February I don't know if I'm doing official romance month but we are going to do romance films and so obviously today we are doing a review of the I guess it would be qualified as a romance film yes Lamhe well I think it qualified why were you doubting there's a gray area here what else would it be psychological thrill but anyways obviously we're doing the 1991 great year by the way Lamhe directed by Yash very unknown actor most I mean director most most people are unfamiliar with produced by him I'm now I also wanted to get to some romance films that he directed as well and I know we've seen some but since he has that big romance kind of series coming out that we are pretty excited for I wanted to get some more films so we can get a lot more references and that kind of stuff yeah produced directed by him screenplay by honey irani and dialogue by Rahi Rahi Masum Reza and starring once again a couple of unknowns Anokabor Shredevi and Anapankar everywhere a whole heap of other people but anyways obviously it came out in 1991 if you haven't watched it we saw it on Amazon you can go watch it come back I think a lot I mean this is it's kind of a cult classic a lot of people know of this film yeah quite a bit but if you haven't watched it go watch it come back because it'll be a spoiler filter big time big time your initial thoughts please Rick well there's a lot that we're going to talk about but my biggest takeaway from this is probably one of the greatest lines in cinematic history cut which is don't hit a retarded child please I'm gonna bleep that out why it's on it's in the movie I'm aware it's so uncomfortable it's it's so awful speaking of which this is probably one of the single greatest compilations of putrefying horrendous you had a split second to just say something awful white acting in the history of all cinema on the planet sometimes they even say anything no they ruined it right it just saw the back of their head and you knew oh god please don't say anything but and they ruined it just not saying anything exactly at all seriousness this is one of the most excruciating movies I've actually ever liked yeah it's it's I this is a very it's a very interesting film and I think the most interesting part is the dialogue you kind of have to have because oftentimes I often say I was like we don't judge films off of our modern-day lens but sometimes it's a little difficult yeah based off of some scenarios but also the there's a whole different aspect of just the filmmaking and the beauty and the the well-made-ness of a film as well that I will talk about but then there's also the I would call it semi-problematic problematic it's some people don't think so as well well that's just subject matter yeah there's also some glaringly hideous moments when it comes to acting and everybody okay the one who has it the least is Shredevi yeah and then and El Capour but pretty much what one of my favorite things and it's just the standard of the day and it just drives me crazy is how many times people are directed to say their line into the middle distance yeah but you know just them so I was talking to them and I told them that I would tell them about the days that I remember so long ago when I was a child back at the river you remember those days when we used to walk along the sand and it's just how it was yeah but on a pump curse should we start with him yeah movie so we'll get into the the subject matter and a little bit then we'll talk about the filmmaking like we normally do first yeah I think the most interesting part is actually the the subject matter and what some people think would be problematic some people don't think it's problematic the biggest issue I have with the film is the age difference how how is an age difference like that ever gonna work between two oh wait I don't know I don't think it's the I don't think it's particularly the age difference as opposed to all the haters who told me that and Ronnie and I would never yeah anyways but yeah let's get into the the acting the story first but actually I will get to an on a pump current a second yeah we will how can you not the most on a pump curve ever seen oh but he was more on a pump curve than on a pump cur that's I want to talk about the our two leads first yeah I especially enjoyed them but Shredevi first and foremost obviously she's a phenomenal actress India loves dual roles more than oh yeah any country I've ever seen in India they just they love their stars have two characters I knew played by the same actor in a movie when she died which I loved I was like oh cool she died but she's not gone no she will be back hour 45 oh yeah how are they gonna bring her back but she will be back absolutely absolutely but I especially in the first hour I was her just beauty was just dripping what okay my word is one of the most gorgeous women what technically would you say the color of her eyes are are they light brown or hazel brown they are stars yeah they're like almost a color I've never seen yeah they're her her her eye color is just magnetic she uh she has she I equate her like to like a zaudition now in terms of like zaudition has these massive massive eyes right and that's one of one of the attributes attributes about her but man she obviously in the beginning when she was just in you know dancing and in the in the Rajasthani traditional clothing and she was just I mean the entire film she was gorgeous but especially that first hour when it was adult Shredavi or I mean I guess they're both technically adults right yeah but mom Shredavi right she was just so gorgeous like it's just you just it's almost like when you sit there but different though like when a shari or rise on screen yeah my god yeah it's must be easy for this director yeah and it must be easy for every leading man I just like one of my complements of Anil Kapoor is I believe 100% he's in love with her oh yeah well how can you not look at Shredavi and just be flabbergasted because she's such a beautiful beautiful lady and I thought outside of obviously when she was like directed to you know yeah obviously it's it's just the it's the time of the day and and like when they want them to act like a little childish it's always like so different to how you know people in the West would do certain scenes but it's very different very common as well you got to have that you have to have those you know what it's like watching Indians particularly Hindi cinema of this day and age you have to have a particular lens on to appreciate it for what it is or you're gonna not enjoy the experience in the same way that if you went into a 3d film without the glasses on you're just gonna be annoyed yeah you have to have a certain perspective to appreciate yeah I thought I don't think this film is totally that because I think there's a lot of good things about this film in terms of like how it was directed and yeah the weaving this of the story and all that but I thought Shredavi did a very good job I thought Anil Kapoor also it was so funny in the beginning he looks so young I know younger than mr. India yeah he lived without the very he was like but like he's a very subdued actor obviously but then obviously there were times as well that white was his character slap happy yes people had a problem with cutter sing but and it was amazing in memory sometimes it was a backhand sometimes it was a forehand I loved the fact that they made a song song about the slap and this her flash dance moments yeah yeah I loved it I was like oh this is like a footloose like yeah yeah angry yeah I did love it moment but then they incorporated the slaps into the over and over which I thought was a lot of fun the songs in this were gorgeous yeah songs are my some of my favorite I wouldn't I mean I I would have been shocked if the songs weren't gorgeous of course just the time of Bollywood the word I've know about this film we see we saw one song when she was given them that glob glob 9000 in the I saw it again yep she was and I also appreciated in the score we had the main theme but there were also variations on the theme throughout the film where you would hear it but it was either changed by tempo yeah we're even changed melodically so it sounded more positive and then yeah it was really nice I 100% believed in Okapur it fell in love with her yeah immediately yeah which is not surprising you could put any man I guess in that or woman in that scenario and they probably would have fallen in love with Shredevi yeah because of how gorgeous she is right yeah and I so I believe there I believe that this young girl was infatuated with him yes creepily so once again yes and she was five so we'll get into that yeah I know couple man there was a moment where someone said how can you tolerate this man and I thought it's really hard it's it's getting really hard for me to tolerate this man we've seen Anil Kapoor and it you mean Anupam Kuru Anupam Kuru sorry in many films be to this style like this yes and we've all seen be very subdued and it's he's I think of a very versatile time sometimes this stuff works really well but I think it's more when this works when everybody else is on his level yeah like in a cabica or whatever the SRK film said he's in right and that right dad right this he was like so drastically above everyone else yeah there was no just shocking to me there wasn't anything charming about it to me in the same way that there is like in DDLJ and things of that nature yeah there wasn't any charm it was just oh good grief somebody tell this guy to ratchet down and that's coming from a New York Italian loud improv comedic guy yeah thinking good grief this hurts do you remember in Harvest you remember Paula Roche yeah yeah oh yeah sorry that's a very personal you know absolutely Paula Roche he's an Anupam Kuru he was a local theater actor that he was in our church yeah and he's very sweet very similar yeah in terms of acting style yes but yeah he was the most like I mean he brought it down at some points and I was like okay especially at the end the last 30 minutes he this is nice there here take it on a boom and obviously I know that was his character I know he was directed to do that yeah but it was just written that way it was like hey it was like a show a punch to the face he gets credit for going all in at the end the whole time because one of the things you want no matter what the genre is one of the things you want your actors to bring if you're the director and the writer and the cast you want them to come in with way more yeah and have them bring it down rather than try to draw something out of them yeah he he brought it all man this is not by unleashed not by Anupam Kuru but there's another obviously the first line that you said which was so I took a picture of it I took a picture of two lines that line that you said because it's just people used it all the time back in the day Hollywood everywhere in regular life and it's just and looking now in 2023 it's like oh my god but it didn't mean any back then I can vouch for this cut being alive in that era at a time when it was used all the time yeah you would call somebody retarded as but because the only definition we had for people at the time who suffered from any kind of what at the time was called mental retardation yeah it was a standard to call somebody retarded it didn't carry the level of yeah insult that it should have yeah as it does today I'm sorry picture that but there's also a line that she Debbie says that when she's talking to honor pump curse would be wife right and she says why are you so emotional she says I'm in that was a great moment I also thought it was written pretty well that moment where they're having the meal or the vanilla ice cream she said he was just straight up she was vicious I was like oh I'm scared of you yeah and but I did it with a smile she did just cold she was like it was and then I guess we could get into some of the obvious I love the the overall score like I said before and it's beautiful I think a lot of the way it was weaved because I thought it was gonna almost be a Casablanca situation in the beginning of yeah it's gonna be like a love triangle right me too interesting right she died he died everybody yeah right it's just so interesting how Indian films just do like you're dead and then how people die also I also this is this is just not good writing but it's kind of endearing for the time where the way you propel the story forward is not just exposition in the opening about hey we've been here for three days and now we all know you've been there for three days but this hello you lost the case losing everything yes in with matter of 60 seconds yeah you gotta you gotta push the story gotta push the three-hour film it took them 60 seconds to do that every was still alive this moment chance to kill her and that was fast too they kill like oh like oh she's she's got oh she was in car crash she yeah he's dead yeah you lost the place what do you want to save the baby or her no save her baby's fine she's dead and the antagonists who get the place we don't know why they won the case maybe her dad did something really heinous and that's why he lost the case yeah we don't know when they show up they're obviously they're practically twiddling their mustaches yeah give us our property yeah it was almost I think was it from the bad guys from what's that dragon movie how to train your dragon no no no the green the green dragon is an old film oh not puff the magic dragon why am I forgetting never ending story that's not a dragon no no no it's an animated dragon oh but everybody else is real oh Pete's drag each dragon yeah the villains and that I forget what their names are I don't remember I asked if their names in the very similar to the yeah the maniacal right so let did you find the story at all problematic any part of it problematic there was a part where there's two things I had particularly the love story yeah yeah yeah here's one thing I had a problem with was another freaking Kashi what Anita man here's another woman loving a guy for years and he just ditches her even though she was totally in love and dedicated to the guy granted you shouldn't good grief grant obviously he shouldn't have pulled her along if he wasn't in love with it exactly that's his fault but that's what I mean she was the Kashi in this don't marry somebody because they love you obviously but don't pull them along either no if there's one thing I had that was problematic for me it was I didn't feel and again look at the film in the time and the kind of film that it is I didn't feel like there was something anything in the script that really did give me a justification for him saying he truly did only love her for her I think that was an entire crock of crap at the end yeah yeah he loved her because she reminded me of her mom yeah that period and I was versus loving her for her I was struggling with once I had the film ended and I wish it it didn't and that's I'm not gonna fault the film for it it I kind of I knew how it was gonna end oh god they're gonna be together they're gonna be together of course but I didn't want it because I was liking how the dynamic of I was like okay Anil Kapoor obviously loved Shredevi he's struggling with the fact that she looks very cool but I was believing him I was like I don't think he loves you right I think he was like he's the idea he loves the idea he's like you look like the person I love right you are not right you also I saw you grow up right which is there's like age difference obviously doesn't really mean anything to me obviously as you as long as your adults right it's like exactly as long as you have adults the issue with the gray area I would say lies in like let's say this is the scenario I saw your granddaughter right grow up right and then once she turned 18 we got together right that's a little strange yeah right yes okay yeah that's that's the problematic part I'm like that's that adds a level of strangeness to it it would be not it's not Woody Allen no right obviously that's point that's a different that's a different thing level right of weirdness because obviously he wasn't her father and obviously she didn't really see him as a father figure but he did see her grow up she and it's not bad for her really to be in love with an older person that's very common for Gary common to believe oh I love this man he's like yeah I want to marry my dad when I'm older kind of thing obviously right and so that's not really the strange part but like if I was to like yeah and then I'd not date her when she was younger but then once she turned 18 she showed interest in me and you'd question how long have you shown interest in this little girl you absolutely right when did that light switch turn on so I guess I think they could have avoided all that and just ended it like with him going his own way and it's almost a tragic tragedy especially for Shredevi yeah and him thinking oh I could have been with this girl even though she wasn't actually her yeah that's how I was or even or even addressed it yeah I mean again it's not that kind of film but I knew I was gonna end but had that been addressed in some way where one of the struggles he's having is that moral conundrum yes if you have a sense of propriety yeah the idea is gonna cross your mind yeah and you'll you'll justify yeah what even in an age difference yeah if you're honest you're going to address it for example my wife and I yeah the the moment we both realized we had been falling in love with each other mm-hmm it was a matter of days we were conversing about how realistic is this relationship yeah you're in India I'm in America that was one thing can we handle the long distance yeah number two age difference do you want children I want more children yeah I'm am I okay with the fact that I could be dead in 20 years mm-hmm is she okay with the fact that could be dead in 20 years you you are brutally honest about that stuff yeah granted it's not the kind of movie no it would have been nice to have seen it I thought someone see the end was very rushed like in terms of them like it happened in the last five minutes right he had a very quick turnaround of yeah no you know what actually you don't smell like her you don't you don't really want her to make me feel like her yes I honestly probably would have preferred it to like you could have I could have I could cut this movie without adding anything to end correctly and it could have been like hours is rough he got yeah yeah that's it but again from this time yeah it could have how I would have preferred it to end once again I'm not taking away from the film this show how why after watching it I think this could have even been a better movie ending this way because you and you leave people with like questions and longing and something what I wanted you tell me what you wanted at the end and I have I had a very could have said he married her whether he did or not means no nothing to me you could speculate as the audience whether he did and then the end her giving that speech at the doing like almost the play kind of thing I think that would have been a perfect ending okay just ended we're on the same page I think that would have been perfect here's what I was hoping for but knew it wasn't gonna happen yeah I knew it wasn't gonna happen he's telling her no I see a different gleam in your eye you smell different that whole nine yards she didn't have any facial change he says I love you yeah no facial change he extends his arms no facial change she starts walking up to him I wanted her to slap and say you're deceiving yourself yeah and you're playing with my emotions yeah and it end with the heartbreak of could they have ever been together yeah that would have been great too and if she had said the monologue there after slapping him that would have been epic for me yeah but you knew it was gonna end now yeah it's you're together all everything's great old-school Bollywood of course you knew it was gonna end up together it's just yes the standard fare of the day if they have to have you happy in the end absolutely and if you don't think this film is probably like you can let us know why you don't think it's problematic at all whether you because I posted it on patreon right after I finished the film I was like just finished it I'm gathering my thoughts of what I think about it do you think this film is at all problematic yes explain why no explain why I got over a hundred comments of people both ways explaining if they think it's but it's problematic or if it's not problematic at all but there's two aspects of it I'm interested in which of the ones they considered because obviously you and I find the aspect of he's known her since childhood yeah not necessarily the age gap yeah no that makes no difference to me me too I like I'm interested to know how many people have a problem with just the age gap itself yeah I mean there are people that will be people of course that but I mean if he just met this random person that he had never met before who looks like her which looks like her hasn't she done a film where she's probably yeah we've seen a film where she's done that and she was like an 18-year-old woman our role as long as she's an adult and they met at his age and her as a legal adult right and but they didn't know each other as children he didn't like see her grow up right I wouldn't have had an issue right I was like okay you're too contending adults you guys do what you'd like to know it to me I thought about that and it's still this morning before I came here I thought if Indrani had died and had a kid that grew up and looked like her it'd be it'd be really weird but granted you've also been dating Indrani she never I never loved never never returned but even still yeah it's that it's still a gray area I can't think of any ex I've had who if they had a kid grew up and looked like them I wouldn't be a little weirded out on myself by the fact that I was yeah liking them it's problematic it's a minimum of gray area right minimum at minimum so you guys got I think the conversation is fun though to for a film like this to stir up yeah I think that's that's good for a film to do as opposed to like oh yeah that's that's standard fair kind of right film I thought it was very interesting I enjoyed the film I think it's a good film yeah even for all of the pain yeah I still what held it together for me was her yeah like so many films we see where it's there's a there's a lone actress who's just holding it all together and that was this should never use the next aspect of that was I for the most part enjoyed a nil in this yeah yeah yeah and I even as dreadful as it is I kind of have this guilty pleasure would be like I don't eat fruit loops or Captain Crunch for breakfast but if there might be this one or two times a year I might just venture out and just have it just to have it and that's how I feel about I don't occur granted though he's gonna like we saw I think it was a really good performance in that year he's a solid actor in that ran beer film right yeah and no no no he's a solid actor and I thought we thought he did a good job in yes he did a very I think he's probably the strongest performance very grounded no he's a solid obviously doing what he was told to do especially for the 90s yeah like I said he he gave it all yeah he did yeah anyways let us know what you thought about the film which we are next kind of romance film from yash I say yash it makes me think of KGF yes Chopra specifically and I know Kapoor and treat it I know they did quite a few films together yes I think click 12 yeah I think so I don't know that exact number but I know that there's I can't remember anyways let us know what it should be down below


Assalamu alaikum, hello my friends. For the last month, Muslims around the world, and here in Canada, have fasted from dawn to sunset. Eid al-Fitr marks the end of the holy month of Ramadan, a time of fasting and reflection, spiritual discipline and renewal, and service to those less fortunate. I am honoured to partake in this special time, which celebrates the common values that unite us all – values like empathy, generosity, discipline and respect. These are tenets of our Charter of Rights and Freedoms and vital principles upon which Canada is founded. As we celebrate the end of Ramadan and look forward toward the future, let us carry these shared values onward – values that built this country and make us stronger every day. On behalf of our family, Sophie and I extend our warmest wishes to all those celebrating Eid al-Fitr. Eid Mubarak.


Live from Atlanta, Georgia, it's theCUBE, covering AnsibleFest 2019, brought to you by Red Hat. Welcome back, everyone, it's theCUBE's live coverage of AnsibleFest here in Atlanta, Georgia. I'm John Furrier, my co-host, Stu Miniman. We're here at Stephanie Charis. He's the Vice President and General Manager of the RELL Business Unit, Red Hat. Great to see you. We've been interviewing you all year through your career at IBM. Now, Ansible, back in the fold. It's the last time we chatted at Red Hat Summit, RELL 8. How's that going? What's the update? Yeah, so we launched RELL 8 at Summit. It was a huge opportunity for us to sort of show it off to the world. Couple of key things we really wanted to do there was make sure that we showed off the Red Hat portfolio. It wasn't just a product launch, it was really a portfolio launch. Feedback so far on RELL 8 has been great. We have a lot of adopters on there early. It's still pretty early days when you think about it. It's been about a little over four, five months. So still early days, the feedback has been good. It's actually interesting when you run a subscription-based software model because customers can choose to go to eight when they need those features and when they assess those features and they can pick and choose how they go, but we have a lot of folks who have areas of RELL 8 that they're testing the feature function of. I saw a tweet you had on your Twitter feed, 28 years old, still growing up, still cool. I mean, 28 years old, it's not in the real world. It's an adult now. No, Linux is running the enterprises now and now it's about how do you bring new innovation in? When we launched RELL 8, we focused really on two sectors. One was, how do we help you run your business more efficiently and then how do we help you grow your business with innovation? One of the key things we did, which is probably the one that stuck with me the most, was we actually partnered with the Red Hat Management Organization and we pulled in the capability of what's called insights into their product itself. So all current subscriptions, six, seven, eight, all include insights, which is a rules-based engine built upon the data that we have from, you know, over 15 years of helping customers run large-scale Linux deployments and we leveraged that data in order to bring that directly to customers and that's been huge for us and it's not only, it's the first step into getting into Ansible, right? I want to get your thoughts on, we're here at Ansible Fest, day one of our two-day coverage. The Red Hat announced the Ansible Automation Platform. Obviously that's the news. Why is this show so important in your mind? I mean, you see the internal, you've seen the history of the industries, a lot of technology changes happening in the modern enterprises now as things become modernized, both public sector and commercial. What's the most important thing happening? Why is this Ansible Fest so important this year? To me it comes down to, I'd say, kind of two key things. Management and automation are becoming one of the key decision makers that we see in our customers and that's really driven by, they need to be efficient with what they have running today and they need to be able to scale and grow into innovation platforms. So management and automation is a core critical decision point. I think the other aspect is, you know, Linux started out 28 years ago proving to the world how open source development drives innovation and that's what you see here at Ansible Fest. This is the community coming together to drive innovation. Super modular, able to provide impact right from everything from how you run your legacy systems to how you bring security to it into how do you bring new applications and deploy them in a safe and consistent way. It spans the whole gambit. So Stephanie, you know, there's so much change going on in the industry.  I actually saw a couple of Hello World t-shirts which were given out at Summit in Boston this year. Maybe help tie together how Ansible fits into this. How does it help customers, you know, take advantage of the latest technology and move their companies along to be able to take advantage of some of the new features? Yeah, and so I really believe, of course, that an open hybrid cloud, which is our vision of where people want to go, you need Linux. So Linux sits at the foundation, but to really deploy it in a reasonable way, in a safe way, in an efficient way, you need management and automation. So we've started on this journey. When we launched, we announced at Summit that we brought in insights in. That was our first step. Included in, we've seen incredible uptick. So when we launched, we've seen 87% increase since May in the number of systems that are linked in. We're seeing 33% more increase in coverage of rules-based and 152% increase in customers who are using it. What that does is it creates a community of people using and getting value from it, but also giving value back, because the more data we have, the better the rules get. So one interesting thing, at the end of May, the engineering team, they worked with all the customers that currently have insights linked in, and they did a scan for Spectre Meltdown, which, of course, everyone knows about in the industry. With the customers who had systems hooked up, they found 176,000 customer systems that were vulnerable to Spectre Meltdown. What we did was we've had an Ansible playbook that could remediate that problem. We proactively alerted those customers. So now you start to see problems get identified with something like insights. Now you bring in Ansible and Ansible Tower. You can effectively decide, do I want to remediate? I can remediate automatically. I can schedule that remediation for what's best for my company. So we've tied these three things together, kind of in the stepwise function. In fact, if you have a rel subscription, you've hooked up to insights, if insights finds an issue, there's a fix it button with Ansible, creates a playbook. Now I can use that playbook in Ansible Tower. So really ties through nicely through the whole portfolio to be able to do everything in a really secure way. It also creates collaboration to these playbooks that can be portable move across the organization, do it once, that's the automation piece. Is that the key? Yeah, absolutely. So now we're seeing automation, how do you look at it across multiple teams within an organization? So you could have a Tower admin be able to set rules and boundaries for teams. I can have a rel, right, IT operations person be able to create playbooks for the security protocols. How do I set up a system? Being able to do things repeatedly and consistently brings a whole lot of value in security and efficiency. Yeah, one of the powers of Ansible is that it can live in a heterogeneous environment. You got your Windows environment, I've talked to VMware customers that are using it, and of course in cloud. Help us understand kind of the rel, why rel plus Ansible is an optimal solution for customers in those heterogeneous environment. And what would love, I heard a little bit in the keynote about kind of the roadmap where it's going. Maybe you can talk about where those will fit together. Yeah, perfect. And I think your comment about a heterogeneous world is key. That is the way we live. And folks will have to live in a heterogeneous as far as the eye can see. And I think that's part of the value, right, to bring choice. When you look at what we do with rel, because of the close collaboration we have between my team and the team in the management BU around Insights, our engineering team is actively building rules so we can bring added value from the sense of we have our Red Hat engineers who build rel, creating rules to mitigate things, to help things with migration. So you asked about rel 8 and adoption. We put in in place upgrades of course in the product, but also there's a whole set of rules curated, supported by Red Hat that help you upgrade to rel 8 from a prior version. So it's the tight engineering collaboration that we can bring, but to your point it's, we want to make sure that Ansible and Ansible Tower and the rules that are set up bring added value to rel and make that simple, but it does have to be in a heterogeneous world. I'm going to live with neighbors in any data center of course. What one of the pieces of the announcement talked about collections. Is there anything specific from your team that which should be pointed out about from a collections and the platform announcement? So I think as collection starts to grow, and it brings out sort of the simplicity of being pulled playbooks and roles and pull that all into one spot. We'll be looking at key scenarios that we pull together that mean the most to rel customers. Migration of course is one. We have other spaces of course where we work with key ecosystem partners. Of course SAP HANA running on rel has been a big focus for us in partnership with SAP. We have a playbook for installing SAP HANA on rel. So this collaboration will continue to grow. I think collections offers a huge opportunity for a simpler experience to be able to kind of do a automated solution if you will kind of on your floor. Automation for all, that's the theme here. I want to get your thoughts on the comment you made about analytics capabilities inside rel. This seems to be a key area for insights tying the two things together. So kind of cohesive, but decoupled. I see how that works. What kind of analytical capabilities are you guys serving up today? And what's coming around the corner because the environments are changing. Hybrid and multi-cloud are part of what everyone's talking about. Take care of the on-premises first. Take care of the public cloud. Now hybrids, now an operating model has to look the same. This is a key thing. What kind of new capabilities of analytics do you see coming? Yeah, so let me step you through that a little bit because your point is exactly right. Our goal is to provide a single experience that can be on-prem or off-prem and provides value across both as you choose to deploy. So Insights, which is the analytics engine that we use built upon our data, you can have that on-prem with rel, you can have it off-prem with rel in the public cloud so where we have data coming in from customers who are running rel on the public cloud. So that provides a single view. So if you see a security vulnerability, you can scan your entire environment, which is great. I mentioned earlier, the more people we have participating, the more value comes. So new rules are being created. So as a subscription model, you get more value as you go and you can see the automation analytics that was announced today as part of the platform. So that brings analytics capabilities to, first, to be able to see who's running what, how much value they're getting out of analytics, that the presentation by JPMorgan Chase was really compelling to see the value that automation is delivering to them. For a company to be able to look at that in a dashboard with analytics automation, that's huge value. They can decide, do we need to leverage it here more? Do we need to bring it value here? Now you combine those two together, right? It's, and being informed is the best. I want to get your reaction to, we made a comment on our opening, Stu and I on our opening segment around the JPMorgan comment, hours to minutes, sometimes days to minutes, depending upon what the configuration is. Automation is a wonderful thing. We're a pro automation, as you know. We think it's going to be a huge category, but we took a survey inside our community and we asked our practitioners and our community members about automation and they came back with the following, and I want to get your reaction. Four major benefits of automation. Focused efforts allows for better results, efficiency. Security is a key driver in all this. You mentioned that. Automation drives job satisfaction. And then finally, the infrastructure DevOps folks are getting re-skilled up the stack as the software abstraction. Those are the four main points of why automation's impacting enterprise. Do you agree with that? Can you give any comments on some of those points? No, I do, I agree. I think skills is one thing that we've seen over and over again. Skills is key. We see it in Linux. We have to help bridge Windows skills into Linux skills. I think automation that helps with skills development helps not only individuals, but helps the company. I think the second piece that you mentioned about job satisfaction, at the end of the day, all of us want to have impact. And when you can leverage automation for one individual to have impact, that is much broader than they could do before with manual tasks, that's just... You know, Stu and I were talking also about one of the key words that kept on coming out in the keynote was scale. Scale's driving a lot of change in the industry at many levels. Certainly software automation drives more value when you have scale, because when you're scaling more stuff, you can't manually configure that stuff at scale. So software certainly is going to be a big part of that. But the role of cloud providers, the big cloud providers, the IBM, Amazon, all the big enterprises like Microsoft, they're driving massive scale. So there's a huge change in the open source community around how to deal with scale. This is a big topic of conversation. What's your thoughts on this? Is there any general opinions on how the scale is changing the open source equation? Is it more towards platforms, less tools, vice versa? Is there any trends you see? I think it's interesting, because I think when I think of scale, I think both volume or quantity as the hyperscalers do, I think also it's about complexity. I think the public clouds have great volume that they have to deal with in numbers of systems, but they have the ability to customize, leveraging development teams and leveraging open source software. They can customize. They can customize all the way down to the servers and the processor chips, as we know. For most folks, they scale, but when they scale across on-prem and off-prem, it's adding complexity for them. And I think automation has value both in solving volume issues around scale, but also in complexity issues around scale. So even, you know, mid-sized businesses, if they want to leverage on-prem and off-prem, to them, that's complexity scale. And I think automation has a huge amount of value to bring there. And that abstracts away the complexity. Automation provides the jobs to abstract, but also the benefits of efficiency. Absolutely. To me, the greatest value of efficiency is now there's more time to bring in innovation, right? It's a... Stephanie, last thing, I wonder in what feedback are you hearing from customers? You know, one of the things that struck me we were talking about the J.P. Morgan is they made great progress, but he said they had about a year of working with the security, the cyber, the control groups to help get them through that knot hole of allowing them to really deploy automation.  What is that something you hear generally? Is that a large enterprise thing? You know, what are you hearing from customers that you're talking to? I think we see it more and more and it came up in the discussions today. The technical aspect is one aspect. The sort of cultural or the ability to pull it in is a whole separate aspect. And you think that technology, right? All of us who are engineers, we think, well, that's the tough bit. But actually the culture bit is just as hard. One thing that I see over and over again is the way companies are structured has a big impact. The more siloed the teams are, do they have a way to communicate? Because fixing that so that when you bring in automation, it has that ability to sort of drive more ubiquitous value across. But if you're not structured to leverage that, it's really hard. If your IT ops guys don't talk to the application folks, bringing that value is very hard. So I think it is kind of going along in parallel, right? The technical capabilities is one aspect. How you get your organization structured to reap the benefits is another aspect. And it's a journey. That's really what I see from folks. It is a journey. And I think it's inspiring to see the stories here when they come back and talk about it. But to me, the most, the greatest thing about it is just start, right? Just start wherever you are. And our goal is to try and help on ramps for folks wherever their journey is. It's a great opportunity for people's careers and certainly the modernization of the enterprise and public sector and governments from how they procure technology to how they deploy it and consume it is radically changing. Very quickly, by the way, too, scale and these things are happening. I got to get your take on it.  You have so many different experiences. The cloud 1.0 was the era of compute, storage, startups can start in, Airbnb start. All these companies are examples of cloud scale. But now as we start to get into the impact to businesses in the enterprise with hybrid, multi-cloud, there's a cloud 2.0 equation. Again, we mentioned observability was just network management at white space. Small category, which companies are going public. It's an important now kind of subsystem of cloud 2.0. Automation seems to feel the same way we believe. What's your definition of cloud 2.0? If cloud 1.0 is simply stand up some storage and compete, use the public cloud. And cloud 2.0 is enterprise. What does that mean to you? How would you describe cloud 2.0? So my view is cloud 1.0 was all about capability. Cloud 2.0 is all about experience. And that is bringing a whole new way that we look at every product in the stack. It has to be a seamless, simple experience. And that's where automation and management comes in in spades because all of that stuff you needed in capability, having it be secure, having it be reliable, resilient, all of that still has to be there, but now you need the experience. So to me, it's all about the experience and how you pull that together. And that's why we're hoping, you know, I'm thrilled here to be at Ansible Fest because the more I can work with the teams that are doing Ansible and Insights and the management aspect and the automation, it'll make the rel experience better. The beautiful thing is software drives it all. Absolutely, absolutely. Stephanie, thanks for sharing your insights on theCUBE. My pleasure. We appreciate you coming back on. And great to see you. Great to be here. Good to see you both. We have coverage here in Atlanta. I'm John Furze, Stu Miniman, CUBE coverage here at Ansible Fest. More coverage after this short break. We'll be right back.


My name is Sam Vaknin and I'm the author of Malignant Self-Love, Narcissism Revisited. In my writings, I often compare narcissists to alien lifeforms, to robots, to automata, to machines. People often bristle at such comparisons. They say, well, surely narcissists are human beings, as human as we are. But you see, narcissists lack a critical piece of equipment. They lack the ability to empathize. Empathy is what binds us together. Empathy is the quintessence, the essence of what it means to be human. In the absence of empathy, emotions and cognitions are skewed, deformed, and in a word, alien. To empathize means to put yourself in the shoes of another person. To understand, to accept, and sometimes to sympathize with that person's needs, emotions, fears, hopes, wishes, preferences. For a split second to be someone else is to empathize. It is this ability to become someone else, however momentarily, that allows us to feel compassion, mercy, pity, and to help altruistically and unselfishly. The narcissist, usually being the victim of early childhood abuse, did not develop this critical capacity. The narcissist is unable to empathize. Everything human is strange to the narcissist alien. He cannot grasp the three-dimensionality of other people. To the narcissist, people are instruments of gratification, mere extensions of himself. Psychopathic narcissists regard other people as playthings, and they can even go to the extent of becoming sadistic, physically, emotionally, verbally and psychologically. It is because of that that I keep saying that narcissists cannot truly communicate with other people. They cannot communicate with their family, with their friends, with their colleagues at work, with underlings, subordinates and bosses, with neighbors, and even with their therapist. This lack of empathy sets them apart. It is an important criterion in diagnosing narcissistic personality disorder, one of the nine in the Diagnostic and Statistical Manual, and it also makes them easily recognizable. This coldness, this aloofness, this detachment is unique to narcissists and psychopaths. Some people call it a reptilian quality. In my book Malignance of Love Narcissism Revisited, I suggested that narcissists and psychopaths actually do possess a form of empathy, which I call cold empathy. They easily hone in on other people's vulnerabilities, weaknesses, frailties and foibles, and they leverage and take advantage of this knowledge in order to exploit, manipulate, maneuver and sometimes simply humiliate and inflicting pain. Yet this is not warm empathy. This is not an empathy that leads to positive feelings. This is the empathy, type of empathy, cold empathy that renders in narcissists and psychopaths the ultimate predator.


So my beautiful people I am back with another out of the ring video and today I'm going to showcase to you guys a very very interesting talisman, a Northwool damage talisman, also it says today we check it out. How's it going guys my name is DPJ and if you do enjoy the video leaving a like who helps out and if you like what you see and want to see more be sure to subscribe.


Hello everybody, I'm Michael Stonkey. I run the platform engineering organization at CircleCI. And I want to talk about what we've learned from looking at the CircleCI data about how teens are performing. So, I'm Mike, I'm at CircleCI. I guess that's the intro, that's the simple intro. The main part of this talk, we're going to have a little bit of a setup about what's the problem set we're trying to ask about, what are we trying to learn, a bit about the data, diving in, seeing what the raw data tells us, and then a little more about insight about how we look at that data and what we can derive from it, what we can maybe hypothesize about it and talk about trends we're seeing in the industry and other influencing factors. So, beginning with the setup. There's a lot of DevOps reports out there that talk about high-performing teams and what these metrics are and what happens. And to me, those have always been fascinating. They've been some of the best research available in our field. I've helped author a number of these at this point. I worked at Puppet for a long time, and they were the people that started this. And I worked with them on a number of these questions and analysis and kind of how teams are working and what makes a team high-performing versus a team that's maybe medium performing or a team that's less successful.  Well, that is an interesting way to approach this, but now that I'm at CircleCI, I had a whole new opportunity of how to do team analysis and that's looking at actual behavior, looking at data. And so the way that I think about this is this is performance derived versus performance described. You describe your performance when you're filling out a survey. You say, well, here's how we behave or here's how my team behaves. Whereas when it's derived, I can say when I look at the data, here's what your team is doing and here's how they're actually working. And those things don't always agree with each other one-to-one. And that doesn't mean that anybody's lying or that they're not telling the truth or that they're, you know, perhaps looking at something with rose-colored glasses or whatever. It means that sometimes people have recency bias or they have a, well, I'm not thinking about this other project. I'm only thinking about this main project or, you know, whatever the case may be. And so we kind of try to, I guess, normalize out that data a little bit and look at what's really going on org-wide versus just maybe a specific data point. So in this data set, we have 44,000 organizations that are in this data set using CircleCI to pull from. 160,000 projects. And that makes this about 1,000 times larger than any state of DevOps survey that's existed over the years. And so this is just a much, much larger data set, which should mean that we can get a little more strong signal from it and a little less, you know, if there's four people that work at one company or 20 that work at one company to fill out something that they can skew the data in one way or another. I don't think that actually happens in those surveys because, again, the data analysis is very thorough and professional and they usually can sort that kind of thing out. But this is just a larger data set overall. The other thing that we can do this year is look at what's changed year over year. We started this question set last year where we were asking a bunch of questions and our data science people were slicing and dicing it and trying to figure out the answers. And so we have some data from last year and even this year we ran the data from last year to make sure that we had processed it correctly and everything. And so we have some trends that we can see of what's changed this year versus last year.  And so you may see that there's some of those factors show up in the data. So we do 30-day increments in data sets. And for some of the trending, we'll do multiple 30-day data sets so you can see month over month in a couple spots. Our organizations, we've grown a little bit in 2020. Our number of projects in the sample grew a little bit in 2020. And that's basically just because of platform growth overall for CircleCI. Traditionally, there are four metrics that are used in DevOps surveys as kind of the indicators of high-performing teams. And that's your deployment frequency, your recovery from failures, your change failure rate, and your lead time to change. And those are generally kind of the big four metrics. If you're high in those, if you cluster in those, you can be in the high-performing group. And that indicates those are characteristics of high performers. Okay. And so how do we look at that now if you map that into a CI world? Well, if we have deployment frequency, the question is really how often do you initiate your work? And so this is how often do you initiate a pipeline and that becomes throughput in our nomenclature. Then we have lead time to change. That has, what's your pipeline duration? And we just shorten that to duration. Your change failure rate, which is your pipeline failure rate, we actually go to success rate instead of failure rate because it's more fun to talk about success than failures. And then mean time to recovery just becomes recovery time in our world. So that was a little bit about the setup. Now I'm going to get into the data. This will be a lot of data coming at you pretty quickly, but then we'll get to the insights. So throughput is what we start with, which is how often do you push code that triggers CI? Most projects are configured to run and push to it, upon push to a get server. Now this can happen, this could be one commit push to a get server, or this could be dozens of commits say in a single poll request. A lot of people like to say they run tests on commit, but usually it's actually they run on push. And so with throughput, you can see that people at the 5th percentile are running 0.03 jobs per day. Well, that's not very many. And then people at the 90th percentile are kicking off a workflow 16 times per day. Okay, that makes a little more sense. But when you think about kind of the narrative that high performing teams are deploying dozens of times a day or hundreds of times a day, I would say that our data don't really support that. Now, if you're in that 95th percentile and above you do start to see that where people are in the 30s and I mean, well above that you can get the several hundred at the 98th or 99th percentile. But on average, you know, people are deploying maybe eight times a day. We should look at that mean at the bottom there. And that's just running a throughput. That might not even be a deploy every time that could be just doing validation or code pushes. So people are not pushing code perhaps as often as they talk about all the time. And so, yeah, this analysis is kind of most projects are not doing not deploying dozens of times per day. So why why does that become such a popular narrative? Well, a lot of times in the surveys they ask, you know, what's the primary application or service you work on? And it could be that a single project you have is deployed a lot or it could be that your single application, your platform is made up of dozens and dozens of projects. And so while any one of those projects may only be running, you know, one to eight times a day in aggregate, you're still deploying 60 or 70 times a day. And for instance, if you were going to look at CircleCI that is exactly what you would see. You would see that our platform has dozens of repositories that make up what becomes CircleCI. And we deploy about 60 to 80 times a day, I would say on average, but any one of those applications may only be changing once or twice a day. And then you can see that like the difference between 2019 and 2020 isn't a lot, but you can tell that, you know, the 90th percentile and the 95th percentile have really increased the amount of CI they're running. And to me, what this tells me is the people that invested in CI are getting more out of it this year than they were last year. And I think that that's a good thing and it's not surprising, but you know, and really across the board, people are just using a little bit more CI, but it's not necessarily the most significant trend. So those leveraging CI well are doing so even more so. And if you had already made the investment to CI, I think you're leaning into that investment more heavily and you're seeing the returns on it. The other thing that we see are there are fewer developers pushing code worldwide in 2020 than there were in 2019, which I think again, if you think of all the factors going into 2020, that isn't too surprising. Now, if you look at duration, that's how long does it take to get results? And so this is basically time after code push, how long does it take me to get signal on what's going on for my code? 5% of our builds finish in less than 12 seconds. And when I say 5%, that's about 500,000 in the sample of this exact sample that I polled. And so that's not a small amount, but less than 12 seconds. You think, well, what happens in 12 seconds? Well, it could be a number of things. It could be you get a very quick failure. It could be the entire workflow is something like I'm putting a static file onto, say an S3 bucket or I'm modifying some documentation. It could be very simple things. It could be your running unit tests that have been really well optimized, but 5% of our builds are finishing in less than 12 seconds. But then you can see that a lot of our builds, they increase quite a bit from there, but 50% of the builds are under that four-minute mark and then the 95th percentile you're at at 34 minutes and our average is right around 25 minutes because you start looking at the outliers that are in there and pulling that number up. I think most of our workflows time out after about five hours, so that's about the longest you can go, but they're a little bit scatter-shotted and a little bit around the world here, but the four-minute mark is the one that I really want to hone in on and that's half of all builds finishing under four minutes. I've seen teams that have an SLO around how long it takes to build their software and if things go above their SLO, they will stop engineering and start engineering how their tests are happening, whether they need to parallelize more, whether they need to be more diligent about flaky tests or which ones can take longer or speed up a test. And so if things take longer than four or five minutes, they may throw a flag and say, hey, it's time to re-engineer our test suite because what matters to us is cycle time and how fast we can give signal back to developers. And so some teams work very, very hard to keep that build time under four or five minutes. And other teams say, hey, 25 or 30 minutes is great for me. I know at a place I used to work 25 or 30 minutes would have been outstanding to get signal back because sometimes it was seven or eight hours. And so it all depends on your perspective about what you're doing, how detailed and thorough your tests are, maybe the number of platforms or permutations you're testing, things like that. So in 2020, you can see that most of the average durations that across the percentiles are a little bit larger in 2020. And that might be that there's more tests happening, perhaps more investment into validation in certain segments, maybe more complicated workflows, things like that. But there are a little more usage going on in 2020 than there was in 2019. So all pipelines are running just a tad longer with one notable exception. And that's that the average time for a pipeline was actually longer in 2019 than in 2020. And what this tells me is maybe the outliers are longer but the average time is shorter. So people have been spending time optimizing and trying to get themselves at the lower end of that measurement. That 24.6 minutes to 26.76 minutes, it's about an 8% change. So the average is about 8% faster year over year. Next is success rate. So how often does your pipeline complete with a green status is more or less what this metric is trying to measure. And as you can see, we have in the lower end of the percentiles zero, it never returns green. And in the higher ends 100% where it's always returning green. And so some of our data samples dabble with CI but they don't necessarily get a working build. This could be somebody starting with their own individual project. It could be a side tool. It could be, you know, that they never get a valid configuration and they look at it once and walk away. And then others of our sample see no failures within a month. It could be they have a really good test suite. They really know what they're doing. They have discipline development practices. It could be they have few tests that could be they have tests that just say return true so they can never fail. And we see all those types of things in our workflows but you do see that some tests or some organizations have workflows that never fail. And so what you look at overall is the overall success rate on average is, you know, it's 54% in the mean and at the 50th percentile is 61%. And what I found really interesting was this really didn't change from 2019 at all. You can see there was a 1% difference at the 50th percentile. But we wanted to look into this a little bit more and see, well, okay, is that true? Is this really that static year over year? And if you do look at the little bit, the 50th, 75th and 85th percentile that things are increasing a little bit. So the people that are using, I guess at that percentile are, have increased their behavior a little bit. They're having a little bit higher success rates than what they were a year ago. I'm not sure it's that significant, but it does seem like people that are really leaning into the CI space are making sure they're getting more out of it than what they were. The last is recovery time in terms of the data. And that's the time a pipeline sits in a failure state. So in 2020, what we see is, you know, the shortest end is about two minutes and that can happen in a couple of different ways. Things could happen where you have a developer, perhaps that pushes a config and right away realizes they pushed an error and they repush a config right after that. It could be you have two developers and one pushes a bad config and one pushes a good one and so the bad one finishes and the good one finishes and now it looks like that pipeline is recovered. And then you can also see that some of these are way, way longer in terms of recovery time. If you want to capture 95% of all builds on the system, how long is the recovery time is 3.4 days, which is very different than two minutes. But on average, if you look at this mean at 14.85 hours, what that basically tells me is sometimes people run a build, they stop their workday and they check it the next morning because that's about the amount of time you're away from your desk on an average weekday. And that's how they go find what happened the next day, which again, I think describes behavior a little differently than what most of those DevOps surveys do in terms of how fast can you recover from a failure or how fast do you recover from problems? And it could be that people look at that question and think more about production problems than about development pipelines being read, but of course, in a classic continuous delivery methodology, a pipeline being read shouldn't be treated any different than a production level outage from a developer point of view. So, and again, recovery time can be for multiple contributors running in parallel, which we talked about already. And then once you get to a certain gap, you can kind of see there's this overnight waiting period that is likely happening. And that's people that are, you know, they kick off a build maybe toward the end of their day and they check it in the morning or they decided to get with it in the morning. In terms of, you know, the 2020 impact here, recovery time went down in every category. And one of the things that I wonder about is, is that because more people are at home and more people are paying attention? Is that because there are fewer distractions? I'm not positive on that, but it does look like the 2020 is slightly better than 2019 was in terms of how long pipelines sit in their red state. Yeah, so they have improved year over year. And so now I want to really get to the insights which I think is the most interesting part of this rather than just read you data on a screen. Let's talk about what this data tells us a little bit more. So what development practices definitely work? Like what can we pull out of this? Well, I can tell you that success rate does not correlate with company size at all. We looked at company sizes and team sizes and org sizes and tried to look at, okay, if you're this size team versus this time size team, do you have a higher success rate? The answer was really pretty much no. So the only thing that we learned for sure was that if you're a team of one, your success rates are lower than if you're not a team of one. Duration is also longest for a team of one. And so this might be that if you're the only one consuming the output of your software project, you may not be trying to make it faster or better because there's no one else relying upon you. It could be these are all personal projects or one-off tools for your organization and not things that other people are relying upon. But a team of one is definitely not, I guess that's always the longest that we see. Recovery time decreases with an increased team size. And so the more people looking at the build, the faster your recovery time is, which makes sense, but it's only up to a point and up to that point was around 200, which was actually much larger than I would have guessed. But the vast majority of our data samples sits there with teams between kind of that five to nine mark and not much above that. Every now and then you get projects that are maybe the core project that many teams contribute to. And so you get many more contributors where it could be in the hundreds because perhaps the entire engineering department or even people outside the engineering department still contribute to the main core project. But that sample is a little bit smaller than kind of just the across the board all the different projects. The longest recovery times are again from teams of one. And so again, if you're working on this yourself, maybe you maybe you're working on your own tool, you get a red build and you think, well, whatever, I'll work on it later. And that could be 10 days later. It could be two weeks later. It could be, you know, three hours later. It just depends on kind of when that fits back into your schedule. So in every way that we can measure performance for a team is better when you have more than one contributor, which I guess you can say, Mike, that doesn't surprise me. You were talking about teams. If you have a team, your team is probably going to perform better. And that does make sense. But if you're chewing things on your own, basically it doesn't look like you will have as good of outcomes. And so I guess one of the things that tells me is you shouldn't be that 10X developer who sits in a corner and says, I can do this all myself because you won't actually have as good of outcomes. And we can show that based on the data sets. Ultimately software is collaborative. And that's one of the things that we were able to pull out of this pretty clearly is that, you know, when you have more than one person or multiple people, more than two people, even as that number grows, you get better outcomes on everything having to do with software delivery. The next question that I asked was, is this don't deploy on Friday a real thing? This has been kind of an old school level of thinking. I think that goes back, you know, to the classic operations days before DevOps was a big movement was, well, we don't make changes at certain times. That's the best way to be stable and don't deploy on a Friday became quite a real thing overall. And so I wanted to see, could I see that in any of our data? Well, we see 70% less throughput on weekends. And that means that there's a whole lot less happening on our on-circle CI on a weekend than there is during the week. And it also means my AWS bill goes down quite a bit. So that's something I usually look forward to. But then on Fridays, we see 11% less throughput than we would say comparative to Tuesday, Wednesday, or Thursday. And I pull off Tuesday, Wednesday, Thursday intentionally because of the middle of the week. And internationally, those days are all weekdays, no matter what time zone you're in. Whereas Monday and Friday, you end up having pieces of weekend for every other slice. I'm at some part in the world. But we also see 9% lower traffic on a Monday. And so if you remember, there was 11% lower on a Friday and 9% lower on a Monday. And what this really tells me is that it's about the same between Monday and Friday. So I don't think people are holding back on pushing code on Fridays. A lot of times people do take Fridays and Mondays off if they're trying to extend a long weekend. And that probably accounts for most of that drop. The other thing that does account for that drop is that Mondays and Fridays are, again, different time zones around the world. We use UTC for all of our logs and all of our data gathering and stuff like that. But at midnight UTC Monday morning is still Sunday evening in North America. So there's not a lot of work happening from that segment. It's mostly other parts of the world at that time. Another interesting set of questions that I love to ask is what do we see about languages and language trends going on in this dataset? Are there certain languages that lend themselves to better outcomes across the CI system, across teams? And generally I say take these with a grain of salt because anytime I put up a chart of languages, it's half of it's just to provoke you to think about what your language choices are and why. And the other half is to make sure that you can argue with your other developing friends about what language choices you made and why. So languages in our sample, when you look at the giant sample that I said that was 160,000 projects, here's basically the breakdown. Anything below 0.83% just wasn't even counted because this is the top 20. But we have a lot of dynamic languages. We have a lot of statically typed languages. We have a lot of more markup and markdown type languages. So there's several different styles of language in our sample here. On throughput, this is basically how often you're running on CircleCI. Ruby is the thing that's happening the most often. That could be because their durations are rather slow or rather quickly you get feedback very quickly. It could be there's just a lot of Ruby projects. It could be there's a number of different things. And you can see that this is just how often different things happen. Now the Docker file being at 20 was a little bit surprising to me because we do have a lot of people that build Docker images via CI. But it could also be that that doesn't just get kicked off that often, it happens less often than a lot of the other software projects.  On the success rate at the 50th percentile, this is the order. I find it interesting that Vue CSS and Shell show up at the top. I don't see a lot of tests for CSS and even in Shell, you don't see a lot of tests. Usually it's kind of just actions. And so those actions may succeed regardless of kind of what happens, a lot of Shell exit zero unless you're very intentional about how you write things.  But most people I think are just using Shell to complete a workflow, maybe copy a file over there and you know, it's going to return zero regardless in a lot of cases. And then Docker file also usually succeeds in that you build a Docker container, whether or not the container was what you wanted or not, maybe something that you need to require is further introspection. But the first real language that I think that has a lot of testing up and down all the way through would be probably go at number eight. And what I find fascinating here is that's the first, you know, that's a strongly type language and then you get into many dynamically type languages and then you get back into the rest of the type language is lower. And so you see, you know, 16 starts with Java again, where you get back into static typing and that I'm really surprised that go it seems like, and you'll see this in a couple of the other slides, go actually clusters more with other dynamic languages, even though it has more of the statically type language, you know, properties going for it. Yeah, recovery time number one is go, which was a little bit surprising to me as well. But apparently the people that are running go programs are paying very close attention to their pipelines and making sure they're good things are happening. The longer things, the things are more at the bottom, the recovery time being slower for some of the statically type stuff, that makes sense to me because their durations are a little bit longer. And a lot of times your recovery time is longer if your duration is longer. If you know your pipeline is going to take 35 minutes to run, you may step away from your desk. You may go to another call, another meeting and then you don't see the result for a while or you don't get back to working on the result for a while. Whereas if you're going to get your results in three minutes, you may be able to correct them very quickly and so your correction time will also kind of correlate with that. In terms of duration, things that happen, the fastest are shell scripts in HCL and CSS. And then you get into the JavaScript stuff which does happen very quickly and then go and then you have kind of those dynamic languages again and then you get back into the static languages toward the end. So, you know, what this tells us is not a lot. I think they kind of cluster together but I think that just the behavior of the go programmers is what I would say is a little bit anomalous and I'd call out there a little bit. In last year's analysis, I can tell you that PHP showed up in a really good light in most of these metrics which was really odd to me. And I remember making recommendations of everybody using PHP which I didn't believe but thought was rather amusing and this year it shows up much more like how I expected to group next to Python and near Ruby and things like that. So, this year the data seems to be a little clearer on how these all grouped together. One of the other things we've talked about was branch information because we, you know, a lot of times people build off a feature branch and then they merge into mainline. How does that all work? And the first question that came up to me for 2020 was have people really worked on renaming their branches from master? You know, there was a lot of talk in the social unrest days throughout this year that master's just, it's harmful language and we want to remove that from our computer science curriculum as much as we can and all of our usage. So, GitHub announced they were going to change the default branch away from master as did Git upstream was very aware, was made so that you can have different default names easily and not just use master. So, did this actually decrease this year? The answer is no. It really didn't in any significant way and I put yet in here because I don't think GitHub has completed all of the changes to make it so that any new project doesn't have master or maybe they did with new projects but they haven't done anything retrofitting existing projects. I think once GitHub does a little more of that work, you'll see it take off a lot more, but right now we don't see any significant movement in people moving away from a branch called master. Another branch topic is that teams are innovating and experimenting on feature branches and so you can tell this because the success rate on the default branch is much higher than on non-default branches and so the default branch in a lot of cases is mainline or main or master and the success rate there is significantly higher than what it would be on some of those branches that lead into it, you know, the branches, you would either be making a pull request or a merge request on things like that. So, we have about an 80% success rate on the main branch versus a, and it's 100% above the 75th percentile and so basically success is happening a whole lot on the mainline branch whereas it's only 58% for that non-default branch and what that tells me is people are using the CI system to get signal at times and so maybe they're merging a thing where they're like, I'm not sure this is going to work, I'm not positive, but I'll let the CI system tell me if this works or it'll run maybe a complicated integration test that I just can't simulate on my workstation too easily. But either way, what this really supports is that things are happening outside of mainline, they're getting merged into mainline and that's how we're figuring, that's how people are getting signal and they're also taking really good care to keep mainline clean and keeping it green, which is really great when you have multiple developers working on something, this is just following the practices of trunk-based development. They really want the trunk to be clean all the way through. The duration on the default branch is faster at every single percentile than it is on the feature branches by default or by feature branches at large and so what this basically means is branches that are not in mainline generally finish slower than branches that are mainline and again, that makes sense because there's new experimentation going on perhaps your tests are flaky, perhaps they need to be optimized but as they get into mainline, that happens and also recovery time is lower while it's supposed to be lower when it's not on the default branch at every single percentile. So when you're on your feature branches your recovery time is significantly longer than what it is on the mainline branch at every single percentile. The last set of questions that I was really interested in from our data team and from the other people that helped put this together was what can we see? What have we learned about a global pandemic on team performance? What is that really told us from our data set? And as you start to graph thing we pulled graphs from 2019 all the way through 2020 and we tried to do 30-day increments on samples but as you can see, there are dips that are noticeable in the March and April timeframe or changes I guess I would say. In this case, throughput went up in March and April. Our peak throughput looked like at least at the 95th percentile was in April of 2020 generally after that since April throughput spent around flat at most of the percentiles. So it does fall just a tiny bit at least at the 95th percentile. In duration we see that a lot of things went up in March and April and to me the way that I thought about this and tried to rationalize this was perhaps there's a bunch of developers that were at conferences that were on vacation that were away from the computer I guess is probably the right way to put it that return to the computer in March and they started working on things maybe they're adding more testing you have more people directly working on the code bases and so your duration goes up and then you see a little bit of optimization after that perhaps people were like hey, I don't like when my test take longer to return I'm going to go work on that and make sure that doesn't happen. So you see duration increased a little bit in February and then that increase accelerated in March and then decreased in April which I think we can basically say that the hypothesis here is that more tests were happening in March driving up the duration and in April there was more of a concentrated effort on optimization and that kind of adds up when you think about the way that people were kind of sheltering in their homes they're working on things and then it was like wow, we're adjusting to this okay, I'm going to work on this I'm going to make this a real thing I'm going to make it good success rates also increased in during the pandemic as everybody went home and sheltered in place they got higher success rates and part of that again could be that they were paying more attention to the direct things that their company was doing they weren't on the road they weren't as distracted they weren't as sidetracked I do love the 25th percentile just falling off there and I wonder if that's just like 2020 in a graph it's just like no, I can't anymore or if it's those are projects that are not core to the business and so they're just being left alone and maybe they failed and somebody was like well, I just don't have time to give on that anymore and so I'm not going to you know, those hypotheses there and I we haven't I don't have a clear narrative there my hypothesis is that that is non-core business inside projects and tools that were just not essential and they're no longer getting the attention they once got the success rates were the highest on record for April of 2020 again, that's kind of when you had full workforce at home fully engaged and getting longer days things like that so the hypothesis there is that people were working hard on core business stability in March and April a lot of companies were going through reduction in workforce and other type of economic you know, things where they were like wow, this little side thing I don't know we want to dabble with that anymore let's make sure the core of our business is clean and works and is stable and I think we saw that show itself through our data through from developers and then recovery time you know, we can see that it hasn't changed significantly throughout the pandemic period but it did drop a little bit and we saw that in some of the 2019 to 2020 data earlier you know, generally the 95th percentile has dropped quite a bit some of the people that were really good are still really good at the recovery times but recovery time has been improving since April and ours with the longest recovery time have definitely improved and that that was kind of indicated by this top line that green bar there where when you had the longest recovery time that has increased or decreased significantly and again this is like this is a golf not bowling thing where you want to be the lowest score to win so people are recovering faster and faster than what they used to and the hypothesis here is that people have fewer distractions when they're working at home they don't get pulled into meetings or go to lunch or go grab a coffee and then forget about it and then come back Oh, I was working on that pipeline I should probably go fix it there's probably a little bit less of that happening and when I say fewer distractions it's a course for some values of distraction some of us have very new distractions to work with whether that's children at home taking care of loved ones or worrying about sickness or just doom scrolling all day about what's going on there's there's a lot that can be distracting but I do think that people generally are a little bit more in front of the computer and paying a little more attention to what their their signals are in their delivery and so I want to take you into a couple of final thoughts as we've dug through this excuse me final thoughts here when mapped against DevOps survey data CI users at the 50th percentile show up between medium work level and so what that tells us is that you know when you look at kind of the classic layout of how are you doing and what are the data sets if you're average at using CI if you're at the 50th percentile generally you're going to be right on the borderline between medium and high performer and the more you're using it the more it will push you up into high performance and that's high performance in terms of the way that DevOps survey measures it and then the way that circle CI measures it in terms of your CI engagement and so you can kind of correlate the CI engagement correlates very highly with DevOps engagement overall or DevOps success overall so if you're using if you're average at using a CI platform you'll be right on the line and people that are really leaning into this have better outcomes in all four of our critical metrics and and the four critical metrics that these DevOps surveys have been using for years and so CI is it's a critical part of being on your delivery path and making sure that you can deliver software when you want to and making sure that your team is high performing they'll last take away that I that I really loved was that more collaborators means better outcomes the more people you have working on this stuff the better you'll be because I you know part of it to me is I think you just care about your your developer experience for other developers if they have to wait 10 minutes for tests when they could wait five people who work on it make sure they're only waiting five and if you know the pipelines read well somebody else needs it to be green to do their work you're gonna make sure it's green and I think that adding collaborators is really great thing. So I hope you found this interesting you know it's definitely a dive into the data this is kind of a cursory survey of the whole set of data because there's a lot you can go into there's a lot of questions you can dig into there's a lot of questions you can ask of the data team they can slice and dice but if this is interesting to you I will remind everybody we are hiring we have been hiring like mad over at Circle CI and we still have many many positions open engineering and data so this stuff is interesting to you you're welcome to apply and I just want to say thank you I'm again I'm Michael Stonkey I'm the VP of platform over at Circle CI you can find me at Stonma on basically any internet service that you care to find me on and I also want to give a special thanks to Ron and Melissa who helped gather all this data and sliced it and diced it to answer a lot of my questions about it I did not do this all on my own I am not a data scientist but luckily I know people who are so thank you so much