Look, you got these nice vents and I hope I've not marked it already. You got these nice vents and When I look on the bottom of this one We got these nice vents, but then you got to use silly stickers And these vents you need these vents to be kept clear because it does get really warm in there sometimes Especially on a warm day flying around and you've got more than a couple of batteries but look The third of me ventilation is covered up and I stick me finger in the butt of it to Check for heat and sometimes it can get really warm in there Like I said, you know a couple of couple of batteries on a warm day And I wonder if you know just having that bit of extra ventilation could help So I'm certainly not gonna put stickers Across here at all I've stuck some I put this back together and I put some of this nice I've got the white stuff last time and it's pretty good. I'm quite impressed with it But this stuff's clear. It's an extra 50p, but it's clear I'm gonna see what this is like But I've put some across the top of there. You can hardly see it. Can you? Which is great. So and then I thought what I could do is I could just set that in get it Sort of glued and then it'll be ready for tomorrow for me to build it up again But of course, I've got to wait for that because that takes quite a while to go off So I found something Loving that we're here now. I've put the I put the ball back in for the camera and It was just proving to be a little bit of a pickle Trying to figure out exactly How to put the antenna in and the reason why I say that is because even with that See what a planar antenna the square antenna that I've got It doesn't matter how close I am if this is turned a certain way I lose signal. It doesn't matter what channel I put it on. I lose signal I'm sure I've mentioned this I've in a video that I have put up or haven't put up I'm gonna actually for close range I'm gonna put the internal antenna that came with it back in and see if that fairs any better But the little antenna Transmitter antenna it did come round And it came over down this leg here and the white seems excessively long But it came down this and this one this leg here or this and Because I can't quite work out where to put it and because it doesn't work as well as what I would hope You know like I say when it turns I've just stuck it down the front here Now chances are I'll have to go into this again at some stage. Anyway, I would swap back out or Yeah, it's a black But there's something as well here is there are on two of these Motors and if I remember rightly my position is here on two of these motors It has these washes Now they're pretty rusty washes and I've looked at I've been looking for a spare set screws not that I need them at all the screws It's just I wonder if they had these washes in there and they don't have them in the spare sets of the screws or fixings for this And I'm wondering why there is actually just two or two and they're on the opposite legs as well There's another another washer there and The wash is down here for this and that goes there Not opposite each other on the same side But on the two opposites nothing as you can see there's there's not been any sort of washes here on this one And also that's the same for this side no washes I'm just wondering why why they're there. Is there a slight imbalance in the actual shell and They found that putting the washes there would help with vibration or something These vibration if it's slightly off I Don't know Honestly don't know but I tell you this Because I did notice that when I used to put this on top of this desk. It did have a slight wobble Not on one side, but on the other side It was like there was a millimeter or something gap where there was a slight bit of movement so you could tilt it Just an observation the worth noting that Putting this back together of course I'm gonna have to put The washes back onto the same places. I have asked about this at Hudson But sometimes it can take quite a few days to get a reply. I'm sure they're very busy there dealing with all sorts of things But I'm gonna put them back in Just because they were there anyway, I just thought that was not worthy Setting in the camera. I've screwed this down, so I'm not gonna take a lot of party And I just want to get it back together. They're getting out early get up a bench I've got other things I want to be able to do. I keep getting notification about this This I'd power supply that I bought that I bought DPS 50 15 about You know if I've got any problems with it I've got to notify them really quickly because they're the warranty I'll run it out. How long have I had it not very long a Couple of few weeks, but the warranties about to run out on it so that's Was I've keep checking this camera as well to make sure that's working So I need to do some stuff with that. I did ask actually the RD tech whatever it's called If it would be okay using inductive Loads like my solid state tells the coil, but he hasn't got back to me That's all with that. I don't know I'm gonna respond because I've asked him to take off the adverts of my Of my videos because I've put adverts on a few of my videos. I'm not really happy with that Don't get me wrong, you know, it's it's okay. I put link in the description, but I don't Advert to my videos so I digress I'm gonna put these back in I'm gonna leave that little antenna down just down the front there sort of sort of loose I don't think it's gonna matter too much. I mean here I'm trying to fit it in down back down here again. It's a bit of a pain in the butt to be fair I'm sure we'll just see how that goes.  Yeah, and earlier on my I looked at these which I thought wow There's quite a lot. So I bagged them In order to work out what to put where again I decided because I didn't know About putting the board down which exact screws they were I went through process all of the nation I know they're for the motors. I know they're for the main show And there's little ones there for the top pieces GPS and compass and these other thin ones are Actually forgot what they are, but these are the ones here for the board. So as I get through I might be running out of screws And then when there's holes, I'll just put in what's left Oh, I know where those thin ones are for there for the elite, you know for the feet Not not here for the feet Underneath You know there We get around to that a little bit I wonder if I should carry on filming this putting it back together again, because it is taking a bit more time It's a lot easier to take it apart because it's just Get everything nice is a bit more time-consuming. I can't really see He's going to be really that interested in watching that There's me tweezers Flatten look like pointy bits Excuse me for Digging into my fingers obviously a bit unsafe with tweezers That should just Snugly fit in there nicely. I'm hoping That's it. Yeah, beautiful. That'll do.  Gotta have it twice So that's why it's nice and snug everything out of the way. Of course I've still got the LEDs to put through Probably will have to pull these out again. So I don't even want bothering to put those in I should probably be just getting these clamped down and And then just working from there. So I'm gonna do that. This is got two screws in it Yeah, I'm here not at the back of just yet just in case I were to take it apart So I wasn't quite sure what this goes from What is quite sure And this has got one two I've got a whole bunch of connections there. This one's only got four. Oh, here we go It probably goes over here That's good Cam is still working. Yep It's gonna get warmed up a bit first and it seems to be once it gets too warm It was all happening a lot quicker when it was 60 frames a second. But I just leave on 30 frames now It doesn't seem to be any movement either on it where these pressed down it doesn't seem to be a clink clink clink clink type There was with the black case That's pretty firm in each corner. I can't tap down and there'd be any movement No No, I was the the base and where there's put the Then the 5.8 gig transmitter aerial antenna. That's where it was supposed to go There's little Grooves Grooves that you can put away Through space quite nicely That's good. It's all in the right place There's two grooves one side LED lights down and the other side you can fit where it's all the space for it This is here where I should try to fit it. I'm pretty sure that just sits in here Fiddling just slides in that's slid in but I think I don't know There's all that lots of gunk so you can't be touching anything but that is slid into that space now It's not gonna be Where the body should be coming together Hopefully that won't Block anything I'll be in the way Alright, so that's outside That's side USB's in Just these two Right, we're in there. It's good to go. So I'm gonna put the top together now There's a glass bit, there's a canopy even and of course there's the wider parts at the bottom So that's pretty self-evident That's gonna get a guy Self-evident I'm trying to put it in from the wrong way around Top bit that just clips into place nice Well, it's definitely in place. It's just not made any real clippy noises, but that's That's the top bit. I don't think we need any sort of gun called gum on there But then I saw quite a lot of videos they all had white ones and I really like to look at the black one So, GPS Let's do the compass first Of course, it's nice and easy. Two screws And it just goes there. Now, I Try doing this without putting the compass in screwed into place correctly When I first took this thing apart And it doesn't want to start up. It doesn't it it won't fly unless you put it in the right way around So there's a little something that's not working So it wants to go this way as well So the screws go in like that And it sits just underneath the canopy Like this rather than Like that Yep First one in place But yeah, it's quite it's quite sensitive To how it wants to be that camera's still working. Yep Surprise There's a thing that I don't want to be Magnetized at all I'm going to assume that's going to be great Get in the hole It's level, it sits firm Now that's something I've noticed about this compass this time I'm sure I've already said this but I could have said it in a bit in the film that I lost It was a screw driver, it doesn't fit under the tiny screws, does it? Yeah, it takes a lot longer now To calibrate the compass It used to be two turns clockwise And then two turns anti-clockwise But now it's more like four or five turns clockwise That doesn't seem to be Fitting in as well I hope it's maybe it's just down to me not applying enough pressure It's because I was pushing on the shadow a little bit and I was trying to be dead careful about Pushing on the shadow but that's certainly That's certainly a bit to there And now we've just got this This side is the part that faces upwards And if I remember correctly this bit goes out the back It doesn't fit nicely into place like the main board does Where you've got a couple of holes to align it up with But never mind I'll get one of these on each corner Opposite corners And then it's got the It's got a lot of lining To go, because that way To go between this It's gonna go whichever way this wire goes through I actually cut this in here to make it easier to get onto here Otherwise it was, this sits down I push this up nice and firm into the top Rather than have it the way it was Because it's supposed to be a screen between those two things I get around it and it's far away from the rest of it as possible But very sensitive to GPS on the point of You put your hand over and this is normal as well I've asked them at Hobson If you just put your hand over the top you'll lose all GPS And if it turns over If it, you know If it spins over you'll lose GPS as well But I don't use GPS so much now I mean I don't use the GPS so I might take it up I might take it up first of all And just let it hover there for a second I wonder if there's people about to show off a little bit Let the GPS hold it up and then just walk away from it Without even looking at it Showing off like Come here little monkey It's probably the 50s bit It's trying to get these pretty screws in Because they're not magnetic Or they don't seem to be They won't be picked up on my screwdriver Ah Damn Light down on my lens So I can see See if I have this force fade off Okay Right So that's the front And so then why is it going to come down in This side So I want to put my This in here Like this And those wires through Push all this up nice and snug Into the top of this I'm hoping that The interference Is going to stay to this area Let it get on and do it's job Across the road for me where there's a A field Unfortunately there's a Transmission validity mask And I figure I used to Ah Just to go and see a friend up there And her place was right by the Transmission mask that I actually Figured it was there I don't know if that had been caused me to Cue little issues so That's that I'm just going to connect these Into these Pop that on there Put the thinner screws through the base Of this into this And then lock these screws into there And we are pretty much Put together But I really need to go Oh and I'm going to hot glue that I've had the The heater on the hot glue now So that should be nice and warmed up On the The gun I should say Hot glue gun so I'm just going to Push that Because we're going to make sure that there's The battery can get in and out So just going to be a take I'm going to Go and find a battery to put in it And make sure that's out the way So I've got a battery I've got my cup of tea We'll leave myself It's good so far The aerial doesn't actually get in the way I've got that in the Right sore place which is good Just going to plug that out again I'm just going to chuck a bit of hot glue down I'm pretty sure it's going to be okay where it is But I'm just going to chuck a little bit down there To just ensure I just move that wire up Away there So I can get the nozzle of my glue gun in I'll show you Turn the bloody thing up when I do that So there's just a little tiny gap down there I'm just going to chuck a little bit of glue Just in there Just so it You know it just doesn't vibrate How it's at any stage So it becomes a problem Putting the battery in and out I'm not sure if you can actually see that I'm really sorry if you can't So I'm going a slightly different angle Because I'm governed by Having to put in the USB cable to charge it At the same time I didn't realise The camera had gone off I just checked it to make sure it was still working It was all Never mind It's all in nice On the black one On the black one It was almost like it was just slightly off So you'd have a bit of movement But at least it was in time The mate has looked like it nicely Look at those Beautiful Okay Well I'm going to put the back door on Even though I've got a white one coming But I'm going to put the back door on anyway Because I was going to think about it Because the batteries are so small What am I going to do with that battery? I'll put them underneath there Right in front of my eyes Because the batteries are so small It's not going to fall out Especially the way I fly I could quite happily fly it like that So I'm just You know Just gentle and smooth And quicker I'd say twice as quick Maybe three times quicker than the GPS But not Full Blastic mode I've had a succession of motorbikes In my time Most of them Well, the last three Were Kawasaki Ninjas 750s Absolutely love the 750s Just got Well Plenty of power Absolutely plenty of power But the one thing I look at is You don't Just because you've got all that With mine With this stage Three tuning and everything It was like a 175 mile hour job But it doesn't mean to say You've got to open that up Everywhere you go You can just gently have it And have nice power And smooth That's the way I look at these Especially since A couple of crashes So I think I might just power it Without Oh, I've got no charge, actually I've got seven volts on those batteries Oh, nice And it's showing up red now On the battery indicator On the handset And in total then That gave me 10.5 flights On a single charge From 4 power, 8.4 Down to 7 volts 10.5 cycles Yeah, that's good That's good Okay, there you are That's good There we go There we go There we go There we go Right, I'm going to drink My cup of tea now And I'm going to say goodbye Fine, I'm going to drink My cup of tea And Come back when I have A little biscuit That's all I'm going to say Lied, I misspoke I'm too excited I drunk half of my tea I've not had a biscuit yet But I'm going to chuck on a I'm going to chuck on the props That I've been using While it was black As you can see Not really damaged I was out of the maintenance field And The printer spares Got all those black ones as well And I took it really quickly Into a turn Going really fast forward In altitude hold mode Because this thing Don't have a shipment It wants to be more When you can make it I think it snapped a prop I think it snapped a prop It's got to be an A then All right Let me just get Well, before I Power up that transmitter Not so bothered about that Not so bothered about putting that on So much We don't want to run it without This on Because I'm not sure But if there's anything to do With like CB radios If you don't have your A real on So it's tuned You can have very nasty VSWR ratio Or VSWR ratio With a voltage standing wave ratio And that's not healthy For Your transmitter I am literally going to start This poppy up here First of all we're going to I hope you can see that So we're in the red on the battery On there So I just tilt that up a little bit We're in the red on the battery That would be alright Because it's got 7 volts And as long as those batteries Don't go below 6.8 Type thing for my comfort So of course we want The all important video To kick in Please don't go Pop Watch Bang That's a good sign That's a good sign And we've got the video The all important video GyroSense is happy It's calling for me to calibrate Compass 1 This might take a bit of a time Because it's been being like that So this is going to be Normally done by now With the old control board That I had Still asking me to calibrate Compass 1 Still asking me to calibrate Now Compass 2 As you can see The light's changed as well This one's going to be done Quick air About 2 on this one That'll be done As it used to be This is half way into the second one That's the second turn Half way into the third Third turn Third turn we're done Okay Let's go to the next part Is the magnet We've got no power On the battery at all On both items I've got 6 GPS On the handset Nothing on the quad That's alright I can tell it to start up Without needing GPS Because this is the new board So I'm just going to tell it to fly When there's no GPS Ready to start it I don't intend on taking it off It's not going to go anywhere I'm just going to get it to go It's in altitude hold mode anyway I've got nothing that's going to Go nuts have I You can see that Beautiful Beautiful beautiful beautiful That's just the test So we're all good We're all good I'm going to charge that up I'm going to stick myself some batteries on charge And Have I got too charged? I have I was charging to the other day I forgot about those So actually all I can do Is I can do a quick blast charge With my SkyRC Into these That gives me about an hour To get something to eat And then I can Rush off down to the fields See how quick I can use my quad Yeah, joking Just give it a natural burst While I put those on charge I'm going to stick another couple In power on charge on that thing And have four batteries Doesn't seem to be particularly windy out there at all So that's it guys That seems to be the mission complete I haven't put the back door on yet But I may I may not Probably will But it works It works, everything calibrates in Everything doesn't space to It starts because it's space to And that's all that's left to say Everything's as it should be So there you go That's the shell swap out Hope you enjoyed the video Hope you enjoyed it as much as I Enjoyed actually making it It's been quite good It's just nice that it worked Little droney And he's got a brand new suit Brilliant Till next time Take it easy And I shall see you soon Thanks for watching


Hello, welcome to SuperCloud 3. I'm John Furrier, host of theCUBE here in Palo Alto for our third edition of SuperCloud. This is where security and AI meet together for the SuperCloud hottest topic in AI, is security and data. We're here for a keynote fireside chat with Kit Colbert, VMware CTO. Kit, great to see you. Thanks for coming on. Well, thanks for having me again. You've been a leader in the cross-cloud, SuperCloud, multi-cloud effort for VMware in the industry. Lots of change since SuperCloud 1. A lot more visibility on this layer, this interaction. Multi-cloud, much more used term with more meat on the bone, so to speak. Cross-cloud has been getting a lot of traction, so congratulations on that. But now as it goes to the next level, you're seeing Jenae, I enter the scene, which is bringing up the conversation of automation. You got Code Whisper from AWS. You got Co-Pilot. People are writing codes. You're starting to see a lot more action on the dev side. Impacting the operational aspect of data. This is a huge thing. There's a lot of hype in Jenae. What are you hearing? Yeah, well, a couple of things. So I think you're right. It's been not quite a year since our first SuperCloud event, which I really think helped us set the stage across the industry and start the conversation that we're continuing now. And I think your point is accurate that we have seen the industry coalesce around this concept, that there's a certain architecture that you need to adopt in order to be successful in this multi-cloud era that we're in. And I think what's interesting is that, ever since ChatGPT came out in November of last year, tremendous interest that we've seen across the industry. I mean, this was very much like an iPhone moment. You saw it and you can never sort of unsee. You can never go back to the way things were before. And so I think what we're seeing across the board is every company trying to figure out, okay, how do I realize the value of generative AI? And how do I do so without necessarily falling into some of the same traps that I've fallen into before when the previous super hot technology came out, right? And so I do think that we have this interesting coalescing of this cross-cloud or multi-cloud architecture with generative AI. You know what's interesting? VMware, if you go back to the roots of VMware, it was a disruptive technology, came out of nowhere, virtual machines. That created a market, Amazon with cloud, that created cloud computing. And now generative AI has got its own market. Each of these inflection points have the same parameters. A lot of developer action, operational impact, meaning changing the game of how companies work. And so now we're in this mode where cloud's gone to gen AI at scale. You've got two types of profile customers, the startups or developers, and then the enterprises that are already out there. So you have people going to reuse gen AI for their existing stuff and net new capabilities. So you're kind of changing the airplane at 35,000 feet if you're an existing player, and then you get the new entrance coming in. So it's an interesting dynamic. How does gen AI get valued in terms of the person looking at a startup clear, go after an opportunity, but for a big company? How do you tackle this? You got to integrate it in, you got to see some low hanging fruit. What's the playbook? How do you see this creating value? Well, I think it's a great point because what you see is something like gen AI. Gen AI, given the importance of it, it really creates an inflection point for the industry. And I think when you find these inflection points happening in the history of business, what you see is it does provide tremendous opportunities for these startups to sort of get in with a very different or potentially you could argue better value proposition. And so I think for a lot of established enterprises, what they're starting to think about is like, okay, I thought I have a solid moat you might think, but how could that get disrupted by this new gen AI technology? And how can I as an established enterprise think about how to defend against some of these startups coming in there? So I think everyone realizes there's huge potential. I think there's also a lot of discovery happening around how to best realize that potential. And I think the other thing that I hear a lot of concern about from large enterprises is on the security side of this. I want to go in, I want to move quickly to take advantage of this technology, but how do I also ensure that I'm not driving greater risk or damaging myself in that way, right? Like, am I potentially leaking proprietary data from leveraging gen AI? Am I potentially doing some sort of IP contamination through the output of the gen AI system? So some of these things are unknown at this point. So I do see both the excitement as well as the risk management side of it and folks trying to balance those two things. And you see companies like Amazon, for instance, doing the bedrock, they have the licensing guarantee with the Titan models. They also have the ability to do training and ECG GPU clusters. So there's costs associated with doing it is one. The question I want to ask you is, how does that relate to SuperCloud? Because members, SuperCloud and multi-Cloud is kind of coming together. Is AI going to help in the short term push us forward operationally towards SuperCloud? That's a really good question. I think it's going to be one of those things where, I can see it going both ways. You saw my smirk, right? I can make arguments in both directions. Typically what you find is that when folks see a new technology like gen AI, what we're seeing now, they're sort of going all in. They're not necessarily thinking about what comes next. They're saying, hey, I just need to get this thing solved, get something in production, start realizing business value from it. But then typically what they find is that, oh, I've kind of painted myself into a bit of a corner. It's inhibiting my ability to scale, my ability to really take advantage, let's say in this case of multi-Cloud. So I do see some thoughtfulness going into these discussions. People are thinking about, okay, what sort of architecture should I be taking here? What sort of dependencies should I be taking and on whom? And how do I ensure that I still get the sort of choice of location that's super important? So like just as another quick data point, a lot of large enterprises, definitely using public cloud for gen AI, but at the same time, they want to know they have a path back to on-prem. It's actually really, really interesting. Both for things like can I do it more effectively, cost-efficiently on-prem, but also from a security and risk standpoint that I mentioned before as well. So there's definitely a lot of thought going in there. And I think the jury's still out on how it will unfold.  Obviously open source is driving a lot of value. I'm not going to poo-poo that, it's a great message. No, I think it's amazing how fast the open source community is moving on this and how fast it's evolving fundamentally. Yeah, and I think that's going to be where the, where the canary in the coal mine will come from. I think that's the answer. And then on the other hand, I hear a lot of people saying, it's not that easy and it's not. There's some technical challenges to get AI into the cloud and then bring in the scale for hybrid and then obviously super cloud. And so I have to ask you, is there or do you see this thinking, do you see the trend being more solution architecture? Because the conversation I'm getting into on that hand is that it feels like the solution architect conversations from years ago. Okay, how do we lay it out? There's a lot more holistic systems thinking into the gen AI than just saying, okay, yeah, just spin up some servers and let's get some GPUs. That's a dev rel, that's a dev test, kind of like a more of a sandbox. But once you want to put something into production, I see a lot more conversations that are serious around architecture, playbook, privacy, compliance and governance, kind of foundationally setting that layer and then enabling that value. What's your thoughts on the whole conversation? Well, I think it goes back to your previous point, which is that you see some folks sort of rushing headlong, necessarily thinking about that broader architecture discussion, whereas you have other folks that are thinking very much about it. And I think one of the amazing things that's, I mean, how long has it been? Seven, eight months since chat GPT sort of came on the scene and caught everyone by surprise. But what you're seeing very quickly is this evolution of what the right sort of reference architecture looks like for a gen AI or large language model type of system. And you're starting to see, you know, just an explosion of startups in the space. And so these people are starting to come in. And I think the key thing, kind of like what we've been talking about with a cross-cloud architecture, is how do you ensure that reference architecture is fundamentally cross-cloud in nature? I think what you find, and what we found at least from other domains, like traditional cloud native apps, if you will, like Kubernetes, is that doesn't actually take that much more effort to make it cross-cloud. You just have to plan ahead a little bit, right? And to really be thoughtful around the dependencies on the APIs, et cetera, that you take. So that's really the direction is going. And that, as I said, gives me some confidence that we can actually approach and take advantage of gen AI while also ensuring that people have the flexibility and choice that comes with multi-cloud. I want to get your thoughts on, is AI a next gen workload, or is it a bolt-on feature natively in the application? I can see it going both ways. It kind of has a depends question, but how do you see that playing out? Because AI workloads might be a big thing coming forward. Yeah, so I think they are really a next gen workload. It's not just a bolt-on to, let's say, anything else you do with Kubernetes, right? Clearly you can build an AI large language model on top of Kubernetes, and you should, it's great, right? But the thing is there's so much additional specialization that's needed there, right? Look at things like prompt engineering or things like vector databases, like all the tooling that goes around that, that's all highly specialized. And so I look at it as a new type of application. And I think because of the scale that you're gonna see, the fact that you're gonna see AI, a large language model based apps, being like a quarter, maybe even a third of the application portfolio for many companies, and let's say three to five years, that you do need to treat it as its own type of app and really get a lot of specialization around supporting it. What's VMware's view? I know you guys do a lot of R&D and data and AI, operationally, you guys have great client base. They're all looking at this future cloud, super cloud, cross cloud, as that next gen VMware, VMware Explorer world that's emerging. And it's going to look kind of the same but different. You're going to have more microservices, you mentioned Kubernetes, but now you've got the notion of a changing data infrastructure to support gen AI, it's infrastructure. This data products layer, I call it's emerging, where data is products, I mean, LLMs could be VMware could have their own LLM, right? So who knows, everyone's going to have their own large language models and foundational models. And then you've got the developer. At the end of the day is coding. How do you see that threading together? Infrastructure, data products, and data developer, I call it. So here's what I'll say, kind of give you a little bit of a sneak peek of what we're going to be talking about at Explore, but of course not give away too much here. But the general point of view, as I mentioned, is that we do see the gen AI and large language model space as a new type of application. And yet at the same time, and while it does require some new sets of capabilities that we just talked about, at the same time, it does leverage a lot of the existing things that are already out there, right? You need infrastructure, right? You need infrastructure that's optimized for these sort of applications. You need to, how do you manage your costs across all these locations? How do you do governance and automation? How do you do security, right? Networking, networking is a huge thing, especially high-speed networking for these sorts of apps. So a lot of our current product portfolio we believe we can bring to bear to support customers doing large language models. We want to do that, of course, across cloud. But I think a big focus for us starting off is, how do we create a simple turnkey solution for on-premises environments? Because again, this is one thing we hear from customers and we hear that there aren't good simple cost-effective solutions there. So you're going to see a big focus from us there. You know, one of the things we talked about when we talked about hybrid and Edge was you move your compute to the data. With cross-cloud and super-cloud, the conversation shifts to move your workload to the data. Because now data is much more agile, much more programmable if you get a lot of the stuff done in the foundation layer. Federated catalogs I've heard is big topic. Data fabrics. Horizontally scalable infrastructure to keep track of. The checkboxes, the compliance and the governance. Once that's kind of set up, another layer emerges to handle the data that needs to be available for applications for the AI to work. Remember, AI is only as good as the data, not just the software anymore. Well, I think, and this is, I think a key part of our perspective is, which is that your data sort of spread out all around. I mean, you certainly have some of the data center, some of the public cloud, but a lot of the data that's getting created or will get created over the next few years is going to be out at the edge. And so that's another big focus for us or a reason for our focus on-prem is this notion that we want to, in your words, like bring the workload, bring the infrastructure to where the data is. And so we're trying to support customers to give them that flexibility to say, okay, where do I want to, maybe I build a model and even train it perhaps in one place, but where am I actually going to serve it? Where am I running inference? Where am I bringing in context information? Maybe that's out at the edge. And so I think having that sort of flexibility is key. The second point, we talk about data. And we've been doing a lot of different things in the data space for a while. And one of the new initiatives we have, this notion of what we call the data services manager. And the exact point there is to provide better flexibility to customers around data to help them provide access to their different data sources in a more dynamic way. You mentioned VMware Explos coming up and you didn't want to reveal too much. I got the sense there's going to be some kind of AI discussion in there, large language and foundation models. Makes a lot of sense because the developers and the operators are going frenzy over this stuff. It's going crazy and it's legit. It's hyped up right now, but people are singing their teeth into it. And it's much more of a sustainable trend as say a hyped up quick turn, a hyped up market. Or like something like, you know, you look at blockchain or web three, which I think do have a lot of applications, but may have been a bit overhyped compared to what they can do. There wasn't enough reality there. Right, actual use cases. I think the long game will prove that will work, I think. But here, AI is impacting immediately. There's low hanging fruit out there. Absolutely. Configuration, even from a developer productivity stamp. Developer productivity. I mean, the other big one we hear about is how do you provide a large language model to support your customer service representatives? So that if a customer calls in with questions, these folks can very quickly search the knowledge base, figure out what they need to get an answer, get a quicker time to value. We see a lot of it also on the, whether it's marketing or social, creating copy there. So there's a lot of these use cases and they're effective and it'll be leveraged today. And data is distributed. We're going to see a lot more distributed computing paradigms coming in. I want to get your thoughts on this because on this super cloud event this year, this episode three, we got a lot of inbound, we put a call for speakers page out there. And we got a lot of people from the Vmug and VMware ecosystem who wanted to submit talks. So it's clearly resonating with your customer base. This isn't a VMware direction that you're taking that has another 10 to 20 year horizon. I'm seeing your ecosystem responding to that. I mean, the VMs was a great run. It's continuing to be relevant. But there's almost another 20 mile stair out there in the industry where people can see a career path operating networks and operating data like they were with vSphere. So like it's a whole nother level of career path where people are going to start to settle in. And that's been a huge focus for us. So we have, I don't know how many actually, VMware administrators are out there, I would guess in the high hundreds of thousands, the small number of millions. But a big focus for us is how do we help them to take forward that skill set? There's a lot of work we did, for instance, with deeply integrating Kubernetes with vSphere. So you have that same sort of management model there in vCenter, but you can now extend that to these cloud native applications, applications that have been containerized. And we want to continue to drive that forward with AI based and gen AI based applications as well. And I think the other, the corollary there is not just within vSphere itself but across all of our product portfolio as we go to this cross cloud architecture really helping them to expand their skill set to support this very dynamic environment. And they're the operators of the networks of the environments with computing. I think the aperture of operations is interesting. DevOps started it, then it's DevSecOps security. The next question is when is it DevSec DataOps? And so we're seeing data kind of weave in the similar pattern to security. Shifting left became a huge wave of value because now developers can be at the point of coding embedding security policies that are given to them as guardrails from ops. Exactly. And that's more efficiency, developer productivity, better supply chain, security. Now the data question is, does that look the same to you for data? Absolutely, has to be. I think it's unquestionable. So the reality is when we're talking about data in some ways, you're getting into even bigger risks for a company. As you know, there's all sorts of compliance and regulatory requirements across many different businesses in many different industries. I mean, of course there's highly regulated ones like banks, healthcare, but across any of them, if you're dealing with things like payment information or personal identifiable information, these sorts of things become really, really critical. And so while you may want to say, hey, let's go a bit fast and loose, get something out there in the JNAI or LLM space, the reality is if you're not really thoughtful about what data you're using to train the model or where that data has come from, as well as the impacts of what data you're putting into the model, then you can have huge repercussions from a risk standpoint. Bill Walsh, the famous football coach, was asked why he was so successful. He said, you know, I just control the inputs and let the scoreboard take care of itself. He had Joe Montana, Jerry Rice. You controlling the inputs are critical QA with anything. And then football example, obviously the talent drives the score, data drives the value. This is becoming another operational linchpin to distributed computing. As a CTO, I'm sure you agree, but as other CTOs out there and other data architects or data engineering, whatever we call them, they're going to be thinking about this problem. What's your advice as SuperCloud continues to push, CrossCloud continues to push, this multi-cloud phenomenon, multi-vendor, it's going to look different. What's the advice to CTOs out there and solution architect? Well, here's what we're trying to do. I think, you know, because by the way, we are both a producer of software to support customers and companies doing Gen AI, but we are also doing it ourselves internally, right? So we're also learning these lessons as I think the rest of the industry does. And so I think a key point that we talked about earlier is developing those guardrails, having really clear policies. You know, we've got kind of a one-pager that we put out for everyone internally around what are responsible use guidelines for Gen AI. Not to mention our ethical guidelines. We've got pretty high standards around that. So things like on the ethics side, explainability, transparency, fairness, trying to reduce bias, these sorts of things. But on the responsible use as well, being really thoughtful about what data goes in, what sort of systems we can or cannot use, et cetera, et cetera. So I think part of that, getting back to your inputs, you know, concept is kind of defining that sandboxing. Okay, here's the guardrails. You kind of play, go ahead and play inside of that. That's fine, you know, have at it. But we also need to make sure that we protect the company appropriately. And so I think that's what it comes back to. How do you ensure that you're protecting the company appropriately, while at the same time enabling your developers, employees, to go as fast as they can within that framework? You got different needs for consumption. You got cloud as a enabler. And you got enterprise needs. This is going to be interesting. I mean, all that coming together and with the developers leading the charge, I've never seen anything more exciting than something developer-led right now. And you see on this same movement when SaaS started with cloud, that was clearly a step function from a startup trying to provision a data center, buy a box and then test everything. Get in your PC, put it on the cloud, put your credit card down, you're the next Dropbox Airbnb. Their new brands are going to emerge. This is a big wave. What are some of the things that you might see out there? Just from Kit's perspective, put the Kit Colbert CTO hat on, looking out of the landscape. What might emerge that no one might see? I think it's, I mean, just kind of some wild stuff that's coming out. Like some of the things I've heard about recently, I don't even know the names of some of these companies, but there's one that will just connect onto a Zoom call and listen to your meeting as you're having a meeting with colleagues. And then what it does is takes a transcript and summarizes it, creates bullets for action items and all that and sends it out, right? And so these are like really small ways in which work is going to get automated and where I think we can create a better work environment and to be more efficient. But honestly, some of the biggest transformations and so forth that are going to happen will be surprises, things we can't even think of. And so I think that's really what goes back to what we were talking about before. It's like to some degree, like I as a CTO, I've got a bunch of ideas, but I'm not necessarily on the ground in the trenches, doing the job of coding day to day or answering customer service queries. And so part of it is, how do you enable those folks to see a problem and say, you know what, maybe there's a large language mom that might be able to solve this? You know, it's an opportunity recognition. You nailed it. It's a human AI relationship. And then ultimately it's like the constraints and the solution, putting it together, all coming together. This is kind of a Cambrian explosion. Absolutely. It's going to take VM where the next level. Looking forward to seeing you explore. Yeah. Thanks for coming on SuperCloud 3. Oh, thanks for having me. All right, Kit Colbert here, fireside chat keynote here. SuperCloud 3 security AI meet. This is the next generation cloud connecting everything together. Humans plus AI is better than AI. A lot of constraints and solutions available, but you got to figure out which models, how are you going to build these apps? These are new workloads, major impact to cloud operations. This is SuperCloud 3. Thanks for watching.


Why do narcissists leave but still contact you? The narcissist has left and they might have made it clear that they are not interested in being in the relationship with you anymore But yet they still continue to contact you. The reason for this is because they need attention They need you to take notice of them They need you to regard them as something interesting or important When they started devaluing you That is when they had found another source of supply Another person to attend to their needs Narcissists have a dualistic mentality Where a person is either all good or all bad Once they have begun devaluing you That is when they see you as being all bad They might try to provoke a negative response from you To make them feel as though they are powerful or significant But from this point on It becomes very difficult for them to get you to admire or respect them This is why they have to find another source of supply Another person to attend to their needs Someone who doesn't know what they're about Someone who will admire and respect the false character that they are displaying to them Although the narcissists has left and moved on with this new source They will continue to contact you because no amount of attention is enough for them They also cannot reveal anything negative to the new source of supply Until they have groomed them until that time they may continue to contact you They will use any contact to emotionally abuse you And they will portray this image or illusion as though they are so much happier with the new source This is all designed to affect you emotionally This is all designed to hurt you Remember the narcissist feeds off of your emotional reactions They provoke jealousy within you which gives them an emotional response and gives them fuel They are not contacting you because they care about you They are not contacting you because they love you All they care about is getting attention All they care about is admiration and respect When you realise that they are not worthy of your admiration and respect That is when they will begin to devalue you even more They will intentionally try to hurt you It's still validating their false self Because it makes them feel powerful Making you feel out of control Provides them with a sense of stability and security In some situations they may act as though they care Or as though they wish the best for you But they really don't They know that you just want them to treat you with their love and respect that you deserve But once you figure them out They can no longer even give you a false sense of what that feels like Because just being in your presence is enough to trigger them to reflect on their insecurities When you're confronting or questioning them It's invalidating their false self and the image or illusion that they are trying to portray They don't want to reflect on their insecurities It's too painful for them to deal with So instead They choose to project their insecurities onto you They assign their negative traits or characteristics onto you This then regulates their emotions and boosts their self-esteem They might also stay in contact with you as a backup In case it doesn't work out with a new source They can often re-idealize old sources of supply But again This doesn't mean that they care about you This doesn't mean that they love you All they care about is themselves All they care about is fulfilling their own needs Everything they do is for narcissistic supply It's not about you It never was They just made you believe that it was Because they knew that that was the only way that they could keep you around The narcissist might continue to contact you And keep you holding on to your feelings of hope That maybe they might come back Or maybe they will change But they will never change And even if they do come back It's only because they have re-idealized you And then they're just going to use you for your energy and resources All over again To fulfill their own needs They do not care about you They are not interested in helping or supporting you And they are not going to be there to comfort you Remember those painful emotions Arouds from you being around them They were the cause of those emotions If you are continuing to stay in contact with them That means that it has created a trauma bond Which is the result of an ongoing cycle With the intermittent reinforcement of reward and punishment Has created a powerful emotional bond between you and them If you want to remove this trauma bond You need to let go of any hope that they are going to change Feel your emotions Understand that the narcissist was not the person that you thought they were Learn to grieve Let go of the fantasy The illusion Set boundaries Identify what is okay And what is not okay for you And most importantly of all Cut contact with the narcissist They are not going to change They might future fake And keep you hoping and respecting Expecting your desired result But this is only to keep you around So they can extract more of your time and attention More of your energy and resources They are not going to change And the more time that you spend in contact with them The more pain and distress it is going to cause you The more difficult it will be to let go Thank you for watching I hope this video resonated with you Please like, comment, share and subscribe Click the bell icon To receive notifications for my future videos If you would like to donate My PayPal link is in the video description Coaching inquiries can email me at narxfathercoaching at gmail.com Check out the emergent dice in the Narx Father store Where you can purchase your own Narx Father t-shirt Tank top or mug The link is in the video description Thank you for watching I'll talk to you soon


All right. Welcome friends. Can everybody hear me? All right. So I hope you're having a great Drupal Con last day. Hope you had a good lunch. I skipped mine. Your food makes me lazy. So yeah, I'm sure you know a few of you would also be feeling a little easy. So let's do a little warm-up exercise. Show of hands might work here. All right.  So show of hands please for all those who are coding developers, programmers, engineers. All right. And what about managers? All right. That's awesome. More managers than engineers. Excellent. Always the case. And what about Agile?  Okay. Good few. So, and how many of you know or have heard about no hashtag, no estimates? Ah, okay. Cool. How many know me? Okay. Great. Thanks. So for all the others, my name is Piyush Poddar. I come all the way from India. I'm working with Accelerant as director of professional services.  So it's almost say 19 years now. And I've been associated with Drupal since last eight years. And I live in India in a beautiful city called Jaipur, a city of forts and palaces, you know, forts that never saw anywhere. So let's jump straight through estimations. We'll start with estimations and then we'll see what are the challenges, you know, you all are facing, and then we'll see what no estimates is all about. What's the philosophy? How can we use it? And then we'll see some use cases. And few people behind this, no estimates who advocated what these approaches are. It's an interesting thing actually, you know, it started with a Twitter hashtag based tweet by someone a few years ago, which led to some conversations led to, you know, more research and interest, people blogging posts, posting blog posts, videos, lots of presentations at conferences and all, and a lot of companies, organizations and individuals are using this going forward to ease some of the pains that are associated with estimations and such. So we'll see how, as we progress through that. So before we go there, you know, let's understand what estimates are, what estimations are, and why don't, you know, what didn't you tell me? What do you think are estimations? What do you think are, you know, estimations? Why do you do it? How do you use estimations?  Clients need them. Excellent. Excellent. I don't need a show of hands to see how many would agree to that. I believe majority of us would. Any other answer to that? All right. So to plan the resources and project planning. Okay. Anyone else? Please? Sorry? Clients think they need them. Yeah. Okay. Which again, right? It's the same thing. Yes. All right. So stakeholders are clients. You know, I believe they are the same clan. So they need, someone else needs them. And that's what, that's why we are asked to do that, right? All right. I think those are very common, common use cases which majority of us, you know, agree with or have been, I've seen that. So I did a quick, you know, Wikipedia search, Google search. And this is what I came across with. Estimations are a rough calculation of the value, number, quantity or extent of something. A judgment of the worth or character of someone or something. Nobody did I find any mention of the word called facts. So, you know, we could safely conclude that estimations are guesses, not facts. Unless, you know, otherwise they would have been called factimates, perhaps not estimates, which they are not. Now, what is a good estimation? Well, that varies from, you know, individual to individual companies, organizations. But a good few years back, you know, a few people decided their estimations. And this was fairly relevant as well. By definition, a good estimate is within 25% of the actual result, 75% of the time. So which means if you are, you've done an estimation of a project for 400 days, if you are able to deliver that within maybe 300 to 500, you're good enough. If out of four such instances, you are able to deliver three, good enough, not bad. Let us look at some industry statistics. I'm sure you have heard about chaos report. Chaos report is an industry report done by, shared by a Standish group. They do it every year. They take data from around 50,000 plus projects across various sizes, including, you know, minor enhancements to mammoth enterprise applications and implementations. And they do this for every year. I believe they have been doing this for at least good 15 years now, which gives us a good benchmark and baseline to compare how industry is, you know, performing in terms of these software IT projects success. And these are some of the numbers. We could focus on 2015. I don't think you'll be able to see the green ones. So that's successful. The second one, the second column, actually the third column, yellow is challenged. And third is failures. And as you can see, 19% of have been absolute failures, which means these are projects that have been canceled prior to completion or delivered and never used. 52% are close to that have been challenged, meaning they have been delivered, but, you know, delivered less features and functions that specified specifications within both time and cost, with to both time and cost overruns. And close to 9% have been successful, which means we are looking at only one third of success here. This is industry data. Furthermore, the unsuccessful project that we see here took around 200 plus, it's actually 222% longer than planned to complete time, 189% more cost than was budgeted, and delivered only 61% of the features and functions specified. So that's not more than two-third of what was expected based on pre-planning and all that stuff. Another data point from Gartner. I've been attending Drupal Cons and hearing a lot about Gartner data, so I thought maybe I'll take my chance as well. So this is a little old data from 2012. It says small, mid to large-size project failures have been close to 20%, between 28% again, one-third. Mackenzie, average cost overrun, 66%. Average time overruns have been in the likes of 44% and average benefits shortfall, which is, you know, they have not been able to deliver the benefits expected close to 17%. So question comes, are estimates reliable? This is an interesting graph adopted from Steve McConnell's book, Software Estimation, Demystifying the Black Art. I'm sure a lot of you would have read it, if not at least heard about it. This shows some plotting of project estimates versus actuals in days, track record for one organization. I'll have to step down because I can't see that. So as you can see, the x-axis is estimated days to completion and the y-axis is actual days of completion. And you can look at some of the data, which is interesting. By the way, this diagonal line is the average, the perfect accuracy. So anything falling below this is a highly profitable project. Anything falling above this is not so. You see this number here? It seems this was estimated to be around 12 days to around 225 days. That's interesting. There's another one here. Close to 100 days were expected, delivered 150, not so bad. Look at this one, 200 days, 250, not so bad. But again, if you overall look at these stars, these projects, these have been those two-third of failure projects, I believe, that we've been talking about in those data points. So this is a chart that has been popularly used in lots of presentations on project management and software failures, examining how and what to do about that. Okay. I'll try to pronounce that. Hofstadter's law. It always takes longer than you expect, even when you take into account this law. So Douglas observed when he was working on the project, which I don't remember the name of the project, but that's the project for playing against chess grandmasters. He observed that no matter how much work went into developing computer programs to play chess against grandmasters, the winning program always seems to be 10 years away. Another one. This is very relevant to software industry, something that we come across every day without realizing Parkinson's law. Work expands so as to fill the time available for its completion. There's a guy called Dhaval Panchal. I stole his diagram here, which was very interesting. This is about depicting what you don't know, you don't know. This relates to how we estimate and where do we actually stumble. The small yellow circle you see is things we know that we know. The bigger circle, the larger yellow circle you see are these other things that we know that we don't know. Then there are things that we don't know that we don't know. I did a Google search on this thing and I also came across a famous saying by Donald Rumsfield where he said, I had a video, I wanted to play that, but it spoke about terrorist activities and Iraq and all that stuff, so I avoid it. Sorry. He said, there are known unknowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know, but there are also unknown unknowns. These are things we don't know, we don't know. He was talking about the issue around weapons of mass destruction and Iraq war and all that stuff, somewhere in 2002, a lot back. The reason why I'm showing this diagram here, this chart here is to take your attention to the big large red circle, which often gets neglected when we try to do estimations. We often pad and add buffers to our estimates for things that we don't know now, but what about all those things which we just don't know at all? They may happen, they may not happen, and if they happen, they'll screw up the whole planning process there. Trying to explain this in a little more technical way, there are two things which basically define the cost of a feature, essential complication and accidental complication. This was explained by J.B. Reinsberger. There's a nice video on Vimeo that you can go and watch. I can share the links later. Essential complications are apparent complications. These are things we know are there with the project, complexity, technical challenges, et cetera, et cetera that we are aware of, how hard a problem is on its own. But then there's also called accidental complications. These are complications that creep in because we suck at our jobs because of inefficient organization structures or blockers because of those structures, because how we code, et cetera, so many other things which are not really related to the problem, the requirement, the project itself. A cost of a feature is often calculated based on these two parameters. While doing an estimation, you do relative estimation. Based on your past experience, if something was X, you imagine that if we have similar size of accidental complication and essential complication, it would be 2X or 5X, that never happens in real life. When was the last time that cost of accidental complication was nearly zero or exact multiple of an essential complication, which is the X there? Cost of feature and functionality is driven by both of these. Oftentimes cost of accidental complication supersedes way beyond much more than the essential complication component. That's where the relative analogy based estimations fail and are not accurate. Later, we realize that we've gone overboard by unnecessary numbers. Woody Zuil is a thinker and agile advocate. He was one of the guys who started talking about this hashtag, no estimates. I'll quickly take you through a brief profile of some of these thinkers later. But one of his thoughts about what estimations are these, he believes those estimations are useless because they are guest based, what we think we know about the unknown. They are wasteful about the work that's not going to be used or the other way. They are harmful because they lead to decisions based on incorrect data, which subsequently leads to consequences later. They are easily gamed. Estimations are easily gamed. I know I'm sure everybody would have done that or at least come across such instances. I did that as well a good few years ago. Reminds me of an instance when I was a manager, I would often ask my team to do estimates and then they'll give me some data. I would have a separate estimation sheet with extra 20% padded there, which these guys won't know. But subsequently, they would start guessing that. They know that there was something extra being padded to these projects. Later in numbers, they automatically started padding those numbers as well. I still kept padding my 20% right. We were looking at failures. You can easily gain things. Again, in planning poker as well, you might have seen so. Breaking the user stories into a larger number of pieces and incorrectly guessing the numbers. One person says five, another says three. Whoever is powerful is able to convince the other guy, of course, with some data and reasoning. Somehow you gain those numbers. Estimations are easily gamed. They are dysfunctional. This whole slide, this whole presentation is about it. Deceptive, that's I've added at the last point for myself because they give you a wrong impression of what you're going to do. I won't go through each of these. This is just a table to show you. These are some of the estimation practices and frameworks that have been developed and evolved over years. Question is, are we trying to get better at estimations or what? The thing is, we need to question our status quo. We need to start doing something less, not keep iterating over the same thing if it's not giving value and being more accurate, or stop doing something and find ways to facilitate that process. Let's talk about no estimations. I believe I have fairly built a background now. No estimates is based on a couple of principles. The first one and most important is embrace agile principles. Second is focus on value. It's all about value and that's what the customer, the client wants in the end. Deliver small slices of working software and it's very particular about how and why should you slice and what advantage will we get from that. Deliver early or frequently. Some of the advocates even say deliver daily. Only do estimate, only have a story which is as small as what you can deliver tomorrow, but we'll see that. Customer collaboration.  No estimation is not about no estimations ever, by the way. It's not about not doing estimations. It's about the minimum amount of estimates that is required to be done and then look carefully at ways to reduce that need even more. I did mention Woody Zule. There's another gentleman called Vasco Duarte who was also one of the early agile advocates of no estimates. He had an approach that I personally like. His approach is what I'm going to talk to you about. Share this with you. He talks about focusing on throughput rather than story points. This is a chart that was shared by Corey Foy on Twitter. This is based on a team's data from few sprints on a particular project. The bottom numbers you see are the story point estimates and the left numbers, the y-axis you see are cycle times in days. For those who don't know cycle time, cycle time is the time spent working on an issue right from the start till the end. That's the cycle time. As you can see, some of these are big lines and some are small lines. These are the story point estimates. You can see those the story point estimates. This is not exactly Fibonacci series I believe. There's one or two numbers different here, but close to that.  You can see how variable they are, how different they are. The chart is not very clear, but there are some small green triangles right at the bottom of most of these lines. Those triangles are average numbers of the cycle times for those particular story points, those particular user stories of those sizes. The point is the actual cycle time is differentials are huge, but the average is not so much. It's better to base your predictions and forecasting on something which is not so variable as opposed to something which is. We'll see how shortly. You can do estimates in couple of steps using no estimates. The first step is you absolutely have to have a stable team because what we're doing is we are trying to use data from the past few sprints, actually two or three sprints, and ensure that you have reached a stable team or stable system state because then the data that you'll have will allow you to make some forecastings. The way you do it is you take the size of these stories. I'll actually take you through that in the next one. Here, when I mentioned stable system, what's important is to identify how would you ensure that a system is stable when the velocity of a system, of a team, or a project would be outside limit three times. I'll show you a chart where you have to have an average size of each story, and then you have a high interval and low interval. As long as all of your stories are getting completed or sized within these two intervals, and if you are having consecutive three, more than three stories outside this limit, your team is not stable. Your system is not stable. You have to plot those numbers on a chart based on data that you received from the past two or three sprints, which will allow you to see if you have a stable system. If you have, then you've got the right data, and based on those, you can do some forecasting. You can plan things and go forward. As well as the chart will show that if there are more than five points going in the same direction, meaning you have a velocity, a lot of acceleration happening in terms of throughput, you have not reached a stable state. A stable system and team is very important here. Second step is you have to select the most important piece of work that you need to work on. You have to focus on value. This is where a lot of projects and a lot of deliveries fail because end of the day, we realize that by the time we delivered something, 80% or 40% of those were not really useful, what was useful for something different. For this, you have to involve product owners and a scrum team because the scrum team and the product owner client stakeholders, they will together assess if this X feature is more valuable than Y feature. This is very important. It should not just be driven by the client stakeholder. Value can be non-monetary. In fact, it has to be non-monetary. If all of you are aware of the user story format as a user, I want to do this so that XYZ is the value that we are expecting from user story, that's the value I'm talking about here. Once you've done these things, then you need to slice your work into smaller pieces, right? All right. So, you have to slice the... Thank you. Thank you. Are your clients stakeholder? All right. Thanks. Let's continue. So, we need to slice features into user stories. So, Vasco suggests that the larger piece of work is a feature and then you break it down to user stories and then you break those user stories further down into what you can deliver. You have to... You can break them down into multiple user stories and generate options. And options will allow you to do those prioritizations, take some decisions. A larger user story may have lesser options and thereby less flexibility of being very flexible. And there's an invest principle which I believe agile developers and managers and everybody are aware of. This is an invest with a slight twist. So, this is about identifying to what level or when a user story is ready to be used, ready to be processed. It has to be independent, vertically slice instead of horizontally. When I say vertically slice instead of horizontally, I mean do not slice the user stories into architectural and system levels. So, saying one is database abstraction, another is maybe API is 30 something else. Rather, slice the user stories into vertical slices so that individual stories are independent and they have all the components required and can be ignored as well if required if they have lesser value. Second, they should be negotiable which is essence. They should have essence of value and not implementation into the technical data, technical details. Third, they should be valuable, most important thing. They should generate value. Fourth is essential. So, agile principles advocate estimable here but no estimate advocates say instead of estimable, it should be essential. Do not work on anything that is not essential at all. Fourth is small. Understood and small enough. Understood when I say you have understood that, yeah, what is required, what is to be done and they should be testable. So, the acceptable criteria is what comes here. You should do slicing along with the whole team, full team. Then there shouldn't be any huge stories. Typically, anything which is greater than half a sprint is a very large story. They are not what you need to work on. You need to slice them further. These are just some interesting valuable rule of thumbs. You can revise and change these to what really works for you as well. Typically, you can have six to 12 user stories delivered in a say two weeks period. If you follow a two week period sprint for your development, five to one mandate per user story is a good enough estimate, not estimate, sizing. What's important to ensure is that the statistical distribution of these user stories, the large and the small ones together, should be spread across the entire project. So, it should be you are working on all the large user stories now and the smaller ones later. It will not allow you to do the forecasting which is required in case you need to answer a few questions for stakeholders and do some planning around that. Each of these user stories are actually going to be working software. So, there should be testable running tested stories. Meaning, once you deliver these stories, you have to either deploy them on a production or production, a like production server so that your user or your customer can use it. That's when you have delivered a value and that story would be completed. Then, you know, once you've sliced and reached on the right sizing of the user story, develop each piece of work delivered in a product ready environment and iterate and refactor. So, another one, another one, another one. Now, while doing this, you have to ensure that you do an active scope management and how do we do that? So, you have to do hard limiting of the duration of certain parts of the project, time boxing. You must ensure that features are, for example, features could be around one month and user story should be around one month. So, because you're not, you haven't been doing, you won't be doing any estimations here. You won't be doing any planning based on those estimations. That's why you need to ensure that the sizes of those pieces of work are more or more or less similar. They may vary, but not very largely. Rest low value user story. So, what happens is once you are breaking down a user story, you find that you broke down one into five. Two are valuable, three are not. Those three don't make the cut and, you know, they get regrouped into a different user story or go back to backlog. The product backlog would keep growing, but the beauty of this, and in fact, it would be that you are talking about embracing change. So, why I say so is that, you know, the next user story that you're going to deliver the day after tomorrow could be the one that otherwise in a maybe waterfall or other project methodologies you may call as scope creeps. So, there are no scope creeps really. We've converted scope creeps into embracing change principle here. Keep prioritizing the backlog regularly to evolve to a more accurate prediction because this is only required if in your project case, you need to do some predictions or forecasting for stakeholders or planning purposes. It could be resource planning. It could be timeline planning. It could be anything else. This entire system helps keeping system of development as stable and thus you actually have to keep your system of development stable and thus you will be staying closer to the forecasted cost timeline target. Another interesting way of, so while you're doing this, you know, some companies and advocates suggest using active scope management via story, story, user story mapping as well. This was popularized by Jeff Patton. It removes the flat user story backlog concept, you know, a bag of contextual free mulch. This is basically visual presentation of a product backlog. I will not go in depth into this. You can read a lot about this approach on the internet. Go to the website of Jeff Patton if you want. But this will allow you to visually look at your product backlog and while slicing push things into a current release plan or the next release plan or push them further down away and say that, okay, this is not required, stuff like that, and give you a visual aspect. This fosters collaboration and build shared understanding because oftentimes, you know, text written requirements may not give you the bigger picture. And this helps you identify gaps in backlog, see interdependencies and helps in release planning activities. In case in your project release planning is not required, the release aspect may be ignored, but I would still recommend using story maps. It's a very good way of managing product backlog and prioritizing. Then comes forecasting. Now, you know, a lot of questions are to be answered like, you know, when can the client or the customer expect X feature? When can he expect something else? How much of resources or investment do we need to do for a typical project? So that's where forecasting comes in. So instead of doing assumptions, we are focusing on forecasting here. Forecasting is based on empirical data based on from from your past experience, calculating or predicting some future events, usually as a result of analysis of available pertinent data. Forecasting uses data while estimation does not. Estimation is purely guess or guesstimate as well what we call it. So the questions that should be answered instead here or asked are, you know, given the rate of progress so far and the amount of work still left, when will the project end? Or when will phase A or phase V end? Given the rate of progress, how much of work can be finalized by, you know, X date or Y milestone or by Christmas? This is a chart that you can map from your user story counts. And this explains the whole idea of how predictions and forecasting works and how the user story averaging would work here. So the red line up at the top is the target. The, in the bottom, you have the user stories. And on the left, on the left hand side on the Y axis is the time taken for completion of these user stories, sorry, the number of user story points that you have achieved. And as you can see, once you are done through three or four sprints, and if your lines are within the interval, the high interval, the interval, you'll have to define those intervals yourself, looking at the data. For example, sprint one, you delivered five user stories. Sprint two, you delivered eight user stories. And sprint three, you maybe delivered, say, two user stories. So you're looking at 15 user stories, so an average would become five. Now that two number is a little catchy here. So maybe instead of two, it's a little larger number. It's like these user stories are closer to each other. Story points, I mean, story counts, I mean. Then you can do an average. The red line you see in the bottom is the average line. And then you just need to ensure that all the user stories are, you know, either above or below, but within the interval. The moment they seem to go beyond those intervals, something is wrong. You're either not slicing your user stories properly, or the system is not stable. So this will allow you to keep within those boundaries. Thus, what you can do is, based on this throughput, you can forecast, right? So if you're looking at an average of five user story counts here per sprint, and the project requires around, say, 500 stories, story counts, you can say, okay, it should roughly take around 100 such sprints. It could be 150, it could be, sorry, it could be 130, maybe it could be 80, but it definitely would not be 200, or it definitely won't be 25 sprints. And based on those sprint numbers, you can make some budget planning, provide some sort of a date idea as to when can X number of features or the entire project be expected to complete. And then as you go, you keep reverting back to this chart on a sprint by sprint basis, and keep tweaking the charts, and you'll see that you have a stable system. End of the day, you'll be able to achieve what you're looking for. So three to five iterations average is suggested here in terms of throughput, because it's sufficient to predict future running testable stories. And if you remember the system stability rule that I mentioned a few slides back, this is where you can see it in play. Velocity outside limit three times in a row. So the interval, the blue and the green line are the intervals. So it should be within those intervals. If it goes out, it's not a stable system. Likewise, if there are four same points in the same direction, the system is not stable. So your system is not good enough at this point of time to make those intelligent forecasts based on your data. Another way of providing forecasting data to your client would be because you are focused on smaller user stories and the current sprint, really, you can create a window saying these F1, F2, F3. So these three features can be delivered in the period of, say, current sprint, remaining Y number of features can be delivered for larger number of longer periods, say one year. And then remaining these seems unlikely to be delivered within this project. So this provides your client, your manager, your management, some actionable data. And based on these, they can make some decisions because, remember, they have not asked for or received any estimations as such. So they need some values to plan things. So this is called rolling wave forecast. Low overhead reporting. It can be updated, tweaked weekly. It's purely based on throughput measurement. It provides actionable information. Some common use cases, not exactly use cases, but the way people are using it is using no estimates plus can ban plus agile contracting with cab TNM and incremental delivery plus some high-level planning. Idea is not to spend too much of time doing planning, not to spend too much of time in estimating. Estimating a backlog is a waste. If estimations and predictions are absolutely required, then use impact mapping. And I would like to invite one of my friends here. I happen to sit with, I happen to share a seat right next to him in a flight and learn that they're actually using all of this process and delivering some real great projects and complicated ones. So I thought maybe me talking about projects that have been delivered by my organization or someone else is why not someone else validate that by saying, yes, this is working out for them. So before I hand over the mic to them, I know we have, let me estimate, we have around 18 minutes. One of the approaches that they may talk about is behavior-driven development, which really works out for them. So Rob, would you like to come please and talk about some of the projects? Thanks. Do you need the slides or something? Hi. Can everybody hear me okay? Yeah. So I'm Rob Knight. I'm a CTO at Fluxus. We're a consultancy based in the UK and in Asia. We've actually been using a no-estimates-based system for the last two years. And I think I'd echo a lot of the sentiments and the kind of the data that Piers has kind of put forward in his slides. This is definitely the experience that we've had as well. I think one of the questions that people often kind of ask us when we talk about moving away from estimation is really how do we know how long things are going to take? But if you actually think about that from the point of view of the person who's leading a project, who's funding a project, that's not normally the most meaningful way of seeing things. People are thinking we've got a certain amount of money to spend and we want to see certain benefits for spending that money. So people are thinking about return on investment rather than thinking purely about how long is something going to take. And what we find with our approach is that by time boxing and by thinking in terms of value you start to drive the question towards okay we're going to spend this amount of money. What limits does that place on the level of complexity that we're able to kind of absorb in our project? So if we've got two days to do something we really can't have super complicated set of features in that time. We need to break that down into a series of features that can be delivered within those constraints. And that actually drives a much more value-driven approach. People can think well for this amount of money do I want to spend that amount of money to get this benefit? And it's kind of much more easy for clients to deal with in those cases because it's thinking in the language of the business and thinking in terms of the business rather than kind of six months, twelve months kind of roadmaps of software delivery. So what we've actually done is we've put together a whole process based around this approach which we're sharing with other companies now. We're sharing that with other Drupal agencies and people outside of the Drupal space and we've done this to name focus because we believe this is really the thing that's most important in project delivery is to actually focus on the outcomes that you want to get and not get lost in the kind of the weeds of some of the details or committing to some kind of long-term plan and you then lose sight of your original objective. And the way that we do this is we begin by establishing what the goal for the project is and we do that using a process called impact mapping. We then work out from that to look at who's going to be affected by the project, who needs to contribute towards achieving that goal. Obviously in a Drupal context content editors are going to be a big part of a lot of CMS-driven projects. Obviously customers and end users which might be existing customers, it might be new customers, it might be different segments, there might be a legal team, there might be an operations team, there might be a marketing team, one of these different stakeholders are involved. We think about what impacts we want to have on those people and only once we have that do we then start to think in terms of what features we can deliver and that focus then is really kind of driven on achieving an impact and not just on kind of ticking off items on a kind of delivery plan or on a road map. We then take that through a lean UX process so we try to avoid creating a large inventory of designs up front because that then is in much the same way as estimates can be wasteful, doing designs can be wasteful in the same way. And we then also go into BDD, so behavior-driven development, thinking about what does this thing actually need to do? What's the interactions that people need to have that they couldn't have before? How does this create value through the behavior of the system? And what we find is that this works really, really well with Drupal. Drupal kind of solves so many basic problems for you out of the box. You don't need to think about how are we going to do access control, how are we going to do user management, how are we going to do basic content creation. So you can think very much in terms of business value, you're not kind of putting a lot of technical tasks into your backlog, you're putting business value directly into the backlog, and then you're delivering that in very small increments, kind of really like two, three days at a time. So in terms of the actual success that we've had with this, I think we've had some very successful engagements where people have kind of said to us that they'd never seen an approach like this used before. They're used to a traditional waterfall process, or they're used to a very rigid scrum process which had kind of become kind of a little bit unmoored from some of the original values of the agile manifesto certainly. And we basically kind of, I think I would say at this point we've got no regrets really from moving away from estimates. I can't really think, at this point, I can't really think what I'd ever want an estimate for ever again because kind of as has been pointed out, a lot of estimates are just wrong. They're just plainly ludicrous, and I think people start to lose confidence in a process when that kind of thing becomes apparent. So I know I've only got a very short space of time just to kind of validate some of these things, so I'd say if anybody wants to talk to me or any of us about how we've implemented no estimates, I'd be more than happy to talk to anybody afterwards and answer any questions. For now, I'll hand you back to Piyush. Thanks, Rob. No principles or practices or philosophies are really valuable without validations. I'll take you through two or three more slides just to touch upon what this movement was about and what it is, what's happening in hashtag no estimates world. So basically hashtag no estimate is a hashtag for the topic of exploring alternatives to estimations for making decisions in software development. It's been fairly active. The hashtag had been fairly active since 2011. You can actually go to Twitter and check out this hashtag. There have been lots of discussion both for and against this. So unless you have against happening, it's not really a good thing, I believe. And this has led to various blog articles, research papers, interviews, podcasts, presentations and conferences that I mentioned at the start. And learnings have inspired lots of teams like what Rob mentioned. And they've stopped doing estimations or minimal estimations and made life and business more happy. These are some of the advocates and thinkers who have been involved in this hashtag, conversations and this world. Woody Zool, Vasco Duarte, Neil Killick, Chris Chapman, Henry Karatsudhawal, Panchal. I have copied some of their ideas and repurposed some of their thoughts and slides into my presentation as well, which you saw. Ron Jeffery, Steve Fenton and many more. And these are some of their, each of them have slightly modified version of how no estimates should be worked upon. In the interest of time, I believe there are a few questions that need to be answered, so I'll skip through this. But you can go online, check out my slides later and read more about their approaches. Vasco Duarte has also written a book called No Estimations, No Estimates Book. You can go online, buy that book. It's a very interesting book with a story of a lady project manager called Karaman and how she manages these challenges and introduces herself to No Estimations and delivered a fairly large, complicated, guaranteed failing project and converts it to a successful project. It's a very interesting read. And in the end, before closing, a quote by Woody Zool that No Estimates is merely a call to refocus on the agile manifesto. We all are aware of what agile manifesto are. And the takeaways from these sessions, don't stop doing what you're already doing, please. I'm not saying stop doing your estimations. You don't want to create challenges with your boss and conflicts in your workplace. Don't stop doing that. Please continue doing that. Start exploring No Estimates in your own way. Try taking out data from your last prints, retrofitting them, see if you are able to trust those throughputs or story counts more than story numbers. See if you were able to time travel, if you would have used this approach instead of estimation, would you have actually reached at a better conclusion without estimations, which will lead you to actually move over from estimations to No Estimates. Run small experiments, analyze measures. That's what I did as well. Try and get better at creating simple and unambiguous slices of functionality. Measure your throughput. Compare story count data with your story point data. Question that, can you take better decisions with this? If yes, then that's the way to go. And discover for yourself if a No Estimate approach is right for you. So there are multiple of these approaches out there. The one I was talking about, the one that I took you through was suggested by Vasco Duarte. Woody says, do not do any predictions. No, nothing. I mean, just focus on value. Deliver the right thing right now and then the next one and the next one. And that's it. And he's written loads of stuff about that. He's talked about one or two large project case studies as well. And questions. And Rob, you're welcome to answer the questions if they are for you. Yes, please. There's a mic here if you could come to that. Yeah, I think, you know, customer education is very important here. So this is a new thing. Customer needs to be educated. They need to be convinced that the end of the day they are paying you to generate value. So, you know, let's actually start it from the reverse and only work with value. Rob, what challenges are such you have faced in trying to educate your clients? And, you know, start saying no to clients if they don't believe that, you know, value is what is required. And value is what you want to commit, not to a number that can be made up or, you know, a number that can just be there because a client wants that, a project manager wants that, or it is to be done because that's how we have done it. Yeah, so I think what we found was that that initial conversation with the client often isn't super detailed. So that isn't at the level of individual acceptance criteria or requirements for a story.  That's kind of the conversation that you can have. What we found was that when we're actually working on delivery and because we're working in an agile way, we're not kind of creating a massive inventory of requirements up front, that we can use that time budget essentially to kind of drive the conversation when we're actually firming up the requirements. So we can say, well, you know, how much complexity, how much risk are we willing to take at that point when we're defining the acceptance criteria. So as long as the kind of the executives or the sponsors or whoever is kind of funding the project is satisfied that they're going to get the value that they want after about, you know, after a certain time period, the actual kind of conversations with product owners and people like that can be kind of driven much more in alignment with, you know, well, this is your budget, this is how long we've got, and these are our value priorities. So we haven't found that to be a particular problem. It's been, we've done that with both. I think it's easier when there is trust. I think that you can have suspicion sometimes that, you know, people want to know that they're getting value for money. They want to know, you know, that you are genuinely kind of giving everything that you possibly can. But, you know, we haven't found that to be particularly in question, really. Yeah. We've found this to be easier with existing clients significantly. It's, I mean, the trust has been established, right? So whatever you suggest, they would be fine in at least trying it out, as opposed to a new client who's yet to be, yet to sign on the dotted lines, right? So yeah, new clients. But the fun is, you know, convincing new clients about this approach. The fun is in convincing new clients. That's when you actually get good at it. This is a sales guy saying, I believe. Any more questions, gentlemen? Yes. So how, how do you get to there? Well, just stop doing estimations. I mean, you know, no, no more planning poker, right? You know, once you start having a stable system, and once you have start building these average story count throughputs on projects, you, you don't need to do those estimations, right?  How can you really make count them and say that we'll deliver them in X number of days? Well, that's the beauty of, you know, doing averaging here, as well. Average really takes care of, you know, on a larger project, on larger timelines, you can actually deliver those numbers of stories. Because remember, you're also slicing them as you go going forward. So that's, that's one of the ways, you know, once you have this process implemented, you would not need to do any more estimations. That's the whole idea, right? It will be hard to convince initially, right?  Yes, focus on the story counts and ensuring that the sizes of those counts are appropriate and, you know, keep delivering the value on a regular basis. A lot of times, so there's an example from my, one of the projects we're delivering, we didn't follow this approach there. It was a 12 month project. We're actually releasing it on end of this month. And after six months, the first six months, the project client comes back to say, hey, we need to cut down the monthly expenses because the markets have changed. We've started our website traffic has gone down by 50%. This particular industry is not using this product as they used to. What about those, you know, 80% of features that we've already delivered in the last six months? It's a waste. That's fine. So had this been an approach from the get go, they would have only delivered those 20, 30, 40% of user stories or features, you know. With time, you realize that, okay, this was really not important, right? So I hope I try to answer your question. Yeah, thanks. Sure. So there's special considerations of your preparing proposal in which it's a kind of bidding situation. This might not work for RFPs and certain cases. So this, I mean, yeah, I believe so. These work with RFPs and those things where a fixed estimate is required. I would say go ahead, you know, based on their estimates, do those estimations, get the project, throw away the estimates and, you know, work on this approach going forward because you got the project, estimations are done. Now deliver value. Yep. Have you been in a situation where you have, as you said before, try to convince a new client, so if you know you're in a competitive bidding situation, actually addressing this model in your proposal? Oh, yeah.  So in your cases, have you done that? Yeah, yeah. So I think kind of echoing the question that we had earlier, I think that that initial process, I think it depends partly on who you're talking to, but I think if you're talking to someone who is sufficiently, you know, senior, who is involved in commissioning the work, what they care about is, are you going to deliver on my objectives, not necessarily are you going to deliver according to what a project manager might think, you know, somebody who has a particular approach that they might favor. If that's not going to deliver value as well as our approach is going to deliver value, then our approach is going to win. And so that's what we sell on, really, is we sell on, you know, this works for our clients, people are happy with the results and it's the best way of getting your objectives to be realized. Okay. Thank you, gentlemen. Thank you. Thank you, Rob, for helping.