Okay, hello everyone. I'm Mustafa. Again, I will introduce myself. I am Mustafa Ferdag from Egypt and now I'm working as an early stage researcher in the European project in Germany, the University of Potsdam, or especially the Helmholtz Center in Berlin, in Potsdam, and what we do is larger scale flood risk modeling. And what I'm doing specifically is that I'm building hydrodynamic models to stimulate floods in all of Germany. And what I'm going to present now was part of my work, was part of my master's thesis with Dr. Gerald Korzon, Professor Demetri Szilmatin in IHE two years ago. And it's not directly in machine learning, but it is in the way that how the resemblance with machine learning is the way how we obtain the parameters for hydrological model as we obtain it in machine learning or neural network. So at the beginning, I will start talking about the conceptual models that we use. So for conceptual models, it provides a simplification of reality, and because of that, it only simulates a small range of hydrological responses. So for the hydrograph, it's consisted of a lot of responses of the catchment, and calibrating the model with one objective function is going to focus mainly on one part of the hydrograph. And if you want to simulate it totally with a high accuracy, there will be a limitation of calibrating the model with one objective function. So with different objective functions, you will have different hydrographs. And by addressing this limitation in the hydrological models, we proposed a fuzzy committee concept to calibrate the model based on not only one objective function, but based on a lot of multi-objective functions. So as Gerald mentioned before, we can cluster high flow or we can divide the hydrograph instead of one hydrograph, we can divide it or cluster it into two flow regimes, high flow and low flow. And here in our method, we didn't make a hard partitioning, so we didn't split the hydrograph into two parts, but we made the whole hydrograph that can be simulated with two models. And we built those two models by multi-objective optimization, and in this case we used the non-dominated sort of genetic algorithm, NSCA2, and with two objective functions. And those two objective functions are root minus square with two weights, and these weights are based on some functions. And by doing this calibration, this multi-objective calibration, we have two models. One simulates the peaks, and that's what we're interested in when we are modeling floods, and the other model simulates the low flow values in order to simulate droughts. And after we have two models or whatever we use multi-objective, how many objectives we use, we can have more than two models, but here we only classify the flow into two models only, so we have two models. After that, the problems come to how can we combine, so we have now two hydrographs, and we only need one hydrograph to be as close as possible to the observations. And so that's the second part of the methods, which is combining these two hydrographs based on membership functions. So for the first part, in the multi-objective optimization, so we have two objective functions, which is the root minus square error, and by adding a weight to each of these objectives. And these weights are based on those weighting screens, which gives a weight to all the values of the simulated hydrograph based on the relative value of the discharge to the maximum value. And after calibrating the model based on those two objectives, we come to the problem of how can we combine both models in order to have one hydrograph by using this membership function. And here it is a trapezoidal function, which gives, this is the low flow model and this is the high flow model, so if the flow is lower than a certain value of the discharge, the low flow model has a whole weight. And if the model is higher than, if the discharge is higher than a certain value, the high flow model has a whole weight. So for this peak or discharge higher than this value, we are only going to consider the flow from the high flow model, and if the discharge is lower than this value, we are going to consider the flow from the low flow model. And the result, and in between, both models are going to be considered with a certain weight, and that's this area in the medium flow regime. And yeah, the model structure that we have used is HPV model. So it is lumped conceptual model, and it is based on Lindstrom 1997. It's a very simple model we scripted in Python, and in order to have it as a distributed, we have implemented a roster-based distributed model where each cell is lumped conceptual model, and the discharge is routed directly with a max pass function to the outlet. So the total hydrographic outlet is the summation of all the discharge from all of these cells. And the case studies that we have used, it is Hebo River in El Salvador. It's 432 square kilometers, and it has a big lake which influences the base flow for the hydrograph, and we simulated the lake separately as a subcatchment in the HPV model. And the data that we used was hourly data, hourly temperature, precipitation temperature, and we have also hourly discharge at the outlet of the catchment. And to test our physical metric concept, how much it improves the model. Here as you see, we have the lake, which is simulated as one model, and the part of the catchment we were trying to explore how much the resolution of the representation is going to affect the performance. So first we considered this whole area as one model, and then we tried to use the after-distributed model with different resolutions. So once we used four kilometers, each cell is four kilometers, and two kilometers, and one kilometer, and 500 meters. And by doing that we were increasing the number of models. So for a single model we have one model for the whole catchment, and the complexity of the system increases a lot when we consider every single cell as a separate model. And that's the case here with its derivative. So this part of the catchment is represented by 32 model. Yeah, and for the result, yeah, here, here, the figures shows in the x-axis here, these are some of the models, and the complexity increased from single models. So this is one model, as I showed here, and the number of model increases, it reaches 32 at this model. So as you see here, and that's the error. So the error here was very high, and it improves more when you go to, this is the committee model, which is using this fuzzy committee model to improve the simulation of the hydrograph. As you see here, the performance improved a lot, and all of this committee model has much better performance than using one, only one model. And yeah, as you see also, that performance did not increase as much as we wanted to be when we made the system is very complex and using 32 models. So the complexity of here was 16 models, 32 and 2 models. So by increasing the complexity of the system, we didn't gain that much. And that's the model that has the best performance. And yeah, so also we, in order to assess how good is all of these models, we use different metrics. So this is root minus square and with this special ways to focus on low flow and high flow, and that's a nice talk with. So as you see here, this is this metrics, the root minus square, the lower, the better. So here at the lower axis, these are the different, the low value of root minus square high, and this is the low value of root minus square. So this is normalized in order to present all the metrics in one graph, and that's the maximum value. And for Nash subcliff, the minimum is up here and the lower is up here. So yeah, to make all the good models in one side, so these are the good models. So it has low value of root minus square and high value of Nash. So, and yeah, by grouping all of these models, we noticed that all of these models are this committee models. So those are, are the ones that has the fuzzy committee as a concept to develop them. So those models are here. So all the committee models are much, much better than all the single models, which are those models. So, and yeah, using this multi-objective optimization, it, it, it results in a variety of set of models. So all of these are all every single dot of these models is a result of the multi-objective optimization. And all of these models are good. This ends the front are good. And by, by using the multi-objective optimization, then combining this best model for low flow and combining this best model of high flow using the committee model, using these different scenarios of committee models, we have those committee models here that are better than every single model. So, yeah, the result at the end, we, we, we concluded that using this committee model to combine this model and this model will result in a better model than any single model. So the best model was, was this one, which is lumped, not distributed and using those two scenarios of membership function A and winning scheme. So, and yeah, for, for, for other models, for other models, as you see here, like all of them has a very good performance. But the observations that we got, increasing the combo list and using 32 model with each cell is, is, is not as good as we thought it would be. So using, using a distributed model might be beneficial, but don't increase the combo list of, of your system. And yes, so that's the end of my presentation. So I would be happy if anyone has a question. Thanks a lot, Mustafa. Thanks for your presentation. We have a few questions for you that I'll just put up on your screen. So now people, you can continue sending your questions and comments to Mustafa as he answers this first one. Question from Nabil Khurchani is how did you combine HPV light model with Python? Yeah, so at the beginning I had it in MATLAB and I, I wrote all of it in Python and actually with Gerald, we developed farsers as HPV and we made it as a distributed model. And now it is in GitHub, so it is publicly available for free access for anyone. And yeah, I guess I will write my GitHub so you can, you can access this. It's freely available in, in, in Python and also in MATLAB in, in, in GitHub. Okay, fantastic. As I put the, as, as I put for you the next question, could you please type the link to the GitHub profile of yours that so people can access it, they might be interested. Okay. All right. The next question is from Seymour Shavalking, who asks, says, very interesting, could you please provide some examples of practical applications of the model as you visualize part from what you started out with the practical application. So that's a practical application what I showed about this hippo river. So the main purpose of the model is that they wanted an operational model. And Seymour is very small, so the calibration, calibration with single model, they didn't, they didn't give a, like, desired results. And that's why we were at the beginning exploring which methods help to improve the simulation of the hydrograph. So, yeah, this case studies is, is a practical application. What's also part of the question as you visualize. So what you meant to us was, you know, practical applications of the model, apart from the one that you kind of started out with the parts of the one that you, you know, do you see some other practical applications of it. Do you think it's, for example, do you think it's relevant, it would be relevant in some other regions as well. So that's not, not only application that has been implemented using this method. So it was originally started by Dimitri so my professor and any of the the implemented in other management in Luxembourg and Italy in in Europe. So yeah, if you just search it and for the publication of the machine or the method itself, you will find like two or three papers talking about other catchments. We have received a question from on Twitter, which is not about your particular presentation per se but it's a more fundamental question. And I'm really hoping you could respond to this. I've seen advantages of artificial intelligence or machine learning over simple data modeling or analysis, like why make the machine learn. Yeah, so that's a hard question and it mainly depends on the purpose of the model so you cannot say like this is better than this and usually people go for machine learning models because it is faster and but it requires more data and in some applications, you cannot use it because it doesn't have a physical meaning. So it just you obtain it from from the calibration of or from the optimization. It's a black box. It doesn't have any physical means at the background. For example, for for some of the one of the questions asking like about flood mitigation or scenarios about land use change and for for this application you are like forced to use physical based models. So it mainly depends on the purpose why you are building this model. Daniel, if you're still connected with us, could you could I please ask you to, you know, come in and respond to this question because it is like a fundamental question and not only specific to the last presentation. If you're still with us, Daniel. Okay, so I will try to answer. Then I agree with Mustafa. It depends on on the specific practical case. And also maybe models that are based on a and m sort of other kind of inter artificial intelligence, they can be suitable for very complex processes that cannot be modeled with with other models. So, as it was said before, the AI model it only analyzes inputs and outputs. So the complex process is hidden inside of a black box and black box and it might be suitable for what is very complex processes. Thank you so much, Daniel, and also Mustafa, I'm afraid we'll have to end there because we're already 17 minutes over time. Thanks everyone. Thanks, especially Daniel Mustafa and of course, Jen, who has now logged out. Thanks to you all the audience. First of all, for, you know, turning up in good numbers. The recording of today's webinar will be available later today on the water channel at the water channel dot TV slash webinars. Let me put up the link over here. Also the website and newsletters and other channels of communication that you're well familiar with. Let me put up one of them over here. And yeah, for now I would just like to say thanks again and goodbye and see you at the next webinar. Thanks a lot guys. Thank you.


Hello, I'm Denshi and in today's video I'm going to be showing you how to use FFmpeg with some basic examples.  And just pretend I only have one file here, outro.mp4, and if I double click on this and I play it back, you'll notice it's just the outro from all my videos. And yeah, that's what I use as my outro, let's say I want to convert it to an MKV for example. Well, I have a program named FFmpeg and if you're running GNU Linux and you have a desktop environment installed, this is already on your system, including all the codecs that you're going to need to do all the convergence. So let's say I want to convert this file. So I'm going to do ffmpeg-i outro, so dash i means input, and if I don't give it any other parameters and just press enter, it will give me information on the file. So as you can see here, it tells me that stream 0 0, it's using the HEVC encoder which is a modern functional encoder, U420p like pixel format, it's at 1920x1080 in terms of resolution, it's at 60fps and all of this stuff. For audio, it's using AAC, it's a 48000 Hz sample rate, stereo and 128 kilobytes per second as the bit rate, so yeah, all of that stuff. Now let's say I want to convert it to an MKV. Without any parameters, ffmpeg defaults to a bunch of stuff that it has pre-built in it, I can just tell it to convert it to outro.mkv. So it might take a little bit of time because this is a 20 second long video, but eventually after I've done running this command, so outro.mp4, outro.mkv, just specifying I want it to go from this is my input to just this output, I get an output and if I double click and play this, you'll see it's pretty much the same thing. Now what if I want to specify different encoders? So this used h264 as the encoder because by default ffmpeg will try to use h264 when converting things to MKV. Let's say I want to use h265 or HEVC, I can do dash cv and specify libx265 as my video codec, same for audio. If I want to use AAC for example which is higher quality, lossless audio, I can do dash c, aac. I can give them more options. So for example, I want to scale the video, I can do dash s and like 1280x720, that's 720p. Let's say I want to give it a rate, I'll keep this for now. In the meantime, before I talk about more options, I'm going to quickly run this and cancel it so I have it saved. I'm going to talk about how to actually get the encoder list. You saw me write out libx265, how do I know what all the encoders are? Well if you want to see all the encoders, you do ffmpeg dash encoders and that will print them all out. If I want all of those to go to like a file, just do ffmpeg dash encoders and send them over to like, I don't know, encoders.txt. So there you go and it will create encoders at txt, you can look through these and there's all the encoders you can possibly use on a system. Now not all of these might work with your system, maybe you don't have the hard drive for some of these or your processor is too old or maybe they're just not supporting enough that certain things downloaded or something like that, but most of these should work. And the reason all of these are here is because video comes in many variations. So the one I use was, let's see if I can spot it, here we are, libx265, so this is h265, also known as HEVC, h264, this is the NVIDIA version, but up here we got h264 VAAPI, so that's the version for video accelerated stuff, and then somewhere up there, here we are, you'll find libx264, so that's h264, that's the actual, you know, standard codec. Anyway, but this isn't what's important now, let's go back to where we were before I think I need to go, wait a second, you know, open up the main toolbar, not main, sorry, settings, show, tabs, go back to the previous file I was in, this one. Anyway, so back to what I was talking about before, let's say I want to take, I don't know, let's just delete this file, let's say I want to take outro.mp4 and convert it with what I was doing before, this, I want to use aac as my audio codec, x265 as my video codec, basically the best ones out there pretty much, and I want the resolution to be 720p, let's say I want to change the audio rate, so colon A and I give it something like 44100, a common audio rate, just give it a couple of spaces so things are a little bit more evenly formatted here, spaces don't matter, let's say I want to change the video bitrate, dash B, the one megabyte, at most it can reach one megabyte per second, most likely won't, but if I really want to, or let's say I want the audio bitrate to be something I can do, dash B, A and bitrate audio instead of 292 kilobytes per second, or kilobytes per second, now setting video bit rates is pretty pointless when you're using something like x265 or h264, all that stuff because there's already an inbuilt utility for it, it's called dash CRF and it's much better, it's varying the bit rate based off a number you give it, so the lower the number is, the better the quality will be but the bigger the file is, so let's say I want it to be 24, 24 is a good number, most of my videos are 20, I set the CRF rate to 24 when I compressed them, including this one, it's 24, and as you can see it looks pretty good, if you need higher quality, 20, but it's pretty indistinguishable, a little bit maybe with more noisy video, but for most video it's perfectly fine, anyway moving on, let's say I want to change the duration as well as this video, I can do dash SS to declare when I wanted to start, I wanted to start at, well, zero hours, zero minutes, zero seconds, and zero milliseconds, and let's say I wanted to end so dash T at zero hours, zero minutes, and zero five seconds, and zero milliseconds, I can also, that's pretty much it, no, so I'm gonna do outside MP4, I'm gonna use x265, I'm gonna set my codec to AAC for audio, I'm going to scale it down to 720p, change the audio bit rate to 44100, I use CRF 24, so you know pretty good quality for H265 and stuff, set my bit rate of audio to 192 kilobytes per second, start the video at zero zero, just the beginning of the video, and end it at five seconds, so let's press enter and let it work, since it only had to do five seconds, it was relatively quick, and if you play this back, as you can see it lasts five seconds, it's in 720p, and everything's been applied, so ffmpeg is a really powerful tool when you want to mass convert files because you can just write a script and and mass convert every single mp4 file to a different format or every single mp4 file, compress it or something else or or something like that, and let's say I want to take this outro.mp4 file and turn it into audio, well that's the same concept, dash I, outro.mp4, if I want to specify a specific audio codec, I don't know, libopus or something, but I'm just gonna say outro.wav, let's just turn it into wav, instantly done, there you go, outro.wav, double click that, and as you can see it plays back because I've converted it to audio, and here right before I end this video, because I've talked about all the main options, let's do a fun little experiment, let's transform this outro.wav and this image into a video, so I'm going to take outro.wav and essentially mix it with this image, so I'm going to do ffmpeg dash I, outro.wav, and then dash I, image.png, and I'm going to specify a specific codec, so let's say I want libx265, relatively good, the crf, I don't know, 2020 is pretty good in terms of quality, images are a little bit messed up when it comes to this kind of stuff, and let's say I want something like audio codecs, okay, I might want to specify a bitrate for audio though, so I'm going to do ba and set it to 192 kHz, so let's press enter on this, and oh yeah, I forgot to set an output file, so wait a second, I'm going to specify I want it to be outro.image.mp4, press enter and just let it convert, there you go, it's already done, double click on this, and as you can see it's the image with the audio, it's just the image playing back and it has the audio from outro.wav, which is just the raw 20 second audio, and it mixes it with this image, so if you ever wanted to just have a still image and you're talking, you can make that pretty easily in the file itself, it's negligible in terms of size, so it's minus good, it's 489 kilobytes, so if you ever need to make a video where it's just like an image, and that's all, you can do that pretty easily with this. So anyway, thanks for watching my video on how to use ffmpeg with some basic options, I'll have all a bunch of examples in the description if you want to take a look at them, I'll just have this entire thing in the description actually, and yeah ffmpeg is a really useful tool, I recommend if you're into video stuff, you learn how to use it, the best encoders for video are at the moment libx265, the best encoder for audio is AAC because high quality, x265 is, you know, as long as you set your CRF rate appropriately, it's going to be perfectly fine in terms of quality, might be a little slow because processors and stuff, if you need to deal with something like Nvidia encoding, you can do obviously what was it called, nvenc underscore HEVC, that's another codec you can use, but it's actually, I think it's HEVC underscore nvenc, and it's fast, but it isn't as precise because GPUs aren't like perfect processors when it comes to video, and the file size will be larger and it won't be as high quality video, but yeah, that's pretty much it, thanks for watching my video, goodbye.


Well, if you can tell from the noise you're in New York and I wanted to welcome you to my studio this is where I work and I'm surrounded by my favorite books here and here are my knickknacks and the Dakar Image the painting is from Dakar Because bbcq my partner and husband is from Dakar and so it Makes me feel like this connection between New York and Africa and then if you can see this This is a drawing from my daughter. I love About like mom stop working. I'm waiting, you know, when are you done? And so here we are. I want to tell you about Why you need to design your life? Why not? You know, I think that our life is our biggest project life is just like a design project. It's full of constraints and problems other people if you want to think about it differently, you need to think about your life creatively like a designer and You know often what we want and what we need are opposing have you noticed that they cancel each other So I want to be on vacation, but I need to work If I'm on vacation, I'm not making money. And if I'm working, I'm not relaxing So if you can make those two things coexist in harmony, you're creating new value So if I can find a gig to work while I'm on vacation that is Making my wants and needs coexist and that's designing your life


Today we're going to be tackling what every woman feels like. I don't care if you're a woman just starting out in makeup I don't care if you're 90 years plus It seems like if we can't get our foundation right our makeup just doesn't look right After 20 plus years of experimenting and doing different things on my face probably more like 30 plus years I feel like I finally have stumbled onto a routine that works so well for me for Longevity for no texture no pores Covering up dark spots my dark circles all of the things that you hope doesn't happen I feel like I've finally tackled and gotten a really good routine down And I wanted to bring you from very start to very finish and I'm breaking this down Normally, this would only take me probably about eight to ten minutes to do it on myself But I'm breaking it down for you in such a way that you can see each Individual step and you can see how I really really work hard on Perfecting my canvas or getting flawless foundation.  Now this I enjoy because it is summer time It's hot and you know, we're getting on the tail end of that but still at the same time I think that we could use this to keep our makeup set down our oils from not breaking through Remember that primer is Important to go in between our moisturizer and our foundation in order to create that barrier So that our foundation doesn't sink into our skin I use a pea-sized amount Hopefully you can see that and then I'm just going to really concentrate here on the areas where I have large pores And then whatever's left.  You can put it up underneath your eyes a little bit It will help create that barrier there as well Next because of my dark dark discoloration through this area and there are a lot of age spots on my face I'm gonna take the pixie corrector in brightening peach online. You can order a little bit darker one Which is apricot. I'm also taking a paddle brush. I was sticking my finger down in here But now it's so deep that it's a little bit hard to do that so I am taking that and I am going to not a lot but lightly go over the places that really need This extra corrector that I know for a fact are gonna have a problem on my particular face I'm going to dot it on my age spots Which I have several that are out here and they might be acne scars that have turned dark through time And I'm going to hit all of these points that are red and then when I'm done doing that I'm just gonna take my fingers and I'm gonna blend it out your finger just kind of seems to be a little bit warmer And what happens for me is it just kind of melts into the skin with that warmth so I am just going to dot everywhere that I have put that and Go ahead and make sure that it's really blended in once you have that done Then the magic happens and the magic happens from putting powder on next if you haven't seen Wayne Goss's video about this This for me as a mature woman has completely changed the game for me being able to keep on my foundation Much much longer use less foundation And it doesn't show up as much texture as I was having before So I'm going to use this. This is the sheer tint from Elf powder It's a pressed powder.  You don't have to use two I particularly am using these two because one is super light and one is a little bit darker So for as best skin ever this has quickly become one of my very very very very favorite all-time Foundations and then of course you if you've been with me, you know that the number seven lift and luminate is a definite favorite So I'm going to put one pump of the number seven that doesn't come out any huge pump But I'm going to come out I'm going to do one pump and then I'm going to see how much I need to get about the same amount of The Sephora on there and they're going to mix and they're going to create just a pretty color for me So you want to start out with about one pump of whatever foundation that you choose to use You don't want to go more than that because you'll have a tendency to want to use it up on your face And you really don't need to I'm using a really fun brush I found on Amazon it has like a buffer on one side and then a shader on the other side I'm going to spray this with my max fix plus if you have any primer spray go ahead and use that I use two pumps on there the reason that I use that is because I feel like Once you put that spray on and then go ahead and put your foundation on the brush You're thinning out that thing foundation just a little bit. Okay, so technique on this is kind of important What we want to do is we want to kind of bounce and I want you to work in sections So if I want to start on my cheek, I'm going to start on my cheek, but I'm only going to do my cheek to begin with I'm not going to go up and over onto my chin or my forehead So working in sections like this can really help you to see where you need to keep a little bit more foundation Where you might need to shear it out a little bit make sure that you go up back into your ear I always do and behind my ear it just really blends things well And I'm just pouncing this bouncing this all over and now I'm going to take a tiny bit And I'm going to do my chin and I am working like I said in sections This is going to go all the way down my chin towards my neck And then I'm going to go up over my lip right down to the lip line paying attention to those nostril folds Little bit more tap and I'm going to do the nose and I'm going to come underneath this eye now You're going to notice that I don't use concealer That is a choice that I have made Because of my aging skin and how difficult it is to make my concealer look good, but thinner foundation just looks perfect So I've really been enjoying that all right I'm going to spray this one more time because now I'm going to go pretty much to the other half I haven't done my forehead yet, but I'm going to go to the other half of my skin or my face and I'm just going to again bounce this all over.  All right at this point I want you to have a damp dampened beauty blender or beauty sponge You guys this one is from eco tools and it's supposed to be completely Biodegradable which means that it's good for the environment which I was surprised at it wasn't very expensive I don't know how long it'll hold up. I've used it a half a dozen times so far But I'm really kind of impressed with that So this is dampened it is wrung out in a towel like what I have on my lap and then in a tissue And now I'm going to take just the round and and I'm going to just very lightly Press across my skin. I am not wiping at all. I am just pressing To pick up any extra Foundation that you have that might make you look cakey through the day when you do this You are definitely going to perfect your makeup a little bit This is going to pick up anything that might be going into your pores it's going to pick up anything that might be going to look like texture and It's just such a good practice to get into you friends that have been with me for a while know how much I love the Becca Light shifter veil powder. It's so good But I know that this powder is not available to everybody anymore So I've kind of come up with what I think mimics it just a little bit are these two powders together the elf This is again the sheer tint and I will just pick this up. I'm not swirling I'm just picking it up a bit and then the Milani prep set and glow not the regular one But the glow one and I'm just going to pick that up just a little bit too So now I have a lot of powder on there and I'm going to tap it off a whole bunch What I want this for more than anything is just to perfect through this area and to set these eyes down ever so slightly That's really important to me because I get creasing under there because I have deep deep wrinkles If you can see I'm going very slowly with I'm trying to touch this very lightly I want powder all over but I don't want to look powdery And I definitely don't want there to be a cakey look that is what we're trying to stay away from as much as possible So hopefully you can see how well that did you can go underneath your eyes a little bit more if you need to This brush is from it cosmetics for Ulta and it will be listed and linked below as well So I know that this is probably full of a lot of techniques that my people that have been with me for a long time Have seen but it is full of techniques that aren't the mainstream So let's spray our brush with that I'm going to put two full sprays on there and now I'm going to apply my setting spray with my brush I'm going to start right here underneath my eyes and again You're going to see me be really careful because I don't want any stroke motion. I just want it to be placed strategically and Very lightly because I don't care what kind of setting spray it is even if it doesn't have any alcohol in it It can dry you out. And so I don't put a lot on I just put enough on to help my powder melt In with my other makeup. I wasn't going to really do any contour and blush I was just going to go off of camera really quick and do that But I'm going to do it for you. This is a new color of contour that I got from Sephora So it's not super great. It's a cross between the Warmth and the contour so you get the cool and the warm tone in here So I guess you could call it neutral I really enjoy putting this way up in my hairline because I've such a high hairline And I will put it right right up in there in my hair I'm not afraid to do that because I don't know I wash my hair every couple of days and I like this look So that's important to me and if you have never had a chance to see one of my videos on contouring and contouring your High forehead your double chin all that kind of stuff I will make sure that I link that below for you one of the things that I want to talk about really quick while We're right here is I've been getting a lot of really really super kind messages from you all about How much better my makeup looks and how you feel like I'm aging backwards and all of that stuff And I appreciate that so very much, but you know what I think it is I think it is going a little bit darker on my makeup for so long. I thought because I was so light I couldn't you know pull off anything that looked very warm or very Tanned type thing, but lately I have been embracing the bronze and that is really I believe what has changed So what I'll do is once I get all this contouring done I will go and take what's left over on my brush And then I'll just go ham all over my face and warm up my face And I really think that's what you guys are seeing more than anything is how much I'm warming up my face And right now I'm going into this Sephora collection This is the micro smooth palette and I'm gonna use the highlighter that's right here on this brush I'm putting this on before my brush and it just gives a pretty look I know that most of you know that already when I talk about that Because if you're aging putting highlighter on top can really sometimes just look much too harsh from Kiko cosmetics They just came out with a new line that is called charming escape This one is their rose color rose garden in their blush.  I love how buttery it is for about a powder product. It's so pretty So I'm just gonna put my blush on over top of that highlighter What do you think that looks so pretty over top doesn't it and in today's look? I did use the Norvina palette which is coming up for sale and Ulta's 21 days of beauty If you do end up getting this I'm planning on right after the sale is over to do at least three or four looks in one video so that you Guys can get a really good feel for what this is all about as a palette So right now. I'm doing a little bit more colorful. I have the blue on the top I think I'm gonna go right here into this purple I love this purple and then I'll just add just a tiny bit of that blue And then I'm going to just go out about a quarter of the outer part of my eye and then I'm just gonna flick it towards my eyebrow tail and Not bring it in hardly at all. I'm gonna add just a tiny bit of mascara out here All right for lips. I'm using this cover girl Exhibitionist lip liner, and I think it's in marvelous and I'm just gonna line my lips all the way around I'm using another Sephora product. This is a lipstick and I can't decide if I like this one or not I can't read this to save my life, and I don't have my glasses over here I'll see if I can throw it up on the screen for you. It's a pretty frosty look But at the same time, I don't know if I'm a hundred percent convinced on it And then this Sephora outrageous plumping lip gloss. This is one of my favorites I've had it for years and keep buying it over and over again So pretty I think this is sparkling pink and that's the final look I hope that you guys do enjoy this what I want to do right this second is I want to turn my lights down Just a bit so you can see a little bit in more of I know this is a dark light I don't normally do this because we really need to see things in a brighter light, but I'm gonna turn that down I'm turning my head so you can see if there's any discoloration. I absolutely love I'm turning them up again I absolutely love this method of being able to do my foundation I feel like after years of experimenting years of trying different things that this has finally become what I love Mostly because it lasts all day It covers everything like a dream. It doesn't look cakey. It looks like natural skin Even if you're wearing full coverage or if you're wearing just a tint It really is a great routine and I realize again that this may have taken longer But boy it is so worth it to me at the end of the day to be able to look in the mirror and go Okay, everything looks good. Everything's looking okay And so I hope that you did enjoy it. Please give it thumbs up if you did I would appreciate that. Let me know if you have any questions at all down in the comments section Please take good care of yourselves. I love you very much and hopefully we will be together very soon in my next video Goodbye my friends


Hello everyone. My name is Fabrizio Fresco. I joined HP one year ago. I work in the Professional Service team based in Galway, Ireland. We're here today, speaking about Freezer. It's kind of a new project, launched by HP. A little bit of history. When HP started to implement his own public cloud, a lot of people, different teams, working on deployment. And as a lot of new things implemented, they started kind of not very well synchronized, and each one of the teams was solving his own backup problem in very different ways. This was causing problems, because a lot of duplication of code, a lot of efforts and maintenance problem, bugs, discovery, and all these classical, operational things. And then they started to implement checks about the consistencies of the backup that runs every a couple of months. And they had very bad news, because as expected, there were many consistencies trying to restore not all these work in the way they were expecting. So we started to investigate possible, already done solution, and we couldn't find a cloud-oriented backup application available at the moment. So we started with development, we started to coordinate things between the different teams, the engineering, the operations, the ops auto, and so on. And so we were embracing the open-source community, and we wanted to be more present, so this project has been open-source since the beginning, and reusing or leveraging all the functionalities or library that OpenStack provided us. At that point, our customers, I am a professional service, so our customers were having absolutely the same problems, and they liked the idea, they started making requests and giving feedback and testing solutions. So they wanted Windows, for example, and they pushed really hard the idea of the disaster recovery capabilities, and all this thing motivated us quite a lot. So in our public cloud we started the tests, and the first address problem, that's kind of a classical one, is our CICD platform. That's quite huge for internal use, mainly, and each node has kind of 400k nodes, half terabyte of data, the classical difficult solution for the backups, because the change rate is quite high, the incremental is kind of a challenge. At the same time, even some customer wanted to solve quite similar problems, mainly telcos using the cloud, a dot moment for internal use, for NFE, and so on, but even for storage for the very critical data in reality. Our feedback was really important for us, it was the things that pushed us to really put a lot of effort in this project. So as today we have that deployant in Helion 1.1.1, and there is under evaluation at the moment the inclusion of Freezer in Helion 2.0, that's developing currently. All these requests, all the things, so we wanted to learn from our own mistakes, and we started to design architectural goals that we wanted to achieve. So the idea was to have a solution, a unique one for the open stack infrastructure, for the virtual machines, or even for the normal computers, let's say laptops, or stations, or whatever. The aspect solution that integrates is our focus completely, because from our own mistakes we realized that having a lot of backups, of useless data, it's not worth. One thing that open stack couldn't help us very much is that we wanted to move the workload away from the infrastructure, because on the customer side they have big implementations with a lot of data and generating backups during the night, all at the same time resulted in a denial of service over the internal open stack control nodes. We have a lot of object storage, so since the beginning we wanted to use that for the backups. It's redundant, it's easy to distribute it over multiple sites, so we thought that it was the way to go. And so we love open stack, and we wanted to do something for helping open stack go to the users, not waiting for the users to go to open stack, or leave open stack only to the operators and not the end users. And so the requests and the challenges grow, they started asking to provide a point in time to make up of the entire infrastructure. This, as you can imagine, is kind of a big problem, because it's a lot of data to a lot of I.O., a lot of transfers, and it's not an easy problem to solve. And then even better they wanted to be able to restore everything in case of a disaster in the center, for example, in another one, at least part of the infrastructure, the more critical pieces. So once this was defined, we realized how big the problem is in the technical perspective, because it's open stack, as I said, it's not helping a lot. There is no automated way of generating the consistencies, backups, volume or images, whatever it is. The file systems won't help us a lot, because you can't sync the file system, but you can't forget about the applications. The easy backup is... There are a lot of solutions commonly in the market, on the open source world or whatever, but we wanted to solve more than only this simple problem. So we started to implement different strategies from the most simple, as I said, flash syncing the file system, and then backing up a simple directory tree. But then we started to do something better. We started to interact with the application, can be your databases or whatever, try to flash what's maintained in memory and take in more consistent backups. These obviously came with a cost, it's not free, and the main idea is to leverage the potentiality over the files, so everything is a file at the end, interact at least as possible with the application, and then work on the files directly. So then the next step was to have multiple choices over the efficiency of the solution, because in some cases we want to have very fast backups, low CPU usage, and we thought that the reason really is something that do this, that it's tar, so it's such an old technology, it's tested and it's there since 20 or more, but customer wanted more advanced solution, they wanted to be able to have a more bandwidth or sites efficient solution, and after a lot of brainstorming we came to the point that probably the ercing way of working was the most intelligent one, so it's kind of a different strategy, it's a lot more CPU intensive and memory, and slow obviously. We had a request about automating the image backups in Nova or Cinder, and Glance didn't help a lot, the only thing that's possible to do there is to create an image from a VM, and Glance have no idea what's happening inside of the VM, so with these alone it's sometimes completely useless as a backup, because not even the file system gets synced inside of the VM, this is very often a big problem. In the Cinder side it's a little bit more advanced, because Cinder already provides the backup, can do compression, can do kind of duplication, and even encryption recently, but again the problem was the same, no consistencies inside of the VM, so our main user cases that we could figure out, we started tracking the most used application, thinking about how many lamp platform are implemented inside of OpenStack these days, and my SQL was kind of a classical problem to be addressed, there are some solutions in the market, but anyway none is automatic and easy, and not clod-oriented, we wanted the object storage for storing our data. So my SQL after a lot of evaluation we realized that probably the best way to go is to interact with the database, connect into the database, lock in it, flash in the tables, flash in the file system, and take the snapshot of the file system. At that point we can remove the lock and everything go on quite well. There is no downtime strictly speaking, but it's only a small interruption of the rides. This is sometimes not acceptable anyway, but even the other solution can do a lot better than that. You can have your, let's say, stand-by replica dedicated for backup, so you can take the data from there and you will not interrupt your service. MongoDB, for example, is quite a lot easier because we really have an internal journal-ed way of working, so we know that flashing the syncing, let's say the file system, is a nog for having binary files in a consistent state. In Windows we have the big problem of SQL server, but with the same strategy we used for my SQL it was mainly a nog, so you can lock your database, flash it, take your snapshot using VSS, that are most the same thing as LVM in Linux, and work from there. We are working on the elastic search, it's widely used, we have it in Helion by default. So, again, the same strategy, different terms, but it's the same as my SQL, we can lock database, take snapshot, and work on the binary files. We are successfully using Freezer now since almost one year in our public cloud, and it's working. We are planning on the implementation of the OSX and BSD clients, we have some ideas, but we are defining the way to go. And then probably it was kind of a mistake, not starting with Cinder or Nova since the beginning, but finally we realize that this is important too. So, we are almost completed the backup leveraging Cinder, so, again, we have our client inside of the virtual machine, we can work through the application and then create backup in Cinder. Without this kind of approach, it's very difficult to have useful backup with Cinder, there is even no way to have that automatically done by default. You can work through Horizon, but you have to go take the snapshot, you can't, at the same time, have sync a different system inside and then create the backup from the snapshot. A lot of virtual machine is kind of a big effort. Obviously, you can work through the APIs, but you need to orchestrate everything and you need to write your own script and synchronize all the stuff by yourself. So, we had that working already, it's in merge state, it's going to be there very soon and we had talked even about doing more than what Cinder do of the volume during the backup because we received some request about having the chance to manage all the keys for the encryption outside of the cloud, the customer won't manage that by themselves. So, here the idea was to, after creating the snapshot, we can create an image from the volume and then allow the image from glance. This is not optimal, but it's impossible to get the volumes directly from Cinder, so the only way to go we couldn't figure out better ideas. And with glance, we have to do everything on the outside because we can create a snapshot of the image, but it's not encrypted, it's not compressed, the duplication, nothing of this. We create the image from the VM and we download that in the freezer, we do our processing and we store the backup in Zwift. How do freezer works in the reality? There is no space usually to store anything inside of the VM because it's kind of illogic, really a lot of IO and it's really something that we didn't want to happen. So, we leverage a lot the multiprocessing and pipelines, we use the Python pipes, so each processor thread will do his piece of work like the compression or the encryption or the duplication and then pass through the pipe to the other processes that will do the sequence work in the chain and then at the end copy it directly in the object storage. These have a lot of advantage because it's almost no additional space required in the inside of the machine that's going to be backed up and only the snapshot, that's really minimal and no additional space required for the restore of the backup and the memory usage is quite low it's not zero but the word is not perfect. For the ErSync way of working we had really big problems because the standard ErSync algorithm that there is no implementation in Python anyway uses both copies of the data the old one and the entire new file to be restored because it uses heavily the duplication so only once is chunk of data is stored over there and can be applied multiple times inside of the file to be restored so it was almost impossible to proceed in that way we struggled a lot to find the solution and so far the best idea we get was to split each big files in blocks of data we used by default the same machine that's 50 megabyte and inside of each one of these blocks we applied the ErSync algorithm that takes 16 kilobytes of blocks he calculates the checksum and we store all this information in the metadata and we are able to compress it afterwards and do the encryption duplication and all the same stuff that the ErSync algorithm will allow us to do this is not optimal but we are still thinking in better solution we have a few ideas but we are trying to write a proof of concept and see if the idea is at the end possible to be implemented in big pictures how do freezer work in the reality it can work alone you install your client in your machine to be back it up and it simply store your backup in Zwift or unamounted an FS file system or throw the data through a SSH machine with storage attached but we wanted more than these obviously so we started implementing the APIs and with these APIs we were able to implement a nice user interface for making things even more easy for the end user so here on the left side you have the freezer client could be whatever as I say the VM or desktop or Windows machine or a physical node if you have the entire architecture implemented you have to register the freezer APIs in Keystone in the catalog and deploy the client with a very basic configuration that's mainly the username the tenant ID the password and the Keystone endpoint at that point the client will auto-register through the APIs so it will show up in your user interface and from there you can create apply your detailed configuration we are using elastic search as the storage for the configuration the events and all the metadata metadata of the current backups and we implemented a dashboard in Horizon for its open stack it's already there what we have so far is a simple list of what we already have implemented we can backup and restore from Zwift we have the MongoDB jubilnaled database functionalities, MySQL, SQL server we have the APIs that are working smoothly we are still working on that adding more functionalities once we have the functionalities in the API we can implement the user interface we have the Windows client working in both ways in the Taren and HerSync way we have the encryption we use SSL whatever kind of encryption we want we can use that we have the incremental differential backup policies and the automatic removal after the expiration period is gone we are actually working on the sender backups it's there but we need a lot more testing we just started to implementation in glance, NOVA images and in our priority there is kind of more functionalities the OSX and VBS implementation the capability of storing the backups in NFS or SSH to SSH creating multiple file system or directory trees in different file system in one passage the concurrency prevention of the client and probably Oracle and SAP and even we are trying to to have collaboration with other product like the internal HP1 that's data protector and we really want to have a mobile client implementation to be done a very nice idea would be even to have a workload integration between different technologies but it's not easy to achieve some script shot of the actual user interface as I said we use elastic search as a behind that allow us to create nice analytics about how things are going the number of successful backup the failures, the size of the files contained in the backups and some nice charts this is the main view you can see all your machines with the name who is executing the backup when I've been added to the freezer and some more additional information the number of backups if there are broken links this is obviously in heavy development so things are changing quite quickly this is the configuration management as I said new machine will pop up automatically over this dashboard and you can create new configurations or you can select one of the configurations like now it's the elastic search one and you will see all the machines to whom the configuration is applied to the status of the last backup in this case there was no backup done and you have the button to be able to restore automatically the backup on the machine here is the configuration creation it's quite simple configuration name in which containers store data in Zwift the mode of the backup if a simple file system or databases or whatever you're willing to backup and the source directory where the data is going to be back up you can choose the way of working as I said before the speed one the fast one or the space efficient you can choose the compression algorithm and the encryption here you will have all your freezer client showing and you can apply the configuration to whatever client you want the scheduling when the backups are going to be taken the hour and after how much time and some more additional efficient like excluding some file patterns where to store the logs or if you need a proxy to access your object storage that's almost it here is where the code is we are in the open stack incubator launch pad you can install freezer directly from the pypy repository and obviously if you want to give some feedback or have bright ideas or you're willing to join the team and help us out you're very welcome that's it if you have any question or file level yes it's not yet implemented in the UI but if you do that by hand let's say you can restore only one file that was a recent request that we implemented quite recently but it has sense so the idea here is to leverage the libvirt and backing up the XML file of your virtual machine so we this is not done yet as I said the disaster recovery is something that we are working on and willing to do that so we have the solution and we are working on the implementation we will retrieve your XML we can show you the network configuration of your virtual machine and we will provide you the way to change to the new addressing from there and restoring then the XML over the new compute node in the new data center let's say it's not easy so it's something that we we want to do and we are working on that in which release of open stack can we use a freezer which release of open stack there's no restriction we develop it in dev stack our public cloud is not completely Juno it's compatible with Juno and you mentioned that your merging code for Cinder we are not working on Cinder we are using Cinder leveraging all the functionalizers that we can use that Cinder will provide us but we are not working in Cinder


Everybody forced here up in 2023 Bowman Chrome Baseball Choice 12 box case and we're 25 random teams with the random right now And then we'll get right to it. Good luck. Thanks for joining This is a 10 6 and 4 Did you puka? Oh geez Names 10 times one two three four five six seven eight nine and ten Felix and down to Natalie M and And ten times on the teams here one two three four five six seven eight nine and ten Cubs Jays down to a move it over Read it off get it posted get ripping We got Felix with the Cubs Jays Joshua G Dodgers Chang S Phillies Brian S Diamondbacks Brayden a Orioles Brian S Giants Jeffrey H Guardians Bradley S Rangers Jeffrey H Mets Michael T. Marlins Tony W raised Jeffrey H Padres Felix and Yankees Ryan D. Twins Jeffrey H Braves Keith J with the Mariners Felix with the Brewers Peter D. Cardinals Lenny C Angels Cody I Red Sox Andrew S Rockies Jonathan L Tigers Andrew S pirates Mark L Reds Mike K White Sox Nats Nathan J Royals Ryan D. Stroze and Natalie with the A's Yeah That's pretty funny puka. I imagine yeah, this is the time of year where you can find cheap life especially last minute It's like empty seats on flights. Oh, yeah, dude this time of year right now The next like month actually for the past month and for the coming like, you know a couple weeks at least Okay guys you may have to revisit the schedule here. We still got a long ways to go on this bowman mixer. Yep 20 to go But basketball also is Getting there contenders optic is 12 to go Like out there the steamer. Oh Really how much Johnny charge for golf lessons first auto axial plaza There you go axial plaza Pirates Diamondbacks here with a Christian Sarah the first bowman base auto is everybody at tonight, man That's quiet. I know Sean is scary guys, but you can talk to me Refractor first auto you have here Valdez the 499 braves. I think they're waiting. I think they see the chain They just leave I Will have to see the dolphins beat those the bills though. All right. Nice here braves Guanipa on the first auto Louise Guanipa next up is a First auto for the twins now Freddie Michelle. Yeah, don't work too hard. Yeah, make sure you're watching more than you're working Yeah, refractor first auto for the Brewers here to 499 Daniel Gilarte We're due doesn't matter home Sean said it's okay Sean approved Really fire Sean. Yeah First auto is an Andy Perez After that, we've got a first auto for the Braves is your Aldo Kintero and a refractor first auto nice one Marco Vargas It's a 499 Miami. Oh Gotcha Justin Even better Marco Vargas My guys Bowman mixer Big-time baseball break under 400 spots. Oh a lot of crazy products in there 2020 Bowman 2019 Bowman 2018 Chrome 2019 Chrome 2020 Chrome choice first base auto is a Leonardo Bernal Ryan. You got twins And Astros Two pretty solid teams you have they give one auto so far. I think of a Michelle base auto Got a Bernal choice out of the 150 for the Cardinals You go Leo not about not Bernal and a gold a true gold of Jack Panetta Kansas City auto 7 of 50 Nice true gold for Kansas City, that's Nathan any true gold first auto in a random teams That's that's what's up Congrats Nathan solid so far nothing huge, but pretty solid so far solid name solid color Hope you get some more Royals again here on a Marin first auto first auto for Atlanta is your Aldo Kintero again on the Kintero and refractor here First Bowman auto of Daniel Gilarte again Milwaukee 359 of 499 box six Your twins on the Urel or Tega first Bowman base auto Urel or Tega Your Cardinals now on a Gordon Graceffo first bowman auto Gordon Graceffo and Purple first bowman auto to 50 of Luke gold for the Tigers Purple gold there we go purple gold Casey closing us out on the Immaculate mixer later guys still 20 to go on the Bowman mixer still 20 to go Well, we also got basketball coming up We're probably in half to pivot and go to basketball while we try to fill this mixer I'm gonna take a look at personals cuz odds are we're gonna finish behind tonight and we're gonna be less time for personals Base auto for the A's is Clark Elliott Another your Aldo Kintero base auto here for Atlanta That's the third one seven boxes and next is who got green true green True green Ruben Santana Diamondbacks first bowman auto 60 of 99 Here we go d-back so the nice green there Luke gold first bowman base auto for the Tigers Reds first bowman base auto atre Faltine and Astros refractor first bowman auto of Kennedy Corona 123 of 499. I mean, it's fine Drink it because it's super hot outside. Yeah, it's a little worse though Oh, yeah, I'm gonna hit it with the line first auto Christopher Toran diamondbacks again Base auto Royals is Hayden Dunnhurst a refractor first bowman auto for the Rockies Jordy Vargas 432 of 499 That is a kid. Yeah, he's still kid. Yeah, he's like younger than everybody in the shop First auto is victories Tories for the Guardians That's crazy to say yeah first auto Dylan array for the Brewers It's probably almost full apocalypse Got purple first auto to 250 is Kristen Monroe For the Angels got a lot of group breaks the fill There's probably sell some space if you order now get it done by the end of the stream Right base first auto is an Eddie Beltray The Padres what a big hit here base first auto Mets is Vincent Peruzo and Refractor first bowman autograph Reds Alfredo. Do you know that's a nice hit 334 499 On the do know the whole box apart there That was that was that was pretty cool First auto for the Braves Javier Valdes again first auto for the Ashos is Kennedy Corona Refractor on the back is first bowman auto for the Royals again junior Marin to 499 Few teams hit a lot in that one. I was hoping to get a more spaced out. Congrats to those teams though You teams hit several times there nothing crazy in this case Some spots help it did very well All right, so we had Corona Valdez Peruzo Veltray Oray Estorys Dunhurst Torian Faltine Gold Kintero Elliot or Tega Graceffo Kintero Marin Perez Kintero Bernal Michelle Juanita plaza Serda refractors of Javier Valdez Daniel Gilarte Marco Vargas Gilarte again Corona Jordi Vargas Alfredo. Do know and junior Marin to 250 We had Kristen Monroe Luke Gold to 150 was Bernal to 99 was Ruben Santana and True gold to 50 was Jack Panetta. Thanks everybody. We're moving on now to Whatever's full I guess come on now


The end developer at the end of the day who's doing their work needs an environment to be able to make sure what they've built is doing what it needs to do, and it solves whatever problem they're trying to solve, and it works. This is your host, Sapil Bhatia, and we are here at KubeCon in Chicago, and today we have with us Tommy McLean, co-founder and CEO of Release. Tommy is here to have you on the show. Great to be here, and thank you for having me. Yeah, it's my pleasure. First of all, the name of the company's interesting release, and you are a co-founder, so I would like to know a bit about the history, which is not very old, because you said it's 2019. That's right. So tell a bit about it. That's right. In 2019, what was the problem that you saw that nobody else saw, useful that needed to be solved that led to the creation of the company? I was the CTO of a public company by the name of Truecar, and 300-ish developers, technologists in that group that worked for me. We had to go through a big modernization effort, and one of the biggest challenges for that organization was making those developers productive, and environments were a big bottleneck. They had a few staging environments, and it really slowed down our ability to ship because everything would bottleneck there. And so as we were doing that modernization effort, I looked in the market to see if anything existed to help with this, and at the time, there really wasn't anything. And so me and my co-founders saw that opportunity, talked to a lot of potential customers, VPs of engineering and CTOs, and this problem exists everywhere. So we jumped into it, and that was how we started it, and the problem we were solving. So when we look at environment, we talk about there are a lot of cultural nuances there. We talk about DevOps, developer, DevOps, we talk about platform engineering. Who is environment targeting? The end developer at the end of the day who's doing their work needs an environment to be able to make sure what they've built is doing what it needs to do, and it solves whatever problem they're trying to solve, and it works quality. So the end consumer of these environments are developers in their SDLC. I might be developing locally, and I need my local setup to look like production, because I can only see a bug that happens in production, and I need to see that now. But I can't go test that on the production environment, so I need that capability close to me. At the end of the day, everything we do is for developers to streamline their ability to produce and get their ideas released to the world as fast as they can. The reason I asked the question was that for a long time, we almost forgot about developers. We start talking DevOps, and DevOps, and SREs, and platform engineering. But recently we have started going back to talk about developer. We have started talking about developer experience. So when we look at environment, how do you make their life easier also so that they don't get overwhelmed with all that nuances? So talk about what kind of technological, cultural nuances that are happening to make it easy. The answer to that is it just works on their behalf. The goal in our design is that developers should stay within their flow. If they're doing Git flow, for instance, and they do a pull request, they shouldn't have to go ask DevOps to build them a test environment or get access to one. It should just appear. And so our design principle is the developer should stay in the tools that they love. It should work within the workflows that they are currently using, and it should be seamless. They shouldn't have to know the details of, you know, I'm using EKS with these kind of instance types with, you know, this much memory allocated to it. They should be able to write their code, and the infrastructure that they need to get their job done should manifest for them right within the workflows that they're in. So I think that's a big part of this, is trying to keep developers developing and not, my co-founder calls it toil. Toil is this work that is non-value add. It's the setup, it's the, you know, getting your environment to work with the right settings, like all of that stuff is not value add. So for us, the more we can keep developers focused on solving customer problems seamlessly and less out of the weeds of the toil, the better off we are. So what are we offering them in the environment as a code? Are we offering them environmental service? What are we doing to help them? We call it environments as a service, but environments as code is a way to describe it too. So ultimately, if you look at an environment, an environment is the settings the infrastructure, the data, the services, everything that is needed to have your application run. That is abstracted into what we call an application template or a blueprint, and that blueprint now is the definition of that environment. So if I click a button, that blueprint is consumed by release, and then, you know, it manifests a version of that environment. So the developer gets a full stack environment of everything needed to run their application on the branch if they're working on a branch. So their code is loaded into that, the data that they need. So if it's production data or sanitized production data is loaded into that environment. And then when they're done with their work, it gets torn down so that they can save money and they don't need to pay for those resources when the when the developer is not actually using it. The environments can be used for development, so I can develop in these environments. I can use them to preview my changes with a QA person, with a product manager. I can run my automated tests against them. And within the SDLC, our vision is that you should have all the resources you need to get your job done as quickly as possible. And typically, what we see with companies is they have like a staging environment that all their developers share, which just is a huge bottleneck. So this parallelizes that process. Talk a bit about some of the core benefits of environmental service or environmental code, you know, whatever we use there. Well, the biggest benefit is velocity. Like you're going to produce software faster and it's going to be the highest quality because you know it is a replica of what it's going to look like when it's in production. Other benefits that we get are developer experience. You get an experience that's much like one of the big technology companies where developers have a CLI, they can get to those environments, they can console, they can terminal in. The developer experience that we deliver is awesome, which is if you're a CTO or VP of engineering, you care a lot about, hey, we have a great streamlined development process. Great developers are going to want to work for you when you have that. So I think the biggest benefit obviously is velocity and quality of your code and consistency. And then there's a lot of tangible benefits that you get just in developer happiness and experience of having something like this within your product or your ecosystem. And they are not developer, they're developers, they're team. So when we look at the whole, you know, sharing, a female environment, I want to talk about... That's a good point too, yeah. Collaboration, you know, you might be working on a project with five or six developers and you want one integration point for all of that. You're all working on the same branch, you use a pull request, everybody works on that branch and you can see that code manifest itself in a very collaborative, multi-team engineer sort of way. That's what these environments, it's a meeting point for engineers to get their work done and see it manifest itself live across that entire group. There's one more problem which is more or less like tribal knowledge or technical jet. If something is running on your own laptop, when you move out, you move out with your laptop, you move with your own environment, how does environment service address that problem? So you don't have to worry about tribal knowledge or technical data. Yeah, it's consistency and like you said, environments as code. It's, you know, you check in the configuration of these environments so that when that developer leaves, the knowledge doesn't leave with him. It's in source control. So the next developer who comes will pick up right where you left off. How does it also kind of address a problem of burnout? Developers have so much on their pipeline so that they are not stressed during Christmas or holiday season because everything is on their own laptop. So I just want to talk a bit about the collaboration aspect of it. So that not every member is stressing out. Yeah, I mean, I think it removes that toil like we talked about before. I want to talk about toil not from an individual developer's perspective, but once again, when they're collaborating, somebody is sharing some burden. Right.  And so I think removing those blockers, 99% of what we're focused on at release is removing blockers. What are the blockers that stop developers from being productive? When anything is locked in one person's hands and they're the only one that can solve whatever that problem is, you have a problem by sharing environments, by sharing those, you know, and having that reproducibility, you're never leaving one developer with too much that they have that I need. You know, it's being shared amongst that group. So environments as a service is also kind of democratizing that so that everybody has what they need and they can share it when they need to. And you can work with a global team as well. It doesn't matter who series we are. We're a fully remote company. So we use release to build release. And the amount of times that we have said, hey, go check out my environment. You know, this is what I'm working on. Tell me what you think. We send them to customers. It even creates collaboration possibilities early in the dev cycle with customers that you normally couldn't do.  But in the process of using release, you can say, hey, I built this feature for you. What do you think before it makes its way all the way out to production? So you're moving all of that left. It's another big benefit that you get from using environments as a service. Can you also talk about, you know, what is your actual offering look like? Yeah, yeah. The offering that we have is environments as a service. You will use release to create environments on demand. So the developer in their process of developing will get an environment with every pull request. And they, again, are full replica environments of what you have in production. You can use remote development environments. So if you want to code in them, you can. You can have staging environments. So if you want to run testing against those, ephemeral environments, preview environments, and even our customers will use us to manage their production environments as well. So it's the orchestration and creation of those. And those environments run within your cloud account. They're not running in a release hosted environment. They actually run within your cloud account under your security policies. So they're as close to production replicas as you can possibly get. And they have full control of what that is. Yeah, full control over what's in those services, what they use in AWS. We leverage open standards. So if you have Terraform, you have Helm, you have Docker compose files, Docker files, that goes into our blueprint. And then we're using that to recreate those environments over and over again. As earlier, you were talking about, you know, when you were looking at solving the problem, you talked to a lot of CTOs, other companies as well.  Use cases, kind of companies, kind of overseas who are already leveraging release. We have companies using us all the way from startups that kind of use us like a better Heroku to large companies who have three, four, or 500 engineers that are using us to solve those bottlenecks. They're across all industries and the commonality amongst them is generally multiple software engineering teams where collaboration and bottlenecks are, collaboration is important to them. Bottlenecks are slowing them down. Generally, they're all building in-house software, right? They have software engineering teams. They're running on Agile.  They're on the cloud. We don't really specifically focus on one industry because this is across the board. If you're a software technology company, these are problems that you solve. So, you know, the best companies for us are larger because that's when the complexity causes slowdown. And that's where we've really focused our energy is on larger, more complex distributed applications. And so, we really look at it like we can do this for virtually any size company that has a large development organization. What kind of adoption have you seen off release? It's been awesome. Like over the years, hundreds of companies have been using us and, you know, this problem is literally ubiquitous. Our biggest competitor is somebody building it in-house a platform engineering team or a DevOps team that's trying to do it themselves. And so, it's literally applicable to almost every software engineering organization. This may be out of the scope of release, but I do want to ask you because we are here at KubeCon. But the couple of things that we talk about when we look at Kubernetes, what is complexity? Kubernetes complexity? Yeah. And second is, of course, cloud cost. Yeah. How do you look at these two problems? Yeah. And do you see that release, because sometimes we don't realize, but in the indirect way, it does affect these two issues as well. Yes, for sure, yeah. So, talk about how do you look at these two challenges and do you see release or is helping them as well? Yeah, on the Kubernetes front, the core of what we've built is on Kubernetes. So, every environment comes up in a namespace within a Kubernetes cluster. And a lot of companies that started with us had zero experience with Kubernetes, none. Like, didn't know what a Kubernetes manifest file was, didn't know how to size a cluster, didn't know how many nodes to put in it. We actually do, it's a managed Kubernetes version that we're basically managing an EKS or a GKE cluster underneath the covers. You come with your code and all of the manifests that are needed in order to run your application are automatically generated by us. So, if you're getting into Kubernetes and you don't know it, you can start with us. And then as you mature, you can actually take over the management of those clusters yourself, directly get to them, use whatever tooling you want to manage, monitor those clusters. But if you're just getting going or you don't want to take on the burden of that, we can manage the whole side of it. And then on the cost side, one of the benefits of using ephemeral environments is they're not there all the time. So, instead of having long-lived environments that are always on, when you do a pull request, maybe what you want to run is an automated test.  So, we believe that those environments should only be needed when you need them. And you can save a lot of money by doing that. Not having those environments running all the time is a big part of the cost-saving story that we talked to a lot of our customers about. And since you are running in their AWS order, so do you also incur some cost as well? It actually is not. It actually a lot of times ends up being a reduction because they can get rid of some of the long-lived infrastructure and only have the environments running when they're needed. There's always a trade-off when you want to go fast. It's time, money, and effort that you put into it, right? And so, in some cases, if they get highly parallelized, let's say you had one staging environment and now you have 100, it may cost more, but you're moving fast. And so, there's always that trade-off, but a lot of times what we see is that on-off ephemeral nature of these actually ultimately ends up saving you money because those long-lived environments are big, beefy machines, cost a lot of money, and by only using environments when you need them, you can dial that back. How do you look at Genetic UI, once again, either from a workload perspective? Yeah. Or to power some of the release? Yeah. I mean, we have a couple of efforts we're undertaking with AI. If you go to release.ai, that's a site that we put up to talk about some of the AI initiatives that we have. We look at two very interesting places. One is, how does generative AI make removing bottlenecks in operations for platform DevOps engineering teams possible? We launched a chat interface that allows you to talk to your infrastructure. So you can literally go in and say, hey, what was my AWS bill yesterday?  You can ask it, what does my networking setup look like? You can set up schedules to send you information out of your infrastructure. And the idea there is more to reduce the knowledge that's generally trapped in one really smart platform engineer or DevOps engineer's head. You always have that one person on your team that you go to to answer the question about how AWS is configured. Again, shifting left, a developer should not be blocked by the one person that has the answer to that question. So our chat interface is connected into your infrastructure. And you can ask questions, query it, have a conversation with your infrastructure. And that was really just for us to kind of get our arms around what's possible here and to learn what the limitations of some of the open models are. So you can use that. It's free. It's unreleased.ai. And then we also look at the infrastructure side of AI as a part of our core business where as AI becomes more pervasive, the ability to run, train, fine-tune your models, it's an environment. You're going to need environments for that, just like you need environments for doing your software development. So we have a lot of work going into, hey, instead of a Kubernetes cluster built with traditional CPUs, how do you run workloads on GPUs that then you can simplify the environment creation and tear down? And a big problem in AI and model training is the cost of those machines is, you want to talk about cost savings. Having the ability to only have those machines while you're running your training, spin them up, spin them down when necessary with the right size for the workload that you're going into. We look at all of that as really interesting use cases of our core product. And that's not going to get less interesting over time. It's only going to get more interesting as companies get deeper and deeper into the AI world. So we look at both of those as really interesting. Do you see a point when those chat DVD clients will talk to each other and negotiate the bill? You know, we actually thought early on with our efforts in AI that you'd be able to take action with it. But I'll give you the example that we used. One of our engineers was playing around with our chat interface and he asked, can you create an EC2 instance? And now that can be taken one or two ways. Like, can you? Sure, I can do it. Or it, yeah, I just did it. And so there's challenges in letting AI act on your behalf. Our chat interface now is really only for that reason. And those are tricky problems to solve. And when you start wanting to do things that are very specific and it has to be accurate, right now you have to have humans evolve still. What are the other things that I look back on? Of course, there are a lot of things that you cannot talk about at this point. We'll talk about them when they're ready. But just give us a teaser or a glimpse of the thing that the release team is working on. Releasing, no pun intended. Well, I just talked about some of the AI things that we're working on. There's going to be more coming from there. And our core product is always evolving around our job is to increase the surface area of the kind of environments that we can create and manage. And that's a never-ending problem for us. So you'll see a lot of innovation on that side as well. What role do you see of culture here within companies? So even if you have technology like release, they can make best use of it. I think having an open mind to the fact that a lot of problems that could have been solved with in-house teams our biggest competitor is Build versus Buy. And the companies that do the best with us or any other technology understand that a lot of these vendors, that's all we do. We're going to be, by far and away, more capable of solving environment problems in a two or three-person team that you have in-house. So culturally, having that mindset of what matters to build that is an intellectual property that is value-add and unique to me and what things should I not build. And what we tend to find are the more advanced, faster-moving organizations can make that decision really, really well. Is managing and building my own IDP or environments as a service platform a core competency that I have to have? Or should I be focusing that effort on maybe I'm a healthcare company and solving my healthcare problem? So culturally, I think that openness and willingness to make that decision to focus on my core competency versus trying to boil the ocean and do everything is, we see the best and fastest-moving companies doing that. Tommy, thank you so much for taking time out today and talk about release. And I mean, this is a very core problem. So I would love to talk more as you focus on more technology. Anytime you want. But I really appreciate your time. Thank you. Yeah, come find us on release.com. Thanks.


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